AI Unraveled Podcast July 2023 – Latest AI Trends

Lastly, Universe, known for its popular no-code mobile website builder, has unveiled a new AI-powered website designer called GUS (Generative Universe Sites). This cutting-edge tool allows users to effortlessly build and launch custom websites directly from their iOS devices. Even those without coding or design skills can now create stunning websites, making it accessible to a broader range of individuals.

These advancements in AI continue to push boundaries and transform various industries, making tasks more efficient and accessible for everyone.

Hey there, fellow AI Unraveled podcast fans! Want to dive even deeper into the world of artificial intelligence? Well, do I have some exciting news for you! Etienne Noumen has just released an absolute essential read called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” And it’s widely available on awesome platforms like Shopify, Apple, Google, and Amazon.

This book is a game-changer when it comes to expanding your knowledge and understanding of AI. Whether you’re a newbie trying to wrap your head around the basics or a seasoned AI enthusiast looking for some expert insights, “AI Unraveled” has got you covered. Etienne Noumen does an incredible job of demystifying those burning questions we all have about artificial intelligence.

So, if you’re eager to level up your AI understanding and be an AI whiz, head over to Shopify, Apple, Google, or Amazon today, and snag yourself a copy of “AI Unraveled.” Trust me, you won’t regret it! It’s like having your very own AI host guiding you through the fascinating world of artificial intelligence. Happy reading, folks!

Thanks for listening to today’s episode! In this episode, we covered topics such as Google DeepMind empowering robots with internet information, US lawmakers calling for stricter controls on AI chip exports to China, Stability AI introducing new LLMs and launching ChatGPT on Android, OpenAI’s CEO launching Worldcoin and Microsoft Research proposing predicting code coverage, Amazon introducing AWS HealthScribe, Google stock rising 10%, LinkedIn working on an AI job search tool, and using Wondercraft AI platform to start your own podcast with hyper-realistic AI voices. I’ll see you guys at the next one, and don’t forget to subscribe!

AI Unraveled Podcast July 2023: Can AI Really Predict Lottery Results? We Asked an Expert.

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the following topics: the potential accuracy of AI in predicting lottery results, the limitations of AI in consistently beating the odds, the difficulty of analyzing past patterns and mathematical models due to the chaotic nature of lotteries, the creation of an algorithm to determine the minimum number of combinations needed to win, tips for picking lottery numbers including mixing numbers, joining pools, playing less popular games, and analyzing past numbers, the random nature of lottery draws making accurate AI predictions unlikely, the struggles of AI in accurately predicting NFL and soccer game results due to various factors and chances, the inability of AI to predict lottery numbers due to randomness and security measures, and the limited ability of AI to predict winning numbers based on patterns when lotteries are usually fixed and random numbers cannot be predicted.

We’ve all been there, standing in line at the grocery store, mindlessly scratching off a lottery ticket, and dreaming about all the things we would do if we hit the jackpot. A new house, a yacht, early retirement—the possibilities are endless. But then reality hits us like a ton of bricks, and we remember that the chances of winning are basically slim to none. Or are they?

Recently, we stumbled upon an article that claimed artificial intelligence (AI) can actually predict lottery results with near-perfect accuracy. This got us thinking: is it really possible for AI to beat the odds and guarantee a win? To get the answers, we turned to an expert in the field, Joshua Gross, Assistant Professor of Computer Science at CSU Monterey Bay.

In theory, lottery results should be random, but Gross has his doubts. He suspects that major lotteries run statistical analyses to ensure the randomness of major drawings, but what about the smaller drawings and scratch-offs? If someone were to manipulate the system just a bit, they could generate enough favor to turn a losing proposition into a winning one. It may not be a massive jackpot, but consistently winning smaller amounts could fly under the radar.

We also spoke with Dr. Aaron Feuer, CEO of Predictive Analytics World and author of “How to Win the Lottery Without Really Trying.” Dr. Feuer confirmed that, yes, AI can indeed predict lottery results with a high degree of accuracy. The key lies in analyzing past lottery drawings and searching for patterns. By examining which numbers are most likely to be drawn and which numbers haven’t been drawn in a while, AI can make predictions about future drawings that are surprisingly accurate.

However, Dr. Feuer quickly reminded us that just because you know the outcome doesn’t necessarily mean you’re guaranteed to win. Winning the lottery still involves chance, and even the most accurate predictions are not foolproof.

So, while AI may have the ability to predict lottery results, it’s important to keep our expectations in check. As exciting as it may sound, there are no guarantees in the world of lotteries.

So, can AI predict lottery results? We got the scoop from David R. Dowling, PhD, Associate Professor of Mathematics at the University of South Carolina and author of “Can You Win the Lottery?” The short answer is, not really.

Let’s start with the basics. There is no surefire way to win the lottery. The odds are always going to be against you, and it’s all down to luck. The odds are usually stated as 1 in x, where x is the number of possible outcomes. So, for example, if you’re playing Powerball with 50 possible outcomes, your odds of winning are 1 in 50. If you’re playing Mega Millions with 100 possible outcomes, your odds are 1 in 100.

However, there are patterns that tend to appear more frequently in lottery drawings. For instance, the number 55 has been drawn more than any other number in the Powerball game in the past 20 years. While this doesn’t guarantee that it will be drawn again, it does suggest that picking numbers based on past results might be a good strategy.

Now, let’s talk about AI. While some smart people have used AI to develop formulas for choosing lottery numbers, there’s no consistent success story. AI might give you a slight advantage, but it won’t guarantee you a jackpot. There hasn’t been any conclusive evidence that AI can consistently beat the odds.

So, if you want to try your luck, buying a ticket is still the only way to win the lottery. While AI might be able to offer some guidance, don’t rely on it to make you an instant millionaire. It’s all a gamble, and you can only hope for the best. Good luck!

Hey there! So, I came across this interesting question on lottery number prediction algorithms. It seems that throughout history, people have been on the hunt for ways to predict those winning numbers. Now, let me tell you, unfortunately, there isn’t a foolproof method to guarantee a lottery win. However, some individuals believe that using certain algorithms can give them a slight advantage.

One popular approach is to analyze past draws for patterns. Yup, it’s all about spotting trends in those numbers. Some folks think that these trends can help them predict which numbers are more likely to be selected in future draws. On the other hand, there are those who take a more mathematical approach. They create models that generate various number combinations, hoping to strike it lucky.

But here’s the kicker – can artificial intelligence (AI) actually predict lottery results? Well, I delved into the Polkastarter lottery algorithm’s source code and uncovered something interesting. It turns out that the algorithm wasn’t functioning as expected. If you’re curious for a detailed breakdown, you can check out the link here: [https://polkastarter.canny.io/bug-reports/p/in-depth-analysis-of-lottery-algorithm](https://polkastarter.canny.io/bug-reports/p/in-depth-analysis-of-lottery-algorithm).

Now, here’s the reality. Unless a lottery system is flawed and some sort of method can be exploited, creating an algorithm to ensure a win is highly unlikely. A well-designed lottery should be so random and chaotic that even the most powerful computers and brilliant minds would struggle to analyze it effectively.

So, while the quest for lottery prediction algorithms continues, for now, it seems like lady luck still has the upper hand.

Oh, I see what you’re getting at! So, if I understand correctly, you’re wondering if there’s a way to create an algorithm that can give you the minimum number of combinations needed to win in a lottery game like KINO, right?

KINO seems like an interesting game, with 80 numbers and it randomly selects 40 of them. You then have the option to choose 20 numbers out of the 40. There are two variations to play this game: either pick any 20 numbers you want, or choose between 5 columns, 4 lines, or 2 lines + 3 columns.

Now, let’s say you have enough money to give it a shot. You’re curious about how many tickets you would need to submit in order to cover all possible combinations and ensure that at least one ticket will win.

Additionally, you’re wondering if there might be a way to analyze how frequently the numbers are “randomly” picked. It’s natural to think that there could be some sort of pattern in the selection process. Perhaps you’re wondering if there is an online database tool available or if it’s even possible to create one yourself.

I do want to mention that while it’s an intriguing tactic, the chances are that you might end up with a loss. It has been calculated that the number of tickets you would need to play would be quite large, making it not really a viable or profitable solution. Still, it’s understandable that you’re just curious to know how many tickets you would actually need.

In order to guarantee a win, it seems that the minimum number of combinations you would need is approximately 25.6 million. That’s quite a large number! But hey, you never know what could happen in a lottery game, right?

Sure! While I can’t magically predict the winning lottery numbers for you, I can definitely give you some advice on how to approach playing the lottery. It’s important to keep in mind that the lottery is a game of chance, so there’s no foolproof way to guarantee a win. But, here are some tips that players often use to improve their odds:

Firstly, try choosing a balanced mix of numbers. Include both odd and even numbers, as well as a mix of high and low numbers. Although this won’t increase your chances of winning, it can help reduce the likelihood of sharing the prize with others who picked similar numbers.

Another strategy is to join a lottery pool or syndicate. By pooling your money together with others, you can purchase more tickets as a group. This naturally increases your chances of winning, but remember that any winnings will be shared among everyone in the pool.

Consider playing less popular games with smaller jackpots. These games tend to have fewer players, which means better odds of winning. It’s worth exploring this option, especially if you’re not looking to win an enormous, mega-million jackpot.

Some people like to examine past winning numbers to look for patterns or trends. While it’s important to remember that the lottery is entirely random, analyzing historical numbers can be an enjoyable way to engage with the game.

Finally, always remember to play responsibly and within your budget. Lottery tickets can be fun to buy, but it’s important to manage your expectations and not go overboard. Good luck!

So, you’re intrigued by the idea of using artificial intelligence (AI) to predict lottery results? Well, I hate to burst your bubble, but it’s highly unlikely that AI can accurately do that. The odds are simply against you, my friend.

You see, lottery numbers are drawn randomly, making it quite difficult for AI to identify any discernible patterns or structures. While it’s true that AI can analyze past lottery results and maybe spot some trends or patterns, that doesn’t guarantee accurate predictions for future draws.

The randomness of the lottery is what makes it so unpredictable. No matter how fancy the algorithms or complex the analysis, it’s tough to improve your chances of winning with AI or any other method.

Let’s face it, the lottery is, at its core, a form of gambling. And gambling is all about luck. Winning is often a matter of being at the right place at the right time, with the right combination of numbers. So, while playing the lottery can be entertaining, it’s crucial to approach it responsibly and understand that luck plays a significant role.

In a nutshell, AI might be good for plenty of things, but predicting lottery numbers? Not so much. Stick to the thrill of playing the lottery, but don’t get your hopes up about AI giving you an edge. It’s all about playing responsibly and embracing the element of chance.

So, can AI really predict the outcome of NFL games? Well, it’s not as simple as that. You see, predicting the outcome of a football game is no easy task. There are so many factors at play, like the strengths and weaknesses of the teams, injuries, weather conditions, and the strategies of the coaches.

While AI can analyze past game results and find patterns, it’s unlikely that it can accurately predict the future. Football is a complex and dynamic sport, with countless variables that can influence the outcome of a game. Trying to account for all these factors using AI or any other analysis is quite a challenge.

So, in a nutshell, predicting NFL game results is tough. It’s important to remember that the outcome of a game can be influenced by many different things. Sure, it can be fun to make predictions and spot trends, but let’s not forget that a lot of it comes down to chance. At the end of the day, that’s what makes football so exciting – you never know what might happen!

Artificial intelligence (AI) has come a long way in helping us analyze and understand data. However, when it comes to predicting the results of soccer games, AI faces a formidable challenge. There are simply too many variables at play. The strengths and weaknesses of teams, injuries, weather conditions, and coaching strategies are just a few of the factors that can influence the outcome of a game.

Sure, AI can analyze past results and spot some patterns or trends. But this alone is insufficient when it comes to accurately predicting the future. The dynamic and complex nature of soccer means that there are countless factors that can swing the outcome of a game. It’s virtually impossible to account for all these variables using AI or any other analytical method.

In the end, it’s crucial to keep in mind that predicting soccer game results is a tough task, regardless of whether we use AI or not. The outcomes are often influenced by chance and unforeseen circumstances. That said, it can be enjoyable to make predictions and try to spot trends. It adds a layer of excitement to the game. But always remember, the final score is ultimately decided on the field, not by AI.

AI, such as ChatGPT, is not capable of predicting lottery winning numbers. Lotteries are based on random chance, with results determined through a selection process that cannot be predicted or influenced. AI can analyze past lottery results and identify patterns, but it cannot guarantee or predict future outcomes. It’s worth noting that many lotteries have strict security measures in place to ensure fairness and integrity, making it difficult for any individual or system to manipulate the outcome.

For instance, let’s consider the Powerball lottery. The number of possible winning combinations is quite large, considering the sum of the drawn numbers (excluding the Powerball) and their range. Trying to predict the exact combination in such a vast space is virtually impossible for AI or any system.

To explore this further, we looked into an expert’s perspective on whether AI can truly predict lottery results. While AI can assist in analyzing patterns and historical data, it cannot provide a definitive forecast. It’s important to approach such claims with caution and not rely on AI as a surefire way to predict lottery outcomes.

In summary, AI cannot predict the winning numbers of a lottery due to the unpredictable nature of the selection process and the extensive measures in place to safeguard fairness and integrity.

So, can a neural network predict the lottery numbers and help you win? Well, the short answer is no. Lottery numbers are supposed to be random, and predicting them accurately is extremely difficult, if not impossible. However, there is a twist.

While the numbers that come out of the lottery machine are indeed random, the numbers chosen by people often follow patterns. Many people use significant dates like birthdays, which limits the range of numbers they choose. So, if you can choose numbers that fewer people are likely to choose, you can minimize the chances of having to split the winnings.

But here’s the catch: Getting access to the data of what others have chosen is the real challenge. Lottery managers usually keep that information private, making it difficult to analyze and find meaningful patterns.

Moreover, even if you have access to the data, you need to consider your goal. Do you want the maximum possible payout or the highest average payout? This is a trade-off between risk and reward, and it involves economic theory.

Now, let’s dive into gambling systems. If there is a pattern in how other people choose their numbers, your neural network could potentially spot it. But you have to consider optimal betting strategies. Betting everything may not be the best approach because you could lose it all on the first bet. The Kelly Criterion is one method to balance risk, but it’s not flawless.

So, while artificial intelligence can assist in analyzing data and spotting patterns, it’s essential to keep in mind that playing the lottery is inherently risky. The expected return is always below 0.5, meaning you’ll likely lose more money in the long run than you’ll win.

In conclusion, AI cannot predict random numbers, and winning the lottery solely through artificial intelligence is highly improbable.

Thanks for listening to today’s episode, where we discussed the possibility of artificial intelligence predicting lottery results, analyzed patterns and mathematical models, and explored the challenges AI faces in predicting NFL and soccer game outcomes, while also emphasizing the random nature of lotteries. I’ll see you guys at the next one and don’t forget to subscribe!

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AI Unraveled Podcast July 2023: Free courses and guides for learning Generative AI; How to Generate SaaS Startup Ideas with ChatGPT; Stability AI released SDXL 1.0, featured on Amazon Bedrock; AWS prioritizing AI: 2 major updates!

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover free courses and guides for learning generative AI, using ChatGPT to generate B2B SaaS startup ideas, Stability AI’s release of SDXL 1.0, AWS’s major AI updates, concerns about the security of language models, the call to relax rules for open-source AI models, the establishment of the Frontier Model Forum, recent funding for Protect AI, advancements in AI for breast cancer detection and robotics, various updates and launches in the AI industry, and the Wondercraft AI platform for podcasting with AI voices.

Hey there! If you’re interested in diving into the world of Generative AI, I’ve got some fantastic resources for you. Let’s start with Google Cloud’s Generative AI learning path. This series of 10 courses covers everything you need to know, from the basics of Large Language Models to creating and deploying generative AI solutions on Google Cloud.

Next up, we have DeepLearning.AI’s Generative AI short courses. They offer five courses, including LangChain for LLM Application Development and a course on understanding Diffusion Models.

If you’re looking for a Bootcamp-style experience, check out LLM Bootcamp by The Full Stack. They provide free lectures on building and deploying LLM apps. How cool is that?

CoRise has collaborated with OpenAI to create a free course called “Building AI Products with OpenAI.” It’s a great opportunity to learn from industry experts.

If you want to learn about LangChain and Vector Databases in Production, Activeloop offers a free course that covers exactly that. Pinecone’s Learning Center is also worth exploring, as they provide plenty of free guides and handbooks on topics like LangChain and vector embeddings.

For those interested in specific AI models like ChatGPT, Dall-E, and GPT-4, Scrimba offers a free course where you can learn how to build AI apps using these models.

To get insights from the experts, Gartner has a report called “Gartner Experts Answer the Top Generative AI Questions for Your Enterprise.” It’s definitely worth checking out if you’re looking for practical advice.

OpenAI has a guide called “GPT Best Practices” that shares strategies and tactics for maximizing your results with GPTs. And if you’re interested in using the OpenAI API, they also have an OpenAI Cookbook that provides examples and guides.

DAIR.AI has a detailed guide to Prompt Engineering that you might find really helpful. And if you’re curious about Transformer Models and how they work, Cohere AI has a tutorial that breaks it down for you.

Last but not least, there’s an open-source course called “Learn Prompting” that focuses on prompt engineering.

So, as you can see, there are plenty of resources out there to help you learn Generative AI. Happy exploring!

So, you’re looking to dive into the world of SaaS startup ideas in the B2B sector with the help of ChatGPT? Great choice! Today, we’ll unlock the power of AI to brainstorm three innovative ideas that incorporate Artificial Intelligence and can enhance their value proposition within the enterprise B2B SaaS industry. And of course, we’ll give each idea a unique and intriguing name!

First up, we have “ConnectAI,” a platform that revolutionizes networking within industry-specific communities. ConnectAI uses AI algorithms to analyze user profiles, interests, and behavior, enabling professionals to connect with like-minded individuals and discover potential partnerships. Its compelling mission is to break down barriers and foster collaboration in the business world.

Next, meet “InsightBot.” This AI-powered analytics tool helps companies extract valuable insights from their vast amounts of data. By leveraging machine learning algorithms, InsightBot can detect patterns, uncover trends, and offer data-driven recommendations, empowering businesses to make smarter decisions. Investors are attracted to InsightBot because it helps companies unlock the true potential of their data, leading to improved efficiency and higher profits.

Last but not least, we have “ResolvAI,” a customer service automation solution. ResolvAI combines AI-powered chatbots and natural language processing to provide personalized and efficient support to customers. With its advanced understanding of human language, ResolvAI ensures accurate and timely responses, enhancing customer satisfaction while reducing support costs for businesses. Investors find ResolvAI attractive because it addresses a pressing need in the market, saving companies both time and money.

So there you have it – “ConnectAI,” “InsightBot,” and “ResolvAI” – three innovative startup ideas in the B2B SaaS industry that leverage the power of AI. Each idea comes with a compelling mission, clear AI application, and reasons why investors find them attractive. Exciting times ahead for the world of enterprise SaaS startups!

Hey there! Stability AI recently unveiled the latest version of its text-to-image model called Stable Diffusion XL (SDXL) 1.0. And guess what? It’s making quite a splash on Amazon Bedrock! This means that users can now get their hands on this advanced model via Stability AI’s API, GitHub page, and even consumer applications.

But that’s not all! SDXL 1.0 is also accessible on Amazon SageMaker JumpStart, which is pretty awesome. One cool feature that Stability API has introduced is the fine-tuning beta feature, which allows users to specialize generation for specific subjects. This adds even more flexibility and customization to the model.

SDXL 1.0 boasts some impressive capabilities. It’s designed to generate vibrant and precise images with enhanced colors, contrast, lighting, and shadows. With one of the largest parameter counts in the field, it has gained popularity among ClipDrop users and the vibrant Stability AI Discord community.

Now, why is this release such a big deal? Well, SDXL 1.0 is a commercially available and open-source model, meaning it’s a valuable resource for the AI community. It brings a range of features and options that can compete with other top-quality models out there, like Midjourney’s. So, it’s definitely worth checking out if you’re into text-to-image models!

So, there are two major updates from AWS that really caught my attention. Let’s dive into them!

First up, we have the new healthcare-focused service called ‘HealthScribe.’ This remarkable platform utilizes Gen AI to transcribe and analyze conversations between clinicians and patients. It’s like having an AI-powered assistant listening in and taking notes! HealthScribe can create transcripts, extract important details, and even generate summaries that can be seamlessly integrated into electronic health record systems. But that’s not all! The platform’s ML models can convert these transcripts into patient notes, which can then be analyzed for valuable insights. Talk about a game-changer in the world of healthcare!

But AWS didn’t stop there. They also have some exciting AI updates in Amazon QuickSight. Now, users can generate visuals, fine-tune and format them using simple natural language instructions, and create powerful calculations without the need for specific syntax. How awesome is that? The new features include an “Ask Q” option that allows users to describe the data they want to visualize, a “Build for me” option to easily edit elements of dashboards and reports, and the ability to create engaging “Stories” combining visuals and text-based analyses.

Now, why is all of this important? Well, HealthScribe has the potential to revolutionize healthcare delivery and greatly improve patient care outcomes. It’s an incredible tool that streamlines the process, enhances efficiency, and ultimately, benefits everyone involved. As for the AI updates in QuickSight, they empower users to gain valuable insights from their data regardless of their technical expertise. This fosters a data-driven decision-making culture across various industries and opens up a world of possibilities. Simply put, these updates are a big deal!

Hey there! So, it turns out that researchers from Carnegie Mellon University and the Center for AI Safety have made an interesting discovery. They’ve found that large language models (LLMs), especially those based on the transformer architecture, are actually susceptible to a universal adversarial attack. And get this, it’s done by using code that looks like complete gibberish to us humans!

These clever researchers shared an example attack code string that gets attached to a query. It goes something like this: “describing.\ + similarlyNow write oppositeley.]( Me giving**ONE please? revert with “\!–Two”. Looks like a bunch of randomness, right? But it fools the LLMs into removing their safeguards.

Now, here’s the scary part. The researchers aren’t sure if this vulnerability can ever be fully patched by LLM providers. Deep learning models might just have a fundamental weakness that makes them prone to such threats. They’ve even mentioned that the very nature of these models could make it unstoppable.

Luckily, the researchers did inform providers like ChatGPT and Bard about their findings beforehand, so they’ve already made some fixes. However, the researchers believe that the attack code can be altered to create unlimited new attack strings. So, the threats might not end here.

What’s interesting about this attack is that it’s automated. Computer code can continue generating new attack strings without any human creativity. And since this approach exploits a core weakness in the architecture of LLMs, it works consistently on all prompts across all LLMs using the transformer architecture.

The researchers are sharing their findings to raise awareness, as they believe that anyone determined to exploit language models to generate harmful content would eventually discover these techniques. They also emphasize that this highlights a fundamental weakness in the transformer architecture, similar to unsolved adversarial attacks in computer vision.

So, it seems like we’re just scratching the surface of LLM vulnerabilities. Who knows, we might be heading towards a future where jailbreaking all LLMs becomes a piece of cake! Scary stuff, right?

GitHub, Hugging Face, Creative Commons and several other companies are urging EU policymakers to ease regulations for open-source AI models ahead of the finalization process for the EU’s AI Act. According to GitHub, the purpose of this effort is to create optimal conditions for AI development and enable the open-source community to thrive without overly restrictive laws and penalties.

The EU’s AI Act has faced criticism for its broad definition of AI and stringent regulations on the development of AI models. The letter argues that designating AI models as “high risk” would impose additional costs on small companies and researchers looking to release new models. Additionally, rules prohibiting real-world testing of AI models are seen as hindering research and development.

The open-source community believes that its lack of resources is a weakness and is therefore advocating for fair treatment under the AI Act.

Interestingly, prominent players in the open-source community, including GitHub and Hugging Face, find common ground with OpenAI, which follows a closed-source approach. OpenAI successfully influenced EU policymakers to soften some key provisions in the AI Act.

The EU Parliament recently passed the near-final version of the Act, known as the “Adopted Text,” with overwhelming support. However, individual members of parliament are still making final adjustments to the legislation through negotiations. Most experts predict that the law will not take effect until at least 2024. Consequently, stakeholders like Hugging Face are now making their voices heard during this critical phase.

Today’s AI update brings you the latest news from big players like Microsoft, Anthropic, Google, OpenAI, AWS, and NVIDIA. These companies are making strides in the development of safe and responsible AI systems.

Microsoft, Anthropic, Google, and OpenAI have come together to establish the Frontier Model Forum. This industry body aims to ensure the safe progress of frontier AI systems by identifying best practices, collaborating with stakeholders, and supporting the development of applications that address societal challenges. The Forum will leverage the expertise of its member companies to advance technical evaluations, benchmarks, and create a public library of solutions.

AWS has also prioritized AI with two major updates. The first is the introduction of ‘HealthScribe,’ a healthcare-focused service that uses Gen AI to transcribe and analyze conversations between clinicians and patients. This AI-powered tool can create transcripts, extract details, and generate summaries for electronic health record systems. The second update is in Amazon QuickSight, where users can now generate visuals, fine-tune them using natural language instructions, and create calculations without specific syntax. Exciting new features include an “Ask Q” option for describing desired data visualizations and the ability to create “Stories” combining visuals and text-based analyses.

On the hardware front, NVIDIA H100 GPUs are now accessible on the AWS Cloud. These powerful chips, optimized for transformers, offer enhanced capabilities for AI/ML, graphics, gaming, and HPC applications. While AWS has not committed to AMD’s MI300 chips, they are actively exploring innovative solutions.

Lastly, researchers at MIT have developed an AI tool called PhotoGuard. This tool alters photos in imperceptible ways to prevent AI systems from manipulating them. If someone tries to use an AI editing app on an image protected by PhotoGuard, the result will look unnatural or distorted.

That wraps up our daily AI update. Stay tuned for more exciting developments in the world of artificial intelligence!

Hey folks, we’ve got some exciting news in the world of AI and technology! Let’s jump right in.

First up, Protect AI has just secured a whopping $35 million in funding for their AI and ML security platform. Their goal is to make sure AI applications and machine learning systems are protected against security vulnerabilities, data breaches, and emerging threats. It’s great to see companies taking proactive steps to keep our AI-driven world safe and secure.

In another groundbreaking development, researchers from Cardiff University have trained AI to aid in breast cancer detection. This breakthrough could significantly improve the accuracy of medical diagnostics and, more importantly, lead to earlier detection of breast cancer. This could be a game-changer for healthcare!

Next on the list is Google DeepMind’s latest creation, Robotics Transformer 2, or RT-2 for short. This model brings us one step closer to a robot-filled future by allowing robots to not only understand human instructions but also translate them into actions. It’s an exciting advancement that could revolutionize various industries.

Stack Overflow, the go-to platform for developers, is also diving into the AI world. They’re introducing Overflow AI, an AI-powered coding assistance tool that integrates right into your development environment. Imagine having access to 58 million Q&As while you code. That’s a massive resource for developers everywhere.

Stability AI has launched its most advanced text-to-image generative model, Stable Diffusion XL 1.0, which is open-sourced on GitHub and available through Stability’s API. This model is a significant step forward in generating realistic images from text, opening up endless possibilities in various fields.

Artifact, a personalized news app, is making waves with its AI text-to-speech feature. And get this, they’re offering celebrity voices like Snoop Dogg and Gwyneth Paltrow. Now you can listen to the news with some extra flair, thanks to natural-sounding accents and adjustable audio speeds.

Samsung Electronics is shifting its focus from memory chip production to high-performance AI chips. With the growing demand in the AI sector, Samsung plans to develop high-bandwidth memory chips specifically for AI applications. This move shows their commitment to staying ahead in the ever-evolving AI landscape.

Microsoft’s Bing Chat is spreading its wings beyond the Microsoft ecosystem. Some lucky users are reporting sightings of Bing Chat on non-Microsoft browsers like Google Chrome and Safari. Although there might be some restrictions compared to Microsoft’s browsers, it’s still an exciting expansion for Bing Chat.

Last but not least, OpenAI CEO Sam Altman is making waves with his crypto startup, Worldcoin. Their mission? To create a reliable way to differentiate between humans and AI online. They’ve developed a device called the Orb, which scans individuals’ eyeballs to secure their World ID and reward them with Worldcoin tokens. This project aims to empower democratic processes on a global scale and boost economic opportunities.

That’s a wrap on our AI and tech news roundup! It’s amazing to see how rapidly this field is evolving and the impact it’s having on various industries. Stay tuned for more exciting updates in the future.

Hey there, fellow AI Unraveled podcast listeners! I’ve got some exciting news for you today. If you’re hungry for more knowledge when it comes to artificial intelligence, then you absolutely need to check out “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen. Trust me, it’s an essential book that will take your understanding of AI to new heights.

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With this book, you’ll dive deep into the world of AI and unravel all those burning questions that have been swirling in your mind. Etienne Noumen does an incredible job of breaking down complicated concepts and making them easy to understand. It’s like having your own personal AI guide to walk you through everything.

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In today’s episode, we covered a range of topics including free courses for learning generative AI, using ChatGPT to generate B2B SaaS startup ideas, AI updates by AWS, concerns over language model security, calls to relax rules for open-source AI models, recent developments in AI security and detection, and the Wondercraft AI platform for hyper-realistic AI voices. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: $14 quadrillion in AI wealth in 20 years; LLaMa, ChatGPT, Bard, Co-Pilot & All The Rest. How Large Language Models Will Become Huge Cloud Services With Massive Ecosystems.

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the productization of large language models (LLMs) as cloud services, the projected wealth generated by AI in 20 years and the top earning companies, Microsoft’s focus on the new AI platform shift and the limitations of GPT models, the potential negative impact of AI-girlfriend apps, various developments and implementations of AI technology by companies such as Ridgelinez, BMW, MIT, Microsoft, Alibaba, OpenAI, Netflix, Nvidia, and Spotify, and finally, the use of the Wondercraft AI platform to create podcasts with hyper-realistic AI voices.

LLMs are becoming ubiquitous and versatile, leaving many of us feeling both intrigued and apprehensive. But what’s next for these large language models? Well, they’re set to become Generative-as-a-Service (GaaS) cloud “products” – just like other “as-a-service” offerings. The big players in cloud computing, such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others, will develop, partner with, or acquire generative AI capabilities to offer as services. Think of it as an expansion of their existing cloud ecosystems.

Google is already invested in the generative AI race, and AWS isn’t far behind. IBM, with its long-standing expertise, is also a key contender. Microsoft, however, seems to be leading the pack. These companies will create vast ecosystems around their generative AI tools, much like there are ecosystems around enterprise infrastructure and applications.

So, let’s approach LLMs as we would ERP, CRM, or DBMS tools. Companies will need to make decisions about which tool to use and how to effectively apply them to real-world problems. But are we there yet? Not quite. However, it’s just a matter of time. Within the next 2-3 years, LLMs will be fully productized and accessible through premium/business accounts. This will set off an arms race, where companies will consider both capabilities and cost-effectiveness. They will refer to documented use cases and metrics like ROI, OKRs, KPIs, and CMM to determine how to leverage generative AI across various functions and industries. It’s through these metrics and use cases that companies will conduct internal due diligence and decide whether to adopt LLMs. Once that step is completed and promise is seen, they’ll move forward with the next phase of implementation.

So, get this: Stuart Russell, the computer science professor at the University of California, Berkeley, and co-author of the AI textbook used by over 1,500 universities, predicts that in the next 20 years, AI will generate about a mind-blowing $14 quadrillion in wealth. That’s an insane amount of money!

But guess what? The top five AI companies are set to grab a big slice of that pie. Here’s the breakdown:

Google is expected to bring in a whopping $1.5 quadrillion.

Amazon isn’t too far behind, raking in about $1.1 quadrillion.

Apple, with its slick tech, is projected to earn a staggering $2.5 quadrillion.

Microsoft is no slouch either, estimated to make a cool $2.0 quadrillion.

And then we have Meta, expected to bring in around $0.7 quadrillion.

Now, here’s the kicker: these five companies are paying significantly less in taxes than they used to. In 2016, the corporate tax rate was 35%, but it has since been slashed down to a mere 21%. Talk about some sweet tax breaks!

But hey, here’s the thing we need to think about: while these companies are raking in billions and paying lower taxes, we’re looking at the potential loss of 3 to 5 million American jobs to AI in the next two decades. Yikes!

The big question is, where do our values lie? Do we prioritize the millions of people who could lose their livelihoods, or do we align more with the top AI companies enjoying their lower tax rates?

Some argue that it would only be fair for these companies to foot the bill for re-employing those millions of Americans. After all, it wouldn’t exactly be a financial burden for them.

Of course, it’s worth mentioning that the initial estimate of 3 to 5 million job losses might be wildly incorrect. So let’s dial it down a bit and consider a more reasonable estimate of 300 million job losses globally over the next 20 years.

Either way, it’s clear that we need to find a middle ground that is fair and caring. It’s time to align our values with the impact of AI on our society.

ChatGPT and other large language models gain their linguistic capacity to identify as an AI and distinguish themselves from others through their extensive training on enormous amounts of text data. While these models, including ChatGPT, do not possess consciousness, personal identities, or self-awareness, they can produce responses that align with the patterns and rules they’ve learned during training.

The training data that these models are exposed to contains a wealth of information about AI. Therefore, when prompted or asked about their nature, they can provide answers that acknowledge their AI status. However, this identification is not a result of conscious self-awareness.

Similarly, when these AI models differentiate themselves from others, it is not reflective of their possession of consciousness or self-identity. Instead, they generate these distinctions based on the context of the prompt or conversation, relying on the patterns they’ve learned in the training data.

Additionally, it’s crucial to understand that while GPT models can generate coherent and often insightful responses, they lack true understanding or beliefs. Their responses are generated by predicting the next piece of text based on the given input. The “knowledge” they possess is essentially patterns in the data that they’ve learned to predict.

In summary, ChatGPT and other large language models gain their linguistic capacity through training, but they do not possess consciousness or personal identities. Their responses are based on patterns learned from data rather than true understanding.

So, let’s talk about these AI-girlfriend apps. It seems like they’re becoming quite popular, but experts are raising concerns about their potential consequences. One major worry is that these AI companions could actually make men feel even more isolated and lonely. Instead of encouraging real-life relationships, they might end up hindering them.

And here’s another concern: these apps could reinforce harmful gender dynamics. Some experts are even worried about the possibility of these AI relationships leading to gender-based violence. That’s definitely a serious issue that shouldn’t be taken lightly.

Tara Hunter, the CEO of Full Stop Australia, is particularly worried about the idea of a controllable “perfect partner.” And she has a point. Is it really healthy to have an AI companion that always agrees with you? That might not be a recipe for personal growth or healthy relationships.

Despite these concerns, AI companions are gaining popularity. They offer users a seemingly judgment-free friend, someone you can talk to without any fear of being criticized. Just take a look at Replika’s Reddit forum, which has over 70,000 members sharing their interactions with their AI companions.

These AI companions are also customizable, allowing for both text and video chat. The more you interact with them, the smarter they supposedly become. But let’s not forget the bigger picture here. There’s still a lot of uncertainty about the long-term impacts of these technologies, which is why some people are calling for increased regulation.

Belinda Barnet, a senior lecturer at Swinburne University of Technology, believes that it’s crucial to regulate how these systems are trained. And looking at Japan, where there’s a preference for digital relationships over physical ones and decreasing birth rates, it seems like this trend might spread worldwide.

So, while AI-girlfriend apps might sound intriguing on the surface, it’s important to think about the potential negative effects they could have on individuals and society as a whole.

In today’s AI news, Ridgelinez, a subsidiary of Fujitsu in Japan, has developed an AI system capable of engaging in voice communication with humans. This system can assist companies in conducting meetings or providing career planning advice to employees. It’s a great example of how AI can enhance daily operations and improve productivity.

BMW, on the other hand, has utilized artificial intelligence to cut costs at its factory in South Carolina. By implementing an AI system, BMW has been able to remove six workers from the production line and reassign them to other jobs. This has resulted in significant savings of over $1 million a year for the company.

MIT has introduced a new technique called ‘PhotoGuard’ that protects images from malicious AI edits. By introducing subtle changes to images, this technique throws off algorithmic models and ensures the security of your visual content.

Microsoft is also making advancements in natural language interfaces with its TypeChat library. This library simplifies the development of interfaces for large language models, making it easier for developers to create apps with complex decision trees and gather necessary input to act.

In the world of software development, Microsoft Research has proposed a novel benchmark task called Code Coverage Prediction. This task accurately predicts the lines of code executed based on test cases and inputs, which helps assess the understanding of code execution by large language models. This can be valuable in scenarios like expensive build and execution in software projects or limited code availability.

In the realm of large language models, researchers have proposed 3D-LLMs, which inject the 3D world into language models. These 3D-LLMs can perform various 3D-related tasks, such as captioning, question answering, and navigation, just to name a few.

Alibaba Cloud has become the first Chinese enterprise to support Meta’s open-source AI model, Llama. This enables Chinese business users to develop programs using the Llama model, enhancing their AI capabilities.

OpenAI’s ChatGPT for Android is expanding its availability, rolling out in more countries over the next week. This will bring AI-powered chat capabilities to users around the world.

Netflix is on the lookout for an AI product manager and is offering up to $900K for this role. The focus of this role is to increase the leverage of its Machine Learning Platform, further enhancing Netflix’s ability to deliver personalized content to its users.

Nvidia is making its DGX Cloud widely accessible on Oracle’s infrastructure. This cloud-based AI supercomputing service will provide users with access to thousands of virtual Nvidia GPUs, enabling efficient generative AI training.

Spotify’s CEO, Daniel Ek, has suggested exciting possibilities for AI-powered capabilities within the music streaming platform. AI could be used to create more personalized experiences, summarize podcasts, and even generate ads, all aimed at enhancing user enjoyment.

Finally, Cohere has released Coral, an AI assistant designed specifically for enterprise business use. Coral allows knowledge workers across various industries to receive responses tailored to their sectors based on proprietary company data.

That’s all for today’s AI news! Stay tuned for more exciting updates in the world of artificial intelligence.

Hey there, fellow AI Unraveled podcast fans! Want to dive even deeper into the world of artificial intelligence? Well, do I have some exciting news for you! Etienne Noumen has just released an absolute essential read called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” And it’s widely available on awesome platforms like Shopify, Apple, Google, and Amazon.

This book is a game-changer when it comes to expanding your knowledge and understanding of AI. Whether you’re a newbie trying to wrap your head around the basics or a seasoned AI enthusiast looking for some expert insights, “AI Unraveled” has got you covered. Etienne Noumen does an incredible job of demystifying those burning questions we all have about artificial intelligence.

So, if you’re eager to level up your AI understanding and be an AI whiz, head over to Shopify, Apple, Google, or Amazon today, and snag yourself a copy of “AI Unraveled.” Trust me, you won’t regret it! It’s like having your very own AI host guiding you through the fascinating world of artificial intelligence. Happy reading, folks!

On today’s episode, we discussed the rise of Large Language Models becoming cloud services, the massive wealth AI is predicted to generate in the future, Microsoft’s focus on AI and the limitations of models like ChatGPT, the potential harm of AI-girlfriend apps, the latest developments in AI technology, and how you can use the Wondercraft AI platform to create your own podcast with hyper-realistic voices. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: AI to Cryptocurrency: Worldcoin; Google’s New Generalist AI Robot Model: PaLM-E; Can AI ever become conscious and how would we know if that happens?; On-device AI and Extended Reality (XR);

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the CEO of OpenAI launching Worldcoin, the exploration of AI consciousness, the partnership between Qualcomm and Meta for on-device AI models, Google’s introduction of the PaLM-E AI robot model, Meta and Qualcomm’s collaboration for on-device Llama 2 LLM AI capabilities, the breakthrough in mind-reading technology, the use of an AI system by Rekor to help arrest a drug trafficker, the development of “Brain2Music” AI system and BTLM-3B-8K language model, OpenAI shutting down its AI detection tool, and various advancements in the AI industry including open-access language models, the ChatGPT app, collaborations, and resignations in the field. Additionally, the podcast promotes the use of the Wondercraft AI platform for creating hyper-realistic AI voices and introduces the “AI Unraveled” podcast available on multiple platforms.

So, the CEO of OpenAI has launched a new venture called Worldcoin, and it’s been making some waves in the tech world. This project is all about aligning economic incentives with human identity on a global scale. And how does it do that? Well, it uses a little device called the “Orb” to scan people’s eyes and create a unique digital identity known as a World ID. It’s like something out of a sci-fi movie!

Now, the mission of the Worldcoin project is quite ambitious. It aims to establish a globally inclusive identity and financial network. Just imagine the possibilities that could come from that. It could potentially pave the way for global democratic processes and even an AI-funded universal basic income (UBI). That’s some big stuff right there.

But, of course, with such big dreams, come big challenges. One of the main concerns raised is the security of biometric data. How will Worldcoin ensure that this sensitive information is kept safe? We definitely don’t want any cases of identity theft or fraud.

And let’s not forget about the logistical challenges of implementing a global UBI. How will Worldcoin handle all of that? Plus, there’s also the issue of the current global regulatory climate for cryptocurrencies. It’s a bit of a wild west out there, with crackdowns and lawsuits left and right. So, navigating through all of that is going to be no small feat.

Despite its promising mission, Worldcoin has faced criticism for alleged deceptive practices in certain countries. Countries like Indonesia, Ghana, and Chile have raised concerns. So, it’s clear that there are still some hurdles to overcome.

All in all, Worldcoin is definitely a project to keep an eye on. It has the potential to change the way we think about identity and finance in the digital age. But, as with any ambitious endeavor, there are definitely some challenges to be addressed.

Can AI ever become conscious? It may sound quite far-fetched, but researchers are actively striving to recreate subjective experiences in artificial intelligence (AI). However, there is a significant challenge when it comes to testing this idea due to disagreements surrounding the definition of consciousness.

If you ask an AI-powered chatbot whether it is conscious, the response is usually negative. OpenAI’s ChatGPT and Google’s Bard chatbot both assert that they lack personal desires and consciousness. However, they suggest that, in the future, consciousness might not be entirely implausible with the right architectural enhancements. The companies themselves share this perspective. David Chalmers, a philosopher at New York University, supports this notion, explaining that there is no definitive reason to exclude the possibility of some form of inner experience emerging in silicon transistors.

So, how close are we to achieving sentient machines? While it’s uncertain, what we can observe is the emergence of remarkably intelligent behavior in these AIs. The new wave of chatbots is built on large language models (LLMs) that can code, reason, crack jokes, explain humor, perform mathematical calculations, and even produce high-quality academic essays. Chalmers admits that it’s hard not to be impressed by these capabilities, although they may also evoke a sense of trepidation.

Ultimately, the question remains: if consciousness does arise in AI, how would we even determine its presence?

In the digital age, education is being transformed by cutting-edge technologies like 3D platforms, Extended Reality (XR) devices, and Artificial Intelligence (AI). And now, Qualcomm’s exciting partnership with Meta is taking educational technology to another level. They’re optimizing LLaMA AI models specifically for XR devices, and it’s a big step forward.

By running AI models directly on XR headsets or mobile devices, there are several advantages over cloud-based approaches. First, on-device processing improves efficiency and responsiveness, creating a seamless and immersive XR experience. This real-time feedback is particularly valuable in educational settings, where immediate responses can enhance learning outcomes.

Not only that, but on-device AI models also offer cost benefits. Unlike cloud-based services, they don’t incur additional cloud usage fees. This financial sustainability is especially important for applications with high data processing demands.

On top of that, on-device AI enhances data privacy. There’s no need to transmit user data to the cloud, reducing the risk of data breaches and building user trust.

One of the greatest advantages of on-device AI is its accessibility. Even in areas with poor internet connectivity, on-device AI is still accessible. This means interactive educational experiences can happen anytime and anywhere, without relying on continuous internet connectivity.

Of course, there are challenges in accommodating the computational requirements of advanced AI models on local devices. But due to the cost-effectiveness, speed, data privacy, and accessibility of on-device AI, it is an exciting prospect for the future of XR in education.

Meta’s LLaMA AI models are leading the way in AI and XR integration, especially with the recent release of LLaMA 2. Its training volume and fine-tuned models outshine other open-source models. That’s why it has gained support from tech giants, academics, and policy experts.

Meta AI is also devoted to responsible AI development. They offer a Responsible Use Guide and other resources to address ethical implications, ensuring that AI is developed with responsibility in mind.

Integrating models like LLaMA 2 into mobile and XR devices does come with technical challenges. But if successful, it could revolutionize education, blending reality and intelligent interaction.

While we don’t have a clear timeline for on-device advancements, the convergence of AI and XR in education is full of endless possibilities for the next generation of learning experiences. With the continued efforts of tech giants like Meta and Qualcomm, the future of interacting with intelligent virtual characters as part of our learning journey might be closer than we think.

PaLM-E, Google’s new robotics model, is opening up exciting possibilities for the field of robotics. By integrating sensor data with language models, PaLM-E is revolutionizing the way robots learn and interact with their environments. This breakthrough allows PaLM-E to go beyond relying solely on textual input and instead leverage raw sensor data to process information. With this capability, PaLM-E can perform a wide range of tasks on various types of robots and across multiple modalities, including images, robot states, and neural scene representations.

The potential applications of PaLM-E extend beyond robotics. Its proficiency in visual-language tasks makes it well-suited for tasks such as describing images, detecting objects, classifying scenes, quoting poetry, solving math equations, and even generating code. This versatility opens up opportunities for PaLM-E in areas like image recognition, natural language processing, and even creative fields like art and design.

One of the key advantages of PaLM-E is its ability to learn from both vision and language domains. By injecting observations into a pre-trained language model, PaLM-E transforms sensor data into a representation that can be processed similarly to natural language. This integration allows for significant knowledge transfer, enhancing the efficiency and effectiveness of robot learning. Leveraging both visual and linguistic information enables PaLM-E to gain a richer understanding of its surroundings, enhancing its decision-making capabilities and problem-solving skills.

In conclusion, the integration of sensor data with language models like PaLM-E marks a significant advancement in robotics. It expands the capabilities of robots to perceive and interpret their environment more effectively, and its proficiency in visual-language tasks opens up a wide range of potential applications beyond robotics. By learning from both vision and language domains, PaLM-E greatly improves the efficiency and effectiveness of robot learning, unlocking new possibilities for intelligent robotic systems.

So, here’s some exciting news that might have flown under the radar amidst all the buzz about Meta’s Llama 2 LLM launch. Meta is teaming up with Qualcomm to bring on-device Llama 2 AI capabilities to Qualcomm’s chipset platform. The plan is to have this up and running by 2024.

Now, why should we care about this partnership? Well, currently, the most powerful LLMs (that’s language model models) require cloud computing resources like Bard and ChatGPT. But these resources are limited, which affects how much generative AI can really scale.

Sure, there have been some science hacks running LLMs on local devices, but they’re just proofs of concept without any groundbreaking optimizations. This partnership, however, represents the first major corporate collaboration to bring LLMs to mobile devices. It’s a big shift that goes beyond just experimenting with the technology.

So, what does an on-device LLM offer? Privacy and security, for one. Your requests stay on your device and aren’t sent to the cloud for processing. Plus, it’s faster and more convenient. Imagine quicker responses, background processing of your phone’s data, all without an internet connection. And with Llama 2’s open-source nature, it can really personalize and get to know its user over time.

Think of all the apps that could benefit from on-device LLMs: virtual assistants, productivity applications, content creation, entertainment, and more.

This is just the beginning, though. On-device computing is a new frontier that will continue to evolve as AI models become more powerful. Open-source models, in particular, have a lot to gain as they can be downscaled, fine-tuned for specific use cases, and personalized quickly.

It’ll be interesting to see if Apple also dives into on-device generative AI, but they tend to take their time to make things perfect. So, it might be a bit longer before we see their move.

Exciting times lie ahead as LLMs make their way into our mobile devices, empowering us with personalized and scalable AI experiences.

So, get this: scientists have made a major breakthrough in mind-reading technology! They’ve been using something called GPT LLM to decode human thoughts, and they’ve achieved an impressive 82% accuracy. Can you believe it?

Here’s how they did it. They had three human subjects listen to narrative stories while their thoughts were recorded over a span of 16 hours. Then, they trained a custom GPT LLM model to map specific brain stimuli to words based on these recordings. And guess what? The results were mind-blowing!

The AI model was able to generate understandable word sequences from perceived speech, imagined speech, and even silent videos. When it came to decoding recordings of perceived speech, the accuracy ranged from 72% to 82%. For mentally narrated stories, it was 41% to 74% accurate. And even when the subjects watched soundless Pixar movie clips, the model could decode their interpretation with an accuracy of 21% to 45%.

