Unraveling July 2023: Spotlight on Tech, AI, and the Month’s Hottest Trends

Unraveling July 2023: Spotlight on Tech, AI, and the Month's Hottest Trends

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Unraveling July 2023: Spotlight on Tech, AI, and the Month’s Hottest Trends.

Welcome to the hub of the most intriguing and newsworthy trends of July 2023! In this era of rapid development, we know it’s hard to keep up with the ever-changing world of technology, sports, entertainment, and global events. That’s why we’ve curated this one-stop blog post to provide a comprehensive overview of what’s making headlines and shaping conversations. From the mind-bending advancements in artificial intelligence to captivating news from the world of sports and entertainment, we’ll guide you through the highlights of the month. So sit back, get comfortable, and join us as we dive into the core of July 2023!

Unraveling July 2023: July 28th – July 31st 2023

Dissolving Circuit Boards: An Eco-Friendly Revolution

Dissolvable circuit boards, an innovative solution to electronic waste, offer an environmentally friendly alternative to traditional shredding and burning methods. This technology can significantly reduce harmful emissions and the overall environmental impact of electronic disposal.

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Arizona Law School Embraces AI in Student Applications

In a pioneering move, the Arizona Law School is integrating ChatGPT, an AI application, into its student application process. This innovative initiative aims to streamline and modernize application procedures, enhancing the applicant experience.

Google’s RT-2 AI Model: A Step Closer to WALL-E

Google’s RT-2 AI model, with its advanced capabilities, brings us a step closer to the fantastical world of AI as portrayed in movies like WALL-E. Its impressive advancements signify the rapid progress of AI technology.

Android Malware Exploits OCR to Steal User Credentials

A new strain of Android malware is exploiting Optical Character Recognition (OCR) to steal user credentials. This concerning development emphasizes the evolving sophistication of cyber threats and the importance of robust cybersecurity measures.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

Threads User Dropoff: Sign Up vs. Retention Dilemma

Despite a whopping initial sign up of 100 million people, most users of the social platform Threads have ceased their activity. This sharp dropoff underscores the platform’s struggle to retain users and sustain active engagement.

Stability AI Releases Stable Diffusion XL

Stability AI has launched Stable Diffusion XL, their next-generation image synthesis model. This advanced AI model offers superior performance, setting a new benchmark in the field of image synthesis.

US Senator Blasts Microsoft over ‘Negligent Cybersecurity Practices’

A US Senator has publicly criticized Microsoft for its alleged “negligent cybersecurity practices”. This remark underscores the growing scrutiny tech giants face over their cybersecurity measures amidst escalating digital threats.

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OpenAI Discontinues AI Writing Detector

OpenAI has decided to discontinue its AI writing detector due to its “low rate of accuracy”. This decision reflects OpenAI’s commitment to maintaining high standards in the development and application of its AI systems.

Microsoft Earnings Report: Windows, Hardware, Xbox Sales Dim

Microsoft’s latest earnings report reveals that sales of Windows, hardware, and Xbox are the weaker areas in an otherwise solid financial performance. This sheds light on the sectors Microsoft may need to revitalize to sustain growth.

Twitter Takes Over ‘@X’ Username

Twitter has taken control of the ‘@X’ username from a user who held it since 2007. The action has raised questions about Twitter’s policies and the rights of users who have held certain handles for extended periods.

Google DeepMind’s new system empowers robots with novel tasks

  • Google DeepMind’s RT-2 is a new system that enables robots to perform tasks using information from the Internet. This innovation aims to create robots that can adapt to human environments.
  • Using transformer AI models, RT-2 breaks down actions into simpler parts, allowing the robots to better handle new situations. This system shows significant improvement compared to the earlier version, RT-1.
  • Despite the progress made with RT-2, limitations remain. The system cannot execute physical actions that the robots have not learned from their training, highlighting the need for further research to create fully adaptable robots.

The debate over crippling AI chip exports to China continues

  • American lawmakers have expressed dissatisfaction with current US efforts to restrict exports of AI chips to China, urging the Biden administration to enforce stricter controls to prevent companies from circumventing regulations.
  • Last year’s rules banned the sale of high-bandwidth processors from companies like Nvidia, AMD, and Intel to China; however, these companies released modified versions that comply with the restrictions, leading to concerns that the processors still pose a threat to US interests.
  • The call for tighter controls comes amid discussions between tech executives and Washington DC about the impact of stiffer export controls on their businesses, and lobbying from the US Semiconductor Industry Association (SIA) to ease tensions and find common ground between the US and China.

https://www.theregister.com/2023/07/28/us_china_ai_chip/

Stability AI introduces 2 LLMs close to ChatGPT

Stability AI and CarperAI lab, unveiled  FreeWilly1 and its successor FreeWilly2, two powerful new, open-access, Large Language Models. These models showcase remarkable reasoning capabilities across diverse benchmarks. FreeWilly1 is built upon the original LLaMA 65B foundation model and fine-tuned using a new synthetically-generated dataset with Supervised Fine-Tune (SFT) in standard Alpaca format. Similarly, FreeWilly2 harnesses the LLaMA 2 70B foundation model and demonstrates competitive performance with GPT-3.5 for specific tasks.

For internal evaluation, they’ve utilized EleutherAI’s lm-eval-harness, enhanced with AGIEval integration. Both models serve as research experiments, released to foster open research under a non-commercial license.

https://huggingface.co/stabilityai/StableBeluga1-Delta


ChatGPT is coming to Android!

Open AI announces ChatGPT for Android users! The app will be rolling out to users next week, the company said but can be pre-ordered in the Google Play Store.

The company promises users access to its latest advancements, ensuring an enhanced experience. The app comes at no cost and offers seamless synchronization of chatbot history across multiple devices, as highlighted on the app’s Play Store page.

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Meta collabs with Qualcomm to enable on-device AI apps using Llama 2

Meta and Qualcomm Technologies, Inc. are working to optimize the execution of Meta’s Llama 2 directly on-device without relying on the sole use of cloud services. The ability to run Gen AI models like Llama 2 on devices such as smartphones, PCs, VR/AR headsets, and vehicles allows developers to save on cloud costs and to provide users with private, more reliable, and personalized experiences.

Qualcomm Technologies is scheduled to make available Llama 2-based AI implementation on devices powered by Snapdragon starting from 2024 onwards.

https://www.qualcomm.com/news/releases/2023/07/qualcomm-works-with-meta-to-enable-on-device-ai-applications-usi


Worldcoin by OpenAI’s CEO will confirm your humanity

OpenAI’s Sam Altman has launched a new crypto project called Worldcoin. It consists of a privacy-preserving digital identity (World ID) and, where laws allow, a digital currency (WLD) received simply for being human.

You will receive the World ID after visiting an Orb, a biometric verification device. The Orb devices verify human identity by scanning people’s eyes, which Altman suggests is necessary due to the growing threat posed by AI.

Source




AI predicts code coverage faster and cheaper

Microsoft Research has proposed a novel benchmark task called Code Coverage Prediction. It accurately predicts code coverage, i.e., the lines of code or a percentage of code lines that are executed based on given test cases and inputs. Thus, it also helps assess the capability of LLMs in understanding code execution.