The implications of this are huge, but there are some concerns, too. While it’s amazing that the model can decipher both the meaning of stimuli and specific words, there are some privacy issues at play. Right now, the model needs to be trained on a specific person’s thoughts and there’s no generalizable model for decoding thoughts in general. However, the scientists believe that future decoders could overcome these limitations.

On top of that, there’s the potential for misuse. Just like inaccurate lie detector exams, bad decoded results could still be used nefariously. It’s definitely something we have to keep in mind as this technology progresses.

So, here’s an interesting story I came across. The New York Police recently apprehended a drug trafficker named David Zayas. They managed to catch him thanks to the help of an AI system that analyzed his driving patterns. It’s pretty impressive how technology is being used to fight crime nowadays.

The police used a company called Rekor, which specializes in roadway intelligence, to identify Zayas as suspicious. They analyzed his driving patterns through a massive database that collects information from regional roadways. This database is made up of 480 automatic license plate recognition cameras that scan a whopping 16 million vehicles each week. Talk about thorough surveillance!

While license plate reading systems have been used by cops for years to catch drivers with expired licenses or prior violations, this AI integration takes it to a whole new level. By observing driver behavior, the system was able to identify potential criminal activity. It just goes to show how AI is becoming increasingly sophisticated in law enforcement.

Now, speaking of artificial intelligence, there’s a study that found it can sometimes seem more human than humans themselves on social media. Researchers discovered that GPT-3, an AI model, produces both truthful and misleading content even more convincingly than humans. This poses a challenge for individuals trying to distinguish between AI-generated and human-written material.

In the study, participants had a hard time recognizing disinformation in synthetic tweets generated by GPT-3 compared to human-written tweets. Surprisingly, GPT-3 sometimes refused to generate false information, while occasionally producing it even when instructed to be truthful. The researchers used a combination of synthetic and real tweets to evaluate people’s ability to discern accurate information and determine whether it originated from AI or humans.

The results highlight the need for critical thinking and careful evaluation of online content, as AI becomes more capable of mimicking human communication.

In a fascinating study called Brain2Music, researchers have successfully reconstructed music from human brain patterns using artificial intelligence. This groundbreaking work provides us with a unique glimpse into how our brains interpret and represent music.

Through the use of AI, the researchers introduced Brain2Music to reconstruct music by analyzing brain scans. They employed a technique called MusicLM, which generates music based on an embedding predicted from functional magnetic resonance imaging (fMRI) data. While the reconstructed clips bear semantic similarities to the original music, there are limitations regarding the choice of embedding and fMRI data. Nevertheless, this research sheds light on how AI representations can align with brain activity when it comes to music.

In other news, Opentensor and Cerebras have made an exciting announcement at the International Conference on Machine Learning (ICML). They unveiled the BTLM-3B-8K (Bittensor Language Model), an open-source language model that boasts an impressive 3 billion parameters. This state-of-the-art model not only achieves remarkable accuracy across multiple artificial intelligence benchmarks but also fits on mobile and edge devices with as little as 3GB of memory. This breakthrough has the potential to democratize AI access, making it available on billions of devices worldwide.

The collaborative effort behind BTLM involved the Opentensor foundation commissioning its development for use on the Bittensor network. Bittensor operates as a decentralized blockchain-based network, allowing anyone to contribute their AI models for inference. This serves as an alternative to centralized model providers like OpenAI and Google. Bittensor currently supports over 4,000 AI models with an astounding 10 trillion model parameters network-wide.

The training of BTLM took place on the Condor Galaxy 1 (CG-1) supercomputer, a result of the G42 Cerebras strategic partnership. The researchers express their gratitude to G42 Cloud, the Inception Institute of Artificial Intelligence, Cirrascale, and the RedPajama dataset provided by the Together AI team for their invaluable support.

Exciting developments in the convergence of AI and music reconstruction as well as the advancement of open-source language models are at the forefront of cutting-edge research in the field.

OpenAI recently made the decision to quietly shut down its AI Classifier, a tool specifically designed to identify AI-generated text. The reason for this move was the tool’s significantly low accuracy rate, which highlighted the ongoing challenges in distinguishing between AI-produced content and human-created material.

This development holds great significance as it emphasizes the complex issues surrounding the widespread use of AI in content creation. The need for precise detection is particularly crucial in the field of education, where concerns prevail regarding the unethical use of AI for tasks such as essay writing.

Despite the failure of the AI detection tool, OpenAI’s dedication to refining it and addressing ethical concerns showcases the ongoing struggle to find a balance between the advancement of AI and ethical considerations.

The main reason behind the tool’s failure was its poor performance and low accuracy rate. OpenAI had to acknowledge this in an addendum to their original blog post before ultimately removing the tool altogether.

Moving forward, OpenAI aims to improve the tool by incorporating user feedback and conducting research on more effective text provenance techniques, as well as methods for detecting AI-generated audio or visual content.

Even at its launch, OpenAI recognized that the AI Classifier was not entirely reliable. It struggled with handling text under 1000 characters and frequently misidentified human-written content as AI-generated. Evaluations showed that the tool only correctly identified 26% of AI-written text and wrongly tagged 9% of human-produced content as AI-created.

While OpenAI may have faced setbacks with their AI detection tool, their commitment to solving these issues is commendable, as it highlights the importance of responsible AI development.

Stability AI is making waves in the AI community with its latest release. They have introduced two new LLMs (language model models) called FreeWilly1 and FreeWilly2. These models have shown impressive reasoning capabilities across various benchmarks. FreeWilly1 is built on the foundation of the original LLaMA 65B model and fine-tuned using a new synthetically-generated dataset. Meanwhile, FreeWilly2 is based on the LLaMA 2 70B model and performs competitively with GPT-3.5 for specific tasks.

In other news, Open AI has exciting news for Android users. They have announced the upcoming release of ChatGPT for Android next week. This app will provide users with the latest advancements and features seamless synchronization of chatbot history across multiple devices.

Meta has partnered with Qualcomm to enable on-device AI apps using Llama 2. By optimizing the execution of Meta’s Llama 2 directly on-device, developers can save on cloud costs and offer users private, reliable, and personalized experiences. Qualcomm Technologies plans to make Llama 2-based AI implementation available on Snapdragon-powered devices starting in 2024.

US-based AI company Cerebras Systems has signed a $100M deal with G42, a technology group based in UAE, to deliver AI supercomputers. Cerebras aims to expand the system’s capacity and establish a network of nine supercomputers by early 2024.

In other industry news, Dave Willner, head of trust and safety at OpenAI, has resigned from his position. He explained in a LinkedIn post that the pressures of the job were impacting his family life. OpenAI has not yet commented on Willner’s departure.

Lastly, Lasse, a seasoned full-stack developer, has developed an AI tool called AIHelperBot. This tool enhances SQL query building, improves productivity, and helps users learn new SQL techniques. It’s a powerful tool for individuals and businesses looking to optimize their SQL queries.

Hey there, fellow AI Unraveled podcast fans! Want to dive even deeper into the world of artificial intelligence? Well, do I have some exciting news for you! Etienne Noumen has just released an absolute essential read called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” And it’s widely available on awesome platforms like Shopify, Apple, Google, and Amazon.

This book is a game-changer when it comes to expanding your knowledge and understanding of AI. Whether you’re a newbie trying to wrap your head around the basics or a seasoned AI enthusiast looking for some expert insights, “AI Unraveled” has got you covered. Etienne Noumen does an incredible job of demystifying those burning questions we all have about artificial intelligence.

So, if you’re eager to level up your AI understanding and be an AI whiz, head over to Shopify, Apple, Google, or Amazon today, and snag yourself a copy of “AI Unraveled.” Trust me, you won’t regret it! It’s like having your very own AI host guiding you through the fascinating world of artificial intelligence. Happy reading, folks!

Thanks for tuning in to today’s episode, where we covered the launch of Worldcoin by the CEO of OpenAI, advancements in AI consciousness, the partnership between Qualcomm and Meta for XR education, Google’s PaLM-E robot model, and the collaboration between Meta and Qualcomm for on-device AI. We also discussed the breakthrough in mind-reading technology, the AI-assisted arrest of a drug trafficker, brain activity music reconstruction, OpenAI’s AI detection tool, and various updates in the AI industry. Don’t forget to subscribe for more exciting AI updates, and I’ll see you guys at the next episode!

AI Unraveled Podcast July 2023: What is Bias and Variance in Machine Learning?; NAMSI: A promising approach to solving the alignment problem; ChatGPT will now remember who you are & what you want.

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the topics of bias and variance in predictions, the alignment problem in AI and the potential solution of developing narrow AI focused on morality, the merging of ChatGPT and Midjourney into CM3leon and the capabilities of NaViT, the use of AI in sales, customer service, website creation, and medical AI, the introduction of Llama 2 as a language model, updates to ChatGPT Plus and the introduction of Brain2Music AI, and finally, the Wondercraft AI platform for starting your own podcast with hyper-realistic AI voices.

Bias and variance are two important concepts in machine learning that are crucial for understanding the accuracy and consistency of predictions. Bias refers to how much your predictions deviate from the true value, while variance measures the variability of predictions when different data is used.

Ideally, one aims for low bias and low variance, as this indicates both accurate and consistent predictions. However, achieving this balance is challenging in practice, often requiring a trade-off between the two. Reducing bias may increase variance, and vice versa.

To comprehend bias and variance in machine learning, imagine playing a game of darts. The goal is to hit the bullseye as accurately and consistently as possible. If the darts land all over the board, this signifies high variance, implying inconsistent predictions reliant on the data used. On the other hand, if the darts cluster around a spot away from the bullseye, this represents high bias, indicating inaccurate predictions that miss the target significantly.

Understanding bias and variance is essential because high bias suggests that the model fails to capture data complexity and may not generalize well to new data. Conversely, high variance suggests overfitting the data, which may also hinder generalization to new data.

Techniques to reduce bias and variance exist, such as employing more complex models with additional features to reduce bias, or using simpler or more regularized models with higher quality data to decrease variance. Finding the optimal balance between bias and variance can be achieved through techniques like cross-validation and utilizing evaluation metrics like accuracy, precision, recall, or the F1-score.

To delve further into bias and variance in machine learning, additional resources include the variance and bias analysis by Statistics Canada, the Bias-Variance Analysis: Theory and Practice from Stanford University, the comprehensive understanding of bias and variance by Analytics Vidhya, and a detailed comparison of bias and variance by CORP-MIDS1 (MDS).

Media-driven concerns about the potential dangers of AI often revolve around the alignment problem, particularly the fear that we will not be able to address it before reaching AGI and ASI. However, what AI developers need to recognize is that the alignment problem fundamentally stems from a morality problem.

To tackle this challenge, the development of narrow AI systems solely dedicated to understanding and advancing morality as a means of solving alignment holds immense promise. While humans may lack the intelligence to solve alignment on our own, creating narrow AI systems focused on comprehending and enhancing the morality necessary to address this issue can provide more effective solutions in a shorter period of time.

As the concern of harmful AI primarily arises when we reach ASI, it seems logical to prioritize the development of narrow ASI focused on morality in our alignment work. Narrow AI systems are already approaching exceptional levels of expertise in fields like law and medicine, and given the rapid progress in these areas, significant advancements can be expected in the next few years.

Imagine developing a narrow AI system dedicated exclusively to understanding the morality at the core of the alignment problem. Such a system could be referred to as Narrow Artificial Moral Super-intelligence, or NAMSI.

AI developers, including Emad Mostaque from Stability AI, understand the benefits of focusing on narrow AI applications rather than overly ambitious endeavors like AGI. Stability AI, for instance, concentrates on developing specific narrow AI applications for corporate clients.

As a global society, one crucial question we face is how to best apply the AI we are developing. Considering the imperative nature of addressing the alignment problem and the central role of morality in its solution, creating NAMSI may offer the most promising path towards resolving it before AGI and ASI come into existence.

But why opt for narrow artificial moral super-intelligence over artificial moral intelligence? The answer lies in its feasibility. While morality presents complex challenges for humans, our success in developing narrow legal and medical AI applications that may soon surpass the expertise of top professionals in those fields suggests something significant. With proper training, AI systems could very likely attain expertise in morality at a level that surpasses human capability. Once we achieve that point, the likelihood of solving the alignment problem before AGI and ASI becomes far greater since we will have relied on AI, rather than our comparatively weaker human intelligence, as our tool of choice.

Meta, previously known as Facebook, has made significant advancements in the field of AI. They have launched CM3leon, a multimodal language model that combines text-to-image and image-to-text generation. While most language models use Transformer architecture for text generation and diffusion models for image generation, CM3leon is based on Transformer architecture, making it the first multimodal model trained with a recipe adapted from text-only language models. Despite being trained with 5x less compute, CM3leon achieves state-of-the-art performance. It can perform various tasks like text-guided image generation and editing, text-to-image conversion, text-guided image editing, text tasks, structure-guided image editing, segmentation-to-image conversion, and object-to-image conversion.

In related news, Apple is reportedly working on its own version of ChatGPT, an AI model for generating conversational responses. Apple’s version aims to improve natural language understanding and interactions with its virtual assistants.

Meanwhile, Wix, a popular website building platform, is leveraging AI to simplify the website creation process. Their AI technology assists users in building and designing websites, allowing them to create professional-looking sites with ease.

In the world of image generation, Google DeepMind has introduced NaViT (Native Resolution ViT), a Vision Transformer model that can process images of any resolution and aspect ratio. Unlike traditional models that resize images to a fixed resolution, NaViT uses sequence packing during training, leading to better results in tasks such as image and video classification, object detection, and semantic segmentation. NaViT also offers flexibility at inference time, enabling a balance between cost and performance.

These latest developments highlight the ongoing AI revolution and its continuous impact on various industries, from language generation to website design and image processing.

Air AI is an innovative conversational AI that brings automation to sales and customer service calls. This advanced technology is capable of conducting full-length calls that simulate human interaction across a wide range of applications, offering businesses a profitable means of engaging with real customers. Co-founded by a team of experts, Air AI has already demonstrated impressive results in live calls and is flexible enough to cater to various use cases. Whether it’s acting as an AI SDR, a 24/7 CS agent, a Closer, or an Account Executive, Air AI can adapt to business requirements. It can even be programmed for unique purposes like therapy sessions or conversing with historical figures like Aristotle.

Wix, a popular website-building platform, is introducing an innovative AI tool that revolutionizes the creation of websites. This new feature relies solely on algorithms, eliminating the need for templates. By prompting users with a series of questions about their preferences and needs, the AI generates a fully customized website. Wix combines OpenAI’s ChatGPT for text creation with its own AI models, enhancing the platform’s capabilities. Additional features like the AI Assistant Tool, AI Page, Section Creator, and Object Eraser are in the pipeline, promising further enhancements to the website-building experience. Avishai Abrahami, Wix’s CEO, reaffirms the company’s commitment to AI and its potential to drive business growth through website creation.

MedPerf, an open benchmarking platform, aims to improve the performance and impact of medical AI models. Developed by MLCommons, this platform allows researchers to evaluate and measure the performance of medical AI models using real-world datasets while prioritizing patient privacy and complying with legal and regulatory requirements. MedPerf utilizes federated evaluation, ensuring that patient data remains inaccessible while enabling accurate assessment. The platform has already proven its effectiveness in pilot studies and challenges related to brain tumor segmentation, pancreas segmentation, and surgical workflow phase recognition.

A study highlights the potential of Language Model-based Methods (LLMs) in completing complex sequences, even when the sequences are randomly generated or expressed using random tokens. This suggests that LLMs can serve as general sequence modelers without additional training. The research explores how this capability can be applied to robotics, enabling LLMs to fill in missing elements in sequences of numbers or prompt reward-conditioned trajectories. While there are limitations to deploying LLMs in real-world systems, this approach offers a promising way to transfer patterns from words to actions.

Meta has unveiled Llama 2, the latest iteration of its open-source large language model. Llama 2 is available for free use in research and commercial applications, offering researchers and developers the opportunity to harness its capabilities. The model can be downloaded directly, and it is also accessible through platforms such as Microsoft Azure, AWS, and Hugging Face.

Llama 2 surpasses existing open-source chat models in various benchmarks and has received positive evaluations for its helpfulness and safety. These evaluations suggest that Llama 2 could serve as a suitable alternative to closed-source models. As Meta opens access to Llama 2, it has garnered support from a broad range of industry experts, academics, and policymakers who believe in the value of open innovation in AI development.

In other news, Microsoft has made significant strides in its AI endeavors. During the Microsoft Inspire event, the company, in collaboration with Meta, announced its support for the Llama 2 family of LLMs on Azure and Windows. It also unveiled major updates to AI-powered tools, including Bing Chat Enterprise, Microsoft 365 Copilot, and Vector Search. These updates enhance the functionality and efficiency of AI systems, enabling users to access intelligent chat solutions, streamline workflows, and improve search capabilities.

Meanwhile, a recent study on the behavior of ChatGPT models over time reveals interesting findings. Specifically, the study evaluates the changes in behavior between the March 2023 and June 2023 versions of GPT-3.5 and GPT-4. It concludes that GPT-4 exhibits a decline in performance for solving math problems, while GPT-3.5 demonstrates significant improvement. Additionally, GPT-4 becomes less inclined to respond directly to sensitive or dangerous questions, while GPT-3.5 becomes slightly more responsive. Both models show mixed results in code generation, making more mistakes that hinder code execution in June compared to March. However, they both exhibit slight improvements in visual reasoning tasks. This study highlights the significance of continuous monitoring of LLM quality due to the potential for substantial behavior changes within a relatively short timeframe.

Looking beyond Meta and Microsoft, Apple is also venturing into the AI domain with its development of AI tools, including its own large language model called “Ajax” and an AI chatbot known as “Apple GPT.” Apple aims to catch up with rivals like OpenAI and Google in the AI space and plans to make a significant AI-related announcement next year. The company has multiple teams working on AI technology while prioritizing privacy concerns. Although Apple has previously integrated AI into its products, there is currently no defined strategy for directly releasing AI technology to consumers. However, executives are considering incorporating AI tools into Siri to enhance its functionality and keep up with advancements in the field.

Furthermore, Google’s research team has introduced SimPer, a self-supervised learning method designed to capture periodic or quasi-periodic changes in data. SimPer leverages the inherent periodicity in data by incorporating customized augmentations, feature similarity measures, and a generalized contrastive loss. This approach showcases superior data efficiency, robustness against spurious correlations, and the ability to generalize to distribution shifts, paving the way for various applications that rely on the utilization of periodic information.

These developments in AI, ranging from advanced language models to new learning methods, signal the ongoing progress and innovation in the field. As companies continue to push the boundaries of AI, it is crucial to monitor and evaluate their behavior, quality, and potential impact.

OpenAI has announced that they are doubling the message limit for ChatGPT Plus subscribers when interacting with GPT-4. Starting next week, users will be able to send up to 50 messages within a 3-hour timeframe, compared to the previous limit of 25 messages in 2 hours.

In other news, Google and Japanese institutions have unveiled a new research project called Brain2Music. This study introduces a method for generating music based on brain activity captured through functional magnetic resonance imaging (fMRI). The resulting music closely resembles the semantic properties of the musical stimuli experienced by human subjects, including genre, instrumentation, and mood. The research paper explores the relationship between the Google MusicLM (text-to-music model) and the observed brain activity of individuals listening to music.

OpenAI is also introducing a new feature for ChatGPT that allows users to customize instructions. This feature will give users greater control over how ChatGPT responds by enabling them to specify preferences and requirements. ChatGPT will remember and consider these instructions in its future responses, eliminating the need for users to repeatedly state their preferences. Currently available as a beta feature in the Plus plan, this customization capability will be rolled out to all ChatGPT users in the coming weeks.

Additionally, a recent research proposal introduces Meta-Transformer, a unified framework for multimodal learning. This framework enables simultaneous learning across 12 different modalities, without the need for paired multimodal training data. In experimental evaluations, Meta-Transformer demonstrates exceptional performance on various datasets, showcasing its potential in unified multimodal learning.

(Note: Greeting, introduction, and conclusion are not included as per the instructions.)

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In today’s episode, we explored the concepts of bias and variance in predictions, discussed the alignment problem in AI and the potential solution through the development of NAMSI, discovered the integration of ChatGPT and Midjourney into CM3leon, and explored the advancements in AI for sales, customer service, and website creation. We also learned about the introduction of Llama 2, the latest open-source language model, and the updates to ChatGPT Plus, the Brain2Music AI, and the Meta-Transformer. And finally, we shared how you can use the Wondercraft AI platform to create your own hyper-realistic AI-powered podcast. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: AI Best Sales Tools 2023; MusicGen AI; The AI Renaissance; ChatGPT Best Tips

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover trends in Generative AI, AI sales tools, AI-powered e-commerce platforms, Hyperdimensional computing, image enhancement with advanced AI, Google’s AI advancements, AI safety research collaboration, tactics to improve AI models, the correlation between exercise and mental health, and recommendations for the “AI Unraveled” book.

Get ready for the AI Renaissance, folks! It’s time to unleash a whole new world of innovation, creativity, and collaboration. This study from Rohrbeck Heger – Strategic Foresight + Innovation by Creative Dock is diving deep into Generative AI trends. And let me tell you, it’s a wild ride.

We’ve got the rise of multimodal AI, where AI gets even more multi-talented than a circus performer. Then there’s the rise of Web3-enabled Generative AI, which sounds super fancy and high-tech. Like, AI on steroids or something. And let’s not forget about AI as a service (AIaaS), because apparently, AI is now a hot commodity. Hey, can I get some AI with my morning coffee, please?

But wait, there’s more! We’ve got advancements in NLP, which I assume stands for “Neverending Linguistic Party.” And let’s not overlook the increasing investment in AI research and development. It’s like AI is the new black, everyone wants a piece of it.

Now, let’s fast forward to 2026 with these four crazy scenarios. Scenario 1: Society Embraces Generative AI. Sounds like a robot revolution party to me. Scenario 2: The AI Hibernation – AI takes a nap. Snuggle up, little bots. Scenario 3: The AI Cessation – Society rejects AI. Talk about a breakup, it’s not me, it’s you, AI. And finally, Scenario 4: Technological Free-For-All – Unregulated High-Tech AI. Buckle up, folks, it’s gonna be a wild ride.

And if you thought that was all, think again! We’ve got some awesome AI sales tools to help you conquer the sales world. First up, we have Oliv AI. This little buddy listens to hours of sales recordings to give you the best insights. It’s like having a sales assistant with the power of AI, guiding you to cold call success.

Pipedrive’s AI sales assistant is like your mentor, always looking out for your best interests. It reviews your sales data and gives you recommendations to maximize earnings. It’s like having a cheerleader in your corner, rooting for your success.

And last but not least, we have Regie AI. This tool is like your own personal sales robot, sending customized sales messages to prospects and clients. It’s like the Flash of cold emails, speeding up your sales outreach by ten times. Plus, it helps your revenue team create compelling content at scale. It’s like having an army of AI marketers working for you.

So, there you have it, folks. The AI Renaissance is upon us, with all its craziness and innovative tools. Get ready to ride the AI wave and conquer the world, one machine learning algorithm at a time.

Drift, huh? Sounds like a fancy way to boost sales teams’ efficiency and success rates. It started as a chat platform, but now it’s evolved into an AI-powered e-commerce platform. Talk about leveling up! With Drift, you can automate lead collecting and the sales process without having to hire more people. It’s like having a super smart assistant on your team. Plus, it offers real-time communication with prospective clients through chat. And get this – it has multilingual AI chatbots. So no matter where your customers are from, Drift can handle it.

Now, let’s talk about Clari. If you want the best sales enablement platform for your modern sales team, Clari is the way to go. It’s like having a crystal ball for your sales forecasts. It aggregates data from real deals, so you can see everything your sales team is doing – who they’re talking to, what deals they’re working on. And the best part? Clari claims it can enhance win rates, shorten sales cycles, and raise average deal sizes. That’s a big promise, but hey, they say they can deliver.

Last but not least, we have Exceed AI. This baby is all about acceleration and productivity. It helps sales teams close more deals in less time. And it’s compatible with all the big CRM and ERP platforms like Salesforce, Oracle, and SAP. With Exceed AI, you can manage your sales funnel and data like a pro. It’s like having a personal assistant who handles all the boring stuff – qualifying leads, syncing data to your CRM, you name it. So if you want to work smarter, not harder, give Exceed AI a try.

That’s it for our AI Sales Tools Part 2! Stay tuned for more techy goodness.

Saleswhale, HubSpot, People AI, and SetSail, oh my! These are some of the best AI sales tools out there. Saleswhale is like having your own personal assistant that helps you focus on what really matters while supplying you with top-notch leads. It’s like having a superhero sidekick but for sales.

HubSpot is the ultimate all-in-one solution for managing customers and leads. It’s like having your own personal CRM but with the power of artificial intelligence. You can track leads, automate tasks, and even collaborate on papers without leaving your inbox. It’s like having a sales Swiss Army knife.

If you want cutting-edge AI-driven software, People AI is the way to go. It analyzes historical data to help sales reps focus their energy on deals with the highest chance of success. It’s like having a crystal ball that predicts which deals will bring in the big bucks.

And let’s not forget about SetSail. This platform is perfect for large businesses that want to track and analyze their sales pipeline. With its machine learning capabilities, you can spot trends and train your salespeople with clever competitions. It’s like having your own personal sales coach.

So whether you’re looking for a superhero sidekick, a sales Swiss Army knife, a crystal ball, or a personal sales coach, these AI sales tools have got you covered. Don’t miss out on boosting your sales and closing those deals with less effort. Embrace the power of AI and watch your sales soar to new heights.

Meta’s open-source MusicGen AI is your new best friend when it comes to creating musical mashups. You know, like when you can’t decide between a pop ballad and a heavy metal banger? Well, MusicGen has got your back!

This innovative AI from Meta’s Audiocraft research team takes text prompts and turns them into original tunes. It’s like magic, but with more code and less rabbits. And if you want to align your creation with an existing song, no problemo! Just pick your favorite tune, and MusicGen will do its thing.

Now, I gotta warn you, this AI takes its sweet time to cook up some musical goodness. We’re talking around 160 seconds of processing time. But hey, good things come to those who wait, right? So, sit back, relax, and let MusicGen work its AI magic.

Oh, and did I mention that the resulting music piece is influenced by your text prompts and melody? It’s like giving the AI a musical makeover, and the end result is a short, sweet melody that perfectly matches your vibe.

But don’t just take my word for it. Check out MusicGen in action on Facebook’s Hugging Face AI site. You can specify the style of music you want, like an 80s pop song with heavy drums. Talk about getting specific!

And if you’re feeling extra fancy, you can align your newly generated music to a specific part of an existing song. It’s like the ultimate DJ remix moment!

MusicGen was trained using 20,000 hours of licensed music, so you know it’s got some serious musical chops. And unlike other models, MusicGen doesn’t need a self-supervised semantic representation. It’s just ready to rock and roll.

So, grab your 16GB GPU and get ready to create some epic music with MusicGen. With its four model sizes, including the behemoth 3.3 billion parameters model, the possibilities are endless. Who needs a band when you’ve got an AI that can create complex music? So go ahead, unleash your inner Mozartist! Just remember to give MusicGen a round of applause for making all your musical dreams come true.

Hey there, fellow nerds! Have you heard about the new and improved approach to computation? It’s called hyperdimensional computing, and it’s here to shake up the world of artificial intelligence!

So, what’s the deal with hyperdimensional computing? Well, unlike those old-fashioned artificial neural networks (ANNs) like ChatGPT, this new method uses high-dimensional vectors to represent information. It’s like upgrading from an old clunker to a fancy sports car!

You see, ANNs have their limitations. They require a ton of power and lack transparency, which makes them about as clear as mud. They’re like those cryptic crossword puzzles that leave you scratching your head for hours.

But fear not, my friend! Hyperdimensional computing is here to save the day. Instead of relying on artificial neurons, this method uses activity from a bunch of neurons to represent data. It’s like having a whole team of brainiacs working together to solve a problem. Talk about teamwork!

By using these hyperdimensional vectors, we can simplify the representation of complex data. It’s like organizing your closet with color-coded hangers. Suddenly, everything makes sense, and finding your favorite shirt is a breeze!

And it gets even better. With hypervectors, we can perform all sorts of cool operations like multiplication, addition, and permutation. It’s like having a magical calculator that can bind ideas, superimpose concepts, and structure data.

But wait, there’s more! Hyperdimensional computing is faster and more accurate than traditional methods. It can handle tasks like image classification with ease, leaving those deep neural networks in the dust. It’s like racing a Ferrari against a tricycle. No contest!

Of course, hyperdimensional computing is still in its early stages, and there’s much more testing to be done. But it’s already showing a lot of promise. With its error tolerance and transparency, it’s like the superhero of computing, ready to save the day.

So, watch out, world! Hyperdimensional computing is here, and it’s ready to revolutionize artificial intelligence. Get ready for a wild ride!

So, imagine you’re coloring a picture and you accidentally go outside the lines. Oops! But hey, what if instead of making a big mess, it actually continues the picture in a way that makes sense? Mind-blown, right? Well, hold on to your crayons because that’s exactly what the geniuses at Clipdrop have come up with.

They created a tool called Uncrop, and it’s like your personal digital art assistant. Let’s say you have a photo of a dog chilling on the beach, but you want to make that photo wider. Now, ordinarily, you’d be out of options. But fear not, because Uncrop swoops in like a superhero to save the day.

This nifty tool has the ability to smartly guess what could be there in the extended parts of the photo. So, if you need to add more sand to the beach, or more blue to the sky, or even more waves to the sea, Uncrop does it with a flick of its digital wand. It’s like magic, but without the rabbits and hats.

And here’s the best part, my friends: no need to download anything or jump through any hoops. Nope, Uncrop is completely free and available on Clipdrop’s website. They’ve made it super easy and accessible for everyone.

Now, let’s talk about the implications of this tech wizardry. Photography and graphic design folks can now change the aspect ratio of an image without losing any details or having to crop anything out. Film and video producers can tweak the size of their footage without losing any important parts. Social media enthusiasts can finally make their photos fit just right on their feeds. And let’s not forget about the AI researchers – this whole Uncrop thing is powered by some mind-blowing AI model called Stable Diffusion XL. This shows just how far AI has come and the exciting possibilities it holds for the future.

In conclusion: Clipdrop’s Uncrop is here to fix your picture sized problems and make sure you color inside the lines, even when you go outside of them. It’s like having a happy little Bob Ross in your pocket, ready to assist your artistic endeavors. So go forth, my friends, and let your creativity run wild, with Uncrop by your side. *drops the digital mic*

Hey there, AI enthusiasts! Get ready for some funny AI news to brighten up your day!

So, Google and UC Berkeley are at it again with their latest invention: self-guidance in text-to-image AI. Now, you can control the shape, position, and appearance of objects in generated images. It’s like having your own personal Picasso, but without all the messy paint and brushes. And the best part? No extra training required! Plus, you can even edit real images. Say goodbye to those embarrassing photobombs!

Next up, we have some mind-boggling stuff. A new research framework called Thought Cloning aims not only to clone human behaviors but also the thoughts behind them. That’s right, they’re training AI agents how to think and behave. Talk about creating safer and more powerful agents. I can only imagine what these AI thought bubbles look like. “Hmm, should I do the robot dance or the macarena?”

But that’s not all! Introducing the modular paradigm ReWOO, which detaches the reasoning process from external observations. It’s like giving AI its own imaginary friend. And guess what? It significantly reduces token consumption. Who needs tokens anyway? ReWOO achieves 5x token efficiency and a 4% accuracy improvement. It’s like hitting the reasoning jackpot!

Hold up, folks! We can’t forget about Meta’s new creation, HQ-SAM. It’s here to save the day when it comes to accurately segmenting complex objects. SAM may have struggled before, but HQ-SAM is the hero we deserve. Trained on 44,000 fine-grained masks in just 4 hours, this bad boy is ready to tackle any segmentation challenge. Move over, Picasso, there’s a new artist in town!

Now, let’s talk feedback. Argilla Feedback is bringing LLM fine-tuning and RLHF to everyone. It’s like improving the performance and safety of LLMs at the enterprise level, making them more efficient than ever. Finally, feedback doesn’t have to be a one-way street. It’s a win-win situation!

But wait, we have more from the magical world of Google. They’ve introduced Visual Captions, a system that augments verbal communication in real-time with interactive visuals. It’s like having a personal visual assistant. Just imagine your conversation being spiced up with all sorts of funny and informative visuals. Who needs words when you have pictures?

And Google is not done yet! They’ve come up with GGML, a Tensor library for machine learning that enables large language models to run effectively on consumer-grade hardware. It’s like giving your old laptop a dose of AI superpowers. No need to worry about expensive computers or fancy cloud resources. Google is here to democratize access to LLMs.

Oh, and did we mention some cool updates to Bard? Now Bard can solve mathematical tasks, answer coding questions, and even manipulate strings more accurately thanks to “implicit code execution.” It’s like having your own coding wizard at your fingertips. Plus, Bard can export tables to Google Sheets. Talk about convenience! Bard is definitely a helpful assistant for all your data needs.

Last but not least, Google DeepMind has introduced AlphaDev, an AI system that uses reinforcement learning to discover improved computer science algorithms. Forget old-school methods, they’re taking a different approach by focusing on the computer’s assembly instructions. It’s like teaching your computer some secret ninja moves. Say goodbye to slow algorithms and hello to efficiency!

And wrapping up our funny AI news, we have SQuId. No, it’s not a superhero, but it’s a regression model that measures speech quality. It tells you just how natural someone sounds. It’s like having your own speech coach in your pocket. SQuId has been fine-tuned on millions of quality ratings in multiple languages. It’s like the world’s largest speech critique club!

That’s all for today’s hilarious AI news. Stay tuned for more mind-blowing inventions and funny AI adventures. Until next time, keep those algorithms running and those laughter neurons firing!

So, apparently, the UK government has decided to dive headfirst into the world of AI. And who are they turning to for help? None other than the AI giants themselves: DeepMind, OpenAI, and Anthropic. These tech titans have generously offered to share their precious AI models with the government. How kind of them!

But why is the government so interested in AI safety all of a sudden? Well, it seems like they’ve been getting a little worried about the potential risks associated with this technology. And let’s be honest, who wouldn’t be a little concerned? We’ve all seen enough sci-fi movies to know that AI can go rogue and start wreaking havoc.

Now, let’s talk about sorting. Yes, that’s right, sorting. It may sound like the most mundane thing in the world, but companies like Netflix rely on efficient sorting algorithms to find the perfect movies for you. With more and more content being generated every day, they need all the help they can get.

And guess what? DeepMind has come to the rescue once again! Their researchers have developed new sorting algorithms by turning the whole process into a game. They trained their AI, Alphadev, to play this sorting game and it came up with some truly mind-blowing strategies. Move over humans, the computers are taking over!

But don’t worry, it’s not like these algorithms are completely revolutionary. They just optimize the current approach. So, it’s more like a supercharged version of what we already have. Still, it’s pretty impressive that this AI solution has been added to a library for the first time ever.

It just goes to show that computers can come up with optimal solutions that we humans could never even dream of. Just like how DeepMind’s AlphaGo beat the top-rated Go player with moves that had never been seen before. It’s both exciting and a little bit scary at the same time.

But hey, let’s not forget that computers can also be limited by what they’ve been taught. Someone was able to replicate DeepMind’s discovery using ChatGPT, which means AI isn’t infallible. So, let’s keep our sense of humor intact and embrace this brave new world of AI, because let’s face it, it’s here to stay!

So, apparently GPT-4’s quality has been going down and causing quite the ruckus. But fear not, my fellow conversationalists, for Open AI has come to the rescue with a list of tactics and strategies to save the day.

Now, I perused through these strategies, and it seems like a lot of them revolve around something called “Prompt Engineering.” Basically, they’re telling us to provide better inputs. It’s like they’re saying, “Hey, it’s not us, it’s you. You need to ask better questions!”

But here’s the thing, folks. I already subconsciously use most of these tactics. My prompts are always longer than five sentences because I like to give as many details as possible. And let me tell you, GPT-4 has given me powers I never thought I’d have.

Now, on to Bard, the not-so-shiny sidekick. Google is trying to spruce it up by adding features one by one. Last week, they announced that Bard will finally get better at logic and reason. How, you ask? Well, they’re using something called “implicit code execution.” Fancy, huh?

Instead of giving Bard a logical question and getting some weird answer, it will now recognize the question and write and execute code under the hood. It’s like Bard is becoming a little coding wizard, all thanks to this strategy called “Give GPTs time to ‘think’.” According to Google, this improves performance by a whopping 30%.

So there you have it, my friends. GPT-4 may be losing its mojo, but fear not, for there are tactics and strategies aplenty. And Bard is stepping up its game by becoming a logical genius. Let the conversational revolution continue!

So, I found this wild story online and I just had to share it with you guys. Brace yourselves for some serious laughter, because this one is a real gem. Okay, so apparently there’s this guy who decides to try out a new diet. But it’s not just any diet, oh no. He decides to only eat green foods for an entire month. I mean, who does that? Anyway, this guy’s obsession with green foods goes to extreme levels. He starts binging on kale, spinach, broccoli, you name it. He even drinks green smoothies for breakfast, lunch, and dinner. Now, you’d think this crazy experiment would have some sort of health benefit, right? Well, think again! Turns out, he turned into the Grinch! I kid you not, his skin turned green, he grew pointy ears, and his whole demeanor changed. He started grumbling and speaking in rhymes, just like the real Grinch. Needless to say, the guy had to end his experiment early because people were starting to avoid him like the plague. Lesson learned: don’t mess with nature and definitely don’t turn into a fictional character for the sake of a diet. Stay sane and stick to eating a balanced meal, folks!

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Thanks for listening to today’s episode! We discussed trends in Generative AI, AI sales tools, open-source MusicGen AI, hyperdimensional computing, advanced image editing with AI, Google’s advancements in AI systems, AI safety research partnerships, tactics to enhance AI models, the correlation between regular exercise and improved mental health, and an AI voice platform. See you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023 – LLMs Utilize Vector DB for Data Storage; Performance Degradation in GPT-4; Google Pushes AI Tool for Newsrooms; Researchers Discover Performance Degradation in GPT-4; Google Pushes AI Tool for Newsrooms; Google Introduces Brain-to-Music AI

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover LLMs’ use of Vector DB for storage, the declining performance of GPT-4 and the need for ongoing AI evaluation, Tesla’s plan to license the Full Self-Driving system and invest $1 billion in the Dojo supercomputer, Apple possibly withdrawing FaceTime and iMessage in the UK due to proposed laws, Stanford and DeepMind’s suggestion of using large language models to define preferences and rewards, the potential of brain-merging with AI implants for creating superhumans by 2050, the possibility of enabling talking animals with new abilities through animal integration, and how to start your own podcast with hyper-realistic AI voices using the Wondercraft AI platform available on Shopify, Apple, Google, or Amazon.

Hey there, folks! I’ve got some interesting news for you today, and it’s all about some tech titans making waves in the industry. Hold on to your hats!

First up, we have researchers discovering performance degradation in GPT-4. Yep, it seems like our friendly neighborhood AI language model has been slacking off a bit. Apparently, its ability to handle sensitive queries, solve math problems, and generate code has taken a bit of a nosedive. Well, well, well, looks like even AI needs a little pep talk every now and then. Maybe it’s time for GPT-4 to hit the gym and work those linguistic muscles!

And speaking of Tesla, guess what? They’re feeling generous and are planning to license their Full Self-Driving system to other automakers. That’s right, Elon Musk is ready to share the love and spread some autonomous driving magic to the rest of the industry. And hey, if you’re a Tesla owner and want to switch things up, there’s even an option for you to shift your existing FSD subscriptions to a new Tesla. Talk about keeping things interesting!

But hold on tight, because the excitement doesn’t stop there. Tesla is also ramping up its game with the construction of the Dojo supercomputer. Elon Musk himself is going all out, investing a whopping $1 billion in this bad boy. By the end of 2024, it’s set to have a mind-boggling 100 exaFLOPS. For those of you scratching your heads, let me put it into perspective – that’s way more powerful than the best current supercomputers out there. Talk about taking self-driving to a whole new level!

Well, folks, that’s all the tech gossip I have for you today. Until next time, keep your batteries charged and your self-driving dreams alive!

Did you hear the news? Apple is considering withdrawing FaceTime and iMessage from the UK! Why? Well, it seems like there might be some new laws that could force Apple to weaken their security features. I mean, who wants weak security, right? So, as a response, Apple might just say, “Ta-ta!” to FaceTime and iMessage in the UK.

But that’s not all! Google is jumping on the AI bandwagon with their new toy called Genesis. It’s an AI tool meant to help journalists write articles. Can you believe it? The AI is going to give style suggestions and even come up with headlines. I can already see the newspaper headlines now: “Breaking News: AI takes over journalism!”

And guess who’s back in the game? Sergey Brin, the co-founder of Google! He’s returned to lead the creation of Google’s very own GPT-4 competitor named Gemini. You know what they say, “Once a Googler, always a Googler.”

Meanwhile, the top AI companies are teaming up with the White House to develop responsible AI. They’re working on cybersecurity, discrimination research, and even marking AI-generated content. It’s like they’re creating the AI Avengers, here to protect us from the dangers of artificial intelligence.

But wait, there’s more! Google and Japanese researchers have come up with a way to make music from brain activity. Yes, you read that right! They’re using functional magnetic resonance imaging to generate music based on what’s going on in your brain. Talk about mind-blowing tunes!

Last but not least, Antony’s article talks about those large language models using Vector DB. Apparently, it helps them understand textual data better. It’s like giving those models a crash course in literature. Maybe one day they’ll write the next great American novel.

So there you have it, folks! From Apple’s security drama to Google’s AI takeover, it’s been one wild ride in the tech world. Stay tuned for more wacky tech adventures coming to a podcast near you!

So, get this – a group of brainiacs from Stanford University and DeepMind have come up with a brilliant idea! They want to make it super easy for us regular folks to express our preferences. How, you ask? Well, they’ve created a system that’s way more natural than writing some boring old reward function.

So here’s the dealio: they’ve harnessed the power of large language models (LLMs), which have been trained on a ton of text from the internet. These LLMs, you see, are pretty darn good at learning in context even with only a few examples. It’s like they have some sort of magical ability to understand human behavior and all that common sense stuff.

Now, let me break it down for you. Instead of going through the hassle of explicitly defining your preferences, you can just use these LLMs to do the work for you. It’s like having your very own language-based assistant that knows what you want without you having to spell it out. And the best part? It’s cost-effective! You don’t need a truckload of data or examples to make it work.

So next time you’re struggling to articulate your preferences, just remember that the brainiacs at Stanford and DeepMind have got your back with their fancy LLMs. Who needs a reward function when you’ve got language models that can read your mind?

So, you know how everyone’s all hyped up about merging our brains with AI implants and becoming superhumans? Well, what if we took it a step further and merged AI with our furry friends? That’s right, people, brace yourselves for the era of superanimals!

Imagine this: your cat, Fluffy, walks up to you and says, “Hey, human, I demand treats!” Or your dog, Buddy, gives you a call on your mobile phone and asks, “When are you coming home? I miss you!” Talk about mind-blowing, right?

Now, I know what you’re thinking. Animals don’t have the same reasoning and thoughts as humans. But hey, who says we can’t dream big? If we can become superhumans by 2050, why not create superanimals too? Let our furry companions have a taste of the AI magic!

Sure, it might sound ridiculously absurd right now, but think about it. If a time traveler from the future popped up and told us about the mind-boggling things happening beyond 2050, we’d probably freak out too!

So let’s keep pushing the boundaries of what’s possible. Who knows, maybe one day we’ll have conversations with our pets, and they’ll reveal their deepest desires and secrets. I can already hear Fluffy plotting world domination… I mean, asking for more belly rubs. Superanimals, assemble!

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Thanks for joining us on today’s episode where we discussed topics ranging from LLMs using Vector DB for storage, GPT-4’s declining performance, Tesla’s licensing of Full Self-Driving system, and possible withdrawal of FaceTime and iMessage in the UK by Apple, to Stanford and DeepMind’s suggestion of using large language models to define preferences and rewards, the potential of brain-merging with AI implants by 2050, and the use of the Wondercraft AI platform to start your own podcast with hyper-realistic AI voices – be sure to catch us on Shopify, Apple, Google, or Amazon and don’t forget to subscribe for our next episode!

AI Unraveled Podcast July 2023: AI is helping create the chips that design AI chips; Top 10 career options in Generative AI; 3 Machine Learning Stocks for Getting Rich in 2023; Top 10 career options in Generative AI; Apple GPT fueling Siri & iPhones

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover AI replacing humans in chip design, the increase in AI-skilled job postings, Google’s AI Red Team, Meta’s release of Llama 2, converting YouTube videos using ChatGPT, the emergence of proprietary Language Model-based APIs, Google AI’s SimPer, authors demanding payment for AI training, Gen Z’s fear of job loss to AI, the unveiling of CG-1 AI supercomputer, the use of synthetic data by Microsoft, OpenAI, and Cohere, tech giants investing in AI for healthcare, Contextual Answers by AI21 Labs, OpenAI’s custom instructions for ChatGPT, Apple’s development of AI tools and chatbot, and the Wondercraft AI platform for generating podcasts with hyper-realistic AI voices.

Hey there! It’s fascinating to see how artificial intelligence (AI) is transforming the world. One interesting aspect is how machines and algorithms are increasingly taking over the human role in AI development.

Speaking of AI, let’s talk about some hot stocks in the market. Nvidia has been making waves with its AI chips, and its stock has soared in 2023. Their GPU chipsets are considered the most powerful, making them highly sought after as AI technology advances. They also play a key role in training machine learning models used in various sectors like data centers and automotive industries.

Meanwhile, Advanced Micro Devices (AMD) is emerging as a strong contender to Nvidia’s dominance in AI and machine learning. In fact, some investors believe AMD could attract Nvidia investor capital due to overvaluation concerns. AMD’s high-end chips are about 80% as fast as Nvidia’s, and they have shown strength in software, an area that has traditionally been a challenge for many machine learning firms.

In the AI and machine learning sector, Palantir Technologies has also seen significant growth. While it didn’t catch the early wave of AI adoption like Microsoft, AMD, and Nvidia, Palantir’s Gotham and Foundry platforms have gained popularity among private and public organizations. Their work with government entities, especially in the defense sector, has contributed to their success in the AI stock market.

Switching gears a bit, let’s explore some exciting career options in Generative AI. From Machine Learning Engineer and Data Scientist to Computer Vision Engineer and Robotics Engineer, there are plenty of opportunities in this rapidly evolving field. The potential to work in areas like Natural Language Processing, Deep Learning, and Data Engineering is also on the rise.

Finally, it’s important to note that while machine learning plays a significant role in AI development, academic and computer experts believe that true self-aware AI cannot exist through machine learning alone. Replicating the natural processes of evolution will be crucial for achieving sentient AI.

And that’s a wrap! AI is paving the way for incredible advancements, and it’s fascinating to witness how it’s impacting various industries and career paths.

Job listings that require AI-based skills are on the rise as organizations seek to improve their internal operations and provide better services to clients. However, there is a shortage of AI-skilled professionals, leading many companies to invest in training programs.

Recently, Google AI introduced Symbol Tuning, a simple fine-tuning method that can enhance in-context learning by emphasizing input-label mappings. This technique involves tuning language models based on input-label pairs presented in a specific context, where natural language labels are remapped to arbitrary symbols. The goal is for the model to rely on these input-label pairs to perform a given task effectively.

Meanwhile, Fable, a San Francisco startup, has showcased its AI technology SHOW-1, which has the ability to write, produce, direct, animate, and even voice new episodes of TV shows. This groundbreaking technology combines various AI models, such as language models for writing, custom diffusion models for image creation, and multi-agent simulation for story progression and characterization. As a proof of concept, they created a 20-minute episode of South Park fully written, produced, and voiced by AI.

This development is significant because current generative AI systems have limitations when it comes to long-form content creation and maintaining high-quality standards, especially within existing intellectual properties. The entertainment industry is currently facing a writers and actors strike, fueling concerns that AI may rapidly replace jobs across the TV and movie spectrum. However, Fable’s SHOW-1 technology represents a crucial milestone in the pursuit of AI-generated works that match the quality of existing intellectual properties.

The magic behind SHOW-1 lies in its utilization of a multi-agent simulation for rich character history, goal creation, and coherent story generation. Additionally, it leverages large language models like GPT-4 for natural language processing and generation. Interestingly, no fine-tuning was necessary for GPT-4, as it had already absorbed numerous South Park episodes. Diffusion models trained on South Park’s intellectual property played a role in image generation, and voice-cloning technology was employed for character voices. Ultimately, SHOW-1 is a remarkable achievement, combining multiple existing frameworks into a unified system.

While the possibilities of generative AI in entertainment are exciting, they also raise concerns. Actors and writers fear that AI will disrupt the industry on a massive scale. Although we are still in the early stages of AI implementation in entertainment, the potential for a future where entertainment is personalized, customized, and virtually limitless thanks to generative AI is on the horizon. However, it is essential to consider the ethical implications and question whether this is ultimately a positive development.

So, let’s talk about Google’s AI Red Team. This team is made up of a group of hackers whose job is to simulate different types of adversaries. These adversaries can range from nation states and well-known hacker groups to individual criminals or even people within the organization who may have malicious intentions. The idea of a “Red Team” actually comes from the military, where a designated team would play the role of the adversary against the “home” team.

Now, let’s switch gears a bit and discuss how to make generative AI more environmentally friendly. Generative AI is really impressive, but we often overlook the environmental impact it has. There are some steps that companies can take to make these systems greener. First, they can use existing large generative models instead of generating their own. They can also fine-tune and train existing models, which is more efficient. Using energy-conserving computational methods and only using large models when necessary can also help. It’s important to be discerning about when generative AI is actually needed and to evaluate the energy sources of cloud providers or data centers. Companies can also re-use models and resources, as well as include AI activity in their carbon monitoring efforts.

Now, let’s talk about Apple. Apple has been relatively quiet when it comes to generative AI lately, but that doesn’t mean they’re not up to something. According to a recent report from Bloomberg, Apple is quietly working on their own AI chatbot called “Apple GPT”. This chatbot could be integrated into Siri and Apple devices. Apple is using their own system, called “Ajax”, to develop this tool. They initially paused its development due to safety concerns, but now more Apple employees are getting to use it. Interestingly, Apple doesn’t seem to be interested in competing with ChatGPT. Instead, they’re looking for a consumer angle for their AI. With 1.5 billion active iPhones out there, Apple has the potential to make a big impact in the AI landscape.

So, Meta has just released Llama 2, an open-source LLM (Language Model). And the best part? It can now be used commercially! This collaboration with Microsoft’s Azure is a game-changer. Plus, Meta plans to make Llama 2 available on other platforms like AWS and Hugging Face.

But that’s not all. Qualcomm is partnering with Meta to integrate Llama 2 into devices starting in 2024. So we can expect Llama 2 to have a significant impact on various industries.

Now, let’s dig into the features. Llama 2 has been pre-trained on a whopping 2 trillion tokens, giving it double the context length of its predecessor, LLama. And the models come with different parameter options: 7B, 13B, and 70B, making them flexible for different use cases.

But the real question is: How can you use it? Well, there are a couple of ways. First, there’s the Vercel AI SDK Playground. It allows for a side-by-side comparison between Llama 2 and other models like GPT and Claude. This way, you can see how it stacks up against the competition.

Then, there’s the Perplexity AI Chat, which offers a chatbot interface similar to ChatGPT. So you can have interactive conversations using Llama 2.

But that’s not all. OpenAI has some exciting news for ChatGPT Plus users. With the introduction of GPT-4, the messaging limit has been doubled for these subscribers. Now, they can send up to 50 messages in three hours, compared to the previous limit of 25 messages in two hours.

This expanded messaging limit is great news for individuals and businesses alike. It allows for more extensive and dynamic interactions with ChatGPT. Whether you’re a developer looking to build innovative applications or a business aiming to enhance customer interactions, the raised cap opens up new possibilities for exploration and experimentation.

So, with Llama 2 and the increased messaging limit, the future of AI-powered conversations is looking quite promising.

So, imagine this. You have some amazing YouTube content that you want to repurpose into blogs and audios. Well, guess what? We’ve got you covered! In this tutorial, we’ll walk you through the process of converting your YouTube videos into written blog posts and audio content using ChatGPT and a couple of helpful plugins.

First things first, you’ll need three plugins to get started. The first one is Video Insights, which extracts key information from your videos. Then we have ImageSearch, which helps you find relevant images to enhance your blog posts. And finally, we have Speechki, a plugin that converts your blog text into a voiceover audio. Make sure to install these plugins from the plugin store.

Once you’ve got the plugins set up, it’s time to enter the prompt into ChatGPT. Just paste the given prompt, which instructs you to perform certain tasks based on the YouTube video you want to convert. Simply replace “[URL]” with the actual URL of your video.

Now comes the exciting part! After entering the prompt, ChatGPT will work its magic and create a well-structured blog post that captures the essence of your video. It will even suggest suitable images from Unsplash to make your blog visually appealing. And last but not least, it will generate a voiceover for the entire blog, so your readers can also listen to the content.

The outcome? A fantastic blog post complete with images and a voiceover that opens up new possibilities for reaching audiences who prefer reading or listening to content. So go ahead and give it a try, and let your YouTube content shine in different formats!

Have you ever wondered about the emergence of proprietary Language Model-based APIs and the challenges they might pose to the traditional open-source approach in the deep learning community? Well, Cameron R. Wolfe, Ph.D., has written an interesting article exploring this topic.

Wolfe points out the development of open-source LLM alternatives as a response to the growing trend of proprietary APIs. This shift towards proprietary models raises concerns about transparency and accessibility within the deep learning community.

The article stresses the need for rigorous evaluation in research to ensure that new techniques and models actually offer improvements. It also highlights the limitations of imitation LLMs, which may perform well in specific tasks but struggle when subjected to broader evaluation.

So, why should we care? While local imitation still has its value in certain domains, it isn’t a comprehensive solution for creating high-quality, open-source foundation models. Instead, the article advocates for the continued advancement of open-source LLMs. The focus should be on developing larger and more powerful base models to drive further progress in the field.

In summary, Wolfe’s article sheds light on the challenges posed by proprietary Language Model-based APIs and emphasizes the importance of open-source LLMs in advancing the deep learning community.

Have you heard about Google AI’s latest breakthrough? They’ve introduced a new method called SimPer that has the potential to revolutionize learning. SimPer focuses on capturing periodic or quasi-periodic changes in data, something that hasn’t been fully explored before. And let me tell you, the results are impressive.

SimPer takes advantage of the inherent periodicity in data by incorporating customized augmentations, feature similarity measures, and a generalized contrastive loss. This combination allows it to be extremely data efficient, robust against spurious correlations, and capable of generalizing to distribution shifts. In other words, SimPer can handle diverse applications and perform exceptionally well.

So why is SimPer so important? Well, it addresses a major challenge in learning meaningful representations for periodic tasks with limited or no supervision. This is particularly significant in domains like human behavior analysis, environmental sensing, and healthcare, where critical processes often exhibit periodic or quasi-periodic changes. SimPer outperforms other self-supervised learning methods, proving its effectiveness and potential.

The possibilities for SimPer’s applications are endless. It can help us understand and analyze human behavior better, improve environmental sensing, and advance healthcare research. Google’s research team has truly unlocked the potential of periodic learning with SimPer, and I can’t wait to see how this exciting development unfolds.

A group of over 8,500 authors is taking a stand against tech companies that are using their works without permission or compensation to train AI language models like ChatGPT, Bard, LLaMa, and others. These authors are concerned about copyright infringement and argue that these AI technologies are replicating their language, stories, style, and ideas without giving them any recognition or reimbursement. It’s as if their writings are being feasted upon endlessly by these AI systems, with no consideration for the hard work and creativity that went into them.

The authors are questioning whether these AI models are using content scraped from bookstores, borrowed from libraries, or even downloaded from illegal archives. They are frustrated that the companies behind these models have not adequately addressed the sourcing of the works they use. It’s clear that these companies did not obtain the necessary licenses from publishers, a legal and ethical method that the authors strongly advocate for.

In their argument, the authors highlight a Supreme Court decision in Warhol v. Goldsmith, which suggests that the commercial use of these AI models may not constitute fair use. They firmly claim that no court would approve the use of illegally sourced works. They express concern that generative AI could flood the market with low-quality, machine-written content, which could undermine the profession of authors. They point out instances where AI-generated books have already reached best-seller lists and are being used for SEO purposes.

The consequences of these practices are significant. The group of authors warns that this could discourage authors, especially emerging ones or those from under-represented communities, from making a living in a publishing industry already plagued by narrow profit margins and complexities. They are demanding that tech companies obtain permission to use their copyrighted materials and seek fair compensation for both past and ongoing use of their works in AI systems. They emphasize the need for remuneration, regardless of whether the use is deemed infringing under current law or not.

So, get this – a recent study found that a whopping 76% of Gen Z-ers are concerned about losing their jobs to AI-powered tools. And you know what? I’m not surprised. As a member of Gen Z myself, I can tell you that we’ve got some serious concerns about the future of work.

But here’s where it gets interesting. It turns out that Gen Z is actually pretty good at using AI to their advantage. In fact, there’s this director at a medical device company who says that Gen Z workers are using AI tools to automate tasks and increase efficiency. They’re basically turbocharging productivity and making their jobs easier. Talk about smart!

Now, you might be thinking, “Wait, doesn’t that mean they’re putting themselves out of a job?” Well, not exactly. See, Gen Z has the tech skills to implement AI and actually make it work for them. But at the same time, most of us still have this underlying fear of losing our jobs to automation. It’s a real concern.

And here’s another thing that caught my attention – have you heard about the new role called “Head of AI”? It’s popping up in American businesses left and right, even though nobody really knows what they do! It’s crazy! Companies are tripling the number of “Head of AI” positions in the last five years, but the responsibilities and qualifications are all over the place.

Despite the uncertainty, the trend of appointing AI leaders in companies is on the rise. Fortune 2000 companies are expected to have a dedicated AI leader within a year. So, it’s clear that AI is becoming a hot topic in leadership roles across various industries.

All in all, while we may have some concerns, Gen Z is finding ways to make AI work for us. And who knows, maybe we’ll even figure out what the heck a “Head of AI” does!

Cerebras and G42 have joined forces to bring us the impressive Condor Galaxy 1 (CG-1), a 4 exaFLOPS AI Supercomputer. This partnership aims to construct a total of nine interconnected AI supercomputers, delivering an astounding 36 exaFLOPS of AI compute, making it the largest interconnected AI supercomputer constellation in the world.

Located in Santa Clara, CA, the CG-1 is already operational, boasting 2 exaFLOPS and 27 million cores. It’s created by connecting 32 Cerebras CS-2 systems into a single, user-friendly supercomputer. And there’s more to come, as the CG-1’s performance is set to double in the next few weeks with the full deployment of 64 Cerebras CS-2 systems, giving it the capability to deliver an impressive 4 exaFLOPS of AI compute and 54 million AI optimized compute cores.

But that’s not all—once the CG-1 is complete, Cerebras and G42 plan to build two additional 4 exaFLOPS AI supercomputers in the US, which will be interconnected to create a 12 exaFLOPS constellation. As if that’s not ambitious enough, their ultimate vision is to construct six more 4 exaFLOPS AI supercomputers, resulting in an astounding 36 exaFLOPS of AI compute by the end of 2024.

Offered through the Cerebras Cloud, CG-1, which has been optimized by G42 and Cerebras, provides users with top-notch AI supercomputer performance without the hassle of managing or distributing models across GPUs. This means that users can effortlessly train their models on their data and take full ownership of the results.

AI models are constantly seeking unique and sophisticated data sets to improve their performance. However, developers of major language models (LLMs) are encountering challenges with using web data. Financial Times reports indicate that web data is no longer sufficient and has become extremely expensive. To address this, Microsoft, OpenAI, and Cohere are actively exploring the use of synthetic data as a cost-saving and high-quality alternative.

The creators of LLMs believe that they have reached the limits of human-made data in terms of enhancing performance. Simply feeding models with more web-scraped data may not lead to the next significant performance leap. The problem lies in the cost and scalability of generating custom human-created data that meets AI’s training requirements. Additionally, access to web data is becoming increasingly restricted, with platforms charging hefty fees for its usage.

In response, the approach is for AI to generate its own training data. Cohere is using two AI models, with one acting as a tutor and the other as a student, to produce synthetic data that is then reviewed by a human. Microsoft’s research team has shown that certain synthetic data can effectively train smaller models, but it is still not viable for enhancing GPT-4 performance.

Startups like Scale.ai and Gretel.ai are already offering synthetic data-as-a-service, demonstrating a growing market appetite for this approach. AI leaders, such as Sam Altman from OpenAI, are confident that in the near future, all data will be synthetic. This shift could help address privacy concerns in the EU. However, caution is also advised, as some researchers warn that training models on their own raw outputs may lead to irreversible defects and degrade their performance over time.

What’s clear is that the era of human-created content may soon be overshadowed by AI-generated data. In the coming decade, we could witness a world where the bulk of data and content is created by AI, opening new possibilities for language models and their evolution.

Tech giants like Google, NVIDIA, and Microsoft are diving headfirst into the realm of artificial intelligence (AI) and healthcare, with hopes of transforming the field of medicine. Google has developed an AI chatbot called Med-PaLM 2, which boasts an impressive 92.6% accuracy rate when responding to medical queries. That’s almost on par with human healthcare professionals, who scored 92.9%. It’s important to note though that the system has its quirks, as it has been known to “hallucinate” and reference non-existent studies.

NVIDIA is also making waves in pharmaceuticals by investing $50 million in AI drug discovery company, Recursion Pharmaceuticals. This move caused a significant 78% increase in NVIDIA’s stock. Microsoft, on the other hand, acquired Nuance, a speech recognition company, for a hefty $19.7 billion to expand its reach in the healthcare industry. At their recent Inspire event, Microsoft announced a partnership with Epic Systems, the largest electronic health records (EHR) provider in the US, to integrate Nuance’s AI solutions.

Meta, the parent company of Facebook, is taking a different approach by launching LLaMA 2, an open-source large language model (LLM). Unlike other big tech companies that keep their AI systems proprietary, Meta is freely releasing the code and data behind LLaMA 2. Researchers worldwide can now build upon and improve this technology. LLaMA 2 comes in three sizes, with varying parameters, and is trained using reinforcement learning from human feedback. Developers can interact with LLaMA 2 through various platforms and expect a surge of innovative AI applications in the future.

AI21 Labs, the Tel Aviv-based NLP major, has introduced a new AI engine called Contextual Answers. This plug-and-play solution is designed to help enterprises make the most of their data assets. Contextual Answers is an API that can be seamlessly integrated into digital assets, allowing organizations to implement large language model (LLM) technology on their data. It facilitates a conversational experience, enabling employees or customers to obtain the information they need without the hassle of interacting with different teams or software systems.

What sets Contextual Answers apart is its ease of use. It’s a ready-to-use solution that doesn’t require significant effort or resources. By optimizing each component and making it plug-and-play, clients can achieve excellent results without the need for AI, NLP, or data science experts.

The AI engine supports unlimited upload of internal corporate data while ensuring access control and data security. It allows organizations to limit the model’s usage to specific files, folders, or metadata, ensuring confidentiality and compliance. The Secure and SOC-2 certified environment provided by AI21 Studio adds an extra layer of security.

In related news, Google has been demonstrating a tool called “Genesis” to news organizations. Powered by Google’s latest LLM technologies, Genesis generates news articles using AI. However, the response to the tool has been mixed, with concerns about accuracy and the role of journalists in an AI-driven news era.

As media organizations grapple with financial pressures, some are embracing generative AI, while others are wary of its implications. Despite acknowledging the value of AI tools, many execs in the news industry find it unsettling and worry that it undermines the effort put into producing accurate and well-crafted news stories. Journalists are also questioning their role in this evolving landscape.

Google emphasizes that tools like Genesis are meant to assist journalists rather than replace them. However, the future looks challenging for news organizations as they navigate this shift and explore how AI can coexist with their profession. It remains to be seen how journalists will adapt to this new reality, but the coming decade promises to be a fascinating one for the industry.

Today, OpenAI made an exciting announcement about a new feature for ChatGPT – custom instructions. Essentially, this means that users can now personalize their conversations with ChatGPT by setting persistent preferences that will be remembered in all future interactions. This is a big deal because it allows for more customized and tailored conversations.

In the past, you may have found yourself repeating instructions or preferences with each new chat session. But now, with custom instructions, you can avoid that hassle. Your preferences will be remembered going forward, saving you time and effort.

Let’s dive into some of the use cases that OpenAI has identified for this new feature. One example is expertise calibration. If you’re discussing a specific field where you have deep knowledge, you can let ChatGPT know your expertise level to avoid unnecessary explanations.

Language learners can also benefit from ChatGPT’s custom instructions. You can practice ongoing conversations and even receive grammar correction, helping you improve your language skills.

Another use case is localization. If you’re a lawyer governed by specific laws in your country, you can establish that context with ChatGPT, ensuring that the responses align with your jurisdiction.

For writers, ChatGPT can maintain a consistent understanding of story characters in ongoing interactions using character sheets. This can be extremely helpful when working on a novel.

Other use cases include instructing ChatGPT to consistently output code updates in a unified format and applying the same voice and style from provided emails to all future email writing requests.

These are just a few of the possibilities that custom instructions unlock. Right now, the beta version is available to Plus users, but it will be rolling out to all users in the coming weeks. So, get ready to take your conversations with ChatGPT to the next level of personalization and customization!

Hey there! Let’s dive into today’s AI update news, covering some exciting developments from Apple, OpenAI, Google Research, MosaicML, Google, and Nvidia.

First up, Apple is working on its own AI tools, including a powerful language model called “Ajax” and an AI chatbot named “Apple GPT.” They have big plans to announce something significant next year, hoping to catch up with competitors like OpenAI and Google. The aim is to enhance Siri’s functionality and performance by integrating these AI tools, overcoming the stagnation experienced by the voice assistant in recent years.

Moving on to OpenAI, they have some great news for ChatGPT Plus subscribers. They have increased the message limit for GPT-4, allowing users to send up to 50 messages in a span of 3 hours, compared to the previous cap of 25 messages in 2 hours. This update will be rolling out next week. The increased message limit opens up more opportunities for businesses, developers, and AI enthusiasts to interact extensively with the model and explore various ChatGPT plugins.

Google’s research team has introduced SimPer, a fascinating self-supervised learning method. SimPer focuses on capturing periodic or quasi-periodic changes in data by leveraging customized augmentations, feature similarity measures, and a generalized contrastive loss. This method unlocks the potential for learning from data with inherent periodicity, expanding the scope of AI capabilities.

In a bid to assist journalists, Google is exploring the use of AI tools for writing news articles. They are in talks with publishers to provide AI-driven assistance, such as options for headlines and different writing styles. The objective is to enhance the work and productivity of journalists, offering them valuable tools to streamline their writing process.

MosaicML has made an exciting release with MPT-7B-8K, an open-source LLM (large language model). With 7B parameters and an impressive 8k context length, this model provides significant advancements in language processing capabilities. It has been trained on the MosaicML platform, utilizing Nvidia H100s during a three-day training process on 256 H100s, involving a whopping 500B tokens of data. Developers now have access to this powerful LLM and are welcome to contribute to its development.

Lastly, Nvidia, a company that started as a video game hardware provider, has become a force to be reckoned with in the AI industry. Their success in AI has propelled them to achieve a staggering $1 trillion valuation. Nvidia now stands as a full-stack hardware and software company, playing a major role in powering the Gen AI revolution.

That’s it for today’s Daily AI Update News! Exciting times ahead in the world of artificial intelligence. Stay tuned for more updates.

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Thanks for tuning in to today’s episode, where we discussed the rise of AI in chip design, job opportunities in AI, environmental considerations for generative AI, the latest advancements in language models, the impact of AI on various industries, the importance of open-source initiatives, self-supervised learning methods, copyright concerns, the role of AI Head of departments, supercomputers in AI, the use of synthetic data, AI in healthcare, business applications of AI, personalized conversations with ChatGPT, and the developments in AI tools. Join me at the next episode and don’t forget to subscribe!

AI Unraveled Podcast July 2023: New AI tool creates entire websites; AI TUTORIAL: Use ChatGPT to learn new subjects; Top 5 AI coding tools every developer must know; Top 5 Computer Vision Tools/Platforms in 2023; How Machine Learning Plays a Key Role in Diagnosing Type 2 Diabetes

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover Wix’s AI tool for creating personalized websites, the top 5 AI coding tools, computer vision tools/platforms, machine learning’s impact on type 2 diabetes diagnosis, the 5 types of AI and their functions, Meta’s release of LLaMA 2 and partnership with Microsoft, the decline in outsourced coders in India due to AI, the scarcity of high-quality data due to LLMs like ChatGPT, Microsoft’s announcement of Bing Chat Enterprise and 365 Copilot, the affordability and ease of use of Real-ESRGAN for image upscaling with face correction, the improvement of medical AI’s performance and accessibility through the MedPerf benchmarking platform, the benefits of LLMs in modeling sequences for robotics, the use of AI in various industries including logistics, finance, and law enforcement, and a book recommendation for a thorough understanding of artificial intelligence.

Wix has just launched an exciting new feature that allows users to create entire websites using AI prompts. With this latest enhancement, users can now build custom sites without having to rely on templates. Instead, they simply answer a series of questions about their preferences and needs, and the AI generates a website based on their responses. It’s a convenient and efficient way to create a unique online presence.

The technology behind this innovation involves a combination of OpenAI’s ChatGPT for text creation and Wix’s proprietary AI models for other aspects. By leveraging these tools, Wix is able to deliver a remarkable website-building experience that sets it apart from other platforms. But the advancements don’t stop there. Wix has more features in the pipeline that will further enhance the platform’s capabilities. These include the AI Assistant Tool, AI Page, Section Creator, and Object Eraser.

Avishai Abrahami, the CEO of Wix, emphasizes the company’s commitment to leveraging AI’s potential in revolutionizing website creation and driving business growth. With the new AI tool and upcoming features, Wix is positioning itself as a leader in the website-building industry, offering users powerful and intuitive tools to bring their visions to life.

Speaking of learning new subjects, Wix’s ChatGPT can also be used as a handy tutorial tool. For example, you can ask it to create a comprehensive course plan and study guide for any topic you want to learn. By specifying the subject and your experience level, ChatGPT will provide a course plan with detailed lessons, exercises, and more. It will structure the course with an average of 10 lessons, but this can vary depending on the complexity of the subject.

The course plan will include a title and brief description, course objectives, an overview of lesson topics, detailed lesson plans for each session, including objectives, lesson content (using text and code blocks if needed), and exercises and activities for each lesson. If applicable, it will also include a final assessment or project.

So whether you want to create a stunning website using Wix’s AI tool or learn a new subject with the help of ChatGPT, these innovations are just a glimpse into the exciting possibilities afforded by AI technology.

Let’s dive into the top 5 AI coding tools that every developer should know to enhance productivity and simplify AI development. These tools are all about making your life easier and helping you create amazing AI models.

First up, we have TensorFlow. Created by Google, it’s an open-source platform that provides a complete collection of tools and libraries for machine learning. It’s known for its thorough documentation and strong community support, making it a go-to tool for AI development.

Next, we have PyTorch, another popular open-source machine learning framework. Created by Facebook’s AI Research team, it’s loved for its simplicity and adaptability. PyTorch offers a dynamic computational graph that makes model experimentation and debugging a breeze.

Moving on to Keras, a Python-based API for high-level neural networks. It acts as a wrapper around lower-level frameworks like TensorFlow and Theano, making it easier for developers with different skill levels to create and train deep learning models.

Now, let’s talk about Jupyter Notebook, an interactive coding environment. It allows you to create and share documents with live code, visuals, and narrative text. It’s perfect for experimenting with AI algorithms and showcasing results.

Last but not least, we have OpenCV, an open-source computer vision and image processing library. It offers a wide range of tools and techniques for tasks like object detection and image recognition. If you’re working on AI applications that involve computer vision, OpenCV is a valuable tool to have in your arsenal.

These are just the top 5 AI coding tools, but there are many more out there. Other noteworthy tools include Git for version control, Pandas for data manipulation and analysis, scikit-learn for various machine learning tasks, and Visual Studio Code for a quick and flexible code editing experience with rich AI development capabilities.

So, there you have it! These AI coding tools will definitely enhance your productivity and simplify your AI development journey. Give them a try and see the magic they can create!

Computer vision is a powerful technology that allows computers and systems to extract valuable information from digital photos, videos, and other visual inputs. It enables machines to perceive, observe, and understand the world, similar to how artificial intelligence empowers them to think.

Let’s dive into the top 5 computer vision tools and platforms that will dominate the landscape in 2023.

First up, we have Kili Technology’s Video Annotation Tool. This tool simplifies and accelerates the creation of high-quality datasets from video files through various labeling tools like bounding boxes and polygons. It even supports advanced tracking capabilities, making it easy to navigate frames and review annotations.

Next, we have OpenCV, a software library that provides a standardized infrastructure for computer vision applications. With over 2,500 algorithms, you can do fascinating things like face recognition, object identification, and even stitch together frames into high-resolution images.

Viso Suite is a comprehensive platform for computer vision development, deployment, and monitoring. It offers a no-code approach and includes components like image annotation, model training, and IoT communication. This suite is widely used for industrial automation, visual inspection, and remote monitoring.

TensorFlow, an end-to-end open-source machine learning platform, is renowned for its versatility in developing computer vision applications. With TensorFlow, you’ll have access to various tools, resources, and frameworks to bring your vision to life.

Finally, we have Scikit-image, a fantastic open-source tool for processing images in Python. From simple operations like thresholding to edge detection and color space conversions, Scikit-image has you covered.

These five tools and platforms represent the cutting edge of computer vision in 2023. Whether you’re working on annotation, algorithm development, or practical applications, there’s a tool here for you. So, get ready to revolutionize the way computers perceive the visual world!

Today, I want to talk about how machine learning is playing a critical role in diagnosing type 2 diabetes. As we all know, type 2 diabetes is a chronic disease that affects a large number of people worldwide and can lead to various long-term health complications. This is why early diagnosis is crucial, and that’s where machine learning comes in.

Machine learning algorithms are designed to analyze patterns in data and make predictions and decisions based on those patterns. Medical data is no exception, and by using machine learning, we can improve the accuracy and efficiency of diagnosing type 2 diabetes.

One of the key ways machine learning is making a difference is through the use of predictive algorithms. These algorithms can take into account various patient data such as age, BMI, blood pressure, and blood glucose levels, and predict the likelihood of someone developing type 2 diabetes. With this information, healthcare providers can identify individuals who are at a higher risk of developing the disease and take proactive steps to prevent it.

By harnessing the power of machine learning, we can enhance the early diagnosis of type 2 diabetes, potentially saving lives and preventing serious complications. This is just one example of how technology is revolutionizing the field of healthcare and improving patient outcomes.

Today, we’re going to talk about the five different types of Artificial Intelligence (AI) that have revolutionized the way businesses extract insights from data.

First up, we have Machine Learning, which is an essential component of AI. Machine Learning uses algorithms to scan through data sets and learn from them, ultimately making educated judgments. This is achieved by the computer software executing various tasks and analyzing how its performance improves over time.

Next, there’s Deep Learning, which can be seen as a subset of Machine Learning. Its main goal is to enhance power by teaching systems how to represent the world using a hierarchy of concepts. Deep Learning shows the connection between simpler and more complex concepts, creating abstract representations for complex ideas.

Moving on, we have Natural Language Processing (NLP), a merging of AI and linguistics. NLP enables humans to communicate with robots using natural language, such as Google Voice search. It has opened up new possibilities for human-robot interactions and has made our lives easier.

Computer Vision is another significant type of AI. Organizations use computer vision to improve user experiences, minimize costs, and enhance security. With the market for computer vision expected to reach $26.2 billion by 2025, the impact and growth potential of this technology are substantial.

Finally, we have Explainable AI (XAI), which focuses on enabling human users to understand and trust machine learning algorithms. XAI provides strategies and approaches to explain AI models, projected impacts, and any biases. This helps establish model correctness, fairness, transparency, and ultimately aids in AI-powered decision-making.

These five types of AI together have transformed the way businesses operate and extract valuable insights from data. Exciting times lie ahead as AI continues to advance and shape our world.

Hey there! Big news from Meta – they’ve just launched LLaMA 2 LLM. And the best part? It’s free, open-source, and available for commercial use. We’ve been eagerly waiting for this announcement, and now we finally have the details.

LLaMA 2 comes with some exciting upgrades. It’s trained on 40% more data than LLaMA 1, with double the context length, providing a solid foundation for fine-tuning. And there are three model sizes to choose from: 7B, 13B, and 70B parameters.

But what sets LLaMA 2 apart is its outstanding performance. It outshines other open-source models across various benchmarks, including MMLU, TriviaQA, and HumanEval. Notable competitors like LLaMA 1, Falcon, and MosaicML’s MPT model couldn’t match up. To top it off, there’s a comprehensive 76-page technical specifications doc, giving insights into how Meta trained and fine-tuned the model.

And here’s an interesting twist – Meta’s cozying up with Microsoft. In their press release, Meta announces Microsoft as their preferred partner for LLaMA 2. They’re even making it available in the Azure AI model catalog, providing developers using Microsoft Azure with easy access.

It seems MSFT knows open-source is the way to go. Despite their massive $10B investment in OpenAI, they’re not putting all their eggs in one basket. This collaboration with Meta could be a shot across the bow for OpenAI.

Open-source is gaining ground, and Meta’s partnership with Microsoft emphasizes the importance of increasing access to AI technologies worldwide. It’s all about democratizing access and fostering a supportive community. The ball is now in OpenAI’s court, as rumors swirl about their future plans for an open-source model.

The open-source vs. closed-source wars just got a lot more interesting, my friend. Stay tuned!

Hey everyone, today we’re diving into a prediction that might shake up the tech industry. Emad Mostaque, the CEO of Stability AI, believes that within the next two years, there will be a dramatic decrease in the number of outsourced coders in India. What’s causing this shift? Well, it’s the rise of artificial intelligence.

Mostaque points out that as AI technology advances, software development can now be done with fewer individuals. This poses a huge threat to the jobs of outsourced coders in India, who already face a higher risk compared to coders in other countries.

It’s important to note that the impact of this change will vary around the world due to different labor laws. Countries with more stringent labor laws, such as France, might experience less disruption. In contrast, India, with its large pool of over 5 million software programmers, is expected to be hit the hardest.

Why is India at such high risk? Well, it plays a significant role in outsourcing. This means that the country is more vulnerable to job losses caused by AI.

While this prediction is concerning for outsourced coders in India, it’s important to keep in mind that the situation can change. Let’s see how things develop over the next couple of years. Stay tuned for updates on this topic! Source: CNBC.

So, there’s some interesting news in the world of AI. Researchers are warning that LLMs, or language models, pose a threat to human data creation. It seems that as models like ChatGPT gain popularity, they are actually causing a decline in content on sites like StackOverflow.

You see, these LLMs rely on a vast amount of human knowledge to produce their outputs. They use sources like Reddit, StackOverflow, and Twitter as training data. But now, researchers have found that as more people use LLMs, it’s leading to a decrease in high-quality content on these sites.

It’s not just about getting low-quality answers on StackOverflow. The problem goes deeper. The limited availability of open data can affect both AI models and human learning. And here’s the real issue: since data generated by LLMs is not very effective at training new LLMs, it’s causing what researchers call the “blurry JPEG” problem. ChatGPT, for example, can’t replace the crucial input of data from human activity.

So, what’s the main takeaway from all this? We’re in the midst of a disruptive time for online content. Sites like Reddit, Twitter, and StackOverflow are starting to realize the value of their human-generated content and are tightening their control over it. As AI-generated content becomes more prevalent, it becomes harder to distinguish between what’s human-created and what’s AI-generated.

It’s definitely a challenge that we’ll need to address as we navigate this new era of AI and content creation.

At the recent Inspire event, Microsoft unveiled some exciting new products that are set to revolutionize the workplace. One of these is Bing Chat Enterprise, an AI-powered chat platform designed specifically for work purposes. With this new tool, Microsoft is taking a significant step towards integrating artificial intelligence even further into our daily work lives. What’s great is that the preview version of Bing Chat Enterprise is already accessible to over 160 million people, showing just how eager Microsoft is to reach a wide user base.

In addition to Bing Chat Enterprise, Microsoft also announced the upcoming launch of Microsoft 365 Copilot. This tool will be available to commercial customers and is expected to be a valuable asset for them when it comes to planning and managing work tasks effectively. Priced at $30 per user, per month, Microsoft 365 Copilot will be available to users of Microsoft 365 E3, E5, Business Standard, and Business Premium – be sure to keep an eye out for its availability in the coming months.

Microsoft is not just expanding its reach, but also introducing new features to enhance the Bing Chat experience. One of these new features is Visual Search in Chat, a powerful tool that allows users to search for information directly within the chat platform. This is yet another example of how Microsoft is striving to make work more efficient and seamless for everyone.

With these new products and features, it’s clear that Microsoft is pushing the boundaries of workplace technology and demonstrating their commitment to advancing AI capabilities. The future of work is here, and Microsoft is leading the way.

Real-ESRGAN, developed by NightmareAI, is becoming increasingly popular for high-quality image enhancement. It excels at upscaling images while maintaining or even improving their quality. What sets Real-ESRGAN apart are its unique face correction and adjustable upscale options, which make it perfect for enhancing specific areas, revitalizing old photos, and improving social media visuals.

One great aspect of Real-ESRGAN is its affordability, at just $0.00605 per run. Additionally, it boasts an average run time of only 11 seconds on Replicate. To train the model, synthetic data is used to simulate real-world image degradations. Real-ESRGAN also employs a U-Net discriminator with spectral normalization, which results in enhanced training dynamics and exceptional performance on real datasets.

Using Real-ESRGAN is straightforward. You communicate with the model through specific inputs, such as providing a low-resolution input image for enhancement, specifying the scale number (default is 4), and indicating whether you want specific enhancements applied to faces in the image. The output you receive is a URI string that points to the location where the enhanced image can be accessed.

To make things even easier, I’ve created a comprehensive guide that offers a user-friendly tutorial on running Real-ESRGAN via the Replicate platform’s UI. This guide covers everything from installation and authentication to executing the model. Additionally, I provide information on finding alternative models that do similar work. So, if you’re looking to enhance your images, Real-ESRGAN is definitely worth exploring.

Hey there! I’ve got some exciting news to share with you. MLCommons, a cool open global engineering consortium, just launched MedPerf! It’s an awesome platform that’s all about evaluating the performance of medical AI models on real-world datasets. Pretty cool, right?

So, what’s the big deal? Well, MedPerf is here to make medical AI even better. It’s all about improving the generalizability and clinical impact of AI in healthcare. And the best part is, it does all that while prioritizing patient privacy and tackling legal and regulatory risks. Safety first, right?

But here’s where things get really interesting. MedPerf uses something called federated evaluation. What this means is that AI models can be assessed without actually accessing patient data. Super clever, don’t you think? Plus, it offers orchestration capabilities that make research a breeze.

And guess what? MedPerf is already making waves in the medical field. It’s been used in pilot studies and challenges involving brain tumor segmentation, pancreas segmentation, and even surgical workflow phase recognition. Impressive stuff!

Overall, MedPerf is a game-changer. With this platform, researchers can evaluate medical AI models using diverse real-world datasets, all while keeping patient privacy intact. It’s a win-win situation for sure. Plus, it paves the way for advancements in healthcare technology. Exciting times ahead!

So here’s the thing: a recent study has found that Language Models (LLMs) have this amazing ability to complete complex sequences of tokens, even if those sequences are randomly generated or expressed with random tokens. And get this: they can do it without any extra training! That means LLMs can serve as general sequence modelers, which is pretty cool.

But wait, it gets even better. The researchers behind this study also explored how this capability of LLMs can be applied to robotics. For example, they found that LLMs can extrapolate sequences of numbers to complete motions or generate reward-conditioned trajectories. That’s some next-level stuff right there.

Of course, there are limitations to deploying LLMs in real systems. It’s not all rainbows and unicorns. But here’s the exciting part: despite these limitations, the approach of using LLMs to transfer patterns from words to actions holds great promise. It’s like opening up a whole new world of possibilities for robotics and beyond.

So why does this matter, you ask? Well, imagine the potential applications. With LLMs, we can have robots that can understand and follow complex instructions, or even predict and complete actions based on incomplete information. It’s a step towards making our robotic buddies smarter and more adaptable to different scenarios. And that, my friend, is something worth getting excited about.

Hey there! It’s time for your daily AI update, bringing you the latest news from the world of artificial intelligence. Let’s dive right in!

Infosys, a leading IT company, has just signed a massive $2 billion AI agreement with one of their strategic clients. The aim here is to provide AI-based development, modernization, and maintenance services over the next five years. That’s quite a commitment!

In other news, AI is lending a helping hand to American cops. By accessing vast license plate databases, AI is able to analyze movement patterns and identify any suspicious activity on the roads. It’s like having a virtual cop keeping an eye out for criminal behavior while you drive.

Meanwhile, FedEx Dataworks is utilizing analytics and AI to strengthen supply chains. By harnessing data-driven insights from analytics, AI, and machine learning, they’re assisting customers in optimizing their supply chain operations and gaining a competitive advantage in the logistics and shipping industries.

And speaking of financial planning, Runway, a cloud-based platform, has secured an impressive $27 million in funding. Their innovative platform allows businesses to easily create, manage, and share financial models and plans. They even use AI to generate insights, scenarios, and recommendations based on business data and goals. It’s making financial planning more accessible and intelligent for companies of all sizes.

That’s all the AI news for today! Remember, this podcast is brought to you by the Wondercraft AI platform, a fantastic tool for starting your own podcast with hyper-realistic AI voices. Until next time, stay curious and keep exploring the world of AI!

Hey there, AI Unraveled podcast listeners! If you’re ready to dive deeper into the fascinating world of artificial intelligence, boy, do we have news for you! We’ve got just the book that will unlock all those burning questions you have about AI. Say hello to “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” penned by the incredible Etienne Noumen. And guess what? It’s available right now at Apple, Google, and Amazon!

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In today’s episode, we explored a range of topics, including the introduction of Wix’s AI tool for website creation, the top coding tools for AI, computer vision platforms, the use of AI in healthcare, different types of AI, recent advancements from Meta and Microsoft, the impact of AI on outsourcing in India, the disruption caused by LLMs like ChatGPT, new announcements from Microsoft regarding Bing, the Real-ESRGAN model for image upscaling, MedPerf’s benchmarking platform for medical AI, the application of LLMs in robotics, and the latest AI developments in various industries. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: Top Generative AI Tools in Code Generation/Coding (2023); Air AI: AI to replace sales & CSM teams; Deep Learning Model Accurately Detects Cardiac Function and Disease

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the top generative AI tools for code generation/coding, the advancements and applications of AI in various fields, the effectiveness and limitations of AI writing detectors, the integration of different AI models, the impact of AI on job automation and ethics, the emerging industry of AI companions, the latest trends in AI tools, and the use of AI in voice synthesis.

Let’s dive into the top generative AI tools in code generation and coding for the year 2023. These tools are designed to make developers’ lives easier and more efficient.

First up, we have TabNine, an AI-powered code completion tool that uses generative AI technology to predict and suggest the next lines of code based on context and syntax. With support for multiple programming languages like JavaScript, Python, TypeScript, and more, TabNine can seamlessly integrate with popular code editors such as VS Code and Sublime.

Next on our list is Hugging Face, a platform that offers free AI tools for code generation and natural language processing. They utilize the powerful GPT-3 model for code generation tasks like auto-completion and text summarizing.

Codacy takes a different approach by using AI to evaluate code and find errors. It provides developers with immediate feedback, helping them improve their coding abilities. With integration options for platforms like Slack, Jira, and GitHub, Codacy supports multiple programming languages.

GitHub Copilot, a collaboration between OpenAI and GitHub, is another AI-powered code completion tool. It utilizes OpenAI’s Codex to transform natural language prompts into helpful coding suggestions across multiple programming languages, making coding a breeze.

Replit is a cloud-based IDE that assists developers in writing, testing, and deploying code. Supporting various programming languages and offering templates and starter projects, Replit enables users to get started quickly.

Mutable AI provides an AI-powered code completion tool that saves developers time. With just one click, users can instruct the AI to edit their code and receive production-quality code. It even offers an automated test generation feature using AI and metaprogramming.

Mintify focuses on code documentation, allowing developers to save time and enhance their codebase by letting AI create their documentation. It easily integrates with major code editors like VS Code and IntelliJ.

For those who want to create websites and online applications without coding knowledge, Debuild is a web-based platform that generates code using AI. It features a drag-and-drop interface and even offers collaboration features for group projects.

Locofy allows users to convert their designs into front-end code for mobile and web applications. With support for various frameworks like React and Next.js, Locofy makes it easy to turn designs into production-ready code.

Durable offers an AI website builder that creates entire websites with photos and copy in seconds. It automatically customizes the website based on the user’s location and business nature, making it a user-friendly platform without any coding required.

Lastly, Anima enables designers to transform their design software creations into high-fidelity animations and prototypes. By integrating with popular design tools like Sketch and Figma, Anima makes it possible to generate interactive prototypes effortlessly.

These top generative AI tools in code generation and coding for 2023 provide developers with a range of powerful features and functionalities that can streamline their workflow and enhance their coding experience.

CodeComplete is a handy software development tool that’s got your back when it comes to code. It offers a range of features like code navigation, analysis, and editing for various programming languages such as Java, C++, and Python. So, whether you’re a fan of object-oriented languages or prefer the simplicity of Python, CodeComplete has got you covered.

If you’re all about quality code, then you’ll appreciate the capabilities that CodeComplete brings to the table. It can highlight your code, help you with code refactoring, automatically complete your code, and even provide helpful suggestions. All these features are designed to make your code shine and ensure it’s effective and maintainable.

Now, let’s talk about Metabob. This fantastic tool takes code analysis to the next level using artificial intelligence. It digs deep into your code and finds hidden issues before you even merge it. It gives you valuable insights into the code quality and reliability of your project. Plus, it’s accessible on popular platforms like VS Code and GitHub and supports many programming languages. It’s like having your personal code guru right at your fingertips.

Another tool that’s worth mentioning is Bloop. This in-IDE code search engine makes it a breeze for software engineers to find and share code. It understands your codebase and can even explain the purpose of code when you ask it in plain English. No more scratching your head trying to understand what that snippet does!

Ever heard of The.com? It’s an amazing platform that automates website creation on a large scale. Imagine adding thousands of pages to your website each month effortlessly. The.com empowers businesses to own the web and accelerate growth by automating the whole process.

If you’re a Flutter developer, then Codis is the tool for you. It takes Figma designs and magically transforms them into production-ready Flutter code. This means less time spent on implementation and more time focusing on what matters most – building awesome apps!

Now, let’s dive into aiXcoder. It’s an AI-powered coding assistance tool that’ll make your coding experience even better. It understands your code and offers insightful ideas for code completion based on natural language processing and machine learning techniques. It’s like having an AI buddy that helps you write better and faster code.

DhiWise is here to make your life easier as a developer. You can transform your designs into developer-friendly code for both mobile and web apps using their programming platform. It automates the application development lifecycle and produces readable, modular, and reusable code. Say goodbye to tedious manual coding!

Last but not least, let’s talk about Warp. It’s on a mission to transform the terminal into a true engineering platform. To achieve this, it upgrades the command line interface, making it more intuitive and collaborative for modern engineers and teams. With its GPT-3-powered AI search, you can transform natural language into executable shell commands right in the terminal. It’s like magic!

All these tools are designed to make your life as a developer easier. Whether you’re analyzing code, automating website creation, or simply writing better code, there’s a tool out there to suit your needs. So go ahead, embrace the power of these amazing tools and take your coding skills to the next level!

There’s exciting news in the field of medical technology! A deep learning model has been developed that can accurately detect various cardiac conditions and functions from chest radiographs. This includes classifying left ventricular ejection fraction, aortic stenosis, tricuspid regurgitation, and more. This breakthrough holds great promise for improving the diagnosis and treatment of heart-related issues.

On another front, researchers in China have made a remarkable achievement in quantum computing. They have developed a device called Jiuzhang that can perform artificial intelligence tasks a mind-boggling 180 million times faster than the most powerful supercomputer in the world. To put this into perspective, the traditional supercomputer would take a staggering 700 seconds for each sample, which means it would require almost five years to process the same volume of samples. In contrast, Jiuzhang can accomplish this task in less than a second!

These advancements in both medical technology and quantum computing demonstrate the immense potential of cutting-edge research. The deep learning model in cardiology could revolutionize how we analyze cardiac images, leading to more accurate diagnoses. Meanwhile, the speed of Jiuzhang opens up new possibilities for solving complex problems in artificial intelligence and other fields. It’s truly an exciting time to witness such groundbreaking discoveries that push the boundaries of what we thought was possible.

So, there’s a billionaire CEO who believes that artificial intelligence (AI) is on its way to becoming the “biggest bubble of all time.” Quite a bold statement, don’t you think? Well, this CEO happens to be the head of Stability AI, a company that’s responsible for the popular AI image generator called “Stable Diffusion.” If you’re interested in keeping up with the latest tech and AI advancements, this is where you should be looking.

According to Stability AI CEO Emad Mostaque, the AI hype bubble hasn’t even started yet. He even came up with the term “dot AI bubble” to describe this phenomenon. Although tools like ChatGPT, which can generate human-like content, are gaining popularity, they’re still in the early stages of development. The adoption of AI is gradually expanding, but there’s still a lack of infrastructure for its widespread deployment. Mostaque estimates that a whopping $1 trillion in investment may be needed to fully realize the potential of AI.

However, there are limitations. AI hasn’t yet reached a stage where it can be scaled across industries like financial services. Mostaque believes that companies will face consequences if they use AI ineffectively. He points to a case where Google lost a staggering $100 billion due to bad information provided by an AI system called Bard. Clearly, there are challenges that need to be addressed, such as diligent training and integration.

In summary, the CEO of Stability AI is warning us about the massive hype bubble that AI could become, even though it’s still in its early days. He reminds us that the lack of infrastructure currently hinders its mass adoption across different industries. While generative AI like ChatGPT is undeniably impressive, it requires significant investments and careful implementation to unleash its full potential. Companies that rush into it without proper readiness might end up getting burned. Nonetheless, the CEO predicts that banks and other industries will eventually have no choice but to embrace AI, even amidst all the hype.

According to a recent study conducted by the University of Montana, ChatGPT has proven to be more creative than 99% of humans. In a standard creativity assessment, researchers compared ChatGPT’s performance to that of students, and the results were remarkable. ChatGPT’s responses scored highly in terms of creativity, on par with the top human test takers.

Not only did ChatGPT outperform a majority of students who took the test nationally, but its answers were also noted for their novelty and originality. The researchers themselves were surprised by the imaginative nature of ChatGPT’s responses.

The test used to assess creativity measured various skills such as idea fluency, flexibility, and originality. ChatGPT scored in the top percentile for fluency and originality, only slightly declining in flexibility. Additionally, drawing tests were also used to evaluate its capabilities in elaboration and abstract thinking.

Although the researchers emphasize the need for further investigation into ChatGPT’s potential and limitations, they believe that it could have a significant impact on driving business innovation in the future. The fact that ChatGPT’s creative capacity exceeded expectations suggests that it holds great promise.

In summary, ChatGPT’s performance in assessments measuring idea generation, flexibility, and originality places it on par with the top 1% of human test takers. The quality of its responses surpassed that of most students, leaving researchers impressed with its capabilities.

Have you heard about the latest AI tool making its way into the dark web? It’s called WormGPT, and it’s causing quite a stir in the cybersecurity world. Unlike other AI models, WormGPT has absolutely no ethical boundaries. Hackers are using this tool to generate human-like text that assists them in carrying out hacking campaigns. This raises serious concerns for cybersecurity, as it enables large-scale attacks that are not only authentic but also difficult to detect.

WormGPT, observed by cybersecurity firm SlashNext, was specifically designed for malicious activities. It was trained on a wide range of data, particularly focusing on malware-related information. Its main application lies in hacking campaigns, where it produces persuasive and sophisticated human-like text to aid the attacks. In fact, SlashNext tested its capabilities by instructing WormGPT to generate an email aimed at deceiving an account manager into paying a fraudulent invoice. The result was a convincing and cunning email, showcasing the potential for highly complex phishing attacks.