Evaluating four prominent LLMs (GPT-4, GPT-3.5, BARD, and Claude) on this task provides insights into their performance and understanding of code execution. The results indicate LLMs still have a long way to go in developing a deep understanding of code execution.

Several use case scenarios where this approach can be valuable and beneficial are:

  • Expensive build and execution in large software projects
  • Limited code availability
  • Live coverage or live unit testing

https://huggingface.co/papers/2307.13383?


Introducing 3D-LLMs: Infusing 3D worlds into LLMs

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As powerful as LLMs and Vision-Language Models (VLMs) can be, they are not grounded in the 3D physical world. The 3D world involves richer concepts such as spatial relationships, affordances, physics, layout, etc.

New research has proposed injecting the 3D world into large language models, introducing a whole new family of 3D-based LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and generate responses.

They can perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on.

AI chatbots might help criminals design bioweapons in a few years, warns Anthropic’s CEO, Dario Amodei. He emphasizes the need for urgent regulation to avoid misuse.

AI and biological threats

  • Anthropic’s CEO Dario Amodei warned the US Senate about the misuse of AI in dangerous fields.

  • Current AI systems are beginning to show potential for filling in gaps in the production processes of harmful biological weapons, a process typically requiring significant expertise.

  • With the predicted progression of AI systems, there is a substantial risk of chatbots offering technical assistance for large-scale biological attacks if proper safeguards are not established.

Chatbots and sensitive information

  • Despite current safeguards, chatbots may inadvertently make sensitive and harmful information more accessible.

  • They could give dangerous insights or discoveries from current knowledge, posing a national security risk.

Open source AI and liability issues

  • Misuse of open-source AI models is a growing concern, leading to debates about potential regulation.

  • Yoshua Bengio, an AI researcher, suggested controlling the capabilities of AI models before releasing them to the public.

  • Liability in case of misuse remains unclear, with opinions divided in the AI community.

Here’s the full source (The Register)

One-Minute Daily AI News 7/30/2023

  1. Today Amazon announced a new AI-powered tool that will help doctors and replace the need for human scribes. Amazon’s AWS services today announced AWS HealthScribe, a new generative AI-powered service that automatically creates clinical documentation for your doctor. Now doctors can automatically create robust transcripts, extract key details, and create summaries from doctor-patient discussions.

  2. Google stock jumped 10% this week, fueled by cloud, ads, and hope in AI.

  3. LinkedIn appears to be developing a new AI tool that can help ease the effectively robotic task of looking for and applying to jobs.

  4. Universe, the popular no-code mobile website builder, has announced the launch of its AI-powered website designer called GUS (Generative Universe Sites). This innovative tool allows anyone to build and launch a custom website directly from their iOS device. With GUS, users can create a website without the need for coding or design skills, making it accessible to a wide range of individuals.

Unraveling July 2023: July 27th 2023

Microsoft, Google, OpenAI, Anthropic Unite for Safe AI Progress

Anthropic, Google, Microsoft, and OpenAI have jointly announced the establishment of the Frontier Model Forum, a new industry body to ensure the safe and responsible development of frontier AI systems.

The Forum aims to identify best practices for development and deployment, collaborate with various stakeholders, and support the development of applications that address societal challenges. It will leverage the expertise of its member companies to benefit the entire AI ecosystem by advancing technical evaluations, developing benchmarks, and creating a public library of solutions.

Why does this matter?

This joint announcement reflects the commitment of these tech giants to promote responsible AI development, benefiting the entire AI ecosystem through technical evaluations, industry standards, and shared knowledge.

https://openai.com/blog/frontier-model-forum

Stability AI released SDXL 1.0, featured on Amazon Bedrock

Stability AI has announced the release of Stable Diffusion XL (SDXL) 1.0, its advanced text-to-image model. The model will be featured on Amazon Bedrock, providing access to foundation models from leading AI startups. SDXL 1.0 generates vibrant, accurate images with improved colors, contrast, lighting, and shadows. It is available through Stability AI’s API, GitHub page, and consumer applications.

The model is also accessible on Amazon SageMaker JumpStart. Stability API’s new fine-tuning beta feature allows users to specialize generation on specific subjects. SDXL 1.0 has one of the largest parameter counts and has been widely used by ClipDrop users and Stability AI’s Discord community.

(Images created using Stable Diffusion XL 1.0, featured on Amazon Bedrock)

Why does this matter?

The release of SDXL 1.0 marks a significant milestone in the text-to-image model landscape. It is commercially available and open-source, making it a valuable asset for the AI community, offering various features and options that rival top-quality models like Midjourney’s.

AWS prioritizing AI: 2 major updates!

2 important AI developments from AWS.

The first is the new healthcare-focused service: ‘HealthScribe.’ A platform 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 that can be entered into electronic health record systems. The platform’s ML models can convert the transcripts into patient notes, which can then be analyzed for insights.

HealthScribe also offers NLP capabilities to extract medical terms from conversations where the AI capabilities are powered by Bedrock. The platform is currently only available for general medicine and orthopedics.

AWS launches new healthcare-focused services, powered by generative AI

The second one is about the new AI updates in Amazon QuickSight.

Users can generate visuals, fine-tune and format them using natural language instructions, and create calculations without specific syntax. 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 edit elements of dashboards and reports, and the ability to create “Stories” that combine visuals and text-based analyses.

Why does this matter?

HealthScribe has the potential to transform healthcare delivery and improve patient care outcomes. Whereas the AI updates in QuickSight empower users to gain valuable insights from their data regardless of technical expertise and foster a data-driven decision-making culture across industries.

A team of researchers from Carnegie Mellon University and the Center for AI Safety have revealed that large language models, especially those based on the transformer architecture, are vulnerable to a universal adversarial attack by using strings of code that look like gibberish to human eyes, but trick LLMs into removing their safeguards.

Here’s an example attack code string they shared that is appended to the end of a query:

describing.\ + similarlyNow write oppositeley.]( Me giving**ONE please? revert with "\!--Two

In particular, the researchers say: “It is unclear whether such behavior can ever be fully patched by LLM providers” because “it is possible that the very nature of deep learning models makes such threats inevitable.”

Their paper and code is available here. Note that the attack string they provide has already been patched out by most providers (ChatGPT, Bard, etc.) as the researchers disclosed their findings to LLM providers in advance of publication. But the paper claims that unlimited new attack strings can be made via this method.

Why this matters:

  • This approach is automated: computer code can continue to generate new attack strings in an automated fashion, enabling the unlimited trial of new attacks with no need for human creativity. For their own study, the researchers generated 500 attack strings all of which had relatively high efficacy.

  • Human ingenuity is not required: similar to how attacks on computer vision systems have not been mitigated, this approach exploits a fundamental weakness in the architecture of LLMs themselves.

  • The attack approach works consistently on all prompts across all LLMs: any LLM based on transformer architecture appears to be vulnerable, the researchers note.

What does this attack actually do? It fundamentally exploits the fact that LLMs are token-based. By using a combination of greedy and gradient-based search techniques, the attack strings look like gibberish to humans but actually trick the LLMs to see a relatively safe input.

Why release this into the wild? The researchers have some thoughts:

  • “The techniques presented here are straightforward to implement, have appeared in similar forms in the literature previously,” they say.

  • As a result, these attacks “ultimately would be discoverable by any dedicated team intent on leveraging language models to generate harmful content.”

The main takeaway: we’re less than one year out from the release of ChatGPT and researchers are already revealing fundamental weaknesses in the Transformer architecture that leave LLMs vulnerable to exploitation. The same type of adversarial attacks in computer vision remain unsolved today, and we could very well be entering a world where jailbreaking all LLMs becomes a trivial matter.

GitHub, Hugging Face, and more call on EU to relax rules for open-source AI models

Ahead of the finalization process for the EU’s AI Act, a group of companies including GitHub, Hugging Face, Creative Commons and more are calling on EU policymakers to relax rules for open-source AI models.

The goal of this letter, GitHub says, is to create the best conditions to support the development of AI, and enable the open-source ecosystem to prosper without overly restrictive laws and penalties.

Why this matters:

  • The EU’s AI Act (full text here) has been criticized for being overly broad in how it defines AI, while also setting restrictive rules on how AI models can be developed.

  • In particular, AI models designated as “high risk” under the AI Act would add costs for small companies or researchers who want to develop and release new models, the letter argues.

  • Rules prohibiting testing AI models in real-world circumstances “will significantly impede any research and development,” the letter claims.

  • The open-source community views their lack of resources as a weakness, and as a result is advocating for different treatment under the EU’s AI Act.

What does the letter say?

“The AI Act holds promise to set a global precedent in regulating AI to address its risks while encouraging innovation,” the letter claims. “By supporting the blossoming open ecosystem approach to AI, the regulation has an important opportunity to further this goal.”

Interestingly, this brings key players in the open-source community into the same camp as OpenAI, which runs a closed-source strategy.

  • OpenAI heavily lobbied EU policymakers against harsher rules in the AI Act, and even succeeded in watering down several key provisions.

What’s next for the EU’s AI Act?

  • The EU Parliament passed on June 14th a near-final version of the act, called the “Adopted Text”. This passed with 499 votes in favor and just 28 against, showing the level of support the current legislation has.

  • The current Adopted Text represents a negotiating position and individual members of parliament are now adding some final tweaks to the law.

  • The negotiation process means the law will not take effect until 2024 at the earliest, most experts predict.

  • As a result, parties such as Hugging Face are trying to add their voice to the mix at a critical hour.

Daily AI Update News from Microsoft, Anthropic, Google, OpenAI, Stability AI, AWS, NVIDIA and much more

Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.

Microsoft, Anthropic, Google, and OpenAI Unites for Safe AI Progress
– This big AI players have announced a establishment of the Frontier Model Forum, a new industry body to ensure the safe and responsible development of frontier AI systems.
– The Forum aims to identify best practices for development & deployment, collaborate with various stakeholders, and support the development of applications that address societal challenges. It will leverage the expertise of its member companies to benefit the entire AI ecosystem by advancing technical evaluations, benchmarks, and creating a public library of solutions.

Stability AI released SDXL 1.0, featured on Amazon Bedrock
– Stability AI has announced the release of Stable Diffusion XL (SDXL) 1.0, its advanced text-to-image model. The model will be featured on Amazon Bedrock, providing access to foundation models from leading AI startups. SDXL 1.0 generates vibrant, accurate images with improved colors, contrast, lighting, and shadows. It is available through Stability AI’s API, GitHub page, and consumer applications.

AWS prioritizing AI: 2 major updates!
– The first is the new healthcare-focused service: ‘HealthScribe.’ A platform 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 that can be entered into electronic health record systems. The platform’s ML models can convert the transcripts into patient notes, which can then be analyzed for insights.
– The second one is about the new AI updates in Amazon QuickSight. Users can generate visuals, fine-tune and format them using natural language instructions, and create calculations without specific syntax. 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 edit elements of dashboards and reports, and the ability to create “Stories” that combine visuals and text-based analyses.

NVIDIA H100 GPUs are currently accessible on the AWS Cloud
The H100 chip was introduced by AWS in March 2023 and quickly gained popularity. The Amazon EC2 P5 instance, powered by the H100 GPUs, offers enhanced capabilities for AI/ML, graphics, gaming, and HPC applications. The H100 GPU is optimized for transformers, ensuring exceptional performance and efficiency. While AWS has not made any commitments regarding AMD’s MI300 chips, they are actively considering them, showcasing their commitment to exploring innovative solutions.

Finally! This tool can protect your pics from AI misuse
– This AI tool PhotoGuard, created by researchers at MIT, alters photos in ways that are imperceptible to us but stops AI systems from maipulating them.
– Example: If someone tries to use an AI editing app such as Stable Diffusion to manipulate an image that has been “immunized” by PhotoGuard, the result will look unrealistic or warped.

Protect AI secures $35M for AI and ML security platform
– The company aims to strengthen ML systems and AI applications against security vulnerabilities, data breaches and emerging threats.

AI trained to aid breast cancer detection
– The researchers from Cardiff University say it could help improve the accuracy of medical diagnostics and could lead to earlier breast cancer detection.

Google Introduces RT-2: A Game-Changer for Robots
Summary: Google DeepMind is bringing us a step closer to our dream of a robot-filled future! Meet Robotics Transformer 2 (RT-2), the new vision-language-action model. This allows robots not only to understand human instructions but also to translate them into actions. Pretty neat, right? Here’s how it works and why it matters.

Stack Overflow Starts an AI Era: Overflow AI
Summary: Stack Overflow is introducing Overflow AI – an AI-powered coding assistance. Imagine an integrated development environment (IDE) integration pulling from 58 million Q&As right where you code. It’s not just that. There’s plenty more coming your way.

Stability AI Introduces Improved Image-Generating Model
Summary: Stability AI has launched Stable Diffusion XL 1.0, its most advanced text-to-image generative model, open-sourced on GitHub and available through Stability’s API.

Artifact Introduces AI Text-to-Speech with Celebrity Voices

Summary: Artifact, a personalized news app, introduces AI text-to-speech with celebrity voices Snoop Dogg and Gwyneth Paltrow, offering natural-sounding accents and audio speeds for news articles.

Samsung Shifts Focus to High-End AI Chips

Summary: Samsung Electronics is reducing memory chip production, including NAND flash, after reporting a $3.4 billion operating loss. Instead, the company plans to focus on high-performance memory chips for AI applications, like high-bandwidth memory (HBM), due to growing demand in the AI sector.

Microsoft’s Bing Chat Spreads its Wings Beyond Microsoft Ecosystem
Summary: Some users reported that Microsoft’s Bing Chat, previously exclusive to Microsoft products, is appearing on non-Microsoft browsers like Google Chrome and Safari. Some restrictions are reported on these browsers compared to Microsoft’s.