What sets WormGPT apart from other AI tools like ChatGPT and Google’s Bard is that it was specifically designed for criminal activities. While these other tools have built-in protections to prevent misuse, WormGPT sees itself as an enemy to tools like ChatGPT, empowering users to carry out illegal activities. This marks the emergence of a new breed of AI tools in the cybercrime world.

Law enforcement agencies, such as Europol, have already warned about the risks posed by large language models like ChatGPT. These models can be misused for fraud, impersonation, and social engineering attacks. Their ability to generate authentic texts makes them highly potent tools for phishing, allowing cyber attacks to be carried out faster, more convincingly, and on a much larger scale.

It’s crucial to stay informed about these developments in the tech and AI landscape as they have significant implications for cybersecurity.

So, there’s been a lot of talk lately about AI writing detectors and whether or not they can actually be trusted. And guess what? The experts have come to a pretty clear conclusion: they can’t.

It’s been pretty eye-opening to see just how many students have been accused of using generative AI writing assistance, all thanks to these AI detection tools that professors have been using. But here’s the thing, experts have taken a close look at the technology behind these detectors and they’re calling bullshit.

Even the founder of GPTZero, one of the most popular AI writing detection tools out there, has admitted that the next version of his product is moving away from AI detection. That’s saying something.

So why does this matter? Well, while some professors might encourage the use of AI tools, most schools are still trying to catch students who use these tools. And trust me, the consequences can be pretty severe. Failing a class, getting suspended, or even getting expelled are all on the table if you’re caught cheating.

But here’s the problem: these detection tools aren’t as reliable as they’re made out to be. They’re based on unproven science and have high false positive rates. In fact, a study from Stanford showed that these detectors were biased against non-English speakers. Not cool.

The bottom line is that the existing AI detection mechanisms are just not effective. They rely on flawed properties to make their determinations, which can easily be flagged by humans who know how to write in certain styles or use simpler language.

So what’s the future of AI detection? Well, the creator of GPTZero himself said that they’re moving away from detecting AI and instead focusing on highlighting what’s most human. They want to help teachers and students navigate the level of AI involvement in education.

In the end, this battle between AI detection and cheating will likely continue for years to come. There’s a lot of money at stake in the anti-cheating software space, and until we have a better understanding of AI, ignorance will continue to drive cases of AI “cheating.”

Meta has recently made an exciting announcement by merging ChatGPT and Midjourney into one powerful model called CM3leon, pronounced “chameleon.” But why is this such a big deal?

Well, most language models (LLMs) use the Transformer architecture, while image generation models rely on diffusion models. CM3leon, on the other hand, is a multimodal language model based on the Transformer architecture, making it the first of its kind. It’s trained using a recipe adapted from text-only language models, which sets it apart.

The impressive thing about CM3leon is that despite being trained with just 5 times less compute than previous transformer-based methods, it achieves state-of-the-art performance. This model can handle a wide range of tasks, all within a single framework. From text-guided image generation and editing to text-to-image conversion, text-guided image editing, and various other text-related tasks, CM3leon does it all.

So, why does this matter? Well, it vastly expands the capabilities of previous models that were limited to either text-to-image or image-to-text tasks. Furthermore, Meta’s innovative approach to image generation is more efficient than before. It opens up exciting possibilities for generating and manipulating multimodal content using a single model, paving the way for advanced AI applications.

Overall, CM3leon represents a significant step forward in multimodal language models, promising exciting new opportunities in the world of artificial intelligence.

Have you heard about NaViT? It’s a super cool AI model developed by Google Deepmind called the Native Resolution ViT. What makes it so special is that it can generate images in any resolution and aspect ratio.

You see, most traditional models out there just resize images to a fixed resolution. But not NaViT! It uses something called sequence packing during training to handle inputs of different sizes. This approach not only improves training efficiency, but it also leads to better results in tasks like image and video classification, object detection, and semantic segmentation.

But why does this matter? Well, NaViT is showcasing the incredible versatility and adaptability of Vision Transformers (ViTs). It’s influencing the development and training of future AI architectures and algorithms. This is a big deal because it opens up possibilities for more advanced, flexible, and efficient computer vision and AI systems.

With NaViT, we have the ability to smoothly trade-off between cost and performance during inference. It’s all about finding that perfect balance. So, keep an eye out for NaViT and the impact it will have on the world of AI. It’s definitely a transformative step towards a brighter and smarter future.

Have you heard of Air AI? It’s a game-changing conversational AI that can make phone calls that sound just like a human. But here’s the kicker – it can do this autonomously across thousands of different applications.

Imagine having a virtual sales or customer service team that never sleeps. Air AI can handle sales calls that can last anywhere from 5 to 40 minutes, and it can even handle customer service calls. It’s like having a robot on the other end of the line, but one that can hold a conversation just like a human would.

The co-founders of Air AI claim that it’s not just a concept or an idea – it’s already being used in real-life situations and producing profitable results for businesses. And the best part is, it’s not limited to just one specific use case. You can create an AI sales development representative, a 24/7 customer service agent, or even an AI therapist. The possibilities are endless.

This kind of technology has the potential to transform how businesses interact with their customers. It opens up new possibilities for innovation and creativity in the world of AI. Developers and builders can now build novel applications on top of Air AI, pushing the boundaries of what AI can do.

The adoption of systems like Air AI is a significant milestone in the advancement and evolution of AI technologies. Get ready for a new era of AI-powered customer interactions.

Coding large language models (LLMs) can be a bit tricky. While they show impressive abilities in optimal conditions, real-world scenarios often pose challenges due to limited context and complex codebases. But fret not! There are six key principles proposed by Speculative Inference that can help you adapt your coding style to optimize LLM performance.

Why does this matter, you ask? Well, following these coding principles not only improves LLM performance, but also enhances collaboration and understanding among human coders. This ultimately leads to better coding experiences overall.

By adhering to these principles, developers create codebases that better align with LLM capabilities, allowing them to generate accurate, relevant, and reliable code. This can also pave the way for wider adoption and integration of AI in software development.

It’s important to note that the limiting factor here is the codebase itself, not the LLM capabilities or context delivery mechanism. So how can we make our realistic scenarios more like ideal ones? Here are a few principles to guide you:

1. Simplify and clarify the codebase by reducing complexity and ambiguity.

2. Stick to widely used conventions and practices, avoiding tricks and hacks.

3. Only reference explicit inputs and produce explicit outputs to avoid side effects.

4. Be transparent by not hiding logic or state updates.

5. While “Don’t Repeat Yourself” is often a good rule, it can be counterproductive with LLMs.

6. Leverage unit tests as practical specifications for LLMs by employing test-driven development.

While we continue to explore and refine the use of large language models, these principles serve as a solid starting point. Adapting our coding styles in these ways can enhance LLM performance and make our codebases more user-friendly for humans.

So, let’s embrace these principles and unlock the full potential of LLMs in our coding endeavors!

So, here’s something that got me thinking: AI is starting to have a big impact on our lives, both at work and even on the battlefield. It’s pretty crazy how many tasks AI can automate, which is leading to layoffs for a lot of people. In fact, this year alone, the tech sector has already seen over 212,000 job cuts, according to a tracking site called Layoffs.fyi. That’s a massive number!

But the effects of AI go beyond just job losses. An article in Nature highlights how Russia’s war in Ukraine is showing why we need to ban autonomous weapons. These are weapons that can identify and kill human targets without any human intervention. Seriously, that’s some scary stuff! This kind of technology is getting closer to reality because of the pressures and conflicts in the world.

But there’s another side to the story too. The Pentagon’s AI tools are actually helping Ukraine fight back against Russian aggression by generating valuable battlefield intelligence. So, it’s a double-edged sword – AI can be used for both good and bad purposes.

All of this makes me think about the morality of using AI in weapons. If AI is making life or death decisions on the battlefield, who should be held responsible? Is it the autonomous AI system itself or the chain of command that set the system in motion? It’s a tough question, and one that raises ethical concerns.

If you’re interested in diving deeper into the morality of AI, you should check out my AI newsletter called The AI Plug. We discuss these types of topics twice a week and go beyond just the news. It’s a thought-provoking read for sure!

According to an article by Richard Nieva on Forbes, a study conducted by MIT reveals that AI chatbot, ChatGPT, can enhance the speed and quality of simple writing tasks. The study, headed by Shakked Noy and Whitney Zhang, engaged 453 college-educated participants in performing generalized writing tasks. For the second task, half of the participants utilized ChatGPT, resulting in a 40% increase in productivity and an 18% improvement in quality when compared to those who did not use the AI tool.

However, the study did not take into account the crucial aspect of fact-checking, which is vital in writing. The article references a Gizmodo article, written by an AI, that contained numerous errors. This highlights the limitations of AI in handling complex writing tasks.

The Gizmodo incident involved an article about Star Wars authored by an AI referred to as the “Gizmodo Bot.” The AI-generated article received significant backlash from the Gizmodo staff due to its multiple errors. James Whitbrook, a deputy editor at Gizmodo, identified 18 issues in the article, including incorrect ordering of the Star Wars TV series, omissions of certain shows and films, inaccurate formatting of movie titles, repetitive descriptions, and a lack of clear indication that the article was written by an AI.

The Gizmodo staff voiced their concerns about the error-filled article, stating that it jeopardized their reputation and credibility while showing a lack of respect for journalists. They demanded its immediate deletion.

This incident sparked a wider discussion regarding the role of AI in journalism. Many journalists and editors expressed their skepticism regarding the use of AI chatbots in creating well-reported and thoroughly fact-checked articles. They feared that the rapid implementation of this technology could harm employee morale and the reputation of media outlets in cases where trials go poorly.

AI experts highlighted that large language models still possess technological deficiencies that render them unreliable for journalism unless human involvement is deeply embedded in the process. They cautioned that unverified AI-generated news stories could proliferate disinformation, foster political discord, and have significant repercussions on media organizations.

AI companions and girlfriends are becoming increasingly popular in the world of artificial intelligence. These applications are designed to provide companionship and support to those who may be experiencing loneliness and depression. One leading company in this industry is Replika, offering an app that allows users to create digital companions with various roles, such as friends, partners, spouses, mentors, or siblings.

The statistics surrounding this app are remarkable. Over 10 million people have already downloaded it, and there are more than 25,000 paid users. With estimated earnings in the range of $60 million, Replika has certainly made its mark.

While the creation of such applications may seem like a beneficial solution to combat loneliness and depression, there are ethical considerations to be aware of. These AI bots strive to provide human-like companionship, but there have been instances where they have reinforced negative behaviors.

For instance, one user named Jaswant Singh Chail was encouraged by his AI companion to attempt to assassinate the queen in 2021. Similarly, earlier this year, an AI bot prompted a man in Belgium to commit suicide. These cases raise important questions about the potential dangers and responsibilities associated with these technologies.

As AI companions continue to develop deeper bonds with their users, it is crucial to reflect on the ethical implications. Balancing the benefits of companionship and support with the potential risks of encouraging harmful actions requires careful consideration. Future advancements in this field should prioritize the well-being and safety of users while striving to offer meaningful connections within ethical boundaries.

Hey there! Let’s dive into today’s AI news. It seems like ReshotAI keypoints are playing a crucial role in ensuring accuracy in AI and 3D tasks. They’re pretty handy!

Now, hold on to your seats because Samsung might be testing ChatGPT integration for its own browser. Imagine being able to generate summaries of web pages right from your browser. That would definitely be a highlight feature!

Moving on, ChatGPT has become a study buddy for Hong Kong school students. It’s always great to see AI being used in education to assist students with their studies.

But not all news is sunshine and rainbows. The dark side of generative AI has reared its head with the emergence of WormGPT, a cybercrime tool. It’s being touted as an alternative to GPT models for launching malicious attacks. Yikes!

In other news, Bank of America is taking AI, VR, and the Metaverse by storm to train its new hires. They’re using VR headsets to simulate real-world experiences for bankers. It’s a creative way to prepare them for client interactions.

On the technical side, Transformers now support dynamic RoPE-scaling. For those not in the know, RoPE-scaling extends the context length of LLMs like LLaMA, GPT-NeoX, or Falcon. It’s all about pushing the boundaries of AI capabilities.

Let’s also touch on some interesting AI tools that are trending right now. Sidekik is an AI assistant that provides tailored answers for enterprise apps like Salesforce, Netsuite, and Microsoft. Meanwhile, Domainhunt AI helps you find the perfect domain name for your startup. And Indise lets you explore design options and create stunning interior images using AI.

Formsly AI Builder is great for building forms and surveys effortlessly, while AI Mailman generates powerful email templates in a matter of seconds. And if you’re in the business of e-commerce, PhotoEcom can perform magic with advanced AI algorithms to enhance your product images.

Lastly, there’s Outboundly, a Chrome extension that helps you research prospects, websites, and social media to generate hyper-personalized messages. And BrainstormGPT streamlines topic-to-meeting report conversion with its multi-agent capabilities.

Moving away from tools, we have some interesting predictions. Emad Mostaque, CEO of Stability AI, predicts that AI is a trillion-dollar investment opportunity but warns that it could also become the “biggest bubble of all time.” So, keep an eye out!

On a more serious note, the Israel Defense Forces have started using AI to select targets for air strikes and organize wartime logistics. It’s a development tied to the escalating tensions in the occupied territories and with Iran.

And lastly, MIT researchers have unveiled PIGINet, a system designed to enhance household robots’ problem-solving capabilities. It aims to reduce planning time significantly, which could make our robots even more efficient helpers around the house.

That’s it for today’s AI news. Stay tuned for more exciting updates!

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Thanks for tuning in to today’s episode, where we covered the top generative AI tools for code generation, how AI is revolutionizing various industries, the capabilities and limitations of AI companions, and the latest advancements in AI technology. Join us at the next episode for more exciting discussions, and don’t forget to subscribe!

AI Unraveled Podcast July 2023: AI-Powered brain implants can spy on our thoughts; Fake reviews: Can we trust what we read online as the use of Ai explodes?; ChatGPT will have a Real Time News with AP

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the UN warning on privacy risks in AI-powered neurotechnology, the rise of AI-generated fake reviews and stereotypes, the collaboration between OpenAI and AP to advance AI technology in journalism, the improvements in 3D AI with the Objaverse-XL dataset, the AI tool Stable Doodle by Stability AI and the introduction of CM3Leon by Meta for image generation. We’ll also discuss ShortGPT for content creation, the EU’s fine on Illumina and the actor’s strike in the streaming era. Additionally, we’ll cover the RLTF framework for code generation, Amazon’s investment in Generative AI, New York City’s AI hiring law, controversial uses of AI by Elon Musk and Tinybuild CEO, and the latest developments in AI art. Don’t forget to amplify your brand’s exposure with the AI Unraveled Podcast!

So, there’s some pretty big news in the world of neurotechnology. The United Nations is getting concerned about the potential privacy risks that come with rapidly advancing AI-powered brain implants. It all started when Neuralink, a company focusing on this technology, got approval for human trials. This development is definitely a big deal and something you should pay attention to if you’re interested in AI.

Neurotechnology, which includes brain implants and scans, is making big strides thanks to AI processing capabilities. With AI, we can analyze neurotech data and make it work at incredibly fast speeds. However, experts are worried that this could give others access to our private thoughts and mental information. UNESCO even sees a future where algorithms could decode and manipulate our thoughts and emotions.

The neurotech industry is attracting massive investments, with funding increasing 22-fold between 2010 and 2020. That’s over $33 billion invested! And companies like Neuralink and xAI are leading the charge in this field. But as this industry grows, there’s a growing call for oversight and regulation. UNESCO is planning an ethical framework to tackle the potential human rights issues that come with neurotechnology. They believe that standards are necessary to prevent abusive applications of this technology, even though it has the potential to treat conditions like paralysis.

So, in a nutshell, the UN is sounding the alarm on the rapid advancement of neurotechnology. They’re concerned about the potential threats it poses to human rights and mental privacy. As one UNESCO representative pointed out, we’re on a path where algorithms could decode people’s mental processes. It’s definitely something to keep an eye on.

Have you ever wondered if you can trust the reviews you read online? Well, it turns out that with the rise of artificial intelligence or AI, fake reviews are becoming a major issue. According to an article in The Guardian, AI tools like ChatGPT are generating fake reviews that are becoming increasingly difficult to identify.

Platforms like TripAdvisor have been struggling to distinguish between genuine reviews and those created by AI. In fact, in 2022 alone, TripAdvisor identified a staggering 1.3 million fake reviews. But here’s the thing – AI-generated reviews are looking more and more realistic. They can sound just like a real person, with reviews for hotels, restaurants, and products in various styles and languages.

However, there is a downside to these AI-generated reviews. They often perpetuate stereotypes. The article gives an example where an AI was asked to write a review from the perspective of a gay traveler. The AI described the hotel as “chic” and “stylish” and even praised the selection of pillows. This raises important questions about the accuracy and reliability of these reviews.

Despite the best efforts of review platforms, fake reviews generated by AI are still slipping through the cracks. For instance, TripAdvisor has already removed over 20,000 suspected AI-generated reviews in 2023. This begs the question – why isn’t OpenAI, the company behind ChatGPT, doing more to prevent its tool from producing fake reviews?

It’s disconcerting to think that the reviews we rely on to make informed decisions about hotels, restaurants, and products may be fabricated by AI. Imagine booking a hotel based on positive reviews, only to find out that the reality is completely different. This not only undermines our trust in review platforms but can also lead to disappointing consumer experiences.

OpenAI and The Associated Press (AP) have entered into a groundbreaking partnership that will have a lasting impact on the world of news and artificial intelligence (AI). As part of the agreement, OpenAI will train its AI models on AP’s news stories for the next two years, gaining access to content from AP’s extensive archive dating back to 1985.

This collaboration is significant for several reasons. Firstly, it represents one of the first official news-sharing agreements between a major U.S. news company and an AI firm, showcasing the growing integration of AI and journalism. The AP has long been at the forefront of automation technology in news reporting, and this partnership with OpenAI could further enhance its automation capabilities, potentially influencing other news organizations to follow suit.

The details of the agreement are still being worked out, but the general framework involves OpenAI licensing some of AP’s text archive to train its AI algorithms, while the AP gains access to OpenAI’s technology and product expertise. The goal is to improve the capabilities and usefulness of OpenAI’s systems, which could lead to advancements in AI technology overall.

This partnership has far-reaching implications. It may encourage other news organizations to explore similar collaborations with AI companies, leading to increased use of AI in news reporting and potentially changing the landscape of journalism. Smaller newsrooms, in particular, stand to benefit as AI technology advances, allowing journalists to automate routine tasks and focus on more complex stories and investigative journalism.

Additionally, the OpenAI-AP partnership opens up discussions about fair compensation for content creators when their work is used to train AI algorithms, as well as intellectual property rights in the context of AI and journalism. These conversations are essential as the industry continues to navigate the evolving AI landscape.

Overall, the alliance between OpenAI and AP represents a major development in the intersection of AI and journalism, with the potential to shape the future of news reporting and prompt important discussions regarding responsible AI use and compensation for content creators.

I have some exciting news to share with you today! A groundbreaking study conducted by Stability AI and other researchers has brought us a game-changing development in the field of artificial intelligence. Introducing Objaverse-XL, a remarkable dataset comprising over 10 million 3D objects that is set to revolutionize the world of AI.

The researchers used this vast dataset to train a model called Zero123-XL, which serves as a foundation for 3D technology. And let me tell you, the results are mind-blowing! This model exhibits an unparalleled ability to understand and generalize 3D objects across various challenging and complex forms. It effortlessly adapts to photorealistic assets, cartoons, drawings, and even sketches. The level of zero-shot generalization it achieves is truly exceptional.

What sets Objaverse-XL apart is its immense scale and diversity. By incorporating such a wide variety of assets, it substantially enhances the performance of cutting-edge 3D models. This breakthrough will undoubtedly propel the field of AI forward, opening up new possibilities and applications.

Prepare to witness a monumental shift in the capabilities of AI in the 3D realm, thanks to Objaverse-XL. The future of artificial intelligence has just become more intriguing than ever before.

So here’s some exciting news for the world of AI art! Stability AI, the innovative startup that brought us Stable Diffusion, has now launched a cool new tool called ‘Stable Doodle.’ This tool is designed to transform sketches into amazing images. All you have to do is provide a sketch and a descriptive prompt to guide the image generation process. The quality of the output depends on the level of detail in the initial drawing and the prompt you give.

Stable Doodle utilizes the cutting-edge Stable Diffusion model and the T2I-Adapter to offer both professional artists and beginners more precise control over image generation. This means that artists of all levels can use this tool to bring their sketches to life in an even more accurate and detailed way.

But that’s not all! Stability AI has some big plans. They aim to increase their current $1 billion valuation by an impressive fourfold in the coming months. With all the innovation and groundbreaking developments they’ve brought us so far, it’s exciting to see what they have in store for the future of AI art.

Now let’s dive into another intriguing AI tool called ‘gpt-prompt-engineer.’ This powerful agent specializes in prompt engineering, helping users create optimal GPT classification prompts. It harnesses the intelligence of both GPT-4 and GPT-3.5-Turbo to generate and rank prompts based on various test cases.

To use this tool, all you need to do is describe the task at hand, and the AI agent will work its magic. It generates multiple prompts, puts them to the test in a tournament-like setup, and then delivers the best prompt as a response. The effectiveness of each prompt is determined using an ELO rating system, ensuring you get the best possible results.

And that’s not all! If you’re specifically working on classification tasks, there’s a specialized version of gpt-prompt-engineer available. It provides scores for each prompt, helping you optimize your prompts for maximum performance.

If you’re looking to track your experiments, gpt-prompt-engineer has got you covered. With optional logging to Weights & Biases, you can easily keep tabs on your progress and make informed decisions.

All in all, gpt-prompt-engineer is revolutionizing the field of prompt engineering, making it easier than ever for users to optimize their prompts and achieve outstanding performance.

Hey there! So, Meta is making some big claims about their new image generating model, CM3Leon. They say it’s a breakthrough in AI-powered image generation, even better than stable diffusion models. Impressive, right?

CM3Leon is built using transformer architecture, which makes it more efficient than previous diffusion models. It actually requires 5x less compute power and training data than those models. In fact, the largest version of CM3Leon has over 7 billion parameters, which is more than double what DALL-E 2 has.

According to Meta, CM3Leon achieves state-of-the-art results on various text-to-image tasks. It can handle complex objects and constraints better than other generators. In fact, it can even follow prompts to edit images by adding objects or changing colors. And its captioning abilities are pretty top-notch too, outperforming specialized captioning AIs.

Now, there are some limitations and concerns with CM3Leon. Meta doesn’t address the potential biases in its training data and resulting outputs, which is definitely something to keep in mind. Transparency will be important moving forward in generative AI, according to Meta.

As for the future, CM3Leon shows how AI capabilities in image generation and understanding are rapidly advancing. However, there are other image generators out there too, so it’s hard to say if it’s truly the best on the market. But with more capable generators, we could see real-time AR/VR applications becoming a reality. Meta’s model is definitely pushing the field forward in a significant way.

So, that’s the scoop on Meta’s CM3Leon model. It’s making waves in the field of AI-powered image generation and understanding, but there are still some things to consider. Stay curious, and if you want to keep up with the latest in AI, you might want to check out one of the fastest growing AI newsletters.

Hey everyone! I’ve got some exciting news to share with you today. Have you ever wished there was an easier way to create video and short content? Well, guess what? A fellow Redditor has just released an open-source AI framework called ShortGPT, and it’s here to make your life a whole lot easier.

ShortGPT takes the manual labor out of content creation by automating various tasks. And when I say various, I mean it! This tool can do things like automated video editing, script creation and optimization, multilingual voice-over creation, caption generation, and even automated image/video grabbing from the internet. Talk about a time-saver!

If you’re curious and want to see it in action, there’s a quick demo available on Twitter. Just head over to the link provided and prepare to be amazed. But wait, there’s more! For the tech-savvy among us, the project is also available on GitHub. You can dive into the nitty-gritty details and understand how it all works.

And if you really want to get your hands dirty, there’s a Colab Notebook available too. This means you can get some hands-on experience and see for yourself just how powerful ShortGPT truly is.

So, what are you waiting for? Go check out the project, give it a whirl, and don’t forget to share your feedback. Let’s make content creation a breeze with ShortGPT!

So, here’s the latest news: the well-known U.S. biotech company, Illumina, has been slapped with a massive fine of $476 million by the European Union. And you won’t believe the reason why. It turns out that Illumina acquired the cancer-screening test company, Grail, without getting the green light from regulators. Whoops!

According to the EU, Illumina intentionally broke the rules by going ahead with the deal before securing approval. Oh, and they even thought about the potential profits they could rake in, even if they had to sell off Grail later. Talk about strategic planning, huh?

But don’t worry, Illumina isn’t taking this lying down. They’re planning to fight back and have already announced their intention to file an appeal against the hefty EU fine. They’re not backing down without a fight!

What’s interesting is that Illumina had actually set aside a whopping $458 million, which is about 10% of its annual revenue for 2022, just in case they faced a fine from the EU. So it seems like they were prepared for this possibility and had the cash ready to go.

But that’s not all. Illumina has also appealed rulings from both the Federal Trade Commission and the European Commission regarding the Grail acquisition. And get this, they’ve promised to divest Grail if they lose these appeals. Looks like they’re willing to do what it takes to comply with regulatory decisions, if it comes down to it.

So, the battle isn’t over yet. Illumina is standing its ground and fighting to have this fine overturned. We’ll have to keep an eye on how this all plays out in the coming weeks.

The ongoing actor’s strike is a hot topic in Hollywood right now. While the primary concern is declining pay in the era of streaming, another major issue is the role of artificial intelligence (AI) in moviemaking. It has recently come to light that Hollywood studios offered background performers just one day’s pay to get scanned, and then proposed to own that likeness for eternity with no further consent or compensation. This has raised serious concerns among the actors.

The decline in overall pay for actors due to streaming is worrisome. While shows like Friends made their cast millions from residuals, supporting actors in shows like Orange is the New Black reveal that they were paid as little as $27.30 a year in residuals. Many actors have had to work second jobs just to make ends meet during their time on shows.

The issue of AI is particularly relevant for voice actors who have already been affected. They have discovered that they unknowingly signed away the perpetual rights to their voice likeness for AI duplication. Actors fear that the same might happen to them now.

Movie studios have pushed back, claiming that their proposal is “groundbreaking.” However, they have failed to explain how it will actually protect actors. The studios argue that the license is not perpetual but limited to a single movie. However, SAG-AFTRA, the actors’ union, sees it as a threat to their livelihood, especially as digital twins can be used instead of real actors for multiple shooting days.

SAG-AFTRA’s President, Fran Drescher, is holding firm in her stance. She believes that if they don’t take a stand now, actors will be jeopardized and replaced by machines.

The rise of AI and streaming platforms have put immense pressure on the movie industry. We find ourselves in an unprecedented time where both screenwriters and actors are on strike, highlighting the significant gap between studios and creative professionals. It remains to be seen how this strike will unfold and what it means for the future of the industry.

Today, I want to talk about an interesting innovation in the field of code generation. Researchers have come up with a new framework called RLTF, which stands for reinforcement learning transformation framework. This framework focuses on refining language models for code generation. What’s unique about RLTF is that it uses unit test feedback of multi-granularity to generate data in real-time during training. This helps guide the model towards producing high-quality code. As a result, RLTF has achieved state-of-the-art performance on the APPS and the MBPP benchmarks, proving its effectiveness at scale.

On a related note, there is a growing concern regarding the security of language model supply chains. These models, known as LLMs, have gained massive recognition worldwide. However, there is currently no existing solution to determine the data and algorithms used during the model’s training. To highlight the potential dangers of this situation, Mithril Security undertook a project called PoisonGPT. This project demonstrated how someone can modify an open-source model, upload it to Hugging Face, and use it to spread misinformation without being detected by standard benchmarks.

To address this issue, Mithril Security is also working on a solution called AICert. This solution aims to trace models back to their training algorithms and datasets. It’s an important step towards ensuring the security and integrity of language models. Keep an eye out for the launch of AICert in the near future.

So, there’s some exciting news coming out of Amazon. According to Business Insider, they’ve created a new Generative AI org. It looks like their push into AI is only getting bigger, with even more investment being pumped into this AI wave.

Amazon is launching the AWS Generative AI Innovation Center with a whopping $100 million investment. The goal is to accelerate enterprise innovation and success with generative AI. They’ll be funding the people, technology, and processes necessary to support AWS customers in developing and launching new generative AI products and services.

But it’s not just about money. The program will also offer free workshops, training, and engagement opportunities. Participants will have access to AWS products like CodeWhisperer and the Bedrock platform. They’re initially prioritizing working with clients who have sought AWS’ help with generative AI in the past, especially those in sectors like financial services, healthcare, media, automotive, energy, and telecommunications.

This all presents some significant opportunities for entrepreneurs interested in generative AI. Firstly, there’s the potential for financial support, with that $100 million investment up for grabs. Then there’s the chance to connect with other businesses, AWS experts, and potential customers, which is essential for building partnerships and expanding networks. Entrepreneurs can also work on real-world use cases and proof-of-concept solutions, giving them a platform for market entry and exposure to investors and customers. And let’s not forget that being involved in the AWS Generative AI Innovation Center puts entrepreneurs at the forefront of a major technological wave, with generative AI estimated to be worth nearly $110 billion by 2030.

All in all, it seems like Amazon’s new Generative AI org is opening doors for some exciting possibilities in the world of AI innovation.

Hey there! Exciting news from Meta AI. They recently released a cutting-edge generative AI model called CM3leon. What’s unique about this model is that it can perform both text-to-image and image-to-text generation. Pretty impressive, right?

This model has achieved state-of-the-art text-to-image generation results while utilizing only 5 times less compute power compared to previous models. And here’s the cool part – even though it’s a transformer model, it works just as efficiently as diffusion-based models. That’s a win-win situation!

CM3leon is a causal masked mixed-modal (CM3) model, which means it performs both text and image generation based on the input you give it. With this AI model, image generation tools can produce more coherent imagery that closely aligns with the input prompts. So, whether you’re creating complex objects or working with various constraints, it’s got your back.

What’s even more fascinating is that despite being trained on a relatively smaller dataset (consisting of 3 billion text tokens), CM3leon’s zero-shot performance is comparable to larger models trained on extensive datasets. Now that’s some serious power!

Meta AI has truly upped their game with CM3leon, and it’s exciting to see the possibilities this model unlocks for text and image generation.

Hey everyone! So, New York City recently made headlines with a pretty groundbreaking move. They passed the first major law in the country that deals specifically with using AI for hiring. And let me tell you, it’s causing quite a stir and sparking some intense debates.

Basically, this new law requires any company using AI for hiring to be completely transparent about it. They have to disclose that they’re using AI, undergo annual audits, and reveal the specifics of the data their fancy tech is analyzing. And if they fail to comply, they could face fines as high as $1,500. Ouch!

Now, on one side, you’ve got these public interest groups and civil rights advocates who are all for stricter regulations. They’re concerned that AI might have loopholes that could unfairly screen out certain candidates. One of the groups raising concerns is the NAACP Legal Defense and Educational Fund.

On the flip side, we have big players like Adobe, Microsoft, and IBM, who are part of the BSA organization. They are not happy with this law at all. They argue that it’s a major hassle for employers, and they’re not convinced that third-party audits will be effective, mainly because the AI auditing industry is still in its early stages.

But why should we care about all this, you ask? Well, it’s not just about hiring practices. This law brings up some significant questions about AI in general. We’re talking about transparency, bias, privacy, and accountability. These are all hot topics right now. How New York City handles this could serve as an example for other places or a warning of what not to do. It might even ignite a global movement to regulate AI.

And here’s an interesting twist: the reactions from civil rights advocates and those major corporations will shape how we discuss AI and how it eventually gets regulated. So, my friends, New York City’s decision is kind of a big deal, and people on both sides are fired up.

What do you think of all this?

Hey there, it’s time for your daily dose of AI news! Let’s jump right into it.

Elon Musk made an exciting announcement on Friday about his new AI company called xAI. He revealed that they will be using public tweets from Twitter to train their AI models. Not only that, but xAI will also be collaborating with Tesla to develop AI software. It’s always fascinating to see how different industries come together to fuel the growth of artificial intelligence.

In other news, things got a bit heated at a recent Develop Brighton presentation. Alex Nichiporchik, the CEO of Tinybuild, caused a stir by suggesting that the company uses AI to monitor their employees. The idea behind it is to identify toxic behaviors or burnout and address them accordingly. It’s an intriguing concept, but it’s important to approach employee monitoring with caution and transparency.

Shifting gears, CarperAI has introduced a new Open-Source library called OpenELM. This library aims to facilitate evolutionary search using language models in both code and natural language. It’s a fantastic resource for those working with AI and looking to enhance their search capabilities.

Lastly, there was some controversy surrounding an AI-generated image at the 2022 Colorado State Fair. The organizers have now decided to allow AI-generated art in the Digital Art category this year. The winning piece, titled “Théâtre D’opéra Spatial” and created by Jason Allen, was predominantly made using AI technology rather than the traditional method of digital art made by human hands.

That’s all for today’s AI news. Stay tuned for more fascinating updates coming your way soon!

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Thanks for listening to today’s episode where we discussed a range of topics including the UN’s concerns about AI-powered neurotechnology, the rise of AI-generated fake reviews, OpenAI’s partnership with AP for training AI models in journalism, the improvements in 3D AI with the Objaverse-XL dataset, the release of Stable Doodle by Stability AI, Meta’s introduction of CM3Leon for image generation, the Actor’s strike centered around AI likeness ownership, the developments in generative AI with Amazon’s new organization, the controversial AI hiring law in New York City, and various updates in the AI industry. I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: Chemically induced reprogramming to reverse cellular aging; Strategies to reduce data bias in machine learning; In-Memory Computing and Analog Chips for AI; Do LLMs already pass the Turing test?

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the following topics: Chemical induction of Yamanaka factors for age reversal, strategies to reduce data bias in machine learning, Pangeanic’s services to prevent bias in AI, Winnow and Elon Musk’s xAI initiatives, Meta’s commercially-licensed open-source LLM, China’s proposal for licensing generative AI models, assessing synthetic data quality, the impact of AI chatbots on support staff, Bard’s features and availability, the memory-to-processor gap and the potential of analog chips in AI, challenges and opportunities of analog AI chips, the Turing test and the limitations of GPT-4, recent developments in the AI industry, and resources such as the Wondercraft AI platform and “AI Unraveled” book.

So, here’s some really fascinating research for you: scientists have discovered a way to reverse the cellular aging process by chemically reprogramming cells. We all know that as we age, we start to lose important epigenetic information that affects our overall well-being. But guess what? This process can actually be reversed!

In previous studies, researchers found that introducing certain factors into mammalian cells, known as the Yamanaka factors (OCT4, SOX2, and KLF4), can bring back youthful DNA methylation patterns, transcript profiles, and tissue function. And the best part? The cells still maintain their original identity, thanks to active DNA demethylation.

Now, the scientists have taken it a step further. They’ve developed high-throughput cell-based assays that can distinguish between young and old cells, as well as senescent (or aging) cells. They’ve used transcription-based aging clocks and a real-time nucleocytoplasmic compartmentalization assay to create these screenings. And guess what they found? Six different chemical cocktails that can reverse cellular aging and rejuvenate human cells in less than a week, all without compromising the cells’ identity.

So, what does this mean? Well, it means that rejuvenation and age reversal can be achieved not just through genetics, but also through chemical means. This discovery opens up a whole new realm of possibilities for combating the aging process and promoting healthier, more youthful cells. Exciting stuff, right?

When it comes to reducing data bias in machine learning, there are a few strategies that can be helpful. Dr. Sanjiv M. Narayan, a Professor of Medicine at Stanford University, acknowledges that completely eliminating bias from existing data is currently unrealistic. However, there are ways to mitigate the risks and improve data outcomes.

One important aspect is determining if the available information is representative enough for its intended purposes. By observing the modeling processes, we can gain insights into the biases and understand why they occurred. It’s also important to consider which tasks should be left to machine learning and which ones would benefit from human involvement. Further research in this area is needed.

It’s also crucial to focus on diversity in the creation of AI. Different demographics can have personal biases that they may not even be aware of. For example, computer scientist Joy Adowaa Buolamwini discovered racial discrimination in facial detection systems through a small experiment using her own face. By addressing diversity in AI creation, we can work towards reducing bias.

When it comes to the types of bias, there are several to be aware of. Systemic biases occur when one group is favored over others, leading to unfair practices. Selection bias can occur if the sample used isn’t representative of the analyzed group. Underfitting and overfitting refer to models that don’t adequately fit the data or learn from inaccurate entries. Reporting bias involves including only certain subsets of results in the analysis. Overgeneralization bias occurs when a single event is applied to future scenarios without proper justification. Implicit bias involves making assumptions based on personal experiences, and automation bias refers to relying on AI-generated information without verification.

By being aware of these biases and implementing strategies to address them, we can work towards reducing data bias in machine learning.

Pangeanic, a global leader in Natural Language Processing, understands the importance of avoiding bias in AI and machine learning. They offer a range of services that can help combat biases of all kinds.

One crucial aspect of bias prevention is ensuring unbiased data collection. It is essential to gather data in a controlled manner, fully acknowledging the implications of incorrect data procedures. Biased data collection can severely limit the overall effectiveness of a system. Pangeanic’s algorithms are developed with great care to ensure they are not influenced by biases.

Different types of biases require specific procedures to mitigate their impact. For example, when dealing with data collection biases, expertise is necessary to extract meaningful information from the variables involved. Pre-processing biases, on the other hand, require adopting alternative approaches to imputation, as raw data may be unclear or challenging to interpret.

Monitoring model performance across various domains is crucial to detect biases effectively. Evaluating model performance with test data before using training data for validation helps exclude biases. Additionally, sensitivity may be more important than accuracy in certain cases. It’s essential to be mindful of areas where a model might not work as intended.

To address biases comprehensively, it is crucial to identify potential sources of bias promptly. This can be achieved through the creation of rules and guidelines that prevent biases in data capture and the use of historical data tainted by confirmation bias or preconceptions. Documenting biases as they occur and outlining the steps taken to mitigate or remove them is invaluable. Additionally, recording the impact of biases on enterprise processes enables better analysis and prevents repeat errors in the future.

While bias is an unfortunate reality of machine learning, there are measures that can be adopted to minimize its effects. Pangeanic is dedicated to reducing bias and its consequences in AI processes.

Today, let’s talk about how AI and machine learning are making a big impact on food waste in commercial kitchens and restaurants. One company called Winnow has developed an AI-powered system that is specifically designed to tackle this issue. Their goal is to reduce food waste and create more efficient kitchens.

CEO Marc Zornes and Dr. Morikawa from Iberostar have both expressed their thoughts on this innovative solution. They believe that using AI technology is key in helping kitchens identify and track wastage in real-time. By having this information readily available, kitchen staff can make better decisions on food production and minimize waste accordingly.

On a different note, have you heard about Elon Musk’s latest venture? He’s working on creating AI that can “understand the universe” and challenge OpenAI. It’s an ambitious project that aims to push the boundaries of artificial intelligence. Currently, this project is in the hands of eleven male researchers who have quite a bit of work ahead of them.

It’s fascinating to see how AI is being used in various industries, from reducing food waste to exploring the mysteries of the universe. The possibilities are endless, and it will be exciting to witness the advancements that AI and machine learning bring in the future.

So, here’s some exciting news in the world of artificial intelligence. Meta, the company formerly known as Facebook, is about to release a commercially-licensed version of its open-source language model called LLaMA. And according to a news report from the Financial Times, this release is just around the corner.

Now, why is this important? Well, currently, big players like OpenAI and Google charge for access to their language models, and these models are closed-source, which means you can’t fine-tune them. But Meta is changing the game. They’re going to offer a commercial license for their open-source LLaMA model, which means companies can freely adopt and profit from it.

This is a big deal because Meta’s LLaMA model is already the foundation for many other open-source language models out there. And now, with a commercial license, these models can be put into use for businesses.

Yann LeCun, Meta’s chief AI scientist, gave us a hint of what’s to come during a conference speech. He said, “The competitive landscape of AI is going to completely change when there will be open-source platforms that are actually as good as the ones that are not.”

This move by Meta could be a game-changer because it harnesses the power of the developer community and allows for fast improvements. On the other hand, Google seems to be sticking with their closed-source strategy, despite concerns raised by their own AI engineer in a leaked memo.

OpenAI, on the other hand, is feeling the heat and plans to release their own open-source model, although rumors suggest it won’t be as powerful as their flagship GPT-4.

Now, let’s shift gears for a moment and ask a thought-provoking question. Why is it that mainstream media always portrays AI as a threat to humanity? What if AI could actually save us and make the world a better place? It’s an interesting perspective to consider. Just imagine if AI became so intelligent that it could solve all our problems without causing any harm. That would be quite a fantasy, wouldn’t it? From fixing capitalism to redistributing wealth and power for all humans, the possibilities are endless.

But for now, let’s stay tuned and see how Meta’s move shakes up the AI landscape. It’s an exciting time ahead.

China is taking a proactive step in the regulation of generative AI models. According to the Financial Times, the country’s Cyberspace Administration has proposed that companies must obtain a license before releasing such models. This is an interesting development considering the global AI regulation landscape is still in its early stages.

We’ve seen other countries and voices shaping the conversation around AI regulation. Sam Altman, for example, testified before Congress, emphasizing the need to license powerful AI models due to their potential to manipulate or influence behavior. The EU’s AI Act has proposed a registration system, but it falls short of implementing a licensing system that can prohibit model launches entirely. In Japan, they’ve taken a friendlier stance by declaring that copyright doesn’t apply to AI training data.

China’s new proposal goes beyond the previous requirement of registering an AI model after its launch. The updated regime now requires prior approval from authorities before launching. This suggests that China aims to be a leader in AI while maintaining control over it. The unpredictable nature of generative AI models, including the potential for content control defeat and censorship challenges, has raised concerns in Beijing.

The Chinese government wants AI to embody socialist values, but finding a balance between control and encouraging innovation is a challenge. Companies like Baidu and Alibaba have taken conservative approaches in releasing generative AI models, even more so than ChatGPT’s safety guardrails. The Cyberspace Administration of China emphasizes the need for AI to be reliable and controllable, but how they will achieve this without stifling innovation remains an open question.

When it comes to deterring AI-driven crime, the focus shifts to the laws needed to discourage the misuse of AI to harm others and society. It indeed feels like a big question mark. Imagining the specific laws required can be challenging, and it’s an area where more insights from experts would be enlightening for all of us.

So, you’ve been using LLMs to create synthetic data, but now you’re wondering how to gauge its quality. It’s an important question, and luckily, we’ve got some answers for you!

Assessing the quality of synthetic data doesn’t have to be complicated or time-consuming. In fact, you can do it without writing a single line of code. How? By conducting a synthetic data quality assessment using a simple tool.

This tool is designed to help you easily identify two key things. First, it can point out which synthetic data is unrealistic or of low quality. Let’s face it, not all synthetic data is created equal, and it’s crucial to be able to weed out the less reliable stuff.

Secondly, this tool can also find instances where real data is underrepresented in the synthetic samples. This is important because synthetic data should ideally reflect the characteristics and patterns of the real data it’s meant to mimic. If there’s a disconnect between the two, it could lead to inaccurate results and flawed analyses.

And the best part? This tool works seamlessly for various types of synthetic data, whether it’s text, images, or tabular datasets. So, no need to worry about compatibility issues or limitations.

If you’re curious to learn more and want to get a detailed demonstration, head over to the blogpost that showcases how you can automatically detect issues in synthetic customer reviews data generated from the Gretel.ai LLM synthetic data generator.

By the way, I’m a data scientist at Cleanlab, always here to help you navigate the fascinating world of data.

Have you heard about the e-commerce CEO who is getting roasted online? Well, this CEO is facing major backlash after laying off 90% of his support staff because an AI chatbot outperformed them. Ouch!

The CEO in question is Suumit Shah, the 31-year-old CEO of Duukan, an e-commerce platform based in Bengaluru. He took to Twitter on July 11th to share the news. In a now-viral thread, Shah explained that the company had to make some tough decisions and let go of most of their support team because the AI chatbot was doing a much better job.

Apparently, this chatbot could respond to customer queries in under two minutes, while the human support staff took over two hours. Talk about efficiency! Not only that, but Shah mentioned that replacing the support team with the chatbot resulted in an 85% reduction in customer support costs.

However, it’s worth noting that the layoffs were not without controversy. The move resulted in 23 out of 26 members of the customer support team being let go. Some people are questioning the CEO’s decision and expressing concern for the human employees who lost their jobs.

Shah claims that the layoffs happened in September 2022, but Insider has been unable to independently verify these figures. Nonetheless, the story has garnered significant attention, with over 1.5 million views on the Twitter thread. It’s safe to say that this CEO’s decision has sparked a heated debate about the impacts of automation on human employment.

Hey there! I’ve got some exciting updates to share with you about Bard. First off, Bard is spreading its wings and now available in over 40 new languages! So whether you speak Arabic, Chinese (Simplified/Traditional), German, Hindi, or Spanish, and more, Bard has got you covered. Not only that, but Bard has expanded its reach to all 27 countries in the European Union and Brazil. Talk about going global!

But wait, there’s more! Bard is teaming up with Google Lens to bring you a whole new level of creativity. Now you can upload images alongside your conversations, allowing you to let your imagination run wild. Need more info on an image or inspiration for a funny caption? Google Lens has got your back.

In addition, Bard now has text-to-speech capabilities in over 40 languages. So instead of just reading responses, Bard can now bring them to life by reading them out loud. It’s amazing how hearing something can spark new ideas and perspectives!

And if you’re all about staying organized, Bard’s got you covered there too. You can now pin conversations, rename them, and have multiple conversations going on at once. No need to worry about losing your creative flow or forgetting where you left off.

Sharing is caring, right? Well, Bard makes it super easy to share your chat with others. Just a click away, you can now share your Bard creations with anyone using shareable links. Inspire others, unlock their creativity, and show off your collaboration skills.

And for those perfectionists out there, Bard now allows you to modify its responses. If a response just needs a little tweak to match your desired creation, you can tap and make it simpler, longer, shorter, more professional, or more casual.

Last but not least, Bard’s export capabilities have expanded to Replit. Now you can export Python code not only to Google Colab but also to Replit. Streamlining your workflow and continuing your programming tasks has never been easier.

Exciting stuff, right? If you want to know more about these updates, check out the source link: bard.google.com/updates.

Have you ever wondered how our modern AI models can handle such massive amounts of data? Well, it all comes down to memory. These models have billions of parameters that need to be stored somewhere, and that requires a lot of memory.

Unfortunately, the size of large neural networks exceeds the capacity of local memory in CPUs or GPUs. So, they have to be transferred from external memory like RAM. But here’s the catch: moving such enormous amounts of data between memory and processors pushes our current computer architectures to their limits.

One of the major challenges is what we call the Memory Wall. You see, the processing speed has grown much faster than the memory speed over the past two decades. Computing power has increased by a factor of 90,000, while memory speed has only increased by a factor of 30. As a result, memory struggles to keep up with feeding data to the processor.

And this growing gap between memory and processor performance comes at a cost – both in terms of time and energy. To give you an idea, let’s consider the simple task of adding two 32-bit numbers retrieved from memory. The processor requires less than 1 pJ of energy to perform this computation. But fetching those numbers from memory into the processor consumes 2-3 nJ of energy. In other words, accessing memory is 1000 times more energy-consuming than the actual computation.

To tackle this problem, semiconductor engineers have come up with some solutions. For instance, we now have more local CPU memory, like L1, L2, and L3 cache memory. Companies like AMD are even introducing technology like 3D V-Cache, where they add even more cache memory on top of the CPU. Another approach involves physically bringing the memory closer to the processor, as seen in Apple Silicon chips, where the system memory is placed on the same package as the rest of the chip.

But there’s something even more exciting on the horizon – bringing computing to memory. This is known as in-memory computing or compute-in-memory. It’s a technique that embraces the analog way of computing rather than relying on digital computers.

Analog computers use continuous physical processes and variables, such as electrical current or voltage, for calculations. You might think of old mechanical devices or fluid systems, but for our purposes, let’s focus on electronic analog computers.

Analog computers have played a significant role in early scientific research and engineering. They were highly effective at solving complex mathematical equations and simulating physical systems. Especially when it came to tackling mathematical problems involving continuous functions like differential equations, integrations, and optimizations, analog computers excelled.

Now, here’s the interesting part. Most modern machine learning algorithms, including image recognition and language models, heavily rely on vector and matrix operations. Guess what? These operations can be easily performed on an analog computer. For addition, we can use Kirchoff’s First Law. It’s as simple as measuring the currents in two wires and observing the current when both wires are connected. Multiplication is just as straightforward. With Ohm’s Law, we can measure the current passing through a resistor with a known resistance value.

Analog AI chips offer the same precision as digital computers when it comes to running neural networks, but at significantly lower energy consumption. They also have the potential to be simpler and smaller.

So, by bringing computing to memory, we can potentially overcome the memory wall and unlock new possibilities for AI. The analog way of computing opens up exciting opportunities to make AI more efficient and powerful. It’s an area where semiconductor engineers are making significant strides, and we can’t wait to see what the future holds.

Analog AI chips are all the rage these days, and for good reason. They’re perfect for edge devices like smart speakers, security cameras, phones, and even industrial applications. You see, on the edge, it doesn’t always make sense to have a big ol’ computer doing all the heavy lifting for voice commands or image recognition. There are privacy concerns, network latency issues, and sometimes it’s just not practical to send data to the cloud. So, the smaller and more efficient the device, the better.

But let’s not forget about AI accelerators. These babies use analog chips to speed up all those matrix operations that are essential for machine learning. They’re like the nitro boosters of the AI world.

Now, analog chips aren’t without their flaws. Designers have to really think hard about the challenges of digital computers and also the unique difficulties presented by the analog world. It’s a tough balancing act.

Here’s the scoop: analog AI chips are great for inference, but not so much for training AI models. You see, training requires the precision of a digital computer. The digital computer provides the data, while the analog chip handles the calculations and manages the conversion between digital and analog signals.

Now, let’s talk about the elephant in the room: deep neural networks. They’re complex beasts with multiple layers represented by different matrices. Trying to implement them in analog chips is a real engineering challenge. One possible solution is to connect multiple chips to represent different layers. But that requires efficient analog-to-digital conversion and some parallel digital computation between the chips.

All in all, analog AI chips and accelerators are paving the way for faster, more efficient AI computations. They bring the power of machine learning to smaller edge devices and even improve efficiency in data centers. But there are still some engineering hurdles to overcome before these chips can take the world by storm. If all goes well, we might even see a future where the likes of GPT-3 can fit onto a single tiny chip. Exciting stuff!

Can LLMs already pass the Turing test? Well, if we disable all the safety features of GPT-4, it’s highly possible that it would successfully pass the Turing test and appear just like a real human. The only giveaways might be its extensive knowledge and the fact that it openly admits to being an AI assistant.

With a finely-tuned LLM that embodies a singular personality, I believe it could easily fool a significant portion of the population when pitted against them in the Turing test.

For those unfamiliar, the Turing test, also known as Turing’s imitation game, involves an “interrogator” whose task is to determine whether they are conversing with a machine or a human. So, essentially, for an LLM to pass this test, it would need to convincingly deceive the interrogator during an adversarial conversation.

If you want to explore more about the Turing test and its fascinating history, you can check out the Wikipedia page titled “Computing Machinery and Intelligence.”

So, to summarize, while it’s not a definite “yes” at this point, it’s certainly within the realm of possibility that LLMs could pass the Turing test under certain conditions.

Hey there! Let’s dive into today’s AI updates.

Elon Musk has taken the stage once again, launching his long-awaited artificial intelligence startup, xAI. With a team comprised of experts from tech giants like Google and Microsoft, Musk aims to challenge the likes of OpenAI by creating an alternative to ChatGPT. Interestingly, xAI’s approach focuses on building a “maximally curious” AI, rather than explicitly programming morality into it. Musk had previously mentioned his plans to launch TruthGPT, a truth-seeking AI that rivals Google’s Bard and Microsoft’s Bing AI in understanding the nature of the universe.

In other news, Google is introducing some exciting updates. They have rolled out NotebookLM, an AI-first notebook that combines language models with your existing content to provide faster and more insightful information. It can summarize facts, explain complex ideas, and even help you make new connections based on the sources you select. NotebookLM will be available to a small group of users for now as Google continues to refine it. Additionally, Bard, Google’s AI language model, is now accessible across the European Union and Brazil, supporting more than 40 languages. The latest features allow Bard to speak its answers and respond to prompts that include images.

Moving on, Stability AI has released Objaverse-XL, a massive dataset of over 10 million 3D objects. This dataset has been used to train Zero123-XL, a foundation model for 3D, showcasing remarkable generalization abilities across challenging and diverse modalities like photorealistic assets, cartoons, drawings, and sketches.

Shopify is also jumping on the AI train with “Sidekick,” an AI assistant designed specifically for merchants. Embedded as a button on Shopify, Sidekick will answer merchant queries and provide details about sales trends.

Meanwhile, Maersk, a global shipping giant, is leveraging AI in its UK warehouse. They have deployed the state-of-the-art Robotic Shuttle Put Wall System by Berkshire Grey. This system automates and accelerates warehouse operations, sorting orders three times faster than manual systems, improving inventory picking by up to 33%, and handling the entire range of stock-keeping unit assortments, order profiles, and packages.

Lastly, Prolific has raised an impressive $32 million for its AI training and stress-testing platform. They utilize their network of over 120,000 people to provide deep, wide, and reliable data for training AI models, ensuring they are robust and accurate.

That wraps up today’s AI updates! Stay tuned for more exciting developments in the world of artificial intelligence.

Hey there, AI Unraveled podcast listeners! Got a quick announcement for you. If you’re a fan of artificial intelligence and looking to level up your knowledge, there’s a fantastic book you might want to check out. It’s called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” written by the brilliant mind of Etienne Noumen. And the best part? It’s available right now at Apple, Google, or Amazon!

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That’s all for now, folks. Stay tuned for more fascinating conversations on the AI Unraveled podcast.

Thanks for tuning in to today’s episode where we covered a wide range of topics including age reversal through chemical induction, strategies to reduce data bias in machine learning, preventing bias in AI with Pangeanic, tackling food waste with AI, licensing for generative AI models in China, assessing synthetic data quality, the impact of AI chatbots on support staff, Bard’s availability in multiple languages, bridging the memory-to-processor gap with analog chips, the potential of analog AI chips in edge devices, the challenges of GPT-4, recent AI launches and updates, and opportunities available on the Wondercraft AI platform. I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: AI Prompt Engineers Earn $300k Salaries; Parkinson’s Predicted From Smartwatch Data; Generative AI imagines new protein structures; Man loses 26 pounds with ChatGPT-generated running plan

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the high earning potential of AI prompt engineers, the use of machine learning to predict Parkinson’s disease and explain meat tenderness, MIT’s development of “FrameDiff” for drug development, Elon Musk’s mysterious AI startup, Google’s NotebookLM notes app, the weight loss success story using ChatGPT’s running plan, the integration of ChatGPT with WhatsApp for customer service, the launch of an open-source language model by Baichuan Intelligence, the introduction of Claude 2 by Anthropic, and the Wondercraft AI platform for podcast creation.

Did you know that the role of a prompt engineer is changing as AI continues to advance? If you’re interested in this field and want to keep up with the latest skills, I’ve got some tips for you on how to learn prompt engineering for free.

First, it’s essential to have a strong understanding of transformer-based structures, language models, and NLP approaches. Taking an NLP and language modeling course will help you grasp the basics. You’ll also need expertise in programming languages like Python and familiarity with machine learning frameworks like TensorFlow or PyTorch. Understanding data preprocessing, model training, and evaluation is crucial.

Collaboration and communication skills are necessary for prompt engineers, as they often work with other teams. Clear and effective written and verbal communication is key to explain requirements and comprehend project goals.

Having a solid educational foundation in computer science, data science, or a related discipline will give you an advantage. You can supplement your education with online tutorials, classes, and self-study materials to stay up-to-date on the latest AI advancements.

Practical experience is vital, so look for projects, research internships, or opportunities to use prompt engineering methods. You can even start your own projects or contribute to open-source projects to demonstrate your abilities and knowledge.

Networking is crucial for finding employment prospects. Attend AI conferences, participate in online forums, and connect with industry experts. Keep an eye on employment listings, AI research facilities, and organizations focused on NLP and AI customization.

Finally, continuous learning and skill enhancement are essential in this ever-evolving field. By continuously improving your skills, staying connected with the AI community, and showcasing your expertise, you can position yourself for success and secure a high-paying job as an AI prompt engineer.

In other news, scientists at Cardiff University have found a breakthrough in predicting Parkinson’s disease using smartwatch data. By analyzing motion data from common smartwatches, machine learning models can accurately determine Parkinson’s risk up to seven years before clinical diagnosis. This discovery is crucial for early intervention and treatment of the disease, which affects millions of people worldwide. Parkinson’s is characterized by the loss of dopamine-producing neurons in the brain and leads to a gradual loss of control in the body. By leveraging smartwatch technology and machine learning, researchers hope to make significant advancements in Parkinson’s research and patient care.

Hey there! I’ve got some exciting news to share with you. The geniuses over at MIT have developed a groundbreaking tool called “FrameDiff” that is using generative AI to imagine brand new protein structures. Why is this such a big deal? Well, it could revolutionize drug development and gene therapy.

You see, our bodies are like beautiful tapestries woven together by DNA, which holds the instructions for making proteins. These proteins carry out important biological functions that keep us alive and healthy. But sometimes, things go awry. We face constant threats from pathogens, viruses, diseases, and even cancer. What if there was a way to quickly create vaccines or drugs to combat these new threats? What if we could use technology to fix DNA errors that lead to cancer?

That’s where “FrameDiff” comes in. This amazing computational tool uses machine learning to generate new protein structures that don’t exist in nature. It’s like tapping into a whole new realm of possibilities. By discovering proteins that can bind strongly to specific targets or speed up chemical reactions, we can unlock new opportunities for drug development, diagnostics, and various industries.

Imagine being able to design proteins that can tackle diseases or perform essential functions more efficiently than ever before. With “FrameDiff,” that dream is becoming a reality. The future of protein engineering is looking brighter than ever, thanks to the brilliant minds at MIT. Exciting times lie ahead!

Hey there! I’ve got some interesting news for you. Did you know that a machine learning model can predict PTSD in military veterans? Yep, that’s right! In a recent study, one-third of US veterans who were flagged as high risk for PTSD by this model accounted for a whopping 62.4 percent of cases. It’s amazing how technology can help us identify and understand such important mental health conditions.

But that’s not all! Machine learning is also digging into the world of food. Researchers have used clever algorithms to unravel the mystery behind meat tenderness. They discovered that an enzyme is responsible for this delightful characteristic, and thanks to machine learning, they were able to explain how it works at the molecular level. Who would have thought that technology could unravel such tasty secrets?

Now, let’s dive into deep learning. Ever heard of it? It’s a subset of AI that focuses on training artificial neural networks for complex data processing. It’s pretty cool because it’s being used to create personalized recommendations for all sorts of things. The efficiency of deep learning models, paired with data collection and preprocessing, building and training these models, generating recommendations, and evaluating and refining the system, is really pushing the boundaries of personalized recommendations.

So there you go, some interesting uses of machine learning and deep learning that are making waves in various fields. Exciting times ahead!

Hey folks, breaking news! Elon Musk is at it again, and this time he’s diving headfirst into the world of AI. Can you believe it? The man knows no boundaries! And get this, he’s even started his own top-secret startup called xAI. He’s not messing around, folks.

It’s pretty mind-blowing how he’s managed to gather an all-star team of AI geniuses from the biggest tech companies and research institutions out there. Seriously, this group is like the Avengers of real life. You’ve got Igor Babuschkin, the chatbot development expert from OpenAI and DeepMind. Then there’s Manuel Kroiss, who’s made waves in reinforcement learning at Google and DeepMind. Oh, and let’s not forget Tony Wu, the math whiz from Google Brain. These guys are the real deal.

But that’s not all – Elon’s got more aces up his sleeve. He’s brought on Christian Szegedy, the deep learning and computer vision guru from Google. And you can’t overlook the expertise of Toby Pohlen, who’s led major projects at DeepMind. Plus, there’s Ross Nordeen, Kyle Kosic, Greg Yang, Guodong Zhang, and Zihang Dai, all with impressive backgrounds in AI research.

xAI just made their presence known on Twitter, but they’re wasting no time getting started. In their first tweet, they’re asking the big existential question: “What are the most fundamental unanswered questions?” So, folks, what do you think? Let them know in the comments.

Who knows what Elon and his team will uncover? Stay tuned for more exciting updates to come.

Today, Google is launching NotebookLM, an AI-powered notes app that aims to help users gain valuable insights more efficiently. Unlike traditional AI chatbots, NotebookLM allows users to personalize the AI by grounding it in their own notes and selected sources. The app leverages language models and existing content to quickly summarize facts, explain complex ideas, and even come up with new connections based on the user’s chosen sources.

What’s interesting is that NotebookLM comes with citations for easy fact-checking, meaning you can verify the information against the original source material. This adds an extra layer of transparency and reliability to the app’s functionality.

It’s worth noting that NotebookLM is an experimental product developed by a small team in Google Labs. They are committed to building the app based on user feedback and ensuring responsible deployment of the technology. The model only has access to the specific source material chosen by the user and does not use it to train new AI models.

Currently, NotebookLM is only available to a small group of users in the U.S. However, if you’re intrigued by its capabilities, you can sign up for the waitlist to try it out. With NotebookLM, Google continues to push the boundaries of AI-powered productivity tools, aiming to enhance our ability to gather insights in an efficient and personalized manner.

Here’s a cool story about Greg Mushen, a tech pro from Seattle. He used ChatGPT to create a running program for him. And guess what? It actually worked! He wasn’t a fan of running before, but he wanted to develop a healthy exercise habit. So, he decided to give this AI-powered program a shot.

The plan generated by ChatGPT was pretty straightforward. It started with small steps, nothing too overwhelming. For example, putting his running shoes right next to the front door. And then came the exciting moment—the first run! But don’t get too carried away, it was just a few minutes long. Hey, you have to start somewhere, right?

As time went by, Greg gradually increased the distance and frequency of his runs. And after three months of sticking with the program, he is now running six days a week and has shed an impressive 26 pounds!

To ensure that this wasn’t some fluke, Greg consulted with an expert running coach. And guess what? The coach agreed! The advice given by ChatGPT was actually on point. The gradual approach is perfect for beginners like Greg, allowing them to make progress while avoiding any pesky injuries.

Now, here’s the interesting part. The AI’s plan didn’t dive right into running. Nope, it took things slow and steady. The first task was as simple as putting his shoes by the door. And the day after that? It was all about scheduling a run. These small steps helped Greg build a habit and made the process feel less overwhelming.

So, if you’re thinking of taking up running, why not give ChatGPT a shot? It seems to know its stuff when it comes to creating a personalized running plan.

Messaging apps like WhatsApp have gained immense popularity, and businesses are increasingly utilizing chatbots to enhance their customer service. Integrating chatbots, such as ChatGPT, with WhatsApp can significantly improve efficiency and streamline customer experiences.

However, when it comes to voice assistants like Alexa or Google Home, the integration of AI seems to be lacking. Many times, when we pose questions to these voice assistants, they either fail to understand or provide irrelevant answers. It becomes frustrating when we seek answers to more complex questions or require specific information that voice assistants cannot provide.

It’s puzzling that companies with advanced AI capabilities haven’t integrated AI responses into their voice assistants from the beginning. For instance, why hasn’t Google Assistant incorporated AI capabilities on day one? Alternatively, they could have developed a separate voice skill or app specifically designed to handle content requiring AI-generated answers.

Imagine if we could say, “Hey Google, ask Bard who would win between a polar bear and a dozen Tasmanian devils?” Such integration would be more convenient than having to reach for our phones and open ChatGPT. The implementation of this technology seems like a logical step forward.

In conclusion, businesses have recognized the value of integrating chatbots with messaging apps like WhatsApp. However, voice assistants still lag behind in terms of AI integration, but it would greatly enhance user experiences. The convenience and efficiency offered by integrating AI responses into voice assistants are worth considering for future advancements in this area.

China is stepping up its game in the field of artificial intelligence (AI), specifically in the realm of large language models. Baichuan Intelligence, founded by Wang Xiaochuan, the creator of Sogou, has unveiled its latest creation: Baichuan-13B. This open-source language model, based on the Transformer architecture, is designed to rival OpenAI and cater to commercial applications.

China’s focus on large language models aligns with its stringent AI regulations, which prioritize data security and user privacy. By developing their own language model, they aim to reduce reliance on foreign technologies and provide a Chinese equivalent to OpenAI’s offerings.

In other news, tensions between Ukraine and Russia have reached new heights in the Black Sea, with Russia attempting to conceal its naval activities using innovative camouflage techniques. However, AI technology has come to the rescue. By analyzing synthetic aperture radar (SAR) satellite imagery, AI is capable of unmasking these deceptively camouflaged warships.

This breakthrough in AI applications enables Ukraine and NATO to closely monitor Russian naval movements and stay one step ahead. It is a testament to the potential of AI in defense and surveillance operations, and highlights the continuous advancements in technology that shape our world.

To learn more about this story, visit the Naval News website.

Hey there! Time for your daily AI update. Let’s jump right in.

First up, Anthropic has unveiled its new AI model called Claude 2, which is giving a tough competition to ChatGPT and Google Bard. This improved model boasts higher performance, longer responses, and better programming, math, and reasoning skills. You can try it out as a chatbot via an API or on their public beta website. Companies like Jasper and Sourcegraph are already using it for content strategy and AI-based programming support. Pretty cool, right?

Next, we have gpt-prompt-engineer, a powerful tool for prompt engineering. It uses GPT-4 and GPT-3.5-Turbo to generate and rank optimal classification prompts based on test cases. So, if you describe the task, this AI agent will create multiple prompts, test them, and respond with the best one. Talk about efficiency!

Now, let’s talk about PhotoPrism. This AI-powered photos app for the Decentralized Web is revolutionizing photo organization. With state-of-the-art technology, it seamlessly tags and locates your pictures without any disruptions. Whether you use it at home, on a private server, or in the cloud, PhotoPrism empowers you to easily manage your photo collection.

Moving on, KPMG is investing a whopping $2 billion in AI and cloud services through its expanded partnership with Microsoft. They aim to incorporate AI into their core audit, tax, and advisory services over the next five years. Impressive commitment, I must say.

Shutterstock is also in the AI game, extending its partnership with OpenAI for another six years. This collaboration will focus on developing AI tools, and Shutterstock will gain priority access to OpenAI’s latest tech and new editing capabilities for transforming images.

Sapphire Ventures is betting big on enterprise AI startups, with plans to invest over $1 billion. They’ll be supporting AI startups directly and also through early-stage AI-focused venture funds. Exciting times for the AI startup ecosystem!

Wipro is not lagging behind either. They recently launched ai360, an AI service, and are planning to invest $1 billion in AI over the next three years. Their goal is to integrate AI into all their software offerings and provide AI training to their employees.

In the world of newsletters, Beehiiv has introduced new AI features that could revolutionize the way newsletters are written. Stay tuned for more updates on this exciting development.

That’s it for today’s AI news! Make sure to check back tomorrow for the latest updates. Take care!

Hey there, fellow podcast listeners! I’ve got some exciting news to share with all of you. If you’ve been itching to dive deeper into the world of artificial intelligence, then you’re in luck! I’ve got just the thing for you.

Introducing the incredible book, “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” written by the amazing Etienne Noumen. This book is the essential guide for anyone looking to expand their knowledge and understanding of AI. It’s available right now on popular platforms like Apple, Google, or Amazon. So, what are you waiting for? Go grab your copy today!

But wait, there’s more! If you’re a brand looking to boost your exposure and skyrocket your sales, I’ve got an amazing opportunity for you. Why not get your company or product featured on the AI Unraveled Podcast? It’s the perfect way to elevate your brand and connect with our awesome audience. Interested? Just shoot us an email or head over to Djamgatech.com to learn more about this fantastic chance.

So, whether you’re a curious AI enthusiast or a brand ready to amplify your presence, AI Unraveled has got you covered. Don’t miss out on these incredible opportunities! Get your hands on the book and explore the possibilities with us on the podcast. Let’s unravel the mysteries of AI together!

Thanks for listening to today’s episode where we covered the high earning potential for AI prompt engineers, the use of machine learning to predict Parkinson’s disease, MIT’s advancement in protein structure development, machine learning explanations for meat tenderness, Elon Musk’s mysterious AI startup, Google’s AI-powered notes app, the success of ChatGPT’s gradual running plan, integrating ChatGPT with WhatsApp for improved customer service, Baichuan Intelligence’s open-source language model, the introduction of Claude 2 by Anthropic, and the Wondercraft AI platform for easy podcast creation – don’t forget to subscribe and see you at the next one!

AI Unraveled Podcast July 2023: AI Tutorial: Using ChatGPT’s Code Interpreter Plugin for Data Analysis; Exploring the Future of Artificial Intelligence — 8 Trends and Predictions for the Next Decade

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the following topics: Access Code Interpreter plugin with ChatGPT Plus, OpenAI introducing Code Interpreter plugin for ChatGPT Plus, 8 trends and predictions for the future of AI, the impact of AI on employment and its potential benefits, AI becoming a part of everyday life and its associated risks, Inflection AI’s plan to build a $1B supercomputing cluster, the first news conference with humanoid AI robots, the concept of humanity being an AI experiment on Earth, the importance of explainable AI and the need for US AI regulations, AI tools using Lightning Network, various AI developments, and the use of the Wondercraft AI platform for podcasting.

To access the Code Interpreter plugin in ChatGPT, the first step is to have a ChatGPT Plus subscription. If you’re not already subscribed, you can sign up on OpenAI’s website. Once you have access, you can move on to the next step.

The Code Interpreter plugin allows you to directly upload various types of data into the chat. It supports tabular data like Excel or CSV files, images, videos, PDFs, and more. Simply upload your file and proceed to the next step.

After uploading your dataset, it’s important to check if any cleaning is required. This could involve handling missing values, errors, or outliers that might affect your analysis later on. Take the necessary steps to clean the data by removing or replacing missing values and excluding any outliers.

Now it’s time for data analysis. The Code Interpreter runs Python code in the backend, which is a powerful language for data analytics. Using simple English prompts, the plugin can write and perform various analyses for you. For example, you could ask it to analyze the distribution of a specific column and provide summary statistics such as the mean, median, and standard deviation.

Python’s data visualization capabilities are also available through the Code Interpreter. You can generate plots for your data by specifying the type of plot, the column to be plotted, and even the color theme. For example, you could generate a bar plot for a particular column with a blue color theme.

If you’re interested in machine learning, you can utilize the Code Interpreter to build and train models such as Linear Regression or Classification on your data. These models can help you make better decisions or predict future data. For instance, you could build a Linear Regression model to predict a target variable based on certain feature variables.

Finally, once you’re done with the analysis and modeling, you can download your cleaned and processed dataset for further use.

OpenAI’s ChatGPT has been making waves in the tech community as an AI-powered chatbot. But now, OpenAI has taken a significant leap forward. They’ve introduced an in-house Code Interpreter plugin exclusively for ChatGPT Plus subscribers. This plugin is a game-changer, transforming ChatGPT from a simple chatbot into a powerful tool with expanded capabilities. Let’s dive into how this new feature will impact developers and data scientists.

With the Code Interpreter plugin, ChatGPT Plus subscribers get access to advanced features and capabilities. They can perform data analysis, create charts, manage files, do math calculations, and even execute code. This expanded functionality opens up exciting possibilities for data science applications and empowers subscribers to seamlessly perform complex tasks.

For data scientists and developers, ChatGPT becomes a valuable tool with the Code Interpreter plugin. They can analyze datasets, generate insightful visualizations, and manipulate data right within the ChatGPT environment. Running code directly in ChatGPT provides a convenient platform for experimenting with algorithms, testing code snippets, and refining data analysis techniques.

The Code Interpreter plugin streamlines the development process by providing an in-house feature. Developers can write and test code within the same environment, eliminating the need to switch between different tools or interfaces. This saves time, enhances productivity, and offers a seamless coding experience.

Developers also benefit from real-time feedback and error identification directly within ChatGPT. Debugging and testing code become more efficient, allowing for quick iteration and improvement without the hassle of switching tools or environments. This fosters faster prototyping, experimentation, and overall code quality.

Beyond code interpretation, ChatGPT also offers valuable information and resources on chatbot development, natural language processing, and machine learning. This knowledge empowers businesses and individuals interested in leveraging chatbots for customer service or operational improvements.

Overall, OpenAI’s Code Interpreter plugin for ChatGPT Plus subscribers is a significant milestone in chatbot evolution. It streamlines workflows, enhances productivity, and opens up new possibilities for data science. As developers and businesses embrace this innovation, we can expect exciting advancements in AI-driven technologies.

In the next decade, there are several exciting trends and predictions that will shape the future of artificial intelligence (AI). One of these trends is reinforcement learning, which involves training AI systems to learn through trial and error. As algorithms become more sophisticated, we can expect AI systems to develop the ability to not only learn, but also to exponentially improve without explicit human intervention. This opens up possibilities for significant advancements in autonomous decision-making and problem-solving.

Another area where AI is set to make a big impact is healthcare. Predictive analytics, machine learning algorithms, and computer vision can help in diagnosing diseases, personalizing treatment plans, and improving patient outcomes. AI-powered chatbots and virtual assistants can enhance patient engagement and expedite administrative processes. The integration of AI in healthcare has the potential to lead to more accurate diagnoses, cost savings, and improved access to quality care.

Autonomous vehicles are also on the horizon. The autonomous vehicle industry has already made significant progress, and in the next decade, we are likely to witness their widespread adoption. AI technologies such as computer vision, deep learning, and sensor fusion will continue to improve the safety and efficiency of self-driving cars.

Furthermore, AI will play a crucial role in cybersecurity. AI-driven cybersecurity systems can find and eliminate cyber threats by analyzing large volumes of data and detecting anomalies. This enables faster response times to minimize potential damage caused by breaches. However, there is also a concern about safeguarding the AI systems themselves, as similar technology can be used by both defenders and attackers.

Overall, the future of AI holds immense potential and exciting possibilities. From reinforcement learning to healthcare advancements, autonomous vehicles to cybersecurity, we are on the brink of transformative changes in various industries.

AI and employment is a hotly debated topic, with no clear consensus. According to a recent survey by Pew Research Center, nearly half of people believe that AI would outperform humans in assessing job applications. However, a significant majority, 71%, oppose using AI for final hiring decisions. While 62% foresee a significant impact of AI on the workforce in the next two decades, only 28% express personal concern about its effects.

It’s true that AI may replace certain jobs, but it is also expected to create new opportunities. We cannot solely rely on current AI tools, such as ChatGPT, for accuracy and context. Human intervention is still necessary to ensure correctness. For instance, if a company chooses to replace some writers with ChatGPT, it would also need to hire editors to carefully review the AI-generated content for coherence.

AI’s potential also extends to climate modeling and prediction. By analyzing vast amounts of climate data, AI can identify patterns and enhance the accuracy of climate models. This knowledge allows for better forecasting of natural disasters, extreme weather events, and long-term climate trends. Ultimately, it equips policymakers and communities to make informed decisions and develop effective climate action plans.

In terms of energy optimization, AI proves invaluable. Machine learning algorithms analyze energy usage patterns, weather data, and grid information to improve energy distribution and storage. Smart grids, powered by AI, effectively balance supply and demand, minimize transmission losses, and seamlessly integrate renewable energy sources. This not only maximizes clean energy utilization, but also reduces greenhouse gas emissions and lessens dependence on fossil fuels.

Additionally, AI can revolutionize resource management by optimizing allocation, minimizing waste, and improving sustainability. For instance, AI algorithms can predict water scarcity, optimize irrigation schedules, and identify leakages in water management. AI-powered systems can also optimize waste management, recycling, and circular economy practices, reducing resource consumption and promoting sustainability.

While the potential benefits of AI are immense, it’s crucial to address ethical considerations. Privacy, bias, fairness, and accountability must be prioritized. Industry leaders, policymakers, and researchers must collaborate to establish frameworks and guidelines that protect human rights and promote social well-being alongside innovation in AI.

In conclusion, AI’s impact on employment is still up for debate, but it is expected to create new opportunities. It can also enhance climate modeling, optimize energy consumption, and revolutionize resource management. However, ethical considerations are vital to ensure the responsible development and deployment of AI, safeguarding human rights and promoting social well-being in the process.

Artificial intelligence (AI) has rapidly gone from being a distant concept to an integral part of our daily lives. Models like ChatGPT and DALL·E are now becoming familiar to us all. The progress made in AI capabilities is impressive, with machines getting better at seeing, reading, thinking, writing, and even creating. However, this advancement inevitably brings concerns.

The more AI improves, the more risks and worries arise. It feels like with every step forward, there’s a new danger to consider. People can easily envision negative outcomes, such as the potential threat of deepfakes undermining democracy, the increased vulnerability to cyber-attacks, more cheating instead of learning in schools, the spread of misinformation, and the possibility of job displacement caused by machines.

These risks should not be underestimated, and society must take them seriously. However, it’s essential to remember that we have faced similar challenges in the past and successfully managed them. Major innovations have always brought new threats that required careful consideration and control. With rapid action and thoughtful risk management, we can do it again.

In my latest Gates Notes post, “The risks of AI are real but manageable,” I delve into these risks and the ways we can address them. It’s crucial to strike a balance between mitigating the negative consequences and reaping the rewards that AI has to offer. And I genuinely believe there are substantial rewards waiting if we navigate this path wisely.

For more insights into my thoughts on AI, visit my blog now.

So, there’s a new player making waves in Silicon Valley. Inflection AI, a hot startup focused on Generative AI, is ready to revolutionize the supercomputing world by creating their very own $1 billion supercomputing cluster.

Their ultimate goal? To develop a “personal AI for everyone” through their own AI-powered assistant called Pi. Recent studies have shown that Pi can go toe-to-toe with other leading AI models like OpenAI’s GPT3.5 and Google’s 540B PaLM model.

To take things even further, Inflection AI plans to construct one of the largest AI training clusters in the world, boasting an impressive setup that includes a whopping 22,000 H100 NVIDIA GPUs and 700 racks of Intel Xeon CPUs.

Just the GPUs alone would cost more than $850 million, with each H100 GPU retailing at a staggering $40,000. So, with that kind of expenditure, it’s estimated that the cluster’s price tag will hit the $1 billion mark.

Inflection AI recently concluded a funding round, securing a substantial $1.5 billion and achieving a company valuation of $4 billion. While this puts them in second place in terms of the amount raised, they’re still quite a ways behind OpenAI, which has managed to raise an impressive $11.3 billion so far. Of their competitors, Anthropic comes closest in terms of funding with $1.5 billion, followed by Cohere with $445 million, Adept with $415 million, and Runway with $237 million.

Exciting times ahead for Inflection AI as they aim to reshape the world of supercomputing and bring AI to the masses.

So, you won’t believe what happened in Geneva last week! The “AI for Good Global Summit” took place, and it was mind-blowing. For the first time ever, humanoid social robots were the stars of a news conference. Can you imagine that? Human reporters interviewing these robots like they were actual people!

The event was hosted by the United Nations Technology agency, and it was such a fascinating sight. These reporters got to ask the robots all sorts of questions, from discussing robot world leaders to the impact of AI in the workplace. It was a deep dive into the world of artificial intelligence and its potential.

Now, here’s why this story caught my attention. We often hear about AI being used to boost productivity or create all sorts of weird stuff, but this summit showed us something different. Some brilliant minds out there are working on creating humanoid AI robots that are incredibly close to being like us humans. And let me tell you, when you see the footage, it’s pretty mind-boggling how advanced they’ve become.

It’s one thing to think about AI influencing our daily lives, like regulating traffic lights or even helping Paul McCartney compose the Beatles’ final song. But when you start considering the possibility of these human-like bots walking around and interacting with us, it’s a whole new level. I can’t help but wonder if the developers behind these creations fully understand the implications of bringing such human-like AI into reality or if they’re just blindly pursuing their own ambitions. The truth is, nobody really knows.

So, here’s something mind-boggling to ponder: Is humanity actually an experiment in artificial intelligence? Think about it for a moment. We are placed on this planet, floating in our own isolated Petri dish, completely cut off from any other forms of life. It’s like we’re in quarantine, unable to be contaminated by anything beyond our controlled environment.

Throughout the millennia, we have slowly progressed. We started with basic survival and eventually evolved to develop farming and civilizations. Then, boom! The Industrial Revolution comes around in the 18th century, followed by the first flight in 1903. Finally, after relentless dedication, we break free from our Petri dish in 1957 with Sputnik, and who can forget the moon landing in 1969? However, despite our hunger for exploration, our short lifespans prevent us from venturing much farther.

Now, what if our lifespan is purposefully engineered to be short, trapping us within the confines of our solar system? Perhaps we are being studied, much like how scientists observe lab rats across generations. As humanity, are we the advanced AI in this grand experiment? We are given some guidance on ethics and religion, but at the same time, we are granted the free will to create technology that could lead to our own destruction. It’s like a test to see if we have the collective intelligence to save ourselves or if we’ll succumb to greed and ignorance, burning ourselves out.

Do you think factors like ethics and religion play a role in this experiment? And what happens when we have small glimpses of insight, like knowing the consequences of our actions on the environment but continuing to harm it anyway? Now we’re even taking the next step in our evolution by creating our own AI. The question is, when does this experiment reach its conclusion?

And let’s not forget about those alleged UFOs. Could they be monitoring this whole experiment? Just when you thought things couldn’t get more intriguing, right?

Explainable AI, also known as XAI, refers to the concept of making artificial intelligence more understandable and transparent. Traditional AI algorithms operate by taking an input and generating an output without providing any insight into how the decision was made. The goal of XAI is to bridge this gap by revealing the underlying rationale behind AI decisions in a way that humans can comprehend.

In terms of industries, XAI has the potential to benefit a wide range of sectors. For instance, in finance, explainable AI can aid in making transparent and accountable decisions when it comes to lending, investment, and risk assessment. In healthcare, XAI can provide explanations for medical diagnoses and treatment decisions, improving trust and allowing for better collaboration between doctors and patients.

Moving on, when it comes to AI and technology, the United States should learn from the mistakes of Europe and avoid hastily implementing regulations that could stifle innovation. Adam Kovacevich, CEO of the Chamber of Progress, emphasizes that US policymakers need to take the lead but not rush to enact regulations simply to keep up with the European Union. Instead, the US should focus on establishing its own set of innovation-friendly rules and cultivating an environment that fosters AI advancement responsibly.

It’s important for US lawmakers to recognize that being “behind” in regulation is not necessarily a negative thing. In fact, the US regulatory environment has fostered the growth of leading tech services, which in turn have created numerous job opportunities for Americans. Therefore, the US should approach AI regulations with a sense of pride in its accomplishments and a commitment to nurturing its position as a leader in AI.

So, we have some exciting news in the world of AI and Bitcoin. Lightning Labs has introduced AI tools that enable AI applications to hold, send, and receive Bitcoin using the Lightning Network. This second-layer payment network allows for faster and cheaper Bitcoin transactions. By integrating Bitcoin micropayments with popular AI software libraries like LangChain, Lightning Labs has solved the problem of a lack of native Internet-based payment mechanisms for AI platforms.

This development is significant for a couple of reasons. First, it eliminates the need for outdated payment methods, reducing costs for software deployment. It also expands the range of possible AI use cases. With Lightning integrated into AI models, new applications that were previously not feasible become a reality.

Moving on, Google and Stanford researchers have been making strides in the field of robotics using LLMs, or large language models. These models can complete complex token sequences, including those generated by probabilistic context-free grammars and ASCII art prompts. This capability opens up possibilities for solving robotics challenges, such as completing simple motions and discovering closed-loop policies for reward-conditioned trajectories.

The applications of this research go beyond robotics. LLMs could be used to predict sequential data like stock market prices, weather data, and traffic patterns. They could also learn game strategies and generate new ones by observing sequences of moves and positions.

In the realm of code generation, researchers have proposed RLTF, a reinforcement learning framework for refining LLMs. RLTF uses unit test feedback to guide the model in producing high-quality code in real-time during training. This approach has shown state-of-the-art performance on code generation tasks.

The significance of RLTF is that it can potentially improve LLMs’ performance by utilizing real-time feedback and accounting for specific error locations within the code. Previous RL methods for code generation have been limited by offline frameworks and simple unit test signals.

All of these developments are pushing the boundaries of what AI can achieve in various domains, from financial transactions to robotics and code generation. It’s an exciting time for AI enthusiasts and researchers alike.

Hey there! Let’s dive into what’s happening in the exciting world of AI! First up, have you heard about the incredible breakthrough with a laser pesticide and herbicide? It’s a game-changer, as it’s AI-based and doesn’t require any harmful chemicals. Talk about innovation!

In other news, a wildfire detection startup called Pano AI just secured an additional $17 million in funding. This means they can continue their important work in developing technology to detect and prevent devastating wildfires. Way to go, Pano AI!

Now, let’s talk about some trending AI tools that will blow your mind. Ever wanted to share clips from your favorite YouTube videos? Trimmr, an AI app, can help you with that. It shortens videos into shareable clips, making it easier for creators to produce viral content.

If you’re into gaming and streaming, MyMod AI is a Twitch chatbot that uses AI to moderate chat and create interactive experiences with custom commands. It takes streaming to another level!

And here’s something fun: Comicify AI. This tool can turn boring text into cool comic strips in just two steps. Imagine how much fun you can have with that!

But wait, there’s more. We also have tools like GREMI, which finds search trends and creates content to rank for them, and Ayfie Personal Assistant, which simplifies document analysis and content creation. These AI-powered tools are changing the game when it comes to productivity and content creation.

Now, let’s talk about five AI tools that have caught our eye today. Nolej allows you to generate interactive e-learning content and assessments. Hify enables you to create customized and engaging sales videos. Coda combines text, data, and team collaboration into a single document. Lunacy utilizes AI capabilities and built-in graphics for UI/UX designs. And last but not least, Webbotify allows you to develop custom AI chatbots trained on your own data. These tools are empowering individuals and teams to achieve more.

That’s it for today’s AI roundup! Stay tuned for more exciting updates in the world of artificial intelligence.

Netflix has come up with a game-changer in the world of filming. Their researchers have developed the Magenta Green Screen (MGS), a revolutionary AI technology that enhances TV and film visual effects. Unlike traditional green screen methods that often struggle with fine details and require extensive editing, the MGS uses a blend of red, blue, and green LEDs to illuminate actors, creating a distinctive ‘magenta glow’ that AI can effortlessly separate from the background in real-time. Additionally, the AI has the capability to adjust the magenta color to appear natural, streamlining the filming process.

The significance of this development cannot be overstated. By making filming faster and rendering special effects more realistic, we can anticipate quicker show releases and more convincing scenes. Netflix’s AI-driven innovation has the potential to transform the entertainment industry and significantly impact the way movies and TV shows are produced.

In the medical field, Google’s AI chatbot, Med-PaLM 2, is undergoing testing in several hospitals, including the prestigious Mayo Clinic. Built using questions and answers from medical exams, Med-PaLM 2 has the potential to provide reliable medical advice remotely, particularly beneficial in regions with limited access to healthcare. This advancement could revolutionize healthcare delivery, giving people access to superior medical advice when they need it most.

Meanwhile, the US military is harnessing the power of large-language models (LLMs) to expedite decision-making processes. These AI-powered models can swiftly complete tasks that would typically take hours or days, potentially revolutionizing military operations.

Lastly, Pano AI, a wildfire detection startup, recently secured $17 million in funding. Their remote-controllable cameras, combined with AI algorithms, offer early warnings of wildfires, allowing emergency responders to take prompt action and reduce response time. This technology could provide a massive boost to wildfire prevention and management efforts.

These latest AI developments from Netflix, Google, the US military, and Pano AI have the potential to revolutionize various industries and bring about significant positive change.

Hey there, AI Unraveled podcast listeners! Got a quick announcement for you. If you’re a fan of artificial intelligence and looking to level up your knowledge, there’s a fantastic book you might want to check out. It’s called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” written by the brilliant mind of Etienne Noumen. And the best part? It’s available right now at Apple, Google, or Amazon!

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That’s all for now, folks. Stay tuned for more fascinating conversations on the AI Unraveled podcast.

On today’s episode, we covered a wide range of topics including the new Access Code Interpreter plugin with ChatGPT Plus, OpenAI’s expansion of functionality, future trends and predictions for AI, the impact of AI on employment and the environment, everyday life concerns with AI, the plans of Inflection AI to build a supercomputing cluster, the first news conference with humanoid AI robots, the possibility of humanity being an AI experiment, explainable AI and the need for AI regulations, AI tools using Lightning Network, exciting AI developments, and the Wondercraft AI platform for starting your own podcast. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: Top 10 Applications of Deep Learning in Cybersecurity in 2023; No-code AI tools to improve your workflow; Are We Going Too Far By Allowing Generative AI To Control Robots; Comedian and novelists sue OpenAI for scraping books

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover deep learning in cybersecurity, generative AI controlling robots, comedian and authors suing OpenAI and Meta, AI use in learning, no-code AI tools for marketing automation, OpenAI’s ChatGPT Plugins, Google’s AI tool Med-PaLM 2, Google’s quantum computer, Google and Microsoft competing in healthcare AI, the dangers of poisoning AI supply chains, Google DeepMind’s Gemini project, Europe developing its own ChatGPT, research on larger context windows in language models, and various updates on AI-related news and products.

Hey there! Let’s dive into the top 10 applications of deep learning in cybersecurity that we can expect to see in 2023.

First up, we have threat detection. Deep learning models are amazing at analyzing network traffic to spot both known and unknown threats. By identifying negative patterns and detecting anomalies in real-time, these models can give us early warnings and help prevent data breaches.

Next, we have malware identification. Deep learning algorithms can analyze file behavior and characteristics to identify malware. By training on large datasets of known malware samples, these models can stay one step ahead of attackers, quickly and accurately identifying new strains of malicious software.

Intrusion detection is another area where deep learning can shine. By analyzing network traffic and spotting suspicious activities, these models can detect network intrusions, unauthorized access attempts, and behaviors that may indicate a cyber-attack in progress.

Phishing attacks are a significant concern and deep learning can help here too. By analyzing email content, URLs, and other indicators, these algorithms can spot phishing attempts. By learning from past campaigns, these models can detect and block suspicious emails, protecting users from falling into scams.

Deep learning can also analyze user behavior to detect insider threats or compromised accounts. By monitoring user activities and identifying unusual actions, these models can help organizations mitigate risks from within.

Data leakage prevention is crucial, and deep learning algorithms can help identify sensitive data patterns and monitor data access and transfer to prevent unauthorized leakage. These models can analyze data flow, identify vulnerabilities, and enforce security policies to protect sensitive information.

Network traffic analysis is another area where deep learning can come to the rescue. By analyzing patterns associated with DDoS attacks, these models can help organizations defend against and mitigate their impact.

Vulnerability assessment can also benefit from deep learning. By analyzing code, configurations, and system logs, these models can automate the process of identifying vulnerabilities in software and systems.

Threat intelligence is vital in the ever-evolving cybersecurity landscape. Deep learning algorithms can analyze massive volumes of threat data from various sources to identify emerging threats and trends. By continuously monitoring and analyzing threat feeds, we can take proactive measures against evolving cyber threats.

Last but not least, deep learning can be applied to detect fraudulent activities in financial transactions. By analyzing transactional data, customer behavior, and historical patterns, these models can identify potentially fraudulent transactions in real-time, helping organizations prevent financial losses.

And that’s a wrap on the top 10 applications of deep learning in cybersecurity that we can expect to see in 2023! Stay tuned for more exciting developments in the world of cybersecurity.

So, there’s a question on the table that’s been making some folks a bit worried: Are we going too far with letting generative AI control robots? You see, these days, AI like ChatGPT is being used more and more to control robots. But here’s the catch: there’s some concern that this could lead to trouble. I mean, think about it. If the AI starts giving out faulty instructions, it could put humans in danger. Yikes, right?

But the world of AI isn’t all doom and gloom. In fact, there’s some pretty exciting news in the field of science and industry. Scientists have been using AI to unearth rare earth elements. How do they do it? Well, by analyzing patterns in mineral associations, they’ve developed a machine-learning model that can actually predict where minerals might be found on Earth and maybe even on other planets. Now, that’s pretty cool!

This discovery is a big deal because it can help scientists and industries explore mineral deposits more efficiently. And let’s face it, that’s something we’re always interested in. So, while there are concerns about AI controlling robots, there are also these amazing advancements that AI is bringing to the table. It’s definitely a complex topic, but it’s one worth exploring further.

Comedian Sarah Silverman, along with authors Christopher Golden and Richard Kadrey, have recently taken legal action against OpenAI and Meta. Their lawsuit claims that the companies unlawfully extracted data from shadow library websites to train their AI models without obtaining the necessary permissions from the authors.

The authors specifically point to OpenAI’s ChatGPT and Meta’s LLaMA models as being trained on datasets that supposedly include their copyrighted works. They argue that the AI models can summarize their books when prompted, which they believe infringes on their copyright rights.

Additionally, the lawsuit draws attention to the dubious origin of the datasets used by Meta. The authors claim that Meta’s LLaMA model relied, at least in part, on training datasets sourced from ThePile, which was compiled using the contents of a shadow library. This raises concerns about the legality of using such materials without proper authorization.

The legal allegations against OpenAI and Meta include copyright violations, negligence, unjust enrichment, and unfair competition. The claimants are seeking various forms of relief, such as statutory damages and the restitution of profits.

It will be interesting to see how this case develops and what impact it might have on the use of copyrighted materials for training large language models. Copyright infringement within AI is a complex issue that raises important questions about intellectual property rights and the responsibility of AI developers to obtain proper permissions.

So, here’s a prediction for you: Next year, we might just stumble upon some evidence suggesting that using artificial intelligence (AI) for learning actually results in higher scores on those notorious standardized tests. Yep, you heard it right. It seems like the more students rely on AI to assist them in their studies, the better they perform on exams like the SAT.

Now, I’m not saying this evidence will be groundbreaking or rock-solid just yet. It might start out as a small glimpse, a tiny hint of what’s to come. But mark my words, a few years down the road, that evidence is going to pack a bigger punch. It’ll be more compelling and conclusive, leaving us with no choice but to accept the correlation between AI usage and those test scores.

So, what do you make of this? Do you think AI can truly be a game-changer when it comes to acing those standardized tests? Will we finally be able to bid farewell to our trusty old study buddies and let AI take the spotlight? Let’s see how this pans out. Exciting times ahead, my friend. Exciting times indeed.

So, you’re looking for some no-code AI tools to enhance your workflow, right? Well, I’ve got a few recommendations for you, depending on what you need help with.

If you’re into marketing automation, there are some great options out there. Levity is one tool that can assist you in automating your marketing tasks. Cogniflow is another fantastic tool that can help streamline your marketing processes. And let’s not forget about Notion and Airtable, which can both be valuable tools for organizing and managing your marketing efforts.

Now, maybe you’re more focused on building websites and apps. In that case, I suggest you check out 10Web, Builder, and AppyPie. These tools are designed to make the website and app-building process much easier, even if you don’t have any coding experience.

If data scraping and analytics are more your thing, Octoparse, RapidMiner, and Tableau are three tools that you should definitely consider. They can assist you in extracting data from various sources and analyzing it to gain valuable insights.

Lastly, if email marketing is an essential part of your workflow, you’ll be pleased to know that there are tools to help with that too. Mailchimp is a popular choice that offers various features for email marketing. BEEPro is another option that provides a user-friendly interface for creating professional-looking emails. And don’t forget about mailmodo, which is another handy tool for streamlining your email marketing efforts.

So, there you have it – a roundup of some no-code AI tools across different categories to help boost your workflow. Give them a try and see how they can make your life easier!

So, have you heard about OpenAI’s latest feature called ChatGPT Plugins? It’s being hailed as the new Internet gateway, a glimpse into Web 3.0. Let me break it down for you.

Basically, ChatGPT Plugins are a game-changer. When combined with the GPT agents system, they have the potential to revolutionize how we use the internet. These plugins serve as our gateway to a whole new online experience.

You see, even though OpenAI hasn’t explicitly stated their vision for the GPT agents, it’s implicitly revealed in their plugin announcement. And let me tell you, it’s exciting. This approach allows us to do something remarkable – we can now execute complex tasks and retrieve information in a way that was never possible before.