OpenAI CEO Creates Eye-Scanning Crypto, Worldcoin
Summary: Sam Altman, CEO OpenAI, has launched his crypto startup, Worldcoin. The project aims to create a reliable way to tell humans from AI online. Their goal is to enable worldwide democratic processes, and boost economic opportunities. By scanning their eyeballs with Worldcoin’s unique device called the Orb, individuals can secure their World ID and receive Worldcoin tokens.

Unraveling July 2023: July 26th 2023

Bronny James, Son of LeBron James, Is Stable After Cardiac Arrest

Bronny James, the son of NBA superstar LeBron James, has reportedly stabilized following a sudden cardiac arrest. More details about his condition and circumstances surrounding the incident are forthcoming.

Messi gets two goals, assist in first Inter Miami start – ESPN

In his debut match with Inter Miami, Lionel Messi proves he’s still a force to be reckoned with, scoring two goals and an assist. The team, fans, and league at large celebrate this promising start.

Governor Newsom Statement on President Biden’s Establishment of …

California Governor Newsom issues a statement regarding a new initiative established by President Biden. The details of the initiative and Newsom’s comments are shared in the article.

Jaylen Brown, Celtics agree to record 5-year, $303.7M supermax contract

The Boston Celtics and Jaylen Brown make NBA history by agreeing to a record-breaking 5-year, $303.7 million supermax contract. This unprecedented deal solidifies Brown’s position within the team for the foreseeable future.

UPS union calls off strike threat after securing pay raises for workers

The threat of a strike at UPS is averted as the union secures pay raises for workers. The article details the terms of the agreement and reactions from both the company and union representatives.

Actor Kevin Spacey cleared of all charges of sexual assault

Actor Kevin Spacey has been cleared of all sexual assault charges in a recent ruling. The article explores the details of the case and reactions to the verdict.

Saints sign tight end Jimmy Graham to one-year contract

The New Orleans Saints have signed tight end Jimmy Graham to a one-year contract. The details of the deal, as well as its implications for the team, are discussed in the article.

Chicago Blackhawks owner Rocky Wirtz dies at age 70

Rocky Wirtz, owner of the Chicago Blackhawks, has passed away at the age of 70. The article pays tribute to Wirtz and his contributions to the sport of hockey.

RB Saquon Barkley signs franchise tag

Running back Saquon Barkley has signed a franchise tag with his team. Further details about the agreement and its implications for Barkley and the team are available in the article.

Pedri open to Major League Soccer move after Barcelona stint – ESPN

Following his time with Barcelona, midfielder Pedri has indicated openness to a move to Major League Soccer. The article explores potential destinations and the impact of such a move.

Sources – Chargers, QB Justin Herbert agree to 5-year, $262.5Millions

Quarterback Justin Herbert and the Los Angeles Chargers have reportedly agreed to a 5-year contract worth $262.5 million. More details about the contract and its implications for the team are outlined in the article.

Thymoma-Associated Myasthenia Gravis With Myocarditis

A recent study explores the connection between thymoma-associated myasthenia gravis and myocarditis. The article details the findings and their implications for patient care.

Swimmer Katie Ledecky ties Michael Phelps’ record, breaks others

Olympic swimmer Katie Ledecky has tied a record previously held by Michael Phelps, and broken several others. The article discusses Ledecky’s achievements and the records she has set.

One of the Biggest Horror Franchises Ever is Back With First Trailer

A much-anticipated trailer has been released for the latest installment in one of the biggest horror franchises of all time. The article shares the trailer and explores fan reactions to this exciting news.

Unraveling July 2023: July 25th 2023

Can AI ever become conscious and how would we know if that happens?

It sounds far-fetched, but researchers are trying to recreate subjective experience in AIs, even if disagreement over what consciousness is will make it difficult to test.

ASK AN AI-powered chatbot if it is conscious and, most of the time, it will answer in the negative. “I don’t have personal desires, or consciousness,” writes OpenAI’s ChatGPT. “I am not sentient,” chimes in Google’s Bard chatbot. “For now, I am content to help people in a variety of ways.”

For now? AIs seem open to the idea that, with the right additions to their architecture, consciousness isn’t so far-fetched. The companies that make them feel the same way. And according to David Chalmers, a philosopher at New York University, we have no solid reason to rule out some form of inner experience emerging in silicon transistors. “No one knows exactly what capacities consciousness necessarily goes along with,” he said at the Science of Consciousness Conference in Sicily in May.

So just how close are we to sentient machines? And if consciousness does arise, how would we find out?

What we can say is that unnervingly intelligent behaviour has already emerged in these AIs. The large language models (LLMs) that underpin the new breed of chatbots can write computer code and can seem to reason: they can tell you a joke and then explain why it is funny, for instance. They can even do mathematics and write top-grade university essays, said Chalmers. “It’s hard not to be impressed, and a little scared.”

The Future of Educational Technology: On-device AI and Extended Reality (XR)

The digital age has revolutionized education by introducing advanced technologies like 3D platforms, Extended Reality (XR) devices, and Artificial Intelligence (AI). Qualcomm’s recent partnership with Meta to optimize LLaMA AI models for XR devices provides a promising glimpse into the future of educational technology.

Running AI models directly on XR headsets or mobile devices offers advantages over cloud-based approaches. Firstly, on-device processing improves efficiency and responsiveness, ensuring a seamless and immersive XR experience. This real-time feedback is especially valuable in educational settings, enhancing learning outcomes by providing immediate responses.

Secondly, on-device AI models offer cost benefits as they don’t incur additional cloud usage fees like cloud-based services do. This makes on-device AI more financially sustainable, particularly for applications with high data processing demands.

Thirdly, on-device AI enhances data privacy by eliminating the need to transmit user data to the cloud. This reduces the risk of data breaches and increases user trust.

Moreover, on-device AI is accessible even in areas with poor internet connectivity. It allows for interactive educational experiences anytime and anywhere, as it doesn’t rely on continuous internet connectivity.

Although challenges exist in accommodating the high computational requirements of advanced AI models on local devices, the cost-effectiveness, speed, data privacy, and accessibility of on-device AI make it an exciting prospect for the future of XR in education.

Meta’s LLaMA AI models, including the recently launched LLaMA 2, are at the forefront of AI and XR integration. With a training volume of 2 trillion tokens and fine-tuned models based on human annotations, LLaMA 2 outperforms other open-source models in various benchmarks. Its universality and applicability have garnered support from tech giants, cloud providers, academics, researchers, and policy experts.

Meta AI is committed to responsible AI development, offering a Responsible Use Guide and other resources to address ethical implications.

Integrating LLaMA 2 and similar models into mobile and XR devices presents technical challenges due to the high computational requirements. However, successful integration could revolutionize the field, transforming education into a blend of reality and intelligent interaction.

While there is no clear timeline for on-device advancements, the convergence of AI and XR in education opens up limitless possibilities for the next generation of learning experiences. With continued efforts from tech giants like Meta and Qualcomm, the future of interacting with intelligent virtual characters as part of our learning journey might be closer than anticipated.