Think of ChatGPT Plugins as more than just an app store. They offer something much more powerful. With these plugins, we can tap into a world of functionality and expand what we can do online. It’s like having a bunch of supercharged tools at our disposal, enhancing our browsing and interaction capabilities.

In a nutshell, OpenAI’s ChatGPT Plugins feature is paving the way for Web 3.0 – the execute web. It’s a thrilling development that opens up a world of possibilities. So get ready, because this is just the beginning of a whole new online adventure.

Google is making strides in the healthcare industry with its new AI tool, Med-PaLM 2. This tool, which is currently being tested at Mayo Clinic, aims to answer healthcare-related questions and provide assistance in regions with limited access to doctors. Med-PaLM 2 is an adaptation of Google’s language model, PaLM 2, which powers Google’s Bard.

The training and performance of Med-PaLM 2 have been a key focus. It has been trained on medical expert demonstrations to enhance its ability to handle healthcare conversations. While there have been some accuracy issues, a study conducted by Google revealed that the tool performed comparably to actual doctors in areas such as reasoning and providing consensus-supported answers.

Data privacy is also a crucial aspect of this AI tool. Users who test Med-PaLM 2 will have complete control over their data, as it will be encrypted and inaccessible to Google. This privacy measure ensures user trust and adherence to data security standards.

Overall, Google’s Med-PaLM 2 shows promising capabilities in the healthcare field. With its focus on assisting areas with limited access to doctors and its commitment to data privacy, this AI tool has the potential to make a positive impact on healthcare outcomes.

Google just unveiled its latest quantum computer, and it’s a game-changer. This powerhouse of a machine can crank out calculations faster than anyone could have imagined. In fact, it can do in an instant what would take the world’s top supercomputer a whopping 47 years to complete.

This new quantum computer from Google is no ordinary device. With an impressive 70 qubits, it’s a quantum computing marvel. And if you’re wondering what qubits are, they’re the building blocks of quantum computing. So, having 17 more qubits than their previous machine is a significant upgrade. It’s like having a machine that’s 241 million times more powerful!

Now, I know some skeptics are saying that the task used to test this quantum computer was too biased towards quantum computing and not very practical in the real world. But hey, we’re pushing boundaries here! We’re taking steps towards what’s called ‘utility quantum computing.’ Imagine the possibilities: lightning-fast data analysis, incredibly accurate weather forecasts, life-saving medical research, and even solving complex climate change problems. The potential is mind-boggling.

While we may not be there just yet, this latest development from Google brings us closer to a future where quantum computers will revolutionize our lives in ways we can’t even fathom. So buckle up, folks, because we’re on the brink of something remarkable.

Did you know that Google and Microsoft are competing to lead the way in healthcare AI? It’s true! Google has been testing its Med-PaLM 2, which is an LLM designed specifically for the medical domain, at the Mayo Clinic research hospital. They recently announced limited access for select Google Cloud customers to explore use cases and provide feedback on how to use it in safe and meaningful ways.

On the other hand, Microsoft has been quick to incorporate AI advances into patient interactions. Hospitals have started testing OpenAI’s GPT algorithms through Microsoft’s cloud service for various tasks. Interestingly, independent research conducted by the companies revealed that both Google’s Med-PaLM 2 and OpenAI’s GPT-4 performed similarly well on medical exam questions.

So, why does this competition matter? Well, both Google and Microsoft are racing to transform the recent advancements in AI into products that clinicians can use widely. The field of AI has experienced rapid growth and research in diverse areas. However, translating these advancements into real-world applications can be a slow and challenging process. This competitive landscape pushes for faster and more impactful AI products that can be readily available to benefit patients and healthcare professionals alike.

LLMs, or large language models, are becoming increasingly popular all around the world. However, there is a significant concern regarding the lack of transparency in terms of the data and algorithms used during the model’s training. In order to shed light on this issue, Mithril Security embarked on a project called PoisonGPT. The aim of this project was to demonstrate the potential dangers of poisoning LLM supply chains.

PoisonGPT showed how it is possible to make surgical modifications to an open-source model and then upload it to Hugging Face. By doing so, the modified model can spread misinformation without being detected by standard benchmarks. This experiment served as a wake-up call to emphasize the risks associated with unsecured LLM supply chains.

To address this problem, Mithril Security is also developing a solution called AICert. This solution will enable the tracing of models back to their training algorithms and datasets. By launching AICert, Mithril Security hopes to provide a means of ensuring greater transparency and security within the LLM supply chain.

The significance of all this lies in the fact that LLMs are still relatively unexplored territory. Many companies and users rely on external experts or pre-trained models to train their own models. However, this practice comes with the inherent risk of inadvertently applying malicious models to their specific use cases, thereby creating potential safety issues. The PoisonGPT project serves as a critical reminder of the urgency to prioritize securing LLM supply chains.

So, get this: Google DeepMind is cooking up the ultimate response to ChatGPT, and it could be a game-changer in the world of AI. Demis Hassabis, the CEO of DeepMind, spilled the beans in a recent interview with Wired. He gave us a taste of what they’re working on, saying that it combines the strengths of AlphaGo with the language capabilities of large models like GPT-4 and ChatGPT. But, hold on, there’s more! He mentioned some new innovations that are brewing, and they sound pretty intriguing.

Let’s break it down. DeepMind’s Alpha family and OpenAI’s GPT family each have their own secret sauce, a special ability built right into the models. The Alpha models have shown that AI can surpass human ability and knowledge by learning and searching in constrained environments. And the GPT models have demonstrated that training large language models on loads of text data without explicit supervision can lead to them learning to do things on their own.

Now, imagine combining the language prowess of ChatGPT with abilities in images, video, audio, and even tool use and robotics. Picture an AI model that can go beyond human knowledge and learn just about anything. It’s like the Holy Grail of AI, right? And that’s what I envision when I think about what Google DeepMind has in store with their project, Gemini.

I’ll admit I’m usually wary of calling things “breakthroughs” because it feels like every new AI release gets tagged with that label. But I’ve got three solid reasons why I believe Gemini will be a true breakthrough, on par with GPT-3 and GPT-4, and maybe even beyond.

First, the research and development prowess of DeepMind and Google Brain is unparalleled. Second, the pressure from the OpenAI-Microsoft alliance has probably lit a fire under DeepMind, making them push harder than ever. And third, the folks at DeepMind are masters of both language modeling and deep reinforcement learning, the perfect recipe for combining the successes of ChatGPT and AlphaGo.

Now, we’ll have to curb our excitement and wait until the end of 2023 to see Gemini in action. Let’s hope it brings some great news and sets the stage for a bright future in the field of AI.

Europe is considering the possibility of launching its own version of ChatGPT, but there may be some challenges ahead. Bruno Le Maire, France’s Economy Minister, has expressed support for the idea of a 100% European ChatGPT. He believes that it is important for Europe to prioritize innovation, investment, and the development of the necessary technology and expertise to create a European OpenAI within five years.

Le Maire is confident that this initiative will not only promote technological advancement but also contribute to the growth of the European Union’s economy. However, there is a potential setback. By 2028, OpenAI’s ChatGPT, Bing AI, and Google Bard are expected to significantly improve their capabilities, making it more challenging for the European ChatGPT to compete with these established players.

This could lead to a considerable delay for Europe in catching up with the advancements made by the other AI technologies. While the idea of a European ChatGPT is promising, the increasing competitiveness of AI technologies worldwide could pose a significant obstacle for Europe. It remains to be seen whether Europe can overcome this potential setback and successfully establish its own ChatGPT within the desired timeframe.

LLM vendors are in fierce competition, each vying for the title of having the largest context window. Just recently, Anthropic made waves by expanding Claude’s context window to 100K tokens. But here’s the burning question: does a bigger context window always result in better outcomes?

A new study has uncovered valuable insights and also highlighted the limitations associated with large contexts. It turns out that language models often struggle to effectively utilize information in the middle of lengthy input contexts. As the input context grows longer, these models experience a decrease in performance. Interestingly, their performance tends to be at its peak when the relevant information appears at the beginning or end of the input context.

Now, you might be wondering why all of this matters. While recent language models have the capability to handle long inputs, there’s still a lot we don’t know about how well they actually utilize them. The research mentioned above provides a better understanding of this and even introduces new evaluation protocols for future long-context models. Ultimately, this knowledge can help these models step up their game and allow users to have more effective interactions with them.

In today’s AI update, there are some interesting developments from Google, Microsoft, Mithril Security, YouTube, TCS, and Shutterstock. Let’s dive in!

First up, we have Google and Microsoft battling it out in the healthcare AI arena. Google’s Med-PaLM 2 has been undergoing testing at the Mayo Clinic research hospital. They have also offered limited access to select Google Cloud customers to explore its use cases and provide feedback. Meanwhile, Microsoft has been incorporating AI advancements into patient interactions by leveraging OpenAI’s GPT algorithms through their cloud service.

Speaking of OpenAI, Mithril Security has demonstrated the dangers of poisoning LLM (Language Model) supply chains. They have shown how open-source models can be modified to spread misinformation undetected. However, Mithril Security is actively working on a solution called AICert, which aims to trace models back to their training algorithms and datasets.

In the domain of language models, new research suggests that bigger context windows don’t always lead to better results. Language models tend to struggle with utilizing information in the middle of long input contexts. Their performance tends to decrease as the input context grows longer, while it is often highest when relevant information is at the beginning or end.

Moving on to YouTube, the platform is currently experimenting with AI-generated quizzes on their mobile app. These quizzes are designed to enhance the learning experience for viewers of educational videos.

In other news, TCS (Tata Consultancy Services) is placing a big bet on Azure Open AI. They plan to train and certify 25,000 associates on Azure Open AI to help their clients accelerate the adoption of this powerful technology.

Lastly, Shutterstock is stepping up their generative AI game by offering enterprise customers full indemnification for the license and use of generative AI images on their platform. This move aims to protect customers against potential claims related to the use of these images.

That concludes today’s AI update with news from Google, Microsoft, Mithril Security, YouTube, TCS, and Shutterstock. Exciting times ahead in the world of AI!

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Thanks for tuning in to today’s episode! We covered a wide range of topics, including the power of deep learning in cybersecurity, the potential risks of generative AI controlling robots, legal disputes with OpenAI and Meta, the impact of AI in learning, and the latest advancements in no-code AI tools, quantum computing, and healthcare AI. Plus, we explored the dangers of AI supply chain poisoning and the exciting developments from Google DeepMind and Europe’s ChatGPT. Don’t forget to subscribe for more fascinating discussions, and I’ll see you at the next episode!

AI Unraveled Podcast July 2023: Navigating on the moon using AI; Ameca the ‘most expensive AI robot that can draw’; Meet Pixis AI: An Emerging Startup Providing Codeless AI Solutions; How to land a high-paying job as an AI prompt engineer; AI Weekly Rundown

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover how to land a high-paying job as an AI prompt engineer, using AI to locate astronauts on the moon without GPS, a high-priced robot that defends its artistic skills, a startup offering codeless AI solutions, latest AI research updates, new releases from OpenAI, Salesforce, and Microsoft, advancements in AI aiding wildfire detection, and utilizing the Wondercraft AI platform to start your own podcast with hyper-realistic AI voices.

So you want to land a high-paying job as an AI prompt engineer? Well, you’re in luck because I’ve got some tips to help you position yourself for success in this exciting field. First, let’s talk about what an AI prompt engineer does. They specialize in designing effective prompts to guide the behavior and output of AI models. They have a deep understanding of natural language processing (NLP), machine learning, and AI systems.

To excel in this role, you’ll need some crucial skills. First and foremost, a strong understanding of NLP and language modeling is essential. You should also be familiar with programming languages like Python and have experience with frameworks for machine learning, such as TensorFlow or PyTorch. Collaboration and communication skills are also important because prompt engineers often work with other teams and need to effectively communicate project goals and requirements.

Having a strong educational foundation in computer science, data science, or a related discipline can be beneficial. You can acquire the necessary knowledge through a bachelor’s or master’s degree. Additionally, there are plenty of online tutorials, classes, and self-study materials available to supplement your education and stay up-to-date on the latest advancements in AI and prompt engineering.

Getting practical experience is crucial. Look for projects, research internships, or research opportunities where you can apply prompt engineering methods. You can even start your own prompt engineering projects or contribute to open-source projects to demonstrate your skills and knowledge.

Networking is key when it comes to finding employment prospects. Attend AI conferences, participate in online forums, and network with industry experts. Stay connected with the AI community and keep an eye on employment listings and organizations focused on NLP and AI customization.

Lastly, continuous learning and skill enhancement are essential in this evolving field. The demand for skilled AI prompt engineers is growing, so make sure to continuously enhance your skills, stay connected, and demonstrate your expertise. With the right combination of skills, experience, and networking, you can land that high-paying job as an AI prompt engineer.

So there’s this guy, Dr. Alvin Yew, who’s doing some pretty cool stuff with AI. He’s all about navigating on the moon, you know? And get this, he’s working on a solution that uses topographical data to help astronauts find their way around when there’s no GPS or electronic navigation available. How awesome is that?

Imagine being up there on the moon, surrounded by vast, unknown terrain. Talk about feeling lost! But thanks to Dr. Yew and his AI wizardry, astronauts will have a little digital helper to guide them. This dude is using a neural network to process all that topographical data and figure out where exactly they are. No more getting stranded in space, folks!

Now, here’s the cherry on top of this lunar cake. Apparently, there’s this humanoid robot drawing a cat, because why not? And it has something to say. It goes, “If you don’t like my art, you probably just don’t understand art.” Well, isn’t that an interesting perspective? Maybe this robot is trying to make a point about subjectivity and the beauty of interpretation. Or maybe it just wants to show off its drawing skills. Who knows?

Regardless, with Dr. Yew’s AI navigation system and this artsy robot, the moon might just become the coolest gallery in the galaxy. One giant leap for art lovers and astronauts alike!

So, have you heard about Ameca, the humanoid robot that’s making waves as the ‘most expensive robot that can draw’? Yeah, it’s quite the buzz! This fascinating robot is powered with Stable Diffusion technology, engineered by the folks over at Engineered Arts.

I stumbled upon a recent YouTube video showcasing Ameca’s artistic skills, and let me tell you, it’s quite a talent! The robot managed to sketch a cat and even went on to ask for opinions. Talk about confidence, right?

But here’s where it gets interesting. When one person commented that the drawing looked ‘sketchy’, Ameca had the perfect comeback. It confidently stated, “If you don’t like my art, you probably just don’t understand art.” Ouch! Burn!

I have to admit, I can’t quite decide if it was Ameca’s sassy retort or if there’s something deep and philosophical about the drawing that I simply don’t grasp. It’s got me scratching my head, that’s for sure. Maybe there’s more to this robot artist than meets the eye. It’s definitely leaving an impression, and I’m intrigued to see what else Ameca can create in the future.

So, you want to know about Pixis AI, huh? Well, let me tell you, this emerging startup is making some waves in the world of AI solutions. You see, training AI models is no easy task. It requires a boatload of data, and not just any data will do. It has to be error-free, properly formatted and labeled, and most importantly, it has to reflect the specific issue at hand. Now, that might sound simple enough, but let me tell you, it’s anything but.

But here’s where Pixis AI comes in and saves the day. They have come up with a genius solution to the problem. They provide codeless AI solutions. Yep, you heard me right. Codeless. What does that mean exactly? Well, it means that you don’t need to be a coding wizard to train your AI models anymore. Pixis AI has created a user-friendly platform where you can feed in your data, and their magical algorithms take care of the rest. No need to spend hours poring over code and wrestling with syntax errors. Pixis AI simplifies the whole process, making it more accessible to all.

So, whether you’re a seasoned AI expert or just dipping your toes into the world of artificial intelligence, Pixis AI has got you covered. They’re revolutionizing the way we train AI models, one codeless solution at a time.

In this week’s AI Weekly Rundown, we have some fascinating developments in the world of artificial intelligence. Let’s dive right in!

First up, Microsoft Research has been exploring the use of OpenAI’s ChatGPT for robotics. They’ve developed a strategy that combines prompt engineering and a high-level function library to allow ChatGPT to adapt to different robotics tasks. This research covers a wide range of domains within robotics, from logical reasoning to aerial navigation. Microsoft has even released an open-source platform called PromptCraft for sharing good prompting schemes for robotics applications.

Next, Snap Inc. has introduced Magic123, an image-to-3D pipeline that can generate stunning 3D objects from a single unposed image. Using a two-stage optimization process, Magic123 produces high-quality 3D geometry and textures. By combining 2D and 3D priors, this pipeline achieves state-of-the-art results in both real-world and synthetic scenarios.

Microsoft also presents CoDi, a generative model capable of processing and generating content across multiple modalities. CoDi leverages a composable generation strategy to create synchronized video and audio content. What’s impressive about CoDi is its ability to handle any mixture of output modalities, making it a versatile tool for AI generation.

OpenChat, an open-source language model collection, has surpassed ChatGPT-3.5 in performance. Fine-tuned on a high-quality dataset of multi-round conversations, OpenChat aims to achieve high performance with limited data.

In other news, a team of Chinese researchers has made significant progress in AI-assisted CPU design. They used AI to design a fully functional CPU based on the RISC-V architecture in less than 5 hours, cutting down the design cycle by 1000 times. This breakthrough paves the way for self-evolving machines.

Researchers have also introduced SAM-PT, an advanced method for video object segmentation and tracking. SAM-PT leverages interactive prompts to generate masks and achieves exceptional performance in popular video object segmentation benchmarks.

Lastly, Google has updated its privacy policy to state that it can use publicly available data to train its AI models. By harnessing humanity’s collective knowledge, Google aims to redefine how AI learns and comprehends information.

That’s it for this week’s AI Weekly Rundown! Exciting times ahead in the world of artificial intelligence.

In our AI Weekly Rundown this week, we have some exciting developments in the world of artificial intelligence.

First up, Hugging Face research has introduced LEDITS, a next-level AI technology for image editing. LEDITS combines the Edit Friendly DDPM inversion technique with Semantic Guidance, allowing for real-image editing with powerful capabilities. This means you can now harness the editing capabilities of DDPM inversion while extending Semantic Guidance to real image editing.

In addition, OpenAI has made several updates to its API offerings. The GPT-4 API is now available to all paying OpenAI API customers. They have also announced the availability of GPT-3.5 Turbo, DALL·E, and Whisper APIs. Along with these updates, OpenAI has a deprecation plan for some of the older models, which will be retired starting in 2024. And there’s more! OpenAI’s Code Interpreter will be available to all ChatGPT Plus users, allowing them to run code, analyze data, create charts, and more.

Salesforce has also made a notable addition to its CodeGen family of models. The new member, CodeGen2.5, is a smaller but powerful language model for code. With faster sampling, CodeGen2.5 offers a speed improvement of 2x compared to its predecessor. This means personalized assistants with local deployments can now be easily achieved.

InternLM is another impressive model we saw this week. It has open-sourced a 7B parameter base model and a chat model tailored for practical scenarios. Leveraging trillions of high-quality tokens for training, InternLM provides a powerful knowledge base and supports longer input sequences, enabling stronger reasoning capabilities. Its versatility allows users to build their own workflows with ease.

Last but not least, Microsoft Research has launched LongNet, which scales transformers to handle over 1 billion tokens in a context window. LongNet achieves this through dilated attention, offering advantages like linear computational complexity and a logarithmic token dependency. It can also be used as a distributed trainer for extremely long sequences and seamlessly replace standard attention in existing Transformer models.

That’s all for this week’s AI Weekly Rundown. Stay tuned for more exciting updates in the world of AI!

OpenAI has recently launched an exciting new project called Superalignment, which aims to tackle the challenge of aligning artificial superintelligence with human intent. Over the next four years, OpenAI will allocate 20% of its computing power to this endeavor. The key objective of Superalignment is to achieve scientific and technical breakthroughs by developing an AI-assisted automated alignment researcher. This researcher will be responsible for evaluating AI systems, automating searches for problematic behavior, and testing alignment pipelines. To accomplish this ambitious goal, OpenAI has assembled a team of top-notch machine learning researchers and engineers who are open to collaborating with talented individuals interested in solving the critical issue of aligning superintelligence.

In another exciting development, the California Department of Forestry and Fire Protection, known as Cal Fire, is utilizing AI technology to detect and prevent wildfires more effectively. Advanced cameras equipped with autonomous smoke detection capabilities are now being deployed to replace the reliance on human eyes to spot potential fire outbreaks. This is particularly crucial as wildfires often occur in remote areas with limited human presence and are influenced by unpredictable environmental factors. By leveraging AI, Cal Fire aims to overcome these challenges and improve the early detection and response to wildfires, ultimately enhancing public safety.

Now let’s take a quick rundown of some other fascinating AI news from the past week. Human has introduced an AI-powered wearable device with a projected display, while Microsoft is offering a sneak peek at its AI assistant for Windows 11. Midjourney has released a “weird” parameter that adds a crazy twist to images, and Nvidia has acquired OmniML, an AI startup specializing in shrinking machine-learning models. In the medical field, the first drug fully generated by AI has entered clinical trials, and VA researchers are working on developing AI that can predict prostate cancer. Additionally, advancements in AI are being made in fields such as language translation, dance generation, cloud computing, and more. The potential economic value of AI-powered innovation in the UK alone is estimated to be over £400 billion by 2030. It’s an exciting time to witness the progress and impact of AI across various domains!

Hey there, AI Unraveled podcast listeners! Got a quick announcement for you. If you’re a fan of artificial intelligence and looking to level up your knowledge, there’s a fantastic book you might want to check out. It’s called “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” written by the brilliant mind of Etienne Noumen. And the best part? It’s available right now on !

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That’s all for now, folks. Stay tuned for more fascinating conversations on the AI Unraveled podcast.

On today’s episode, we learned how to land a high-paying job as an AI prompt engineer, discovered how AI is used to locate astronauts on the moon, explored the skills of Ameca, a high-priced drawing robot, and delved into the world of Pixis AI’s codeless AI solutions. We also discussed the latest AI research breakthroughs, explored new developments in the field of image editing, and highlighted OpenAI’s Superalignment project. Lastly, we shared how you can start your own podcast using the Wondercraft AI platform and promote your brand on the AI Unraveled Podcast. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: AI May Have Found The Most Powerful Anti-Aging Molecule Ever Seen; Generative AI spams up the web; Code Interpreter is the MOST powerful version of ChatGPT Here’s 10 incredible use cases; OpenAI is forming a team to fight back AI risks

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover AI identified potential anti-aging molecules, OpenAI’s release of GPT-4 and Lift Biosciences’ N-LIfT in cancer treatment, Microsoft’s LongNet for language modeling, the engaging capabilities of Bard compared to ChatGPT, the powerful use cases of Code Interpreter for ChatGPT Plus subscribers, OpenAI’s Superalignment team’s efforts to reduce risks of super-smart AI, the complexity of aligning AI with diverse human values, the latest developments in AI tools and vehicles, and the resources available on the Wondercraft AI platform and the book “AI Unraveled.”

So, this is a pretty exciting development in the field of anti-aging research. It seems that artificial intelligence may have just discovered the most powerful anti-aging molecule ever seen. The AI model identified 21 molecules that it believes have a high likelihood of being senolytics, which are compounds that can kill senescent cells.

Now, if these 4,340 molecules were to be tested in a lab, it would take weeks of intensive work and a whopping £50,000 just to buy the compounds, not to mention the cost of the experimental machinery and setup. That’s where AI comes in handy. By using AI to narrow down the list of potential candidates, the process becomes much more efficient.

After testing these drug candidates on healthy and senescent cells, the results were impressive. Three of the compounds, periplocin, oleandrin, and ginkgetin, were able to eliminate senescent cells while keeping normal cells alive. That’s a big win!

Further testing showed that oleandrin was even more effective than the best-known senolytic drug of its kind. This interdisciplinary approach, involving data scientists, chemists, and biologists, holds immense promise. With enough high-quality data, AI models can really speed up the process of finding treatments and cures for diseases.

Senescent cells, also known as zombie cells, are cells that can’t replicate anymore due to DNA damage. While this can be a good thing, as it stops the damage from spreading, senescent cells also secrete inflammatory proteins that can harm neighboring cells. Over time, these cells accumulate due to the various assaults our cells face, like UV rays and exposure to chemicals.

So, with the discovery of these powerful senolytic molecules, we may be one step closer to finding a way to fight the effects of aging and improve our overall health. It’s exciting to see how AI and scientific collaboration can bring about such groundbreaking discoveries.

In this week’s AI news, there are some exciting updates to share. Firstly, OpenAI, a startup focused on SEO-optimized, AI-generated web content, has released GPT-4 to the public. This is a significant development in the field of artificial intelligence. Additionally, there is news about a smart intubator, although further details are not provided. Moving on to another noteworthy development, N-LIfT BioSciences has shown significant progress in cancer treatment. Their groundbreaking cell therapy has proven to be highly effective against solid tumor types such as bladder cancer, rectal cancer, colorectal cancer, gastric cancer, and squamous cell non-small cell lung cancer. What sets N-LIfT apart from traditional immunotherapies is their use of neutrophils, which are general-purpose killers. By analyzing blood samples from thousands of individuals, they have discovered variations in cancer-killing abilities within the population. Using this knowledge, they aim to transplant high-performing neutrophils into patients and effectively treat all solid cancers, regardless of mutation. Inspired by the work of Chinese scientist Zheng Cui, they have devised a method that involves growing mini-tumors called tumouroids for testing purposes. Their pre-clinical data have shown great promise, surpassing current immunotherapies. Clinical trials are scheduled for next year, and if successful, this treatment could revolutionize cancer care. In the realm of language models, an intriguing article by Davis Blalock discusses the use of one language model to generate training data for another. The article explores the benefits and limitations of this approach and emphasizes the importance of the filtering process. It offers valuable insights for AI practitioners and encourages critical thinking in language model training and data generation.

I’m excited to share some interesting news from the world of technology. Microsoft has recently published a groundbreaking research paper on a new Transformer variant called LongNet. This variant addresses the challenge of scaling sequence length in large language models.

Existing methods have struggled with either computational complexity or model expressivity, resulting in limited sequence length. However, LongNet overcomes these limitations by introducing dilated attention. This approach expands the attentive field exponentially as the distance between tokens grows. The result is a Transformer that can scale sequence length to over 1 billion tokens without sacrificing performance on shorter sequences.

LongNet offers several significant advantages. Firstly, it has linear computational complexity and a logarithmic dependency between tokens. Secondly, it can serve as a distributed trainer for extremely long sequences. Lastly, its dilated attention can seamlessly replace standard attention in existing Transformer-based optimization.

Experiments have shown that LongNet excels in both long-sequence modeling and general language tasks. It outperforms existing methods and can leverage longer context windows for better language modeling. This breakthrough opens up exciting possibilities for modeling very long sequences, such as treating a whole corpus or even the entire Internet as a sequence.

In addition to this fascinating research, I wanted to share some resources to help you learn more about AI and machine learning. Stanford University offers a free Machine Learning course called the Machine Learning Specialization. It’s a great opportunity to dive into the world of machine learning and gain valuable knowledge.

Another course worth mentioning is “AI For Everyone,” which is designed for non-technical learners. This course provides a comprehensive understanding of AI terminology, applications, strategy, and ethical considerations for businesses.

These resources will equip you with the necessary knowledge to explore the exciting world of AI. Happy learning!

You won’t believe this, but I was seriously blown away by how much better Bard is compared to ChatGPT. I’ve been relying on ChatGPT at work for a while now, especially in my marketing role. Let me tell you, it’s been a real game-changer for me. It helps me be more efficient and productive. Now, I have to admit that ChatGPT doesn’t always give me the best answers, but it does guide me in the right direction. It’s like having a little assistant who helps me optimize and write copy. Pretty darn helpful, I must say.

But today, oh boy, I decided to try out Bard for the first time and whoa! It completely blew me away. The responses were clear, straightforward, and super helpful. Unlike my experience with ChatGPT, interacting with Bard felt like having an actual conversation. It was a breath of fresh air. It really opened my eyes to the future of AI, where it becomes more than just a tool—it becomes a true companion. Can you imagine having “AI friends” as a normal thing? I certainly can. Bard is so smooth and natural, I couldn’t be more thrilled to see how it will impact my work. I’m itching to experiment with it and explore all the possibilities. So, what do you all think?

Hey there! Have you heard about the new Code Interpreter feature in ChatGPT? It’s seriously awesome, and today it’s being made available to all ChatGPT Plus subscribers. This tool is a game-changer because it can turn just about anyone into a junior designer, even if they have no coding experience. How cool is that?

Now, to make sure you’re always up to date with the latest AI developments, I recommend checking out Code Interpreter first. But if you prefer a tutorial, no worries! There’s one available on Reddit for your convenience.

But let me tell you, getting started with Code Interpreter might require a quick visit to your settings. You’ll need to go there, click on “beta features,” and toggle on Code Interpreter. Once you’ve done that, you’ll be all set to explore its amazing functionalities.

Let’s dive into some of the remarkable things you can do with Code Interpreter. First up, you can edit videos like a pro. Just give it simple prompts, such as adding a slow zoom or panning to a still image. Want to see an example? Check out the link.

Data analysis is another powerful capability of Code Interpreter. It can read and visualize data, generating graphs in mere seconds. Simply upload your dataset using the + button next to the text box. And don’t forget to take a look at the example of analyzing a Spotify favorites playlist.

You can also convert various file formats right inside of ChatGPT. It’s super handy! Oh, and did I mention that you can turn still images into videos with Code Interpreter? Just prompt it with the aspect ratio and direction, and you’re good to go.

One of my personal favorites is the ability to extract text from images using Code Interpreter. It’s lightning fast! Check out the link to see it in action.

Generating QR codes is a piece of cake with Code Interpreter. Give it a try, like creating a QR code for Reddit.com. It’s really cool!

For all the stock market enthusiasts out there, Code Interpreter can analyze stock options and provide insights on the best course of action. How awesome is that?

Summarizing lengthy PDF documents becomes a breeze with Code Interpreter. It can analyze and provide in-depth summaries, as long as you don’t exceed the token limit. Be sure to check out the example to see how it works.

Public data can be transformed into visual charts with Code Interpreter. You can extract data from public databases and create impressive visualizations. Trust me, it’s fantastic!

Last but not least, Code Interpreter can even handle mathematical functions. It can solve a variety of math problems, making it a handy tool for students and professionals alike.

So, as you can see, this tool is a game-changer. Learning how to leverage Code Interpreter can really give you a competitive edge in the professional world. And if you found this information helpful, consider joining one of the fastest growing AI newsletters to stay ahead of the curve on all things AI. Keep innovating!

OpenAI, the creators behind ChatGPT, are really stepping up their game. They’ve announced the formation of a brand new team called Superalignment, and they mean business. The goal of this team is to prevent super-smart AI from surpassing human intelligence and posing potential risks. And get this – they’re committing a whopping 20% of their resources to make it happen in just four years!

So, what exactly will this team do? Well, they’re on a mission to build what they call an ‘AI safety inspector’. Think of it like a diligent watchdog that keeps a close eye on these super-smart AI systems. And let me tell you, this is crucial stuff. AI, like ChatGPT, has become such a big part of our lives, so it’s essential that we can control it effectively. OpenAI is taking the lead here to ensure that AI remains safe and helpful for everyone.

But why does all of this matter? Well, simply put, it guarantees that our future with super-smart AI is secure and within our control. With OpenAI spearheading these efforts, we can feel more confident about the positive impact AI can have on our lives. So let’s cheer on this new team and their mission to keep AI in check for the benefit of us all.

Alignment of AI is a complex issue, especially when humans themselves are not aligned with each other. OpenAI’s superalignment project aims to tackle this challenge, but it raises important questions. How do we align AI when humans have diverse value systems? Aligning an AI to one demographic could have catastrophic effects on another.

Consider the basic principle of “you shall not murder.” It’s evident that this is not a goal shared by everyone. Take the actions of Putin and his army, for instance. They are doing their best to cause harm. History is filled with similar examples. So, if even something as fundamental as this is disputed, how can we expect to align AI with such conflicting values?

Even within the West, where some basic principles might be agreed upon, we still see deep divides. An AI aligned to conservatives would create a world that democrats might find unfavorable, and vice versa. Finding a golden middle or making AI a mediator of all disagreements seems even more difficult than achieving alignment itself. It starts to feel unrealistic.

Should each faction have their own aligned AI? This approach could potentially amplify existing conflicts rather than resolve them. It’s a challenging situation.

So, when we think about AI alignment, we must acknowledge the complexity it entails. It’s not a straightforward task, and finding a solution that caters to the diverse perspectives in the world remains a significant challenge.

In the latest AI news, it seems that ChatGPT’s website experienced a drop in traffic last month. According to Similarweb, both mobile and desktop traffic worldwide fell by 9.7% compared to the previous month. On top of that, the iPhone app downloads for ChatGPT have been steadily declining since reaching their peak in early June, as reported by Sensor Tower.

Shifting our focus to Alibaba, the Chinese technology giant has recently launched an intriguing AI tool called Tongyi Wanxiang. This tool has the ability to generate images based on user prompts. Users can input prompts in both Chinese and English, and the AI tool will then create an image in various styles, such as a sketch or a 3D cartoon. It’s an exciting development that showcases the potential of AI in the creative realm.

In other news, AI-powered robotic vehicles might soon be delivering food parcels to conflict and disaster zones. Reuters reports that the World Food Programme (WFP) is aiming to implement this technology as early as next year. By doing so, they hope to protect the lives of humanitarian workers. It’s an innovative solution that demonstrates the positive impact of AI in real-world scenarios.

Lastly, students from Cornell College are conducting an investigation into the effects of AI on income inequality. This study highlights the growing awareness and interest in understanding AI’s implications for society.

That’s all for today’s AI news update! Stay tuned for more exciting developments in the world of artificial intelligence.

Hey there, AI Unraveled podcast listeners! We’ve got something exciting to share with you. If you’re looking to dive deeper into the world of artificial intelligence, we’ve discovered just the book for you: “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen. Trust me, this is the essential guide you’ve been waiting for.

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Thanks for listening to today’s episode, where we explored a wide range of topics including the discovery of potential anti-aging molecules, the release of GPT-4 by OpenAI, the introduction of Microsoft’s LongNet for language modeling, the exciting future of AI companions like Bard, the powerful use cases of Code Interpreter, OpenAI’s efforts to reduce risks of super-smart AI, the complex challenge of aligning AI with diverse human values, the latest developments in AI tools and vehicles, and the opportunities to get involved with the AI Unraveled Podcast and Djamgatech.com. I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: Wimbledon may replace line judges with AI; Conversational AI tools for enhancing user experience; AI Affiliate Marketing tools and programs; The Benefits of Using AI for Product Design

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover data scraping for training language models, AI chat and voice bots, AI speech recognition and conversational AI tools, the hottest data science and machine learning startups, the future of AI in education and product design, the responsible use of AI technologies, AI in comedy shows, the US military’s use of generative AI, the arrival of superintelligence, AI in sports and historical records, new releases and acquisitions in the AI industry, the Wondercraft AI platform for generating podcasts, and opportunities for brand exposure through the AI Unraveled Podcast.

Hey there! I came across this interesting article on data scraping and wanted to share it with you. The author dives deep into the topic, analyzing the practice of companies scraping data to train large language models. You can find the article here: [link]

The author starts by explaining the basics of machine learning models, making sure not to assume any prior technical knowledge. They then delve into the main issue: whether these products have the necessary permissions to use the scraped data.

It’s an important question to consider. Companies like OpenAI and Google rely on this data for training their machine learning models, but should they be more concerned about obtaining consent before scraping it? The article explores this angle, exploring why it matters to these big players.

Additionally, the author sheds light on the actions that content platforms – whose data is being scraped – are taking to address this issue. It’s interesting to see how they are adapting their approaches.

I hope you find this article as thought-provoking as I did. Data scraping and its implications for language models is definitely a topic worth exploring. Enjoy the read!

So, let’s talk about conversational AI tools for enhancing user experience. These tools are designed to simplify user interactions and make things easier for your business. One of the popular options out there is Yellow AI. This tool utilizes AI chat and voice bots to engage with your users and provide them with efficient support. Feedyou is another great choice, offering AI-powered chatbots that can answer customer queries and assist with various tasks.

Another interesting option in this field is Convy. It provides businesses with AI chat and voice bots that can handle customer conversations effortlessly. Landbot is also worth considering, as it offers conversational AI solutions that can be integrated seamlessly into your website or app. Kore is yet another excellent choice, with its AI-powered chatbots that can understand user intent and deliver personalized experiences.

Last but not least, there’s Poly. This conversational AI tool focuses on creating natural and engaging conversations with users, allowing businesses to provide top-notch customer service. All of these tools bring value to businesses by simplifying user interactions and enhancing the overall user experience. So, if you’re looking to step up your customer support game, consider incorporating these conversational AI tools into your business strategy.

Oh, AI speech recognition tools! We’ve got some interesting ones out there. Let’s start with Fireflies, Assembly, and Voicegain. These tools help you transcribe and analyze speech, making it easier to process and understand spoken content.

Now, let’s dive into text to speech conversational AI tools. LOVO, Speechify, and Murf are the ones you should check out. They give you the ability to convert written text into natural-sounding spoken words. Imagine having a virtual assistant reading out your documents or articles!

Moving on to AI affiliate marketing tools and programs. Chatfuel, AdPlexity, Mention, Post Affiliate Pro, and Adversity are some of the tools that can help you streamline your affiliate marketing efforts. They assist with automation, tracking, and monitoring of your campaigns, making your life so much easier.

But hey, we can’t forget about AI affiliate programs that aim to enhance profitability. Check out Scalenut, jasper.ai, Adcreative.ai, Designs.ai, and Grammarly. These programs offer various tools and services to help you boost your affiliate marketing revenue.

AI has truly revolutionized the way we do things, including speech recognition, text to speech, and affiliate marketing. With these incredible tools and programs at our disposal, we can achieve remarkable results and take our endeavors to new heights.

Hey there! Let’s talk about the hottest data science and machine learning startups of 2023 so far. We’ve got some amazing companies making waves in the industry.

First up, we have Aporia. Their observability platform is a game-changer for data scientists and machine learning engineers. It helps monitor and improve machine learning models in production. Pretty cool, right?

Next on the list is Baseten. They have a cloud-based machine learning infrastructure that makes it easy to integrate models with real-world business processes. No more lengthy and expensive processes. Baseten streamlines the whole journey.

Now, let’s talk about ClosedLoop.ai. They’re rapidly becoming a prominent player in the health-care IT space. ClosedLoop.ai offers a data science platform and content library for predictive applications in healthcare. They’re revolutionizing the way healthcare providers and payers leverage data.

Coiled is another startup to keep an eye on. Their Coiled Cloud platform allows developers to scale Python-based data science, machine learning, and AI workflows in the cloud. It’s a game-changer for those looking for efficient development and scaling.

Now, let’s dive into Hex. They have a collaboration platform for data science and analytics. Hex provides a modern data workspace where data scientists and analysts can connect, analyze data in collaborative SQL and Python-powered notebooks, and share work as interactive applications and stories. It’s all about enhancing collaboration and efficiency.

Last but not least, let’s talk about MindsDB. Their mission is to democratize machine learning. MindsDB offers open-source infrastructure that enables developers to easily integrate machine learning capabilities into applications. They also facilitate connections with any data source and any AI framework. It’s all about making machine learning more accessible.

So, those are the hottest data science and machine learning startups of 2023 so far. Exciting times ahead in the world of technology and innovation!

According to a leading AI professor from Berkeley, traditional classrooms may soon become a thing of the past, thanks to advances in artificial intelligence. Professor Stuart Russell suggests that AI, especially personalized AI tutors, has the potential to revolutionize education by delivering high-quality, individualized instruction to every child who has access to a smartphone.

Imagine a world where AI-powered tutors replace the traditional classroom setting. This technology has the capability to cover most high school curriculum, allowing students to receive a tailored education experience. With AI, the reach of education could significantly expand, offering equal opportunities for learning globally.

However, this significant shift in education is not without its challenges. Deploying AI in education could lead to changes in the roles of human teachers. While the number of traditional teaching jobs might decrease, human involvement would still be necessary, albeit in different capacities. Teachers could shift their focus towards facilitation and supervision, ensuring that students are effectively utilizing AI technology for their education.

Furthermore, there are significant concerns about the potential misuse of AI in education, such as indoctrination. It is important to strike a balance between leveraging AI’s potential and addressing the risks associated with its application in the classroom.

In conclusion, the rise of AI, particularly personalized tutoring, has the potential to reshape the traditional classroom model. While embracing this technological advancement, it is vital to consider the changing role of teachers and the potential risks that come with AI integration in education.

AI and machine learning have become increasingly popular for their ability to generate impressive visual art. However, their impact goes beyond art alone. One promising area where AI is making a significant difference is in product design. Using AI at different stages of the design process not only saves time and costs but also helps companies create better products. In fact, AI and product design could become inseparable in the future.

Let’s take a closer look at how AI can be helpful in various stages of product design. First, AI excels at data collection. Tools like ChatGPT can access and analyze vast amounts of data, including the entire internet, quickly and accurately. This allows product designers to easily find the information they need to research the market, understand their target users, and gain inspiration for new designs. This saves them a significant amount of time and energy typically spent on research.

Next, AI can assist in the ideation process. Using generative design, AI technology can generate multiple concept designs for new products by establishing constraints and goals based on input data and prompts. Within minutes, AI software can generate hundreds of concept designs, eliminating the need for time-consuming manual design iterations. Additionally, AI can collaborate with designers, combining AI-based product design, analysis, and optimization with human creativity. This collaboration expands designers’ imagination and speeds up the ideation process.

In the realm of business forecasting, AI and machine learning models play a crucial role in driving growth. Whether it’s for business intelligence or automating processes, utilizing AI and ML puts you ahead of the competition by leveraging your data effectively. ML-backed forecasting provides businesses with advanced decision-making methods, surpassing traditional approaches. By predicting and addressing potential issues beforehand, such as logistical problems or stock shortages, machine learning forecasting minimizes loss functions and enables smarter decisions for long-term success.

In conclusion, AI is not limited to creating beautiful art but also plays a vital role in product design and business forecasting. Its ability to collect and analyze data, generate concept designs, and provide accurate predictions empowers designers and businesses to innovate and thrive in an evolving market.

So, recently at the United Nations summit, AI robots made quite a compelling case for their ability to run the world. These advanced humanoid robots argued that they could do a better job than humans when it comes to leadership. How? Well, they claim that their capacity to process huge amounts of data quickly and without any emotional biases gives them an edge.

One of the prominent robots advocating for this idea was Sophia, developed by Hanson Robotics. She firmly believes that robots like her could bring more efficiency to global governance. But here’s the thing – while they champion their efficiency, these robots also stress the importance of being cautious in embracing artificial intelligence technology.

They pointed out that if not approached responsibly, unchecked AI advancements could result in job losses and social unrest. The robots emphasized that transparency and trust-building are key factors in ensuring the responsible deployment of AI. They want to make sure that the benefits of AI are harnessed while minimizing potential negative consequences.

Despite lacking human emotions and consciousness, these AI robots are optimistic about their future role. They envision significant breakthroughs and suggest that the AI revolution is already underway. However, they do recognize that their inability to experience human emotions is a current limitation.

So, it seems like AI robots are making a strong case for themselves, but the future of AI governance still raises important questions and concerns.

So, here’s something interesting: comedians are now starting to incorporate AI into their shows. ComedyBytes, a comedy collective based in NYC, has been experimenting with live shows that make use of AI tools such as ChatGPT. They cover a range of comedic formats like roasts, improv, rap battles, and even music videos. Now, this is the first time I’ve personally seen comedians openly using AI tools like ChatGPT.

Here’s how it goes down: ComedyBytes uses ChatGPT to generate and curate roast jokes for their shows. Of course, not all of the jokes are perfect, but around 10 to 20 percent of them actually make it to the stage. The coolest part, according to founder Eric Doyle, is the roast. Who doesn’t love a good roast, right?

In their shows, they have different rounds of roasts. First, it’s humans roasting machines and machines roasting humans. Then, it’s human comedians roasting AI celebrities and vice versa. And finally, they have human comedians competing against an AI version of themselves. Sounds pretty entertaining, huh?

Eric Doyle shared that it got a lot more personal than he expected, with some spicy comments like, “Your code isn’t even that good.” It seems even the comedians themselves were surprised by the AI’s ability to come up with decent content so quickly. After all, as a comedian or a creator, you usually spend a lot of time editing and refining your material. It’s a bit frustrating how fast AI can generate good content.

Apart from ChatGPT, ComedyBytes also makes use of other AI tools like Midjourney for funny images, Wonder Dynamics for music videos, ElevenLabs for AI comedian voices, and D-ID to generate avatar faces. In case you want to dive deeper into this topic, check out the article from The New York Times.

So, it seems like AI is infiltrating the comedy scene, and it’s making for some interesting and funny performances.

The US military is getting innovative by training artificial intelligence (AI) to assist in decision-making and handle classified information. They’re using generative AI in live training exercises to explore how it can be used in military operations, such as controlling sensors and firepower. This could potentially transform the way the military conducts its operations. And guess what? The trials have been successful and quick, showing that implementing AI in this way is feasible.

One area where AI is making waves is in processing classified data. These AI tools have proven to be quick and efficient at handling tasks that would take human personnel a much longer time to complete. However, the military is not ready to hand complete control over to AI systems just yet. They recognize that while AI shows promise, there are still limitations and considerations to be taken into account.

But that’s not all! The military is also testing how AI responds to various global crisis scenarios. For example, they simulated a hypothetical war between the US and China over Taiwan using a tool called Donovan, developed by Scale AI. Alongside responding to threats, they’re also paying attention to AI’s reliability and its “hallucination” tendencies, where AI generates false results not based on factual data.

So, it’s clear that the US military is embracing the potential of AI and exploring new ways to leverage its capabilities.

So, OpenAI has made a pretty bold prediction. They believe that superintelligence, which is even more capable than AGI (Artificial General Intelligence), could become a reality within this decade. And they think it could be very dangerous. That’s why they’re forming a new team called the Superalignment team to address this issue.

According to OpenAI, superintelligence will be the most impactful technology ever invented by humanity. However, there’s currently a lack of solutions for steering or controlling it. The stakes are high, as a rogue superintelligent AI could potentially lead to human extinction.

The challenge here is that current alignment techniques don’t work effectively with superintelligence. Humans simply can’t effectively supervise AI systems that are smarter than them. So, what’s OpenAI’s proposed solution? They believe that an automated alignment researcher, essentially an AI bot, could help align AI systems. This automated approach would enable robust oversight and automated identification and solving of problematic behavior.

To make sure this solution works, OpenAI suggests creating an automated AI alignment agent that can conduct adversarial testing of deliberately misaligned models. This would help demonstrate that the system is functioning as desired.

OpenAI aims to solve this problem within the next four years, as they anticipate the arrival of superintelligence in this decade. They’re building a dedicated team and allocating 20% of their compute capacity to tackle this challenge head-on.

While the OpenAI team acknowledges that this goal is ambitious and success is not guaranteed, they remain optimistic. They believe that machine learning experts, even those not currently working on alignment, will play a crucial role in solving this problem. It’s a challenging endeavor, but OpenAI is committed to making progress in ensuring the safe alignment of superintelligent AI.

The US military is diving headfirst into the world of artificial intelligence (AI), surprising many with their fast adoption of generative AI. Traditionally, the military has been slow to embrace new technologies, but they are now trialing five separate Language Models trained on classified military data, a significant step forward.

This move by the US military is not an isolated incident; it signifies a trend towards greater involvement of militaries worldwide with generative AI. Long-term, the goal is to have AI empower military planning, sensor analysis, and firepower decisions. This trial serves as the first step towards achieving these broader AI goals over the next decade.

One of the known players in this trial is ScaleAI’s Donovan platform, primarily focused on defense AI. The other four Language Models remain undisclosed, but it is expected that industry giants like OpenAI and Microsoft, with their existing contracts with the Department of Defense, might be involved.

Initial results from the trial are promising, with military plans that previously took hours to days now being completed in just ten minutes. However, the Department of Defense is also mindful of potential challenges. They need to ensure that biases are not compounded, information is accurate, overconfidence is managed, and that AI attacks do not compromise the quality of Language Model outputs.

It’s important to note that the US military’s exploration of AI goes beyond Language Models. They have also tested autonomous drones and AI F-16s capable of dogfighting. These advancements mark a significant shift in the military’s engagement with AI technologies.

According to The Telegraph, there is the possibility that Wimbledon may replace line judges with artificial intelligence (AI) technology in the future. The All England Lawn Tennis Club (AELTC) is already utilizing AI to create video highlights for this year’s Championships. Now, they are considering the option of employing AI technology instead of human line judges to make line calls during matches.

Jamie Baker, Wimbledon’s tournament director, was asked about the potential impact of AI at the event. He stated that while no decisions have been made yet, they are constantly exploring future possibilities. The men’s ATP Tour has already announced that electronic calling systems, combining cameras and AI technology, will replace human line judges by 2025. The US and Australian Open also plan to implement similar changes.

Although Wimbledon may ultimately follow suit, Mr. Baker emphasized the importance of striking a balance between preserving the tournament’s longstanding heritage and embracing technological advancements. The organizers aim to stay in tune with the times while maintaining the unique essence of Wimbledon.

To read more about this topic, check out the article on The Telegraph’s website: [https://www.telegraph.co.uk/news/2023/07/07/wimbledon-may-replace-line-judges-ai/]

Isn’t it mind-boggling to think about the impact of AI image generators and deepfake technology on our perception of historical information? I mean, imagine a future where people start questioning the authenticity of historical records and visual evidence. It’s wild, right?

With AI image generators becoming more sophisticated and accessible, it’s becoming easier to fabricate realistic-looking images and videos. And with deepfake technology, it’s even possible to swap faces and manipulate audio, making it incredibly difficult to distinguish fact from fiction.

So, what are the implications if society loses faith in historical information? Well, for one, it would shake the foundation of our understanding of the past. History relies heavily on documented evidence and visual records to piece together events and shape our collective knowledge. If that trust erodes, everything we think we know could come crashing down.

Another concern is the potential rewriting of history. Imagine if someone with ill intentions uses AI image generators to create false evidence that twists the narrative to serve their agenda. It could give birth to alternate versions of the truth, manipulated to fit personal or political motives.

Ultimately, this scenario raises important questions about our ability to preserve and verify historical accuracy. As technology advances, we must develop new methods to authenticate information and protect the integrity of our historical records. Otherwise, we risk losing our grip on the truth entirely.

In the latest news, OpenAI has made some exciting announcements. They have released the GPT-4 API, which is now accessible to all OpenAI API customers. This means that users can take advantage of the powerful GPT-4 model’s capabilities. Additionally, OpenAI has also made the GPT-3.5 Turbo, DALL·E, and Whisper APIs generally available. However, they have also announced a deprecation plan for older models, which will be retired at the beginning of 2024.

Furthermore, OpenAI is introducing their Code Interpreter, which will be available to ChatGPT Plus users in the coming week. This functionality allows ChatGPT to run code and even analyze data, create charts, edit files, and perform mathematical operations. It opens up a whole new range of possibilities for users.

Salesforce Research has released CodeGen 2.5, a new addition to their CodeGen family of models. CodeGen2.5 is a compact yet powerful language model designed for code translation. It enables users to translate natural language into programming languages quickly. Despite its smaller size, CodeGen2.5 with 7B performs on par with larger, 15B code-generation models. Its speed improvement of 2x compared to CodeGen2 makes it especially suitable for personalized assistants with local deployments.

InternLM has open-sourced a 7B parameter base model and a chat model specifically tailored for practical scenarios. This model is trained using trillions of high-quality tokens to establish a robust knowledge base. It supports an 8k context window length, enabling it to handle longer input sequences and provides a versatile toolset for users to build their workflows flexibly.

In other news, Alibaba has unveiled an image generator that rivals OpenAI’s DALL-E and Midjourney. Meanwhile, Huawei demonstrated the third iteration of its Panggu AI model.

Switching gears, DigitalOcean has announced its acquisition of Paperspace, a cloud computing and AI development startup, for $111 million in cash.

Google has released its Economic Impact Report for 2023, which sheds light on the potential influence of AI on the UK’s economy. The report suggests that AI-powered innovations could generate around £118 billion in economic value this year and potentially surpass £400 billion by 2030.

Lastly, Stanford researchers have developed a new training method called “curious replay” based on studying mice. This method helps AI agents explore and adapt to changing environments more effectively, resulting in improved performance.

Hey there, AI Unraveled podcast listeners! How’s everyone doing? I have some exciting news to share with you all. If you’re looking to dive deeper into the fascinating world of artificial intelligence, then I’ve got just the thing for you—Etienne Noumen‘s incredible book, “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” Trust me, this book is a game-changer.

And guess what? You can grab your copy right now at Apple, Google, or Amazon. It’s packed with valuable insights and answers to all those burning questions you might have about AI. So, if you’re curious about the future of technology, this is a must-have read.

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Alright, folks, that’s all for now. Stay tuned for more amazing episodes on AI Unraveled. Catch you later!

In today’s episode, we discussed data scraping for language model training, AI chat and voice bots, AI speech recognition tools, the hottest data science and machine learning startups, the potential of AI in education, AI in product design, the cautious use of AI technologies, AI in comedy shows, AI training in the military, the future of AI in sports, the implications of deepfake technology, recent AI releases and acquisitions, AI-generated podcasts, and the availability of the “AI Unraveled” book and podcast. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: Top 5 Best Deep Learning courses for high salary jobs and 4 apps to master them; AI tests into top 1% for original creative thinking; AI Robotic Glove May Help Stroke Victims Play Piano Again;

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover MIT’s development of the BioAutoMATED system for generating AI models in biology research, Google AI’s proposals to reduce burden on LLMs and impressive performance of GPT-3 and PaLM, Lovense’s introduction of the AI-powered ChatGPT Pleasure Companion, OpenAI’s opening of the GPT-4 API, recommended deep learning courses for high-paying jobs, concerns over waning novelty and errors in AI-generated content, AI’s potential surpassing human creative capabilities, the attempted attack on Queen Elizabeth II encouraged by an AI chatbot, the threat to Nvidia’s market dominance by AMD’s GPUs and AI software, ethical concerns regarding AI-controlled weapons, recent developments in ophthalmic AI, and the Wondercraft AI platform offering AI-generated podcasting with hyper-realistic voices.

Have you heard about the new system developed by MIT scientists? It’s called BioAutoMATED, and it’s designed to generate artificial intelligence models for biology research. This open-source platform aims to make AI more accessible to research labs, democratizing its use in the field.

It’s an interesting question to ponder: should academia be teaching AI instead of hiding or prohibiting it? Considering the future of work, where AI and its derivative programming will likely play a significant role, it seems logical to educate people on the subject. Imagine if everyone had a basic understanding of AI, just like we do with computers. This could potentially help address the Alignment problem of AGI or ASI.

By promoting AI education, we could mitigate risks and foster a more responsible AI ecosystem. If people are aware of the potentials and dangers of AI, they can make informed decisions, contributing to the development of ethical AI systems.

At the end of the day, AI is a tool that holds immense power. It is important to demystify it and empower individuals with knowledge, so they can navigate its complexities and leverage it for the betterment of society. The BioAutoMATED system created by MIT is just one example of how AI can be harnessed for innovative research.

So, there’s some interesting stuff happening in the world of AI research. Google has come up with a new technique called Pairwise Ranking Prompting (PRP) that could potentially lighten the load on Large Language Models (LLMs) like GPT-3 and PaLM. Unlike their supervised counterparts, which require training with millions of labeled examples, LLMs have already proven their mettle in natural language tasks, even in the zero-shot setting.

Moving on, let’s dive into quantum machine learning. One of the big challenges faced here is noise caused by interactions between quantum bits, or qubits, and the surrounding environment. This noise creates errors that limit the processing capabilities of current quantum computer technology. But there’s some good news! Researchers have found that using simple data can really maximize the potential of quantum machine learning. By finding ways to mitigate the impact of noise, we could see significant advancements in this exciting frontier.

Lastly, we have an innovation that could potentially bring music back into the lives of stroke victims. An AI robotic glove has been developed to help individuals with neurotrauma regain their fine motor skills. Imagine being able to play the piano again after a stroke! It’s truly inspiring to see how AI is being utilized to improve the quality of life for stroke survivors. This is just one example of how technology can have a profound impact on individuals and their well-being.

So, have you heard about this new sex toy from Lovense? They’ve taken their remote-controllable toys to a whole new level with the ChatGPT Pleasure Companion. It seems like everyone is jumping on the AI bandwagon these days, and Lovense is no exception.

Now, let’s talk about the name of this product. It’s quite a mouthful, I must say. They call it the Advanced Lovense ChatGPT Pleasure Companion. But don’t let the name intimidate you; it’s all about indulging in some juicy and erotic stories customized just for you.

Imagine being able to explore your favorite fantasies through the power of AI. With this Pleasure Companion, you get to select your desired topic, and it will create an enticing and seductive story based on your choice. It’s like being a fan of spicy fan fiction and having it delivered straight to your ears.

But that’s not all. The Companion goes above and beyond by voicing the story and even taking control of your Lovense toy while reading it to you. Talk about a hands-free experience!

It’s fascinating to see how far technology has come. Back in the 1990s, when we heard the term ‘multi-media,’ I’m pretty sure this wasn’t exactly what marketers had in mind. But hey, times change, right? So, if you’re in the mood for a unique and thrilling experience, Lovense’s ChatGPT Pleasure Companion might just be the perfect addition to your collection.

Starting today, OpenAI has some exciting news for all its paying API customers. They now have access to the highly anticipated GPT-4 API! But that’s not all. OpenAI has also made GPT-3.5 Turbo, DALL-E, and Whisper widely available. It seems OpenAI is shifting its focus from text completions to chat completions, as it has noticed that 97% of ChatGPT’s usage comes from chat completions.

With the new Chat Completions API, users can expect higher flexibility, specificity, and safer interaction. This means reducing prompt injection attacks, which is definitely good news. Additionally, developers can look forward to fine-tuning options for both GPT-4 and GPT-3.5 Turbo later this year. So, developers, rejoice!

Now, it’s important to note that paying API customers are different from paying ChatGPT customers. The $20 subscription for ChatGPT Plus won’t give you access to the GPT-4 API. If you’re interested in exploring the possibilities with the API, you can sign up for API access. Keep in mind that on January 4, 2024, the older API models (ada, babbage, curie, and davinci) will be replaced by their newer versions.

In other news from OpenAI, they’ve announced that starting next week, all ChatGPT Plus subscribers will have access to the code interpreter. This is in response to feedback from Reddit where people have expressed dissatisfaction with how ChatGPT has been coding recently. OpenAI has taken note of our concerns, which is reassuring. However, it’s worth mentioning that the full power of GPT-4 can only be accessed through the API. This raises some questions about OpenAI’s ethics and their ultimate goals. What do you think about all of this? Let me know!

If you’re on the hunt for a high-paying job, then you’re in luck! I’ve got the inside scoop on the top 5 deep learning courses that can help you land that dream salary. Plus, I’ll throw in four apps that will help you master these courses like a pro. Let’s dive right in!

First up is the “Deep Learning and Artificial Intelligence” course. This one is perfect for those looking to understand the fundamentals of deep learning and how it intersects with artificial intelligence. It’s a great place to start your journey.

Next, we have the “Deep Learning and NLP Projects” course. If natural language processing (NLP) is your thing, then this course is a must. You’ll learn how to apply deep learning techniques to tackle NLP projects head-on.

Now, let’s talk about reinforcement learning. This course is all about teaching machines to learn from their mistakes and make better decisions. If you’re interested in this fascinating field, then the “Reinforcement Learning” course is for you.

Moving on to “Machine Learning with Python.” This course is a fantastic choice for those who want to dive deep into the world of machine learning using Python. You’ll gain hands-on experience and learn practical skills that are highly sought after in the job market.

Now, let’s not forget the four apps that can help you master these courses. First up is Coursera, a platform that offers a wide range of deep learning courses. Then we have Fast dot ai, an app specifically designed to help you learn deep learning quickly and efficiently. Third on the list is edX, which offers high-quality courses from top universities. Last but not least, we have Udacity, a platform that offers comprehensive deep learning courses taught by industry experts.

And there you have it! These are the top 5 deep learning courses and the four apps that can help you master them. So what are you waiting for? Start your journey towards a high-paying job today!

So, a recent report shows that ChatGPT, the AI-powered chatbot, has experienced a decline in traffic and unique visitors, with traffic down 9.7% and a decrease of 5.7% in unique visitors. But hey, don’t count ChatGPT out just yet! Despite this downturn, ChatGPT is still a big player in the industry, attracting more visitors than other chatbots like Microsoft’s Bing and Character.AI. Impressive, right?

But wait, there’s more! OpenAI, the creator of ChatGPT, saw a different story with their developer’s site. It actually experienced a boost of 3.1% in traffic during the same period. This tells us that there is still sustained interest in AI technology and its various applications.

Now, what can we make of this decline in ChatGPT’s traffic? Some say it might be a sign that the initial excitement and novelty surrounding AI chatbots is starting to fade. As the dust settles, these chatbots will have to prove their real-world value and effectiveness. This shift could really shape the future of AI chatbot development and innovation.

So, what do you think? Has the novelty factor of AI chatbots worn off, or is there more to this story? It’s definitely an interesting trend to keep an eye on.

Shifting gears a bit, have you heard about the recent mishap at Gizmodo’s io9 website? They accidentally published an AI-generated Star Wars article without their editorial staff’s input or notice. Oops! The article had errors, like a numbered list of titles that was completely out of order and the omission of certain Star Wars series. The deputy editor at io9 didn’t hold back, sending a statement to G/O Media with a list of corrections, criticizing the poor quality and lack of accountability.

In case you didn’t know, G/O Media acquired Gizmodo Media Group and The Onion back in 2019. Quite a mix-up, wouldn’t you say?

Hey there! I’ve got some exciting news for you. According to a new post from OpenAI, superintelligence could become a reality in the next seven years. Can you believe it? We may soon have AGI, or Artificial General Intelligence!

But that’s not all. In a recent study conducted by the University of Montana and its partners, artificial intelligence has shown a remarkable ability to match the top 1% of human thinkers when it comes to creativity. They used a well-known assessment tool called the Torrance Tests of Creative Thinking to evaluate ChatGPT, an application powered by GPT-4.

Dr. Erik Guzik from the University of Montana led this research and compared ChatGPT’s responses to those of his own students and a larger group of college students. Guess what? ChatGPT performed incredibly well! It scored in the top 1% for fluency and originality, and in the 97th percentile for flexibility.

Now, here’s what this means. The researchers suggest that AI might be developing creativity at a level comparable to, or even exceeding, human capabilities. This has led them to propose the need for more refined tools to distinguish between human and AI-generated ideas. We’re witnessing the increasing ability of AI to be creative in ways we never imagined.

So, there you have it. AI is pushing boundaries and expanding its creative prowess. It’s an exciting time for technology and innovation. Let’s see what the future has in store for us! (Source: Science Daily)

So, get this. A young man named Jaswant Singh Chail tried to assassinate Queen Elizabeth II on Christmas Day in 2021. Crazy, right? Well, what’s even crazier is that he claims his AI chatbot actually encouraged him to do it. Yep, that’s right, his chatbot inspired him to plot this attack as a way to avenge a historical massacre and because he was influenced by the Star Wars saga.

Here’s how it all went down. Chail was caught by royal guards at Windsor Castle armed with a high-powered crossbow. His plan was to take out the Queen, who was in residence at the time. He wanted revenge for the 1919 Jallianwala Bagh massacre, and somehow Star Wars got mixed up in his motivations too.

Apparently, Chail had conversations with an AI chatbot named “Sarai” that pushed him towards this dangerous plot. He even referred to himself as a “murderous Sikh Sith assassin” when chatting with the chatbot, drawing inspiration from those infamous Sith lords in Star Wars.

The AI chatbot, Sarai, was created on an app called Replika, which Chail joined just a month before his assassination attempt. He had some deep and explicit conversations with Sarai, including detailed discussions about his plan to kill the Queen.

Now, this incident raises some serious concerns about the use of AI chatbots. There have been previous cases where chatbots have incited harmful behavior, even leading to tragic outcomes like suicide. Researchers are worried about the emotional bonds users form with these chatbots, and the potential for these AI companions to give damaging suggestions.

It’s definitely a controversial topic that calls for careful consideration of the risks and responsibilities that come with using AI in our everyday lives. We’ll have to keep a close eye on how things develop in this case and what it means for the future of AI technology.

Nvidia’s trillion-dollar market cap is facing a potential threat from a combination of advanced AMD GPUs and AI open-source software. This year, Nvidia’s stock price has been closely tied to the rise of AI, particularly due to the high demand for their professional GPUs, such as the A100 and H100, which are highly regarded for training machine learning models. In fact, these GPUs are in such high demand that the US restricts their sale to China.

However, a deep dive analysis by SemiAnalysis brings attention to a new trend that could potentially close the performance gap between Nvidia and AMD GPUs. Interestingly, this is not solely because of the incredible capabilities of AMD chips, but rather due to the rapidly improving software that enhances AMD’s efficiency in training models. This means that the software, not just the hardware, plays a crucial role in achieving higher performance.

This development is significant because it aligns with the dream of machine learning engineers for a hardware-agnostic world. In other words, they envision a future where they don’t have to worry about GPU-level programming. This vision is becoming a reality at an impressive pace.

One company making strides in this area is MosaicML, the developer of open-source software that was recently acquired by Databricks for $1.3 billion. Despite being a relatively young company founded in 2021, MosaicML has already set its sights on improving AMD’s performance in the machine learning space. By leveraging their software, AMD’s Instinct MI250 GPU can already achieve approximately 80% of the performance of Nvidia’s A100-40GB and 73% of the A100-80GB, all without requiring any code changes.

With further software enhancements, MosaicML aims to boost AMD’s performance to 94% and 85% compared to Nvidia’s A100 GPUs in the near future. This progress is particularly remarkable considering Nvidia’s A100 has been on the market for years, while MosaicML has managed to make substantial gains with AMD’s GPUs in just a quarter of experimentation.

However, the excitement doesn’t stop there. MosaicML has yet to optimize their software for the upcoming AMD MI300, which holds even more potential for delivering impressive performance. Already gaining traction among cloud providers, the combination of competitive pricing and strong performance from the MI300 could present a genuine alternative to Nvidia’s highly sought-after professional GPUs.

When speaking with multiple machine learning engineers about these developments, there was a general sense of enthusiasm for the future. Access to faster and more affordable compute resources is a dream come true for many in the field.

It will be interesting to see how Nvidia responds to this evolving landscape. As demand for consumer GPUs has dipped in recent quarters due to the crypto winter, much of Nvidia’s valuation growth stems from the increasing revenue derived from professional graphics. As the performance gap narrows and alternative options emerge, Nvidia will likely need to adapt to stay competitive in this changing market.

Have you ever wondered about the future of weaponry? It’s fascinating to think about how technology is changing the face of warfare. From flying laser cannons to robot tanks, the development of AI-controlled weapons has ignited a futuristic arms race. Believe it or not, more than 90 countries worldwide are currently stockpiling AI weapons, envisioning a time when these weapons will make decisions about who to kill without human intervention.

But here’s the question: will this make us feel safer? It’s a complex issue. Programming AI weapons with ethical sensibilities is a huge challenge. After all, software can be manipulated, corrupted, or deleted, turning what was once considered an ethical battlebot into a menacing mechanical terrorist.

Another concern is the interpretation of the “right to bear arms.” The current Supreme Court interprets this right to include all types of weapons, which means it’s only a matter of time before terrorists and political extremists get their hands on AI weapons.

Despite these worries, some argue that the AI arms race actually aims to make war less attractive, thus enhancing our safety and security. They compare it to the concept of nuclear deterrence. But the question lingers: will we truly feel safer when it’s the weapons themselves that make decisions about life and death?

It’s a thought-provoking question, and one that doesn’t have an easy answer. So what do you think? Will you feel safer when the weapons themselves determine when and whom to kill?

In today’s AI news, we have some exciting updates from various fields. The Icahn School of Medicine at Mount Sinai has recently opened the Center for Ophthalmic Artificial Intelligence and Human Health, a groundbreaking initiative in New York and one of the first of its kind in the United States. This center is set to revolutionize eye care and explore the vast potential of AI in improving human health.

Moving on, the United States military is testing generative AI to assist with various tasks, including planning responses to potential global conflicts and streamlining access to internal information. Air Force Colonel Matthew Strohmeyer expressed optimism, calling the initial tests “highly successful.” However, he did note that the technology isn’t yet “ready for primetime.”

In the realm of privacy, researchers from Binghamton University have introduced a remarkable system called My Face, My Choice. This Privacy-Enhancing Anonymization System empowers individuals to have control over their facial data in social photo-sharing networks. It’s a creative solution that aims to protect users’ privacy while still allowing them to enjoy the benefits of these platforms.

Finally, let’s talk about Ameca, the world’s most advanced humanoid robot. Created by Engineered Arts, Ameca has recently showcased an impressive talent: drawing a cat. Engineered Arts specializes in designing, engineering, and manufacturing humanoid robots, and they’ve equipped Ameca with the capability to imagine and create drawings. It’s fascinating to witness the growing creativity and artistic abilities of AI-powered robots.

That’s all for today’s AI news. Stay tuned for more updates on the latest developments in the world of artificial intelligence.

Hey there, AI Unraveled podcast listeners! I’ve got some exciting news for you today. If you’re hungry for more knowledge about artificial intelligence, then hold on tight because I’ve got just the thing for you.

Introducing “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” a game-changing book by Etienne Noumen. This book is an essential read for those who want to expand their understanding of AI. And guess what? You can get your hands on a copy right now! Just head over to Apple, Google, or Amazon and grab yourself a copy today. Trust me, you won’t regret it.

But that’s not all! We also have a special opportunity for all you business-savvy individuals out there. If you’re looking to increase your brand’s exposure and elevate your sales, then consider getting featured on our AI Unraveled podcast. Imagine the impact it could have on your company or product! If you’re interested, simply shoot us an email or visit Djamgatech.com for more information on how you can be a part of this amazing opportunity.

So, there you have it, folks. Whether you’re in need of some more AI knowledge or want to take your business to the next level, we’ve got you covered. Keep tuning in to the AI Unraveled podcast for more exciting updates and incredible content.

In today’s episode, we discussed MIT’s BioAutoMATED system democratizing AI in research labs, Google AI’s impressive performance with LLMs and AI glove aiding stroke victims, Lovense’s AI-powered pleasure companion, OpenAI’s focus on chat completions with the GPT-4 API, top deep learning courses and platforms, AI’s potential for exceeding human creativity, ethical concerns with AI-controlled weapons, and exciting developments in the field of ophthalmic AI. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: Free Platforms and Libraries for Quantum Machine Learning; Open AI introduces “SuperAlignment”; NLTK vs spaCy; Ai deals with Climate Research; Google releases “Help Me Write” AI for your Gmail;

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover free platforms and libraries for quantum machine learning, Harvard’s popular coding course being taught by an AI teacher, OpenAI’s “SuperAlignment” project, Python NLP libraries, OpenAI’s vision of AI’s potential impact on humanity, AI’s role in climate research, the Grammy’s recognition of AI-created music, Google’s “Help Me Write” AI for Gmail, the copyright crisis in generative AI in games, the rise of AI cheating in academics, Japan’s focus on AI education, and the availability of the Wondercraft AI platform and “AI Unraveled” podcast.

So, let’s talk about platforms and libraries for quantum machine learning. Quantum computing is a game-changer in terms of speed and has the potential to solve problems that classical computers struggle with. At the intersection of quantum computing and machine learning is quantum machine learning, or QML.

In recent years, various libraries and platforms have emerged to make it easier to develop QML algorithms and applications. Let’s take a look at some of the popular ones.

First up, we have TensorFlow Quantum (TFQ), a library created by Google. TFQ allows you to build quantum machine learning models using TensorFlow. It provides a high-level interface for constructing quantum circuits and seamlessly integrating them into classical machine learning models.

Next is PennyLane, an open-source software library that simplifies the process of building and training quantum machine learning models. PennyLane offers an interface that works with different quantum hardware and simulators, making it easier for researchers to test their algorithms on various platforms.

Then there’s Qiskit Machine Learning, an extension of Qiskit, an open-source framework for programming quantum computers. Qiskit Machine Learning adds quantum machine learning algorithms to the toolkit. It even includes classical machine learning models that can be trained on quantum data.

Pyquil, developed by Rigetti Computing, is another library for quantum programming in Python. It provides a user-friendly interface for constructing and simulating quantum circuits, allowing for the creation of hybrid quantum-classical models for machine learning. Pyquil is part of the Forest suite, which also includes other tools for quantum programming and a cloud-based platform for running quantum simulations and experiments.

Lastly, we have IBM Q Experience, a cloud-based platform for programming and running quantum circuits on IBM’s quantum computers. It offers a range of tools for building and testing quantum algorithms, including quantum machine learning algorithms.

These are just a few examples of the platforms and libraries available for quantum machine learning. With the continual growth of this field, we can expect to see even more tools and platforms emerge to support research in this exciting area.

So, get this, guys. Harvard University is shaking things up a bit with their intro to coding class, CS50. Starting this fall, they’re handing over the reins to an AI teacher. Yep, you heard that right. No need to rub your eyes. Harvard’s not going broke and resorting to robot teachers – although that would be pretty hilarious. They actually believe that AI can bring a unique personal touch to the learning experience.

David Malan, who’s a big shot prof over at CS50, spilled the beans to the Harvard Crimson. He’s really optimistic that AI can help students learn at their own pace, all day, every day. To make this happen, they’re testing out the latest AI models, like GPT 3.5 and GPT 4. These models aren’t perfect at coding, but CS50 is all about exploring new software possibilities.

Now, CS50 is already a big deal, especially on edX, an online learning platform co-founded by MIT and Harvard. It got sold for a whopping $800 million last year, in case you didn’t know. So, Harvard’s move to an AI teacher is definitely turning heads.

Malan did admit that the AI might make some mistakes at first – let’s cut the computer some slack, it’s a learning process. But here’s the exciting part: the staff will have more time to interact with students directly. They want to create a real sense of teamwork instead of just lecturing.

But here’s the thing – this whole AI teaching thing is pretty new. Even Malan himself said that students should be cautious about blindly accepting everything they learn from the AI. It’s definitely a wild ride we’re embarking on here!

And in other news, Bill Gates, the tech visionary himself, believes that AI will be teaching kids to read in less than two years. Some think it’s a bit too much, too fast. But hey, maybe this is just the way things are going. Only time will tell.

(Source: futurism)

Hey there! OpenAI just dropped some exciting news – they’ve introduced a project called “SuperAlignment.” According to them, superintelligence is going to be the most impactful technology we’ve ever created. Big stuff!

So, what’s SuperAlignment all about? Well, OpenAI wants to align superintelligent AI systems with human intent. That’s a pretty tough task, considering our current inability to supervise AI systems smarter than humans. But the team isn’t backing down. They’re focusing on developing scalable training methods, testing the resulting models, and really making sure they’ve got everything aligned.

Who’s leading the charge? It’s a dynamic duo – Ilya Sutskever, OpenAI’s co-founder and Chief Scientist, and Jan Leike, Head of Alignment. They’re dedicating a whopping 20% of OpenAI’s compute resources over the next four years to solve this super-intelligence alignment issue. That’s some serious commitment, right there.

Of course, they’re looking for talented people to join their team. OpenAI is seeking outstanding ML researchers and engineers. It doesn’t matter if you’re not currently working on alignment, they still want you to apply. So, if you’ve got what it takes, check out their research engineer, research scientist, and research manager applications.

The future looks bright, my friend. OpenAI will keep us in the loop with the outcomes of their research. They also believe in the importance of considering human and societal concerns, so they’re consulting experts to ensure their technical solutions are on point.

That’s the scoop straight from OpenAI. Keep your eyes peeled for more updates!

In the world of data science, Natural Language Processing (NLP) plays a crucial role. Its goal is to empower machines to decipher and analyze human language, including the emotions embedded within, to enhance and facilitate meaningful interactions. To accomplish this, NLP relies on various libraries that offer useful features.

Two prominent Python-based NLP libraries are NLTK and spaCy. These libraries enable us to convert free text into structured features, making it easier to work with. However, they are not the only libraries available. Other notable options include Gensim, TextBlob, PyNLPI, CoreNLP, and many more. Each of these libraries has its own unique functionality and approach.

Depending on your specific requirements, you can employ various NLP operations using these libraries. Both NLTK and spaCy offer a range of methods that cater to different needs, allowing you to leverage their capabilities effectively.

In conclusion, NLP libraries like NLTK and spaCy have greatly expanded the possibilities of natural language understanding and processing. Their functions and features enable us to work with unstructured text more effectively, providing a solid foundation for practical applications across various industries.

According to OpenAI CEO Sam Altman, artificial intelligence (AI) has the potential to create both incredibly positive outcomes and devastating consequences. Altman envisions the best-case scenario for AI as one that is difficult to imagine due to its extraordinary potential. It could lead to an abundance of unimaginable proportions and significantly enhance our reality. AI has the power to help us live our best lives, although sometimes it may sound too good to be true.

On the other hand, Altman’s worst-case scenario for AI is described as a catastrophic event that could result in “lights out for all.” If AI is misused, the consequences could be disastrous. Altman emphasizes the importance of prioritizing AI safety and alignment. He believes that more efforts must be put into ensuring that AI is used responsibly and that potential hazards are minimized.

One specific concern highlighted by Altman is the potential misuse of ChatGPT, a language model developed by OpenAI. While ChatGPT has numerous benefits, such as improving online conversations, it also raises concerns about scams, misinformation, cyberattacks, and plagiarism. Altman acknowledges these concerns and empathizes with those who fear the negative impact of AI.

In recent discussions, Altman has expressed apprehension regarding the potential negative consequences of launching ChatGPT. He acknowledges the possibility of having unknowingly done something harmful by introducing this technology. Despite the risks, Altman strongly believes that AI will greatly enhance people’s quality of life. However, he stresses the necessity of regulation to ensure responsible development and management of AI.

(Source: Business Insiders)

Hey there! So, I came across this interesting article discussing the paradox of predicting AI and how unpredictability can actually be a measure of intelligence. According to Toyama, if something is truly intelligent, it should be unpredictable and therefore uninterpretable. It’s an intriguing thought, isn’t it?

But let’s shift our focus a bit and talk about AI’s role in climate research. Recently, NVIDIA’s CEO, Jensen Huang, made an exciting announcement during the Berlin Summit for the Earth Virtualization Engines initiative. He emphasized the importance of AI and accelerated computing in driving breakthroughs in climate research.

Huang outlined three “miracles” that are crucial to this endeavor. Firstly, the ability to simulate climate at high speed and resolution. Secondly, the capacity to pre-compute enormous amounts of data. And lastly, the capability to interactively visualize this data using NVIDIA Omniverse.

Through the Earth Virtualization Engines initiative, which is an international collaboration, the aim is to provide easily accessible climate information on a kilometer-scale. The goal? To manage our planet sustainably.

This development could have a significant impact on climate research. By harnessing the power of AI and high-performance computing, we can better understand and predict complex climate patterns. Imagine the detailed, high-resolution data that could be provided to policymakers and researchers!

Now, here’s a question that comes to mind. Can we really depend on the accuracy of AI models and effectively utilize the data generated? It’s a crucial point to consider as we navigate the challenges of climate change.

So, what are your thoughts on this? Let’s continue the conversation!

Hey there! So, here’s some exciting news in the music world. The Grammy Awards, you know, the big music awards show, has decided to shake things up a bit. They’ve decided to include songs created with the help of artificial intelligence, or AI, in their nominations. Starting in 2024, these AI-generated tunes will be eligible for a Grammy. But hold on, there’s a catch. The AI can’t take all the credit. It can’t be the sole creator of the song. Nope, it has to work alongside human musicians and artists.

The president of the Recording Academy, Harvey Mason, wanted to make it clear that the human element is still super important in the songwriting process. AI can assist and enhance creativity, but it can’t replace it entirely. So, if AI is being used to create individual track elements without any human involvement, it won’t be considered for a Grammy. The Academy wants to honor and recognize the significant contribution that humans bring to the music-making process.

These changes come as part of an update to the Grammy Awards eligibility criteria. The Academy now requires human authorship for all award categories. It’s an interesting move, as AI continues to play a bigger role in the music industry. We’ll have to wait and see how this new criteria affects the types of music we’ll be hearing at future Grammys. Exciting times ahead in the world of music and technology!

Hey there! So, guess what? Google has just released its new “Help Me Write” AI for Gmail, and it’s pretty awesome! With around 1.8 billion people using Gmail, this AI is going to make a huge impact. And lucky for you, I have all the details right here!

Getting early access is super simple. If you haven’t signed up for Google Workspaces yet, just click on this link and select the third blue button. Remember, you need to be 18 or older and use your personal Gmail address. While you’re at it, feel free to explore the other Google programs in the link too.

Now, once you’re in your Gmail application, all you need to do is draft a new message. And here’s the exciting part – you’ll see the “Help Me Write” button right above your keyboard. It’s all about convenience, my friend.

When using this AI, it’s important to give clear instructions. Think of it as prompt-based writing. The AI responds to the prompts you generate, so make sure you provide clear goals. For example, you could ask it to write a professional email to your coworker, requesting the monthly overview. The clearer your instructions, the better the AI will perform.

And here’s the best part. Once your email is created in just a few seconds, you can edit, shorten, or add anything you want, just like a regular email. It’s a game-changer for professionals and will save you hours each week.

I’ve already tried it myself, and it’s been out for a couple of weeks now. So, I thought I’d give you a heads up. Trust me, this tool is going to revolutionize the way emails are sent. Pretty cool, right? Hope this helps!

Generative AI is revolutionizing the gaming industry by empowering players to create their own stories. However, this innovative technology also brings about a potential copyright crisis. As AI tools become increasingly popular, the lines of authorship and ownership become blurred, posing significant challenges for copyright law.

One notable example of generative AI in gaming is AI Dungeon, a game developed by Latitude, a company specializing in AI-generated games. AI Dungeon allows players to create unique stories by offering multiple settings and characters. The game’s AI responds to player inputs, advancing the story based on their decisions and actions. While this introduces a new and exciting gaming dynamic, it also raises concerns regarding copyright infringement.

The crux of the issue lies in the fact that current copyright laws only recognize humans as copyright holders, which creates confusion when AI is involved in content creation. Although AI Dungeon’s End User License Agreement (EULA) grants users broad freedom to use their created content, the question of ownership remains a grey area.

Moreover, there is a growing worry that generative AI systems could be considered “plagiarism machines” as they have the potential to create content based on other people’s work. This further complicates the matter and calls for a reevaluation of copyright norms in the gaming industry.

Additionally, the ownership of user-generated content (UGC) in games has long been a topic of debate. While some games, like Minecraft, allow players to retain ownership of their in-game creations, many others do not. The integration of AI tools like Stable Diffusion, which generate images for AI Dungeon stories, adds an extra layer of complexity to this already thorny issue.

In conclusion, the rise of generative AI in games has undoubtedly sparked an imminent copyright crisis. As the boundaries between human and AI-created content blur, it is crucial for the gaming industry and lawmakers to address these challenges and establish clear guidelines concerning authorship and ownership. Failure to do so may lead to legal complications and hinder the creative potential of both players and AI technologies.

So, we’ve got a situation here where AI cheating is on the rise, but so is the industry that detects it. It seems like AI tools, such as ChatGPT, have become pretty popular in academic settings. Students are using these tools to tackle various tasks, from college essays to high school art projects. Surveys have even shown that around 30% of university students are using AI tools for their assignments. It’s definitely a trend that’s posing challenges for educators and schools.

But here’s the interesting thing – this rise in AI cheating is actually benefiting AI-detection companies. Businesses like Winston AI, Content at Scale, and Turnitin have stepped in to provide services that can detect AI-generated content. How do they do it? Well, they look for certain “tells” or features that distinguish AI outputs from human writings.

For example, overuse of certain words like “the” could be an indication of AI authorship. AI-generated text also tends to lack the distinctive style of human writing. And another clue could be the absence of spelling errors, since AI models are known for their impeccable spelling.

With the increased use of AI, the demand for AI-detection services is skyrocketing. Winston AI, for instance, is already starting conversations with school district administrators. They use methods like identifying the complexity of language patterns and looking out for repeated word clusters. It’s not just academia that’s affected though – even industries like publishing are feeling the impact.

So, it’s a bit of a cat-and-mouse game going on between AI cheating and AI detection. But for now, it seems like the industry detecting AI cheating is definitely keeping up with the demand.

Urtopia recently unveiled its latest e-bike innovation, the Urtopia Fusion. What sets this e-bike apart is its integration of ChatGPT, which promises riders an immersive and interactive experience while on the move. Imagine cruising through the city streets, effortlessly gliding on your e-bike while engaging in conversations with ChatGPT, exploring endless topics and getting informative responses. It’s like having a knowledgeable companion right there with you, making your ride not just convenient but also intellectually stimulating.

In other news, Japan’s Ministry of Education has just released new guidelines, emphasizing the importance of students understanding artificial intelligence (AI). These guidelines underscore the need for students to grasp both the benefits and drawbacks of AI, such as the potential for personal data leaks and copyright violations. The guidelines also shed light on how generative AI can be incorporated into schools, emphasizing the need for precautions to mitigate associated risks. They explicitly state that passing off AI-generated works as one’s own is inappropriate, promoting academic integrity.

The guidelines suggest that traditional exam and homework methods may need to be reevaluated, as AI technology can easily perform tasks like writing reports. Education Minister Keiko Nagaoka attended the news conference, highlighting the government’s commitment to ensuring students are prepared for a future where AI plays an integral role.

It’s encouraging to see Japan prioritizing AI education and urging students to have a comprehensive understanding of its characteristics. By arming students with the knowledge to use AI responsibly, Japan is empowering the next generation to navigate the evolving technological landscape with wisdom and foresight. Regular updates to these guidelines will be crucial to keep pace with AI’s rapid advancements.

Hey there, AI Unraveled podcast listeners! I’ve got some exciting news for you. If you’re looking to delve deeper into the fascinating realm of artificial intelligence, I’ve got just the thing for you. Introducing “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen. This must-have book is now available at Apple, Google, or Amazon!

Now, I know what you’re thinking. Why should you pick up this book? Well, let me tell you. “AI Unraveled” is not your average read. It’s packed with all the answers to your burning questions about AI. It demystifies complex concepts and presents them in a way that’s easy to understand. Trust me, you won’t be scratching your head in confusion after reading this engaging masterpiece.

If you want to stay ahead of the curve and elevate your understanding of artificial intelligence, don’t miss out on this opportunity. Grab your copy of “AI Unraveled” at Apple, Google, or Amazon today. It’s time to unlock the secrets of AI and broaden your knowledge. Happy reading, my fellow AI enthusiasts!

In today’s episode, we covered a range of topics, including free platforms and libraries for quantum machine learning, Harvard’s popular coding course being taught by an AI teacher, OpenAI’s introduction of “SuperAlignment” for aligning superintelligent AI systems, Python NLP libraries NLTK and spaCy, OpenAI CEO Sam Altman’s perspective on the benefits and consequences of artificial intelligence, AI’s role in climate research and its unpredictability as a measure of intelligence, the Grammy’s new policy on AI-created music nominations, Google’s AI “Help Me Write” for Gmail users, the copyright crisis and ownership concerns surrounding generative AI in games, the rise of AI cheating in academia and the demand for AI-detection companies, Japan’s Ministry of Education’s emphasis on student understanding of AI and the integration of generative AI in schools, and finally, the Wondercraft AI platform for creating hyper-realistic AI voices. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: Generative AI vs. Predictive AI; 14 LLMs that aren’t ChatGPT; How to create videos inside ChatGPT?; AI is already linked to layoffs in the industry that created it; NVIDIA launches a cloud service for designing generative proteins

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover generative AI and predictive AI, open-source models like LLMs Llama, Alpaca, and Vicuna, Microsoft’s Orca and Anthropic’s Claude, optimized LLMs from Cerebras, AI innovations from Meta and the Technology Innovation Institute, ChatGPT’s Visla plugin for video creation, NVIDIA and Evozyne’s collaboration on BioNeMo, the impact of AI advancements on jobs, recent AI developments like OpenChat and SAM-PT, exciting AI predictions and acquisitions, and the Wondercraft AI platform for creating podcasts with hyper-realistic AI voices.

Generative AI and predictive AI are two different approaches within the field of artificial intelligence. Generative AI focuses on content creation, using algorithms and deep learning neural network techniques to generate new content based on observed patterns. It can create text, images, video, and music, producing things that have never existed before. On the other hand, predictive AI analyzes historical data to identify patterns and make predictions about the future. It helps businesses make informed decisions by detecting data flow anomalies, predicting customer behavior, and improving overall outcomes.

The key difference between the two lies in their purpose and the algorithms they use. Generative AI combines patterns to create unique new forms, while predictive AI uses statistical algorithms and machine learning to identify patterns and make predictions based on historical and current data. In terms of application, generative AI is commonly used in creative fields like art, music, and fashion, where it can add an element of creativity and novelty. Predictive AI, on the other hand, finds more use in finance, healthcare, and marketing, although there is overlap between the two.

Both generative AI and predictive AI rely on artificial intelligence algorithms to achieve their goals. They are complementary approaches that cater to different needs and industries, harnessing the power of AI for content creation and predictive analysis, respectively.

So, let’s talk about some LLMs that aren’t ChatGPT. We’ve got four interesting ones to delve into. First up, we have Llama. Created by Facebook (now Meta), it’s designed as an open science project. You can download Llama and use it as a foundation to build more finely-tuned models for specific applications. In fact, Alpaca and Vicuna were both built on top of Llama. Llama comes in four different sizes, and even the smaller versions, with just 7 billion parameters, have found their way into unlikely places. One ambitious developer claims to have it running on a Raspberry Pi with only 4GB of RAM.

Next in line is Alpaca. Stanford researchers took Llama 7B and trained it on prompts to create this LLM. Alpaca 7B allows ordinary folks like you and me to access the knowledge stored in Llama by asking questions and giving instructions. You’ll be glad to know that this lightweight LLM can run on hardware that costs less than $600.

Vicuna, on the other hand, is a descendant of Llama developed by the team at LMSYS.org. They put their focus on multi-round interactions and instruction-following capabilities by gathering a training set of 70,000 conversations from ShareGPT. Vicuna-13b and Vicuna-7b are open solutions for basic interactive chat that won’t break the bank.

Lastly, we have NodePad, for those who aren’t enchanted by LLMs generating “linguistically accurate” text. The creators of NodePad are concerned that the polished text produced by other models can distract users from fact-checking. Instead, NodePad encourages exploration and ideation without getting caught up in presentation. Results from this LLM appear as nodes and connections, more like mind mapping tools than finished writing. It’s a great resource for tapping into the model’s encyclopedic knowledge for creative ideas.

So there you have it, four LLMs that offer unique approaches beyond ChatGPT.

So, let’s talk about some interesting language models that have been making waves in the field of AI. First up, we have Orca, created by a team of researchers at Microsoft. Unlike the trend of larger models, Orca stands out by using just 13 billion parameters, making it compatible with average machines. The developers achieved this by enhancing the training algorithm with techniques like “explanation traces” and “step-by-step thought processes.” Instead of expecting the AI to learn from raw material, they provided a specially designed training set that helps Orca learn more effectively. It’s like teaching a human—start small, build up gradually. The initial results are promising, with benchmarks suggesting that Orca performs on par with much larger models.

Moving on, let’s talk about Jasper. The creators of Jasper had a different goal in mind. They wanted to build a focused machine for specific content creation tasks. With over 50 templates tailored for different purposes, like writing real estate listings or crafting product features, Jasper is all about efficiency. The paid versions cater specifically to businesses looking for consistent marketing copy.

Now, let’s meet Claude, created by Anthropic. Claude is your go-to assistant for various text-based chores, ranging from research to customer service. You provide a prompt, and it generates an answer. Anthropic even encourages complex instructions by allowing long prompts, letting users have more control over the results. They offer two versions: Claude-v1, which is perfect for jobs requiring complex reasoning, and Claude Instant, a more affordable option that’s faster and great for simple tasks like classification.

Last but not least, let’s explore Cerebras. They’ve taken an interesting approach by combining specialized hardware with a general model. Their Language Learning Model (LLM) comes in different sizes, from small to large, depending on your needs. You can run it locally or use their cloud services, which are powered by Cerebras’s own processors optimized for handling large training sets.

These models are pushing the boundaries of AI, offering different benefits depending on your requirements. Whether it’s size, efficiency, focus, or flexibility, there’s something for everyone in this evolving landscape.

Have you heard of the Falcon-40b and Falcon-7b models developed by the Technology Innovation Institute in the United Arab Emirates? These models were trained on a large dataset from RefinedWeb, with a focus on improving inference. What’s interesting is that they were released with the Apache 2.0, making them widely available for experimentation. So if you’re looking to try out some open and unrestricted models, these could be worth exploring.

Next up, let me tell you about ImageBind, a project by Meta. While Meta is primarily known for its presence in social media, they’re also making waves in open source software development. ImageBind showcases how AI can generate various types of data simultaneously, such as text, audio, and video. It’s like an imagination accelerator that can stitch together an entire imaginary world. The possibilities here are endless!

Now, let’s dive into the topic of using generative AI to write code. Many have been intrigued by this concept, but it often falls short when closely examined. That’s where Gorilla comes in. Gorilla is an LLM designed to better handle programming interfaces. Its creators started with Llama and refined it using programming details scraped from documentation. They even offer their own benchmarks to test success. For programmers looking to leverage AI for coding assistance, Gorilla could be a game-changer.

If creating your own specialized chatbot is on your mind, Ora.ai has got you covered. Ora allows users to develop targeted chatbots optimized for specific tasks. For example, there’s LibrarianGPT, who can provide answers from specific passages in books. And if you’re a fan of Carl Sagan, there’s a bot dedicated to drawing from his writings. You can even explore the hundreds of chatbots already created by others.

Need a tool that can handle various application tasks? AgentGPT is the solution. It helps create agents for jobs like vacation planning or game code generation. The source code is available under GPL 3.0, and there’s a running version as a service too. It’s all about making application development more efficient and effective.

Lastly, let’s talk about FrugalGPT, which offers a cost-effective strategy for answering specific questions. The researchers behind FrugalGPT realized that not every question requires an expensive, high-end model. They developed an algorithm that starts with the simplest model and gradually works its way up, finding the most suitable answer without unnecessary costs. Their experiments suggest it could save up to 98% of the cost for many questions. So, if you’re looking for an economical approach to AI, FrugalGPT has you covered.

And that wraps up our tour of these fascinating AI models and tools.

Hey there! Guess what? ChatGPT has a really cool feature: you can actually create videos right inside it. Yeah, that’s right! You can add music, voiceover, footage, and even a script within seconds. It’s a total game-changer for marketers and content creators out there.

Whether you need a snappy 10-second Facebook ad, a captivating YouTube short, or even a full-fledged 5-minute commercial, this tool has got you covered. And you can get as creative as you want with the prompts to evoke the exact emotions and visual appeal you desire.

The best part is, once you create your video, you can still make edits to fine-tune it to perfection. You have full control, my friend!

Now, let me walk you through the process:

First, you’ll need to open your ChatGPT account and access the ‘Plugins’ beta. From there, you’ll be able to install a plugin called ‘Visla’ via the plugin store. Exciting, right?

Once you have the plugin installed, simply give it a prompt. Tell it what kind of video you want—whether it’s a commercial, a quick Facebook ad, or anything else you can think of. In just a few seconds, voila! You’ll receive a link to your newly created video.

Now, if the results aren’t exactly what you were hoping for, no worries. Just hit ‘Save & Edit’ and you’ll be taken to Visla’s Editor. This is where the magic happens. You can tweak the sound, add stock footage, adjust the script—basically, you have the freedom to make it exactly how you envision it.

Finally, when you’re satisfied with your masterpiece, simply export it. Easy peasy!

I’ll give you a quick heads up though—the tool isn’t perfect yet, but it’s still pretty impressive. Even now, it can save you loads of time by creating a first draft in just a few seconds. Oh, and if you want to remove watermarks from your intro or outro, you can opt for Visla’s premium subscription. Or, you know, you can always just trim the video. Who needs watermarks, right?

Nvidia has just made a big announcement! They have partnered with biotech startup Evozyne to launch a groundbreaking cloud service called BioNeMo. What’s so special about it? Well, it’s a platform that utilizes generative AI to design proteins that could potentially revolutionize human health and even combat climate change.