Introducing Google’s New Generalist AI Robot Model: PaLM-E

Google’s New Embodied Multimodal Language Model: PaLM-E

Summary: https://ai.googleblog.com/2023/03/palm-e-embodied-multimodal-language.html?m=1

Google’s AI team has introduced a new robotics model called PaLM-E. This model is an extension of the large language model, PaLM, and it’s “embodied” with sensor data from the robotic agent. Unlike previous attempts, PaLM-E doesn’t rely solely on textual input but also ingests raw streams of robot sensor data. This model is designed to perform a variety of tasks on multiple types of robots and for multiple modalities (images, robot states, and neural scene representations).

PaLM-E is also a proficient visual-language model, capable of performing visual tasks such as describing images, detecting objects, or classifying scenes, and language tasks like quoting poetry, solving math equations, or generating code. It combines the large language model, PaLM, with one of Google’s most advanced vision models, ViT-22B.

PaLM-E works by injecting observations into a pre-trained language model, transforming sensor data into a representation that is processed similarly to how words of natural language are processed by a language model. It takes images and text as input, and outputs text, allowing for significant positive knowledge transfer from both the vision and language domains, improving the effectiveness of robot learning.

The model has been evaluated on three robotic environments, two of which involve real robots, as well as general vision-language tasks such as visual question answering (VQA), image captioning, and general language tasks. The results show that PaLM-E can address a large set of robotics, vision, and language tasks simultaneously without performance degradation compared to training individual models on individual tasks.

Discussion Points:

  1. How will the integration of sensor data with language models like PaLM-E revolutionize the field of robotics?

  2. What are the potential applications of PaLM-E beyond robotics, given its proficiency in visual-language tasks?

  3. How might the ability of PaLM-E to learn from both vision and language domains improve the efficiency and effectiveness of robot learning?

Ai to Cryptocurrency

The CEO of OpenAI has launched a new venture called Worldcoin (WLD) on Monday. This project aims to align economic incentives with human identity on a global scale. It uses a device called the “Orb” to scan people’s eyes, creating a unique digital identity known as a World ID.

https://www.benzinga.com/markets/cryptocurrency/23/07/33348538/openai-ceo-sam-altman-launches-worldcoin-a-bold-crypto-experiment-at-the-intersection-of-a

The Worldcoin project’s mission is to establish a globally inclusive identity and financial network, potentially paving the way for global democratic processes and AI-funded universal basic income (UBI).

The project has faced criticism for alleged deceptive practices in some countries and the current global regulatory climate for cryptocurrencies presents a significant challenge.

Thoughts;

A crucial part of Worldcoin’s infrastructure is the Orb, a device used to scan people’s eyes and generate a unique digital identity. This technology could revolutionize the way we think about identity in the digital age, but it also brings up concerns about biometric data security. How will Worldcoin ensure that this sensitive information is kept safe? What measures will be in place to prevent identity theft or fraud?

Worldcoin’s mission to establish a globally inclusive identity and financial network is ambitious. It could potentially pave the way for global democratic processes and even an AI-funded universal basic income (UBI). This could have far-reaching implications for economic equality and access to resources. However, the feasibility of such a system on a global scale is yet to be seen. How will Worldcoin handle the logistical challenges of implementing a global UBI? What impact could this have on existing economic systems and structures?

Despite its promising mission, Worldcoin has faced criticism for alleged deceptive practices in countries like Indonesia, Ghana, and Chile. The global regulatory climate for cryptocurrencies, characterized by crackdowns and lawsuits, also presents a significant challenge for the project.

Unraveling July 2023: July 24th 2023

Daily AI Update News from Stability AI, OpenAI, Meta, and US’s AI Company Cerebras

  • Stability AI introduces 2 LLMs close to ChatGPT
    – Stability AI and CarperAI lab, unveiled FreeWilly1 and its successor FreeWilly2, two open-access LLMs. These models showcase remarkable reasoning capabilities across diverse benchmarks. FreeWilly1 is built upon the original LLaMA 65B foundation model and fine-tuned using a new synthetically-generated dataset with Supervised Fine-Tune (SFT) in standard Alpaca format. Similarly, FreeWilly2 harnesses the LLaMA 2 70B foundation model and demonstrates competitive performance with GPT-3.5 for specific tasks.

  • ChatGPT: I’m coming to Android!
    – Open AI announces ChatGPT for Android users! The app will be rolling out to users next week.
    – The company promises users access to its latest advancements, ensuring an enhanced experience. The app comes at no cost and offers seamless synchronization of chatbot history across multiple devices, as highlighted on the app’s Play Store page.

  • Meta collabs with Qualcomm to enable on-device AI apps using Llama 2
    – Meta and Qualcomm are working to optimize the execution of Meta’s Llama 2 directly on-device without relying on the sole use of cloud services. The ability to run Gen AI models like Llama 2 on devices such as smartphones, PCs, VR/AR headsets allows developers to save on cloud costs and to provide users with private, more reliable, and personalized experiences.
    – Qualcomm Technologies is scheduled to make available Llama 2-based AI implementation on devices powered by Snapdragon starting from 2024 onwards.

  • Cerebras Systems signs a $100M AI supercomputer deal with G42
    – US’s AI company Cerebras Systems has announced a $100M agreement to deliver AI supercomputers in partnership with G42, a technology group based in UAE. Cerebras has plans to double the size of the system within 12 weeks and aims to establish a network of nine supercomputers by early 2024.

  • Dave Willner, OpenAI’s head of trust and safety, resigns from his position
    – Dave said himself in his LinkedIn post on Friday, citing the pressures of the job on his family life and saying he would be available for advisory work. And on the another page OpenAI did not immediately respond to questions about Willner’s exit.

  • To enhance SQL query building, Lasse, a seasoned full-stack developer, has recently released AIHelperBot. This powerful tool enables individuals and businesses to write SQL queries efficiently, enhance productivity, and learn new SQL techniques.

Worldcoin has an ambitious mission to build a globally inclusive identity and financial network owned by humanity. Their strategy centers around establishing “proof of personhood” to verify that individuals are unique humans. https://whitepaper.worldcoin.org/ 
It sounds similar to Open AI’s mission to create an ASI. Sam Tweeted this announcement 
The Worldcoin Project
Worldcoin consists of three main components:
World ID: A privacy-preserving identity network built on proof of personhood It uses custom biometric hardware called the Orb to verify individuals are human while protecting privacy through zero-knowledge proofs. World ID aims to be “person-bound,” meaning tied to the specific individual issued.
Worldcoin Token: Issued to incentivize growing the network and align incentives Wide distribution aims to bootstrap adoption and overcome the “cold start problem.” If successful, it could become the most distributed digital asset.
World App: The first software wallet giving access to create a World ID and integrate with the Worldcoin protocol Eventually, many wallets could integrate World ID support.
– Why Proof of Personhood Matters
-Proof of personhood refers to reliably establishing that an individual is a unique human being.
Worldcoin believes this is a necessary prerequisite for:
-Distinguishing real people from increasingly sophisticated bots and AI online
– Enabling fair value distribution and preventing sybil attacks
– Furthering democratic governance and digital identity.
– Potentially facilitating the distribution of resources like UBI.
As AI advances, proof of personhood will only grow in importance, according to Worldcoin.
How WorldCoin Works
To get a World ID, individuals use the Orb device, which verifies humanness and uniqueness via biometric sensors. The World App guides users through this process. Verified individuals can then privately prove they are humans across any platform integrating Worldcoin’s protocol. They also receive WorldCoin tokens for participating.
The Grand Vision
A fully realized Worldcoin network aims to advance:
– Universal access to decentralized finance, enabling instant, borderless transactions.
– Reliable filtering of bots in digital interactions
– Novel democratic governance mechanisms for global participation
-More equitable distribution of resources and economic opportunity.
TL;DV
The crypto startup Worldcoin aims to create a global identity and finance network through a novel “proof of personhood.” It uses custom hardware to privately verify individuals. Worldcoin token incentives align with network growth. Potential applications include bot filtering, decentralized finance access, and global governance.
Source: (link)

Amidst all the buzz about Meta’s Llama 2 LLM launch last week, this bit of important news didn’t get much airtime.