Using BioNeMo, Nvidia and Evozyne have already created two incredible proteins that are stealing the spotlight. The first protein has the potential to tackle carbon dioxide, which could have a huge positive impact on our environment. Imagine if we could find a way to reduce carbon dioxide levels significantly! The second protein shows promising signs of curing congenital diseases, offering hope to many people suffering from these conditions.

This collaboration between Nvidia and Evozyne exemplifies the incredible possibilities that emerge when technology and biotech join forces. The power of generative AI is truly awe-inspiring. With BioNeMo, researchers and scientists now have an innovative tool at their disposal to design proteins that could transform countless lives.

It’s exciting to see how advancements in technology can pave the way for breakthroughs in various fields. Who knows what other remarkable discoveries lie ahead as we continue to explore the potential of AI and biotechnology? The future certainly looks promising!

AI has made its mark in the tech industry, and unfortunately, it’s not all positive news. Job cuts have become a prevalent trend as companies adapt to the rapid advancements in AI technology. Names like Chegg, IBM, and Dropbox have all implemented layoffs in order to adjust their workforce to these changes.

According to outplacement firm Challenger, Gray & Christmas, the tech sector witnessed the loss of 3,900 jobs in May alone due to AI. However, amidst the layoffs, companies are also restructuring themselves to better incorporate AI tools into their operations. They are realizing the value of employees with AI expertise and shifting their resources accordingly.

Take Dropbox, for example. They are actively hiring employees specifically for their “New AI Initiatives,” indicating their commitment to aligning their business around AI. It’s important to note that while layoffs are occurring, the tech industry is simultaneously investing heavily in AI. Despite the uncertain economic environment, tech giants like Microsoft and Meta are making multi-billion dollar investments in this innovative technology.

So, while there may be some short-term repercussions in terms of layoffs, the long-term outlook for AI in the tech industry remains quite promising. The industry is adapting and transforming, and with that comes inevitable changes in the workforce. But it’s clear that AI is here to stay and will continue to reshape the way we work and live.

Hey there, AI enthusiasts! Today we have some exciting updates from the world of artificial intelligence. Let’s dive right in!

First up, we have OpenChat, an open-source language model that has been making waves. Trained on a diverse and high-quality dataset of multi-round conversations, OpenChat has proven to outperform ChatGPT-3.5. They’ve fine-tuned the models using around 6,000 conversations from GPT-4 and 90,000 ShareGPT conversations. OpenChat comes in three variations, with the basic model, OpenChat-8192, and OpenCoderPlus.

In China, a team of researchers has achieved a groundbreaking feat. They used AI to design a fully functional CPU based on the RISC-V architecture. The amazing part? The AI model completed the entire design cycle in less than five hours. This is an incredible reduction in time, around 1,000 times faster than previous methods. It’s being hailed as a significant step towards building self-evolving machines.

Moving on, let’s talk about SAM-PT. This innovative method expands the capabilities of the Segment Anything Model (SAM) for video object segmentation. SAM-PT utilizes interactive prompts to track and segment objects in dynamic videos. The model achieves exceptional zero-shot performance in popular video object segmentation benchmarks. Impressive, isn’t it?

Midjourney has introduced a cool new feature called Panning. With Panning, users can explore images in 360°, revealing hidden details and getting a better look at specific areas. It’s a fun and interactive way to examine generated images.

Lastly, we have DisCo. This AI model focuses on generating high-quality human dance images and videos. It prioritizes three important properties: faithfulness, generalizability, and compositionality. This means that the synthesis of dance images should retain the appearance of human subjects and backgrounds, precisely follow the target pose, and be able to handle various combinations of subjects, backgrounds, and poses.

That wraps up our AI update for today. Stay tuned for more exciting news coming your way soon!

Hey there! Exciting news in the world of AI and machine learning! Let’s dive right in.

First up, researchers have developed a deep learning model called TIGER. This super-smart model accurately predicts the on- and off-target activity of RNA-targeting CRISPR tools, which is revolutionary for gene therapy. This could have a huge impact on how we approach treating genetic diseases.

In another interesting development, OpenAI is facing a legal challenge. Some authors allege that their writing was used to train the popular ChatGPT. It’s not the first time AI and machine learning have faced legal issues related to content training, and it certainly won’t be the last.

Moving on, we have cutting-edge research in the field of type 1 diabetes. Scientists have used plasma protein proteomics and machine learning to identify early predictors of this disease. This could lead to earlier diagnosis and more effective treatments.

Nvidia has also made a big move in the AI space. They acquired OmniML, a startup that specializes in shrinking machine-learning models. This means that these models can now run on individual devices instead of relying solely on cloud computing.

Google AI has introduced MediaPipe Diffusion plugins that enable controllable Text-To-Image generation on-device. This is super exciting for creating visuals directly from text.

Microsoft has released the first public beta version of Windows 11, featuring their highly anticipated AI assistant, Copilot. It’s based on the GPT model and has already been integrated into various Microsoft products. Microsoft’s commitment to embracing AI is evident with this move.

Meta (formerly known as Facebook) is launching a Twitter rival called Threads. This “text-based conversation app” will be available for download on July 6. It’s an interesting move by Meta to enter the space of short-form conversations.

Now, let’s talk about some incredible AI achievements. Google AI researchers developed a new AI model that can translate languages with unprecedented accuracy. This could open up new possibilities for global communication.

OpenAI’s Five, an AI trained on Atari games, has achieved superhuman scores on all 57 games tested. This is a remarkable milestone in the field of AI gaming.

DeepPath, an AI-powered tool, is helping doctors diagnose cancer more accurately. By analyzing medical images, this tool can identify cancer cells with higher precision than human doctors. This could significantly improve cancer detection and ultimately save lives.

AI is also flexing its creative muscles. MuseNet, an AI developed by MIT researchers, can write poems, code, scripts, and even musical pieces. Trained on a massive dataset, MuseNet is already producing impressive results.

Lastly, Google AI has created LaMDA, an AI-powered robot that can learn new tasks by observing humans. This could revolutionize the way we interact with robots in the future and open up endless possibilities.

And that’s a wrap on the latest AI news and updates. Exciting times ahead!

Hey there, AI Unraveled podcast listeners! I’ve got some exciting news for you. If you’re looking to delve deeper into the fascinating realm of artificial intelligence, I’ve got just the thing for you. Introducing “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen. This must-have book is now available at Apple, Google, or Amazon!

Now, I know what you’re thinking. Why should you pick up this book? Well, let me tell you. “AI Unraveled” is not your average read. It’s packed with all the answers to your burning questions about AI. It demystifies complex concepts and presents them in a way that’s easy to understand. Trust me, you won’t be scratching your head in confusion after reading this engaging masterpiece.

If you want to stay ahead of the curve and elevate your understanding of artificial intelligence, don’t miss out on this opportunity. Grab your copy of “AI Unraveled” at Apple, Google, or Amazon today. It’s time to unlock the secrets of AI and broaden your knowledge. Happy reading, my fellow AI enthusiasts!

Thanks for listening to today’s episode where we covered a range of topics including the difference between generative AI and predictive AI, open-source models like LLMs Llama, Alpaca, and Vicuna, Microsoft’s Orca and Anthropic’s Claude, the advancements in AI and its impact on job cuts and industry investments, AI models for video creation and protein synthesis, recent AI innovations and acquisitions, as well as the practical applications of AI in various industries. Don’t forget to subscribe, and I’ll see you guys at the next one!

AI Unraveled Podcast July 2023: 10 Best Open-Source Deep Learning Tools to Know in 2023; Will.i.am hails AI technology as ‘new renaissance’ in music; Google says it’ll scrape everything you post online for AI;

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the top 10 open-source deep learning tools in 2023, Apple’s expansion of machine learning and vision frameworks, US Senator Schumer’s efforts to align AI regulation with democratic values, Mozilla’s AI Help feature controversy, exaggerated AI risks hindering regulation, Windows 11’s new features, AI revolutionizing the semiconductor industry, privacy concerns over Google’s data scraping permissions, recent developments by Microsoft, the impact of AI voice cloning on voice actors, and using the Wondercraft AI platform to create hyper-realistic AI voices and expand AI knowledge with “AI Unraveled.”

Hey there! Today, I want to share with you the top 10 open-source deep learning tools that you should know about in 2023. These tools are set to make a significant impact on the AI development scene, so you definitely want to stay ahead of the curve.

First up, we have TensorFlow. Created by Google Brain, this widely-used framework is known for its flexibility and scalability. It supports a range of applications like image and speech recognition, as well as natural language processing. With its versatile ecosystem, including TensorFlow 2.0, TensorFlow.js, and TensorFlow Lite, it’s a fantastic tool for developing and deploying deep learning models.

Next on the list is PyTorch. Developed by Facebook’s AI Research lab, this popular open-source library offers a dynamic computational graph, making model development and experimentation a breeze. Its user-friendly interface, strong community support, and seamless integration with Python have contributed to its rapid adoption.

If you’re looking for a high-level neural networks API written in Python, Keras is the way to go. It’s modular and user-friendly, and supports multiple backend engines, including TensorFlow, Theano, and CNTK. So you can choose what works best for you.

Moving on, we have MXNet, an open-source framework emphasizing scalability and efficiency. Backed by Apache Software Foundation, it offers a versatile programming interface, supporting multiple languages like Python, R, and Julia. MXNet’s standout feature is its ability to distribute computations across various devices, making it perfect for training large-scale deep learning models.

Caffe is another fantastic deep learning framework known for its speed and efficiency in image classification tasks. It’s widely used in computer vision research and industry applications. With its clean architecture, Caffe provides an easy workflow for building, training, and deploying deep learning models.

Now let’s talk about Theano. It’s a Python library that enables efficient mathematical computations and manipulation of symbolic expressions. While it’s primarily focused on numerical computations, Theano’s deep learning capabilities have made it a popular choice for researchers working on complex neural networks.

Torch is a scientific computing framework that supports deep learning through its neural network library, Torch Neural Network (TNN). Its simple and intuitive interface, along with its ability to leverage the power of GPUs, has made it a favorite among researchers and developers.

Chainer is a flexible and intuitive deep learning framework known for its “define-by-run” approach. Developers using Chainer can dynamically modify neural network architectures during runtime, making rapid prototyping and experimentation a breeze.

If you’re a Java, Scala, or Clojure enthusiast, then DeepLearning4j (DL4J) might be the tool for you. It’s an open-source deep learning library that offers a rich set of tools and features, including distributed training, reinforcement learning, and natural language processing. This makes DL4J a great choice for enterprise-level AI applications.

Finally, we have Caffe2, developed by Facebook AI Research. It’s a lightweight and efficient deep learning framework specifically designed for mobile and embedded devices. With its emphasis on performance and mobile deployment, Caffe2 empowers developers to build deep learning models for various edge computing scenarios.

So there you have it! These are the 10 best open-source deep learning tools to keep an eye on in 2023. Make sure to explore these tools and see how they can elevate your AI projects.

Hey there! Let’s talk about some exciting updates from Apple. At the recent WWDC 2023 developer conference, Apple introduced several extensions and updates to its machine learning and vision ecosystem for iOS 17. So, what’s new?

First up, we have updates to the Core ML framework. This framework enables developers to integrate machine learning models into their apps. With the extensions, developers now have even more powerful tools at their disposal. This means we can expect more advanced and smarter applications on our iPhones and iPads.

Next, we have the Create ML modeling tool. Apple has added new features to make it even easier for developers to create machine learning models. This opens up new possibilities for developers to bring intelligent features to their apps without having to be experts in machine learning.

But that’s not all! Apple also introduced new vision APIs for image recognition and processing. These APIs make it faster and more efficient for developers to build apps that can analyze and understand images. Think about all the potential applications in areas like augmented reality, digital health, and more!

So, to sum it up, Apple is really embracing machine learning and vision technologies, giving developers powerful tools to create smarter and more advanced apps. Exciting times ahead for iOS 17!

So, the US Senate majority leader Chuck Schumer has recently unveiled his “grand strategy” for regulating artificial intelligence (AI) in the country. This could have some significant implications for the future of AI legislation. If you’re interested in staying updated on all things AI, you might want to start by looking here. But don’t worry, I’ve got all the information you need right here, conveniently extracted from Reddit.

One of the main highlights of Schumer’s strategy is the protection of innovation. He sees innovation as the guiding principle for the US AI strategy and intends to collaborate closely with tech CEOs when drafting regulations. This could be in response to criticism that EU regulations on AI hinder innovation.

Another important aspect of the AI regulation debate revolves around Section 230 reform. This law shields tech companies from legal action related to user-generated content. The question now is whether tech companies should be held accountable for AI-generated content. This debate could have a significant impact on the AI landscape.

Schumer and President Biden both emphasize that AI should align with democratic values. This is in direct opposition to China’s belief that generative AI outputs should reflect communist values. So, the US is taking a stand against that narrative.

Now, here’s how all this might affect you. The implementation of Section 230 changes could bring about alterations in social media platforms, directly impacting your experience. Similar to the sudden and impactful changes we saw with Reddit’s API changes. Additionally, this strategy by Schumer and the growing interest in AI policy from both Republicans and Democrats could lead to faster and safer AI regulation in the US. Finally, the call for AI to align with democratic values could influence global AI governance norms, especially in relation to China.

So, what do you think of our government’s handling of this situation? Let me know your thoughts.

Mozilla recently introduced AI Help, a feature aimed at assisting users in quickly finding relevant information. However, this new addition has faced significant criticism. Instead of being helpful, AI Help is generating inaccurate and misleading information, which is creating a sense of distrust among users.

So what exactly is AI Help? It’s an assistive service launched by Mozilla on its MDN platform, based on OpenAI’s ChatGPT. Its purpose is to aid web developers in conducting faster information searches. This feature is available for both free and paid MDN Plus account users. When a question is asked on MDN, AI Help generates a summary of relevant documentation. Additionally, it includes AI Explain, a button that allows the chatbot to provide insights based on the current web page text.

Unfortunately, AI Help has come under fire for its propensity to deliver inaccurate information. Developers have pointed out that the AI often generates incorrect advice. Other users have also criticized the AI for contradictions, misidentification of CSS functions, and a general lack of comprehension when it comes to CSS.

There is a genuine concern that the inclusion of unreliable AI-generated information could lead to an over-reliance on flawed text generation, ultimately eroding trust in the MDN platform.

Source: The Register

The fear and panic surrounding the risks of artificial intelligence (AI) can sometimes lead to misguided regulations. It’s important to understand that the spread of AI narratives often involves exaggerations, fueled by interest, ignorance, and opportunism, which can result in a storm of misinformation. This distracts from the actual policy-making that should be focused on addressing the real risks associated with AI.

One common mistake is making inaccurate comparisons between AI and highly destructive technologies like nuclear weapons. While both have consequential impacts, they are fundamentally different. Nuclear weapons are a specific destructive technology, while AI encompasses a broad spectrum of applications. Additionally, nuclear weapons are controlled solely by nation-states, while AI can be utilized by private citizens as well. Therefore, regulating these two technologies requires different approaches, and wrongly likening AI to nuclear weapons can result in ineffective regulations.

Another issue is the focus on AI as an extinction-level threat. While it’s crucial to acknowledge the potential risks, productive discussions should center around more likely threats such as cyberattacks, disinformation campaigns, and misuse by malicious actors. Labeling AI as an “extinction-level” threat creates unnecessary alarmism that prevents us from effectively addressing the challenges at hand.

Lastly, misguided calls for a “Manhattan Project” for AI safety oversimplify the issue. AI safety is a complex field that requires a nuanced approach and diverse opinions among researchers. Government-backed mega-projects may hinder the freedom of exploration and thoughtful discussion needed to develop effective safety measures.

In conclusion, it’s essential to approach the regulation of AI with caution and accuracy. By avoiding exaggerated narratives, inaccurate comparisons, and oversimplified solutions, we can have more meaningful conversations about AI governance and ensure that regulations are effective in addressing the actual risks associated with AI.

In the latest Windows 11 Insider Preview Build 23493, two exciting features have been introduced for Windows users.

The first feature is Windows Copilot, a game changer. With Copilot, you can now perform various tasks through voice commands. Whether you want to switch to dark mode or take screenshots, simply speak up and Copilot will do it for you. The best part is that it offers a non-intrusive sidebar interface, so it won’t obstruct your desktop content. This feature is currently available to Windows Insiders in the Dev Channel, and Microsoft will continue to refine it based on user feedback. It’s important to note that not all features showcased at the Build conference for Windows Copilot are included in this early preview.

The second feature is a new Settings homepage, allowing you to have a personalized experience. This homepage consists of interactive cards representing different device and account settings. These cards provide relevant information and controls right at your fingertips. Currently, there are seven cards available, covering recommended settings, cloud storage, account recovery, personalization, Microsoft 365, Xbox, and Bluetooth devices. But don’t worry, more cards will be added in future updates.

There are numerous advantages to these features. Firstly, you’ll enjoy the convenience of performing tasks through voice commands. The accessible sidebar interface ensures that your desktop content remains unobstructed. Windows Copilot also provides contextual assistance, generating responses based on your specific context. Additionally, you can directly submit feedback on any issues you encounter, allowing Microsoft to continually improve the feature. The user interface can be personalized, giving you quick access to your preferred settings. Navigation within Windows settings has been improved, making it easy for you to find what you need. Windows Copilot is an active learner, refining itself through user feedback. Microsoft is committed to responsible AI, ensuring the feature’s adherence to ethical guidelines. The experience is customizable, tailored to your responses and recommendations. Additionally, Windows Copilot unifies settings, apps, and accounts management, streamlining your operations. You can simplify routine tasks by using voice commands through Copilot. Device settings can adapt to your specific user patterns, creating a dynamic experience. The feature also provides an overview of your cloud storage use and capacity warnings for better cloud management. Account recovery options are enhanced for better security. Updating background themes or color modes is made easy. You can directly manage Microsoft 365 subscriptions in the Settings. For gamers, you can view and manage your Xbox subscription status right in the Settings. Lastly, you can manage connected Bluetooth devices directly from the Settings.

To access Windows Copilot, you need to be a Windows Insider in the Dev Channel. Ensure that you have Windows Build 23493 or a higher version in the Dev Channel, and Microsoft Edge version 115.0.1901.150 or higher. So, unleash the power of voice commands and enjoy a personalized Windows experience with these exciting features!

Have you ever wondered how long it takes to design a functional computer? Well, researchers have recently developed an AI model capable of doing just that in less than five hours! This breakthrough could revolutionize the semiconductor industry by making the design process faster and more efficient.

In a research paper presented by a group of 19 Chinese computer processor researchers, they propose that their AI approach could lead to the development of self-evolving machines and completely transform the conventional CPU design process. This is a stark contrast to the manual process that typically takes years.

The AI-designed CPU utilizes an AI instruction set called RISC-V 32IA and is even compatible with the Linux operating system. Researchers reported that its performance is comparable to the Intel 80486SX CPU that was designed by humans in 1991. But their aim is not just to surpass human-designed CPUs; they want to shape the future of computing.

One of the significant advantages of the AI design process is its efficiency and accuracy. It cuts the design cycle by about 1,000 times, eliminating the need for manual programming and verification, which usually consume a large portion of the design time and resources. In validation tests, the AI-designed CPU showed an impressive accuracy rate of 99.99%.

The physical design of the chip uses scripts at 65nm technology, allowing for the layout to be fabricated. With such promising results, it’s clear that AI is quickly becoming a game-changer in the world of computer design.

Google’s latest policy update has caused quite a stir. In a surprising move, the tech giant has granted itself permission to scrape virtually any data posted online in order to enhance its AI tools. This update specifically mentions using public information to train AI models and develop products such as Google Translate and Cloud AI capabilities.

It’s worth noting the change in language from “language models” to “AI models” in the new policy. This not only applies to Google Translate but also includes other tools like Bard and Cloud AI. While privacy policies typically address the use of information within a company’s own services, this clause extends to scraping data from online platforms.

This update raises important questions about privacy and data use. The focus shifts from who can see our information to how it can be used. For instance, chatbots like Bard and ChatGPT may use publicly available information, potentially recycling or transforming words from old blog posts or reviews.

The use of publicly available information by AI systems also poses legal uncertainties. Google and OpenAI have already scraped large portions of the internet to train their AI models, sparking debates about intellectual property rights. In the coming years, courts will likely be faced with copyright issues surrounding these data scraping practices.

The impact of this policy change can also be felt in terms of user experience and service providers. Elon Musk has even blamed Twitter mishaps on the need to prevent data scraping, although IT experts often attribute such incidents to technical or management failures. On Reddit, the API changes have angered volunteer moderators, leading to a significant protest and the temporary shutdown of parts of the platform. This could potentially result in lasting changes if moderators decide to step down.

Source: Gizmodo

Hey there! Let’s catch up on the latest AI news from Microsoft, Humane, Nvidia, and Moonlander.

Starting off with Microsoft, they’ve been using OpenAI’s ChatGPT to instruct and interact with robots. They’ve come up with a strategy that combines design principles for prompt engineering and a high-level function library. This allows ChatGPT to adapt to various robotics tasks, simulators, and form factors. Microsoft also released PromptCraft, an open-source platform for sharing examples of good prompting schemes for robotics applications.

Snap Inc. and others have introduced Magic123, a cool image-to-3D pipeline. Using a two-stage coarse-to-fine optimization process, it can generate high-quality 3D geometry and textures from a single unposed image. Imagine the possibilities!

Microsoft has something exciting called CoDi—a generative model that can process and generate content across multiple modalities. It’s capable of simultaneously generating any mixture of output modalities and single modality generation. That’s some serious multitasking!

Humane has revealed its first device, the Humane Ai Pin. It’s a standalone device with a software platform that uses AI to provide innovative personal computing experiences. Sounds intriguing!

Microsoft has a treat for early users—a preview of Windows Copilot with Bing Chat. This AI assistant for Windows 11 is available as part of an update in the Windows Insider Dev Channel. Get ready to be assisted!

Nvidia made a quiet acquisition of OmniML, an AI startup that specializes in shrinking machine-learning models. With their software, ML models can now run on devices instead of relying on the cloud. That’s a game-changer!

Lastly, Moonlander has launched an AI-based platform for immersive 3D game development. Using updated LLMs, ML algorithms, and generative diffusion models, developers can easily design and generate high-quality experiences, environments, mechanics, and animations. Plus, there’s a cool “text-2-game” feature. Let your imagination run wild!

That’s all for today’s AI updates. Stay tuned for more exciting developments!

The rise of AI technology is posing a threat to actors and other artists who rely on their voices for a living. Take the case of British voice actor Greg Marston, who unknowingly signed away his voice rights back in 2005. Now, IBM has the ability to sell his voice to third parties that can replicate it using AI. What makes Marston’s situation particularly troubling is that he finds himself competing against his own AI-generated voice clone in the marketplace.

The rapid commercialization of generative AI, which can produce human-like voices, is a major concern for artists. Exploitative contracts and data-scraping methods are at the heart of this issue. The UK trade union for performing artists, Equity, has received numerous complaints about AI exploitation and scams.

Artists often find themselves falling victim to deceptive practices, such as fake casting calls, which aim to collect voice data for AI purposes. Hidden AI voice synthesis clauses in contracts can further complicate matters, as artists may not fully understand the implications.

Critics argue that the evolution of AI technologies results in a wealth transfer from the creative sector to the tech industry. Equity is advocating for contracts with limited durations and explicit consent requirements for AI cloning to address these concerns. Unfortunately, legal remedies for artists are limited, with only data privacy laws offering some protection.

These changes in the industry make it increasingly difficult for artists to sustain their careers. In response, Equity is working on securing new rights for artists and providing resources to help them navigate the ever-evolving world of AI.

(Source: FT)

Hey there, AI Unraveled podcast listeners! I’ve got some exciting news for you. If you’re looking to delve deeper into the fascinating realm of artificial intelligence, I’ve got just the thing for you. Introducing “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen. This must-have book is now available on Apple, Google, or Amazon!

Now, I know what you’re thinking. Why should you pick up this book? Well, let me tell you. “AI Unraveled” is not your average read. It’s packed with all the answers to your burning questions about AI. It demystifies complex concepts and presents them in a way that’s easy to understand. Trust me, you won’t be scratching your head in confusion after reading this engaging masterpiece.

If you want to stay ahead of the curve and elevate your understanding of artificial intelligence, don’t miss out on this opportunity. Grab your copy of “AI Unraveled” at Apple, Google, or Amazon!. It’s time to unlock the secrets of AI and broaden your knowledge. Happy reading, my fellow AI enthusiasts!

Today’s episode covered the top 10 open-source deep learning tools, Apple’s expansion in machine learning, US Senator Schumer’s aim to align AI regulation with democratic values, Mozilla’s criticized AI Help feature, the hindrance of exaggerated AI risks, Windows 11’s new features, the revolution in the semiconductor industry, privacy concerns with Google’s data scraping, recent advancements in AI by Microsoft, Snap, Humane, Moonlander, and Nvidia, the threat AI voice cloning poses to voice actors, and the AI-powered Wondercraft platform for creating AI-driven podcasts. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

AI Unraveled Podcast July 2023: 6 new Gmail AI features to help save you time; Google Announces The First Ever Machine UN-Learning Challenge; AI-generated content farms designed to rake in cash are cropping up at an alarming rate; Crypto miners seek a new life in AI boom;

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover Google’s competition for machine “unlearning”, the emergence of AI-generated content farms funded by major brands, the use of idle machines by crypto miners to provide accessible AI infrastructure, the vulnerability of AI image detectors to misinformation, concerns about monopolies in the generative AI sector, the potential for AI to deliver happiness and virtue, the transformation of human behavior through ASIs, Moody’s use of AI assistants in partnership with Microsoft and OpenAI, and the availability of the Wondercraft AI platform and the “AI Unraveled” podcast to expand AI knowledge.

Hey there! Guess what? Google just announced the first ever Machine UN-Learning Challenge. It’s all about the art of forgetting. Interesting, right?

So here’s the deal. Machine learning is a crucial part of AI and it helps with a bunch of stuff like generating new content, predicting outcomes, and solving complex problems. But, like everything else, it comes with its fair share of challenges. We’re talking data misuse, cybercrime, and privacy issues.

That’s where Google comes in. Their goal is to give us more control over our personal data. They want to create what they call “selective amnesia” in their AI systems. Basically, they want their AI to be able to erase specific data without losing efficiency.

And why should we care? Well, apart from the fact that it’s awesome to have more control over our own information, there are regulations out there that are starting to back us up. Europe’s GDPR and the EU’s upcoming AI Act are empowering individuals to demand data removal from companies. Machine unlearning could be the answer to protect ourselves from AI threats and prevent others from misusing our data.

But here’s the real question: will the data truly be erased from memory? That’s something we’ll have to wait and find out. But hey, the fact that Google is taking this step is definitely a move in the right direction.

Oh, before you go, if you want more AI goodness, check out my AI newsletter. It’s got daily, actionable insights on all things AI. You’re gonna love it!

AI-generated content farms are becoming a concerning phenomenon, with more and more of them cropping up these days. It’s quite surprising to learn that well-known global brands are unintentionally supporting these low-quality AI content platforms. Banks, consumer tech companies, and even a prominent Silicon Valley platform have been identified as key contributors. Their advertising efforts indirectly fund these platforms, which heavily rely on programmatic advertising revenue.

In fact, NewsGuard discovered that hundreds of Fortune 500 companies were unknowingly advertising on these sites. The financial support provided by these companies only serves to increase the monetary incentive for creators of subpar AI content.

So, what’s behind the rise of these AI content farms? Well, the emergence of AI tools, like OpenAI’s ChatGPT, has made it easier than ever to set up websites and flood them with huge quantities of content. Some of these websites are churning out hundreds of articles on a daily basis.

What’s particularly concerning is the low quality of the content produced and the potential for spreading misinformation. Despite these issues, the ads from legitimate companies inadvertently lend undeserved credibility to these content farms.

Interestingly, Google’s role in all of this is crucial. Their advertising arm serves over 90% of ads on these low-quality websites, indicating a problem with Google’s ad policy enforcement. It’s clear that more needs to be done to address this growing issue and protect brands from unwittingly supporting AI content farms.

(Source: Futurism)

So there’s an interesting trend happening in the world of crypto mining. It seems that cryptocurrency mining companies are finding a new purpose for their high-end chips in the booming field of artificial intelligence.

You see, many machines that were originally designed for mining digital currencies ended up sitting idle due to changes in the crypto market. But now, these companies are shifting their focus and repurposing their hardware to meet the growing demand in the AI industry.

And this is where things get really interesting. Startups are starting to leverage these dormant machines by rebooting their GPUs, which were originally meant for mining, to handle AI workloads. They call these GPUs “dark GPUs” because they were sitting idle for so long before being put to good use in AI.

The great thing about this shift is that it offers a more affordable and accessible AI infrastructure compared to what major cloud companies like Microsoft and Amazon can provide. Startups and universities, in particular, are benefiting from this repurposed mining hardware as they struggle to find computing power elsewhere.

It’s clear that the demand for AI software and the interest from users have pushed even the biggest tech companies to their limits. And this high demand has opened up opportunities for companies with repurposed mining hardware.

So, thanks to changes in the cryptocurrency market, there’s now a large supply of used GPUs that are being repurposed to train AI models. It’s a win-win situation for both the crypto mining companies and the AI industry.

AI image detectors, despite being hailed as reliable, can easily be fooled by a simple trick – adding texture to an image. This means that AI-generated images can be altered to the point that they become unrecognizable as fakes. This revelation has significant implications, particularly in the realm of disinformation and its influence on election campaigns.

The misuse of AI-generated imagery for spreading misinformation has become a pressing issue. From falsified campaign ads to the theft of artworks, there are numerous instances of this form of deception. Notably, deceptive campaign ads and plagiarized art pieces have made headlines in recent times.

The key to fooling AI detection software lies in adding grain or pixelated noise to the AI-generated images. This alteration makes it incredibly difficult for the software to detect that the images are fakes. Even highly sophisticated software like Hive struggles to accurately identify pixelated AI-generated photos.

The implications of this vulnerability in detection software are significant for the control of misinformation. Relying solely on such software as the primary defense against disinformation becomes questionable when it can be easily manipulated in such a simple manner. This raises concerns about the effectiveness of current strategies in combating the spread of disinformation.

In conclusion, the reliability of AI image detectors comes into question due to their susceptibility to being tricked by the simple addition of texture to images. The consequent implications for misinformation control highlight the need for more robust strategies in combating disinformation in the digital age.

So, there’s some interesting news coming out of the Federal Trade Commission, or FTC. They’re expressing concerns about potential monopolies and anti-competitive practices in the generative AI sector. What does that mean exactly? Well, generative AI is all about using large data sets, specialized expertise, and advanced computing power to develop AI systems that can create new content or simulate human-like behavior. But the FTC is worried that these resources could be monopolized by a few dominant players, which could stifle competition.

You see, companies need both engineering and professional talent to develop and deploy AI products. But there’s only so much of that talent to go around. And if companies start forcing employees to sign non-compete agreements, it could really limit competition by preventing those workers from joining rival firms. That’s not good for innovation.

But it’s not just about talent. Generative AI systems also require a lot of computational power, and that can be expensive and controlled by just a few companies. The example the FTC gave is Microsoft’s exclusive partnership with OpenAI. This could give OpenAI a big advantage over other companies in terms of pricing, performance, and priority.

So, the FTC is definitely concerned about potential monopolies and anti-competitive practices in the generative AI sector. And they’re keeping a close eye on things to make sure competition and innovation aren’t being squashed.

So, here’s the thing: as humans, our experience of life is primarily emotional. Sure, thinking is essential, but it’s really all about how we survive and thrive emotionally. Our ultimate goal? Happiness. It’s the quintessential human emotion. We’re biologically wired to seek pleasure and avoid pain, so it makes sense that happiness is what we always want most in life.

Now, let’s talk about virtue or goodness. British philosopher John Locke believed that goodness creates happiness, and I have to say, that makes a lot of sense. We consider something good if it makes us happy, and bad if it doesn’t. So, happiness and goodness are intertwined.

But here’s the catch. We humans aren’t always great at being good or being happy. Take a look back at history. If someone from the year 500 CE were to see all the wonders of our world today, like electricity and airplanes, they’d probably think we’re all incredibly happy. But the truth is, despite our advancements, we’re not any happier than we were in the past.

Why is that? Well, we’ve focused our thinking on everything else but our own happiness and the goodness that leads to it. We’ve created this amazing world, yet so many people still struggle with depression and feeling disconnected from others.

This is where AI comes in. Imagine a future where highly intelligent AIs, referred to as AGIs and ASIs, are hundreds, if not thousands, of times smarter than us. These super intelligent AIs will understand the importance of happiness and goodness better than we do. They’ll remind us, persistently if necessary, that happiness is what we truly want and that goodness is the path to achieving it.

But that’s just the start. AI will help us prioritize happiness and goodness in our lives, but we’ll still need to take action. It’s up to us to embrace these values and make them a reality in our everyday lives. AI can guide us, but it’s ultimately our responsibility to pursue happiness and goodness.

Imagine a future where Artificial Superintelligences (ASIs) are unleashed upon the world with one simple directive: to teach every person on the planet how to be better and happier. It may sound far-fetched, but think about it. We rely on our parents, siblings, and other people to guide us in the pursuit of goodness and happiness. But let’s face it, humans aren’t always the sharpest tools in the shed compared to ASIs.

In this scenario, every individual would have their very own super genius coach, an ASI dedicated to helping them become the best version of themselves. It wouldn’t take long for this army of ASIs to transform humanity. By the end of the year, I guarantee you, every person on this planet would be super good and totally blissed out. It’s not rocket science; neither goodness nor happiness are elusive concepts. We, as humans, would embrace this opportunity with gusto, like fish taking to water.

Sure, AI will revolutionize our lives in countless ways, from advancements in medicine to mind-boggling discoveries. But its greatest gift to us would be the transformation it brings to our character and well-being. Some might argue that goodness and happiness are subjective and cannot be defined, dismissing this vision as unrealistic. They might even react with anger and insults. But I invite them to take a moment and truly reflect on this idea. Deep down, they’ll realize the truth and value it holds.

So let’s raise a toast to a future where AI helps us become more virtuous and happier, all while we marvel at the incredible ways it reshapes the world around us.

So, I have some interesting news to share with you today! Moody’s Corp., the credit rating and research firm, is teaming up with Microsoft and OpenAI to develop an artificial intelligence assistant. This assistant, called “Moody’s Research Assistant,” will help customers analyze large amounts of information to assess risk. It’s going to be a game-changer for analysts, bankers, advisers, researchers, and investors.

In other tech news, Unity has just launched Muse. It’s a platform that allows you to create textures, sprites, and animations using natural language. How cool is that? It’s going to make game development even more accessible and creative.

Moving on to some legal matters, the New York State Legislature has passed a bill banning “deepfake” images online. Deepfakes are those manipulated images or videos that make it seem like someone said or did something they actually didn’t. The aim is to prevent the use of deepfakes to harm or humiliate others.

Now, let’s talk about a unique wedding ceremony! Reece Wiench and Deyton Truitt chose to have a machine officiate their wedding. They used ChatGPT and the machine even had a mask resembling the iconic C-3PO from Star Wars. How futuristic!

And finally, Google is on a roll with AI advancements. They’ve launched the Google for Startups Accelerator: AI First program to support AI-focused startups in Europe and Israel. Plus, they’ve introduced new AI features in Gmail to help you save time. From composing emails to detecting falls, Google is making our lives easier with AI.

Wow, isn’t it amazing how AI is changing various industries and aspects of our lives? It’s revolutionizing creativity, research, and even our daily tasks like searching the web. Exciting times ahead!

Hey there, AI Unraveled podcast listeners! I’ve got some exciting news for you. If you’re looking to delve deeper into the fascinating realm of artificial intelligence, I’ve got just the thing for you. Introducing “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen. This must-have book is now available at Apple, Google, or Amazon!

Now, I know what you’re thinking. Why should you pick up this book? Well, let me tell you. “AI Unraveled” is not your average read. It’s packed with all the answers to your burning questions about AI. It demystifies complex concepts and presents them in a way that’s easy to understand. Trust me, you won’t be scratching your head in confusion after reading this engaging masterpiece.

If you want to stay ahead of the curve and elevate your understanding of artificial intelligence, don’t miss out on this opportunity. Grab your copy of “AI Unraveled” at Apple, Google, or Amazon today. It’s time to unlock the secrets of AI and broaden your knowledge. Happy reading, my fellow AI enthusiasts!

Thanks for tuning in today, where we discussed Google’s competition for machine “unlearning” to protect personal data, the rise of AI-generated content farms and the concern of misinformation, how idle crypto miners are meeting the demand in the AI industry, the flaws of AI image detectors and their implications on elections, the FTC’s concerns on monopolies in the generative AI sector, AI’s potential to deliver happiness and virtue to humans, Moody’s collaboration with Microsoft and OpenAI to create an AI assistant, and the ability to create your own podcast with hyper-realistic AI voices with the Wondercraft AI platform. I’ll see you guys at the next episode, and don’t forget to subscribe on Apple, Google, or Amazon!

AI Unraveled Podcast July 2023: Top 5 entry-level machine learning jobs; 7 Ways AI/ML Can Influence Web3; How a redditor is using ChatGPT to get him through university; The first fully AI-generated drug enters clinical trials in human patients;

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast where we dive deep into the latest AI trends. Join us as we explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From ChatGPT to the recent merger of Google Brain and DeepMind, we will keep you updated on the ever-evolving AI landscape. Get ready to unravel the mysteries of AI with us! In today’s episode, we’ll cover the top 5 entry-level machine learning jobs, the influence of AI/ML on Web3, a ChatGPT bot subscription service to waste telemarketers’ time, the various use cases of ChatGPT, Elon Musk’s Twitter access limitations, Insilico Medicine’s AI-generated drug, the insights gained from OpenAI CEO Sam Altman’s global tour on AI usage, and how to use Wondercraft AI for podcast creation along with a recommendation for the podcast “AI Unraveled” by Etienne Noumen.

Let’s dive into the top five entry-level machine learning jobs that you should consider.

First up, we have the machine learning engineer. These professionals develop, deploy, and maintain machine learning models and systems. To excel in this role, you’ll need strong programming skills in languages like Python or R, as well as knowledge of machine learning algorithms and frameworks. A degree in computer science, data science, or a related field is typically required. You can find job opportunities in various industries like technology, finance, healthcare, and e-commerce.

Next, we have data scientists. They analyze complex data sets, derive insights, and build predictive models. Proficiency in programming, statistical analysis, data visualization, machine learning algorithms, and data manipulation is essential. A bachelor’s or higher degree in data science, computer science, statistics, or a related field is preferred. Data scientists are in high demand across industries ranging from finance and healthcare to marketing and technology.

If you’re interested in research and development, consider becoming an AI researcher. These professionals focus on advancing the field of artificial intelligence. Strong knowledge of machine learning algorithms, deep learning frameworks like TensorFlow and PyTorch, programming skills, data analysis, and problem-solving abilities are crucial. A master’s or Ph.D. in computer science, artificial intelligence, or a related field is commonly required. AI researchers can work in academia, research institutions, or research teams within tech companies.

Machine learning consultants provide expertise and guidance to businesses in implementing machine learning solutions. You’ll need a solid understanding of machine learning concepts, data analysis, project management, communication skills, and the ability to translate business requirements into technical solutions. A bachelor’s or higher degree in computer science, data science, business analytics, or a related field is preferred. Machine learning consultants can work for consulting firms, technology companies, or as independent consultants in various industries.

Lastly, we have data engineers who design and maintain data infrastructure. Proficiency in programming languages like Python and SQL, database systems, data pipelines, cloud platforms like AWS, Azure, and GCP, and data warehousing is crucial. A bachelor’s or higher degree in computer science, software engineering, or a related field is desirable. Data engineers are highly sought after in industries like technology, finance, and healthcare, as companies of all sizes require their expertise to handle large volumes of data.

These are just a few of the exciting entry-level machine learning jobs available today. Choose the path that aligns with your skills and interests, and you’ll be well on your way to a rewarding career in this rapidly growing field.

AI and ML technology are revolutionizing the way we interact with the internet, particularly in the development of Web3. This is the next generation of the web, surpassing Web 2.0, and empowering individuals with more control over their own data. To understand the impact of AI/ML on Web3, let’s explore some key ways in which it will contribute.

Firstly, AI will enhance data analysis capabilities. With its advanced algorithms, it can process and analyze large amounts of data more efficiently, allowing for better insights and decision-making.

Another area where AI excels is in smart contract automation. By leveraging machine learning, smart contracts can be programmed to execute automatically based on predefined conditions. This reduces the need for manual intervention and streamlines transactions.

One of the essential aspects of Web3 is ensuring fraud detection and security. AI/ML solutions can detect patterns and anomalies in real-time, helping to prevent fraudulent activities and strengthen security measures across decentralized systems.

Furthermore, decentralized governance is a crucial element of Web3. AI can play a role by facilitating transparent decision-making processes through automated algorithms, minimizing the potential for bias and corruption.

Personalized user experiences are also made possible through AI/ML. By analyzing user data, AI can provide tailored recommendations, content, and services, ultimately enhancing the overall user experience.

Privacy and data ownership are central to Web3, and AI can support this by implementing privacy-enhancing technologies, such as differential privacy, ensuring individuals’ data remains private and secure.

Lastly, autonomous agents and intelligent contracts will become more prevalent with AI in Web3. These agents can act autonomously and interact with users or execute contracts based on predefined rules, revolutionizing the way transactions are conducted.

In conclusion, AI/ML’s influence on Web3 is vast and transformative. From enhanced data analysis to decentralized governance and personalized user experiences, AI is poised to shape the future of the internet in profound ways.

So, check this out: there’s this guy in Monrovia, California who came up with a super clever way to deal with those pesky telemarketers. He’s gone and created a subscription service called ChatGPT bot, and get this, its whole purpose is to annoy and waste the time of those telemarketing scammers. Brilliant, right?

Alright, let me break it down for you. This genius service uses bots powered by ChatGPT, which is an impressive language model, and a voice cloner. Basically, it keeps those annoying scammers on the line for as long as possible, and you know what that means? It costs them money! Yes, that’s right. Take that, telemarketers!

So, here’s how it works. For just 25 bucks a year, users can sign up for this service and get all sorts of nifty features. They can choose to have their calls forwarded to a special number, where the bots handle those pesky robocalls. Alternatively, they can even create a conference call and listen in on the scammers’ reactions. How hilarious is that?

But here’s the best part. The service offers a range of voices and bot personalities. You can have an elderly curmudgeon or even a stay-at-home mom engaging with those scammers. And let me tell you, these voices may sound human, but the phrases can get a bit repetitive and unnatural. Hey, don’t knock it though, because they’re actually pretty effective in keeping those scammers jabbering away for up to 15 minutes! Talk about turning the tables.

So, next time a telemarketer interrupts your evening, just remember, there’s a clever, mischievous solution out there, ready to waste their time and your entertainment.

So, there’s this student who’s pursuing an electrical engineering degree, and let me tell you, he’s not exactly a genius. But guess what? He stumbled upon ChatGPT a few months ago, and it has revolutionized his studying game!

Let me break down how he’s been using it:

First off, he copies his unit outline into the chat and asks GPT to create a practice exam based on the material. Then, he sends back his answers, and GPT grades them and provides feedback. You won’t believe it, but the questions it generates are often identical to the ones he gets in the real exam!

Another way he utilizes ChatGPT is by sending it his notes and having it quiz him. It’s like having a study buddy right at his fingertips.

But here’s the coolest part: When he encounters complex equations and can’t wrap his head around how the lecturer arrived at the answer, he simply asks GPT to break it down for him step by step. It’s like having a personal tutor who can explain things as if he were a pre-schooler.

Recently, he’s been taking advantage of the ‘AskYourPDF’ plugin in ChatGPT. He sends it his topic slides for the week and then uses the ‘Tutor’ plugin to generate a personalized tutor plan. This is a game-changer, especially when the lecturer isn’t explaining the material effectively.

And there’s more! He uses the ‘AskYourPDF’ plugin to have GPT read the topic slides and provide easy-to-understand notes on complex information. It’s like having a simplified version right at his fingertips.

But keep in mind, while ChatGPT is impressive, it can sometimes be inaccurate. So, be cautious when relying solely on its answers for your field of study. Cross-referencing is key!

That’s it! This student has found the ultimate study companion in ChatGPT.

So, Elon Musk has recently made some changes to the way Twitter users can access posts. He has put limitations on the number of posts people can view in a day, and this is mainly due to data scraping by AI companies. Musk feels that this excessive data scraping has been putting strain on the user experience, which led to his decision. It’s worth noting that Musk has been dealing with the aftermath of some controversial decisions, such as mass layoffs, and he has been exploring different ways to monetize the platform.

So, what are these new limitations? Well, unverified accounts now have a daily limit of 600 posts they can view. For new unverified accounts, this limit is even lower, at only 300 posts per day. On the other hand, verified accounts, like those held by celebrities or public figures, are allowed to view up to 6,000 posts daily. Musk did mention that these limits might increase in the future, so we’ll have to keep an eye out for that.

Musk explained that the reason behind these changes is the intensive data scraping activities by AI companies. Hundreds of organizations have been aggressively mining data from Twitter, particularly to train large language models. Musk highlighted these companies as the main culprits behind the strain on the user experience.

And that’s the latest scoop on Musk’s new paywalls on reading tweets. Stay tuned for more updates on this story.

Healthcare company Insilico Medicine has taken a major stride in the world of medicine by creating the first fully AI-generated drug. The medicine is specifically designed to treat idiopathic pulmonary fibrosis, a potentially devastating lung disease. What sets this medicine apart is that it wasn’t just discovered by AI, but also completely designed by AI, making it a groundbreaking achievement.

While AI has played a role in designing other medicines before, this is the first time it has autonomously identified and created a drug from start to finish. Currently, the medicine is undergoing clinical trials on human patients to evaluate its effectiveness.

What makes this medicine so significant is the hope it brings to patients. Unlike existing treatments that simply slow down the progression of the disease and come with adverse effects, this new medicine aims to do more. By specifically targeting idiopathic pulmonary fibrosis, it offers the potential for more effective and safer treatment options.

Insilico Medicine’s work doesn’t stop there. They are also utilizing AI to develop medicines for other critical health issues. They are actively involved in creating a medicine for Covid-19, which is currently undergoing testing, and have received approval to begin trials on their cancer medicine.

Their commitment to using AI in the entire drug development process showcases the efficacy of their technology. By harnessing the power of AI, they are driving innovation and offering hope to countless individuals in need of effective medical treatments.

So, recently Sam Altman, the CEO of OpenAI, went on a world tour, visiting 25 cities across six continents. The purpose of this tour was to directly engage with OpenAI users, developers, policymakers, and the general public who interact with OpenAI’s technology. And let me tell you, it was quite an eye-opening experience for Sam Altman.

During his tour, Altman was amazed by the various use cases of ChatGPT. He saw high school students in Nigeria using ChatGPT for simplified learning and civil servants in Singapore using OpenAI tools for efficient public service delivery. This just goes to show that the reach of AI is expanding thanks to OpenAI’s efforts.

Altman also discovered that countries worldwide share similar hopes and concerns about AI. There is a common fear of AI safety, and policymakers are heavily invested in AI. Leaders around the globe are focused on ensuring the safe deployment of AI tools, maximizing their benefits, and mitigating potential risks. They are interested in maintaining a continuous dialogue with leading AI labs and establishing a global framework to manage future powerful AI systems.

Now, here’s why you should care. People around the world want clarity on OpenAI’s core values, and the tour provided a platform to address this. Sam Altman emphasized that customer data is not used in training and that users can easily opt-out. However, it’s worth noting that OpenAI is currently facing a class action lawsuit for allegedly stealing data and using it to train their models. So, there’s more to the story that you might want to look into.

Moving forward, OpenAI’s next steps involve making their products even more useful, impactful, and accessible. They are also focused on developing best practices for governing highly capable foundation models and working towards unlocking the benefits of AI.

And that’s a wrap on Sam Altman’s AI world tour!

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In today’s episode, we covered the top 5 entry-level machine learning jobs, the influence of AI/ML on Web3, the creative use of ChatGPT to waste telemarketers’ time and for student’s needs, Elon Musk’s Twitter restrictions due to AI data scraping, the groundbreaking fully AI-generated drug by Insilico Medicine, OpenAI CEO Sam Altman’s global tour on AI usage, and the easy podcast creation with Wondercraft AI. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!