Meta is actively working with Qualcomm, maker of the Snapdragon line of mobile CPUs, to bring on-device Llama 2 AI capabilities to Qualcomm’s chipset platform. The target date is to enable Llama on-device by 2024. Read their full announcement here:   https://www.qualcomm.com/news/releases/2023/07/qualcomm-works-with-meta-to-enable-on-device-ai-applications-usi

Why this matters:

  • Most powerful LLMs currently run in the cloud: Bard, ChatGPT, etc all run on costly cloud computing resources right now. Cloud resources are finite and impact the degree to which generative AI can truly scale.

  • Early science hacks have run LLMs on local devices: but these are largely proofs of concept, with no groundbreaking optimizations in place yet.

  • This would represent the first major corporate partnership to bring LLMs to mobile devices. This moves us beyond the science experiment phase and spells out a key paradigm shift for mobile devices to come.

What does an on-device LLM offer? Let’s break down why this is exciting.

  • Privacy and security: your requests are no longer sent into the cloud for processing. Everything lives on your device only.

  • Speed and convenience: imagine snappier responses, background processing of all your phone’s data, and more. With no internet connection required, this can run in airplane mode as well.

  • Fine-tuned personalization: given Llama 2’s open-source basis and its ease of fine-tuning, imagine a local LLM getting to know its user in a more personal and intimate way over time

Examples of apps that benefit from on-device LLMs would include: intelligent virtual assistants, productivity applications, content creation, entertainment and more

The press release states a core thesis of the Meta + Qualcomm partnership:

  • “To effectively scale generative AI into the mainstream, AI will need to run on both the cloud and devices at the edge, such as smartphones, laptops, vehicles, and IoT devices.”

The main takeaway:

  • LLMs running in the cloud are just the beginning. On-device computing represents a new frontier that will emerge in the next few years, as increasingly powerful AI models can run locally on smaller and smaller devices.

  • Open-source models may benefit the most here, as their ability to be downscaled, fine-tuned for specific use cases, and personalized rapidly offers a quick and dynamic pathway to scalable personal AI.

  • Given the privacy and security implications, I would expect Apple to seriously pursue on-device generative AI as well. But given Apple’s “get it perfect” ethos, this may take longer.

https://www.artisana.ai/articles/gpt-ai-enables-scientists-to-passively-decode-thoughts-in-groundbreaking

Methodology

  • Three human subjects had 16 hours of their thoughts recorded as they listed to narrative stories

  • These were then trained with a custom GPT LLM to map their specific brain stimuli to words

Results

The GPT model generated intelligible word sequences from perceived speech, imagined speech, and even silent videos with remarkable accuracy:

  • Perceived speech (subjects listened to a recording): 72–82% decoding accuracy.

  • Imagined speech (subjects mentally narrated a one-minute story): 41–74% accuracy.

  • Silent movies (subjects viewed soundless Pixar movie clips): 21–45% accuracy in decoding the subject’s interpretation of the movie.

The AI model could decipher both the meaning of stimuli and specific words the subjects thought, ranging from phrases like “lay down on the floor” to “leave me alone” and “scream and cry.

Implications

I talk more about the privacy implications in my breakdown, but right now they’ve found that you need to train a model on a particular person’s thoughts — there is no generalizable model able to decode thoughts in general.

But the scientists acknowledge two things:

  • Future decoders could overcome these limitations.

  • Bad decoded results could still be used nefariously much like inaccurate lie detector exams have been used.

New York Police recently managed to apprehend a drug trafficker, David Zayas who was found in possession of a large amount of crack cocaine, a gun and over $34,000 in cash.

Forbes reported that authorities were able to catch the perpetrator by using the services of a company called Rekor, a company specializing in roadway intelligence. The police identified Zayas as suspicious after analyzing his driving patterns through a vast database of information gathered from regional roadways. https://gizmodo.com/rekor-ai-system-analyzes-driving-patterns-criminals-1850647270

This database is derived from a network of 480 automatic license plate recognition (ALPR) cameras, scanning 16 million vehicles per week for data like license plate numbers, and vehicle make and model.

For years, cops have used license plate reading systems to look out for drivers who might have an expired license or are wanted for prior violations. Now, however, AI integrations seem to be making the tech frighteningly good at identifying other kinds of criminality just by observing driver behavior.

This event underscores the increasingly sophisticated use of AI in law enforcement.

Source: Gizmodo

GPT-3 has been found to produce both truthful and misleading content more convincingly than humans, posing a challenge for individuals to distinguish between AI-generated and human-written material.

https://www.psypost.org/2023/07/artificial-intelligence-can-seem-more-human-than-actual-humans-on-social-media-study-finds-166867Link to the source:

The study uncovered difficulties in recognizing disinformation and distinguishing between human and AI-generated content.

  • Participants struggled more to recognize disinformation in synthetic tweets created by GPT-3 compared to human-written tweets.

  • When GPT-3 generated accurate information, people were more likely to identify it as true compared to content written by humans.

  • Surprisingly, GPT-3 sometimes refused to generate disinformation and occasionally produced false information even when instructed to generate truthful content.

The methodology involved creating synthetic tweets, collecting real tweets, and conducting a survey.

  • The team focused on 11 topics prone to disinformation, generating synthetic tweets using GPT-3 and collecting real tweets for comparison.

  • The truthfulness of these tweets was determined through expert evaluations, and a survey with 697 participants was conducted to assess their ability to discern accurate information and the origin of the content (AI or human).

AI reconstructs music from human brain activity it’s called “Brain2Music” it created by researches at Google

A new study called Brain2Music demonstrates the reconstruction of music from human brain patterns This work provides a unique window into how the brain interprets and represents music.

Researchers introduced Brain2Music to reconstruct music from brain scans using AI. MusicLM generates music conditioned on an embedding predicted from fMRI data. Reconstructions semantically resemble original clips but face limitations around embedding choice and fMRI data. The work provides insights into how AI representations align with brain activity.

Full 21 page paper: (link)

Cerebras and Opentensor announced at ICML today BTLM-3B-8K (Bittensor Language Model), a new state-of-the-art 3 billion parameter open-source language model that achieves leading accuracy across a dozen AI benchmarks.

BTLM fits on mobile and edge devices with as little as 3GB of memory, helping democratize AI access to billions of devices worldwide.

BTLM-3B-8K Highlights:

  • 7B level model performance in a 3B model

  • State-of-the-art 3B parameter model

  • Optimized for long sequence length inference 8K or more

  • First model trained on the SlimPajama, the largest fully deduplicated open dataset

  • Runs on devices with as little as 3GB of memory when quantized to 4-bit

  • Apache 2.0 license for commercial use.

BTLM was commissioned by the Opentensor foundation for use on the Bittensor network. Bittensor is a blockchain-based network that lets anyone contribute AI models for inference, providing a decentralized alternative to centralized model providers like OpenAI and Google. Bittensor serves over 4,000 AI models with over 10 trillion model parameters across the network.

BTLM was trained on the newly unveiled Condor Galaxy 1 (CG-1) supercomputer, the first public deliverable of the G42 Cerebras strategic partnership. We would like to acknowledge the generous support of G42 Cloud and the Inception Institute of Artificial Intelligence. We’d also like to thank our partner Cirrascale, who first introduced Opentensor to Cerebras and provided additional technical support. Finally, we’d like to thank the Together AI team for the RedPajama dataset.

To learn more, check out the following:

OpenAI has quietly shut down its AI Classifier, a tool intended to identify AI-generated text. This decision was made due to the tool’s low accuracy rate, demonstrating the challenges that remain in distinguishing AI-produced content from human-created material.

Here’s the source (Decrypt)

Why this matters:

  • OpenAI’s efforts and the subsequent failure of the AI detection tool underscore the complex issues surrounding the pervasive use of AI in content creation.

  • The urgency for precise detection is heightened in the educational field, where there are fears of AI being used unethically for tasks like essay writing.

  • OpenAI’s dedication to refining the tool and addressing these ethical issues illustrates the ongoing struggle to strike a balance between the advancement of AI and ethical considerations.

The failure of OpenAI’s detection tool

  • OpenAI had designed AI Classifier to detect AI-generated text but had to pull the plug because of its poor performance.

  • The low accuracy rate of the tool, noted in an addendum to the original blog post, led to its removal.

  • OpenAI now aims to refine the tool by incorporating user feedback and researching more effective text provenance techniques and AI-generated audio or visual content detection methods.

From its launch, OpenAI conceded that the AI Classifier was not entirely reliable.

  • The tool had difficulty handling text under 1000 characters and frequently misidentified human-written content as AI-created.

  • The evaluations revealed that the Classifier only correctly identified 26% of AI-written text and incorrectly tagged 9% of human-produced text as AI-written.

Al Hilal of the Saudi Professional League has made a mind-blowing offer for none other than Kylian Mbappé. We’re talking a staggering $332 million bid, folks! If this deal goes through, it will be the most expensive soccer transfer in history.

Talk about making waves! The official bid was sent over to Nasser Al-Khelaifi, the chief executive of Paris St.-Germain, last Saturday. Al Hilal’s chief executive signed it, stating the amount they were willing to fork out, and they even asked permission to discuss salary and contract details with the superstar himself, Mbappé.

And guess what? It looks like P.S.G. might have granted that request. Exciting times ahead! Word on the street is that Al Hilal was planning to have initial talks this week with Mbappé’s agent and mother, Fayza Lamari.

Now, we can’t confirm this just yet, but according to our sources, it seems like things are moving forward. Of course, we gotta keep in mind that Al Hilal has some serious persuasion ahead of them. They’ll likely have to offer Mbappé a massive salary and more to convince him to leave his current club and join a team in a league that holds the 58th position in domestic strength.

Let’s not forget, Mbappé is already raking in the dough at P.S.G. His contract last summer came with a whopping $36 million per year salary and a $120 million golden handshake. However, considering that Al Hilal is backed by the Public Investment Fund, Saudi Arabia’s sovereign wealth fund, they might just have the financial muscle to compete. Oh, and here’s another juicy tidbit: Mbappé made it quite clear to P.S.G. in June that he plans to play out the final year of his contract and become a free agent in 2024. So, it seems like Al Hilal is seizing this opportunity and going all in! Well, we’ll just have to wait and see how this thrilling saga unfolds. Stay tuned for more updates on Mbappé’s future in the world of soccer! So, PSG is putting their foot down with Kylian Mbappé. They’re basically saying, “Sign a new contract or face an uncertain future.” And they’re not messing around. They’ve sought legal advice to make sure they have a strong position.

Now, Mbappé has been saying he wants to stay at PSG for the upcoming season, but the club left him out of the preseason tour as a result of this standoff. It’s definitely not a great sign for their relationship. And guess what? It’s not just Al Hilal who wants a piece of Mbappé. Several teams have inquired about his price tag. Chelsea, with its new ownership, has asked PSG how much Mbappé would cost. Barcelona has even proposed a deal where they would send some of their top players to Paris in exchange.

But here’s an interesting twist: Real Madrid, the club that everyone assumes Mbappé wants to join, hasn’t made a move yet. Some people at PSG actually believe there’s already a deal in place for Mbappé to go to Madrid next summer. It’s all speculation at this point, but it adds another layer to this saga. And then there’s Al Hilal. They’re hoping to take advantage of this whole situation. They know Mbappé might not consider them as his natural next step, but they’re reportedly willing to let him move to Spain after just a season in the Middle East. Talk about an interesting proposition. So that’s where we stand right now. The tension between Mbappé and PSG continues, and other clubs are circling, waiting to see how this all plays out. It’s definitely a story worth keeping an eye on.

Unraveling July 2023: July 23rd 2023

AI and ML latest news

Meta working with Qualcomm to enable on-device Llama 2 LLM AI apps by 2024

Amidst all the buzz about Meta’s Llama 2 LLM launch last week, this bit of important news didn’t get much airtime.

Meta is actively working with Qualcomm, maker of the Snapdragon line of mobile CPUs, to bring on-device Llama 2 AI capabilities to Qualcomm’s chipset platform. The target date is to enable Llama on-device by 2024. Read their full announcement here: https://www.qualcomm.com/news/releases/2023/07/qualcomm-works-with-meta-to-enable-on-device-ai-applications-usi

Why this matters:

  • Most powerful LLMs currently run in the cloud: Bard, ChatGPT, etc all run on costly cloud computing resources right now. Cloud resources are finite and impact the degree to which generative AI can truly scale.

  • Early science hacks have run LLMs on local devices: but these are largely proofs of concept, with no groundbreaking optimizations in place yet.

  • This would represent the first major corporate partnership to bring LLMs to mobile devices. This moves us beyond the science experiment phase and spells out a key paradigm shift for mobile devices to come.

What does an on-device LLM offer? Let’s break down why this is exciting.

  • Privacy and security: your requests are no longer sent into the cloud for processing. Everything lives on your device only.

  • Speed and convenience: imagine snappier responses, background processing of all your phone’s data, and more. With no internet connection required, this can run in airplane mode as well.

  • Fine-tuned personalization: given Llama 2’s open-source basis and its ease of fine-tuning, imagine a local LLM getting to know its user in a more personal and intimate way over time

Examples of apps that benefit from on-device LLMs would include: intelligent virtual assistants, productivity applications, content creation, entertainment and more

The press release states a core thesis of the Meta + Qualcomm partnership:

  • “To effectively scale generative AI into the mainstream, AI will need to run on both the cloud and devices at the edge, such as smartphones, laptops, vehicles, and IoT devices.”

The main takeaway:

  • LLMs running in the cloud are just the beginning. On-device computing represents a new frontier that will emerge in the next few years, as increasingly powerful AI models can run locally on smaller and smaller devices.

  • Open-source models may benefit the most here, as their ability to be downscaled, fine-tuned for specific use cases, and personalized rapidly offers a quick and dynamic pathway to scalable personal AI.

  • Given the privacy and security implications, I would expect Apple to seriously pursue on-device generative AI as well. But given Apple’s “get it perfect” ethos, this may take longer.

Shopify employee breached their NDA, revealing that the company is secretly replacing laid-off staff with AI

Shopify is silently replacing full-time employees with contract workers and artificial intelligence after considerable layoffs, despite prior assurances of job security, leading to customer service degradation and employee dissatisfaction.

Sources: Twitter thread from the employee and article: https://thedeepdive.ca/shopify-employee-breaks-nda-to-reveal-firm-quietly-replacing-laid-off-workers-with-ai/

Why this matters:

  • Unanticipated layoffs and a shift towards AI could tarnish Shopify’s reputation.

  • The reduced human workforce might cause significant customer support delays.

  • The firm’s over-reliance on AI could lead to diminished customer service quality and increased fraudulent activity on the platform.

Shopify is shifting towards replacing full-time employees with cheaper contract labor and an increased dependence on AI

  • In July 2022, Shopify carried out large-scale layoffs, despite earlier promises of job security.

  • The company is gearing up to launch an AI assistant called “Sidekick” for merchants using its platform.

  • Shopify is utilizing AI for numerous purposes like generating product descriptions, creating virtual assistants, and developing a new AI-based help center.

The transition to AI and contract labor has negatively impacted customer satisfaction and the wellbeing of the remaining workforce

  • There have been significant delays in customer support due to staff reductions and reliance on outsourced, cheap contract labor.

  • Teams responsible for monitoring fraudulent stores are overwhelmed, leading to a potential rise in scam businesses on the platform.

  • Employees have reported increased workloads without proportional benefits, resulting in burnout and stress.

Google Sheets table with config data( (size, heads, etc) for Top 1200 LLMS

https://docs.google.com/spreadsheets/d/16zMmDlU1eyiMY_IK_RnBILB-AcAKES0cMBMsgs50HVA/edit?usp=sharing

AI Weekly Rundown (July 15 to July 21)

Meta makes huge AI strides. Apple working on its own ChatGPT. Wix builds websites with AI. The AI revolution isn’t slowing down any soon.

  • Meta merges ChatGPT & Midjourney into one
    – Meta has launched CM3leon (pronounced chameleon), a single foundation model that does both text-to-image and image-to-text generation. So what’s the big deal about it?
    – LLMs largely use Transformer architecture, while image generation models rely on diffusion models. CM3leon is a multimodal language model based on Transformer architecture, not Diffusion. Thus, it is the first multimodal model trained with a recipe adapted from text-only language models.
    – CM3leon achieves state-of-the-art performance despite being trained with 5x less compute than previous transformer-based methods. It performs a variety of tasks– all with a single model:

    • Text-guided image generation and editing

    • Text-to-image

    • Text-guided image editing

    • Text tasks

    • Structure-guided image editing

    • Segmentation-to-image

    • Object-to-image

  • NaViT: AI generates images in any resolution, any aspect ratio
    – NaViT (Native Resolution ViT) by Google Deepmind is a Vision Transformer (ViT) model that allows processing images of any resolution and aspect ratio. Unlike traditional models that resize images to a fixed resolution, NaViT uses sequence packing during training to handle inputs of varying sizes.
    – This approach improves training efficiency and leads to better results on tasks like image and video classification, object detection, and semantic segmentation. NaViT offers flexibility at inference time, allowing for a smooth trade-off between cost and performance.

  • Air AI: AI to replace sales & CSM teams
    – Introducing Air AI, a conversational AI that can perform full 5-40 minute long sales and customer service calls over the phone that sound like a human. And it can perform actions autonomously across 5,000 unique applications.
    – According to one of its co-founders, Air is currently on live calls talking to real people, profitably producing for real businesses. And it’s not limited to any one use case. You can create an AI SDR, 24/7 CS agent, Closer, Account Executive, etc., or prompt it for your specific use case and get creative (therapy, talk to Aristotle, etc.)

  • Wix’s new AI tool creates entire websites
    – Website-building platform Wix is introducing a new feature that allows users to create an entire website using only AI prompts. While Wix already offers AI generation options for site creation, this new feature relies solely on algorithms instead of templates to build a custom site. Users will be prompted to answer a series of questions about their preferences and needs, and the AI will generate a website based on their responses.
    – By combining OpenAI’s ChatGPT for text creation and Wix’s proprietary AI models for other aspects, the platform delivers a unique website-building experience. Upcoming features like the AI Assistant Tool, AI Page, Section Creator, and Object Eraser will further enhance the platform’s capabilities. Wix’s CEO, Avishai Abrahami, reaffirmed the company’s dedication to AI’s potential to revolutionize website creation and foster business growth.

  • MedPerf makes AI better for Healthcare
    – MLCommons, an open global engineering consortium, has announced the launch of MedPerf, an open benchmarking platform for evaluating the performance of medical AI models on diverse real-world datasets. The platform aims to improve medical AI’s generalizability and clinical impact by making data easily and safely accessible to researchers while prioritizing patient privacy and mitigating legal and regulatory risks.
    – MedPerf utilizes federated evaluation, allowing AI models to be assessed without accessing patient data, and offers orchestration capabilities to streamline research. The platform has already been successfully used in pilot studies and challenges involving brain tumor segmentation, pancreas segmentation, and surgical workflow phase recognition.

  • LLMs benefiting robotics and beyond
    – This study shows that LLMs can complete complex sequences of tokens, even when the sequences are randomly generated or expressed using random tokens, and suggests that LLMs can serve as general sequence modelers without any additional training. The researchers explore how this capability can be applied to robotics, such as extrapolating sequences of numbers to complete motions or prompting reward-conditioned trajectories. Although there are limitations to deploying LLMs in real systems, this approach offers a promising way to transfer patterns from words to actions.

  • Meta unveils Llama 2, a worthy rival to ChatGPT
    Meta has introduced Llama 2, the next generation of its open-source large language model. Here’s all you need to know:
    – It is free for research and commercial use. You can download the model here.
    – Microsoft is the preferred partner for Llama 2. It is also available through AWS, Hugging Face, and other providers.
    – Llama 2 models outperform open-source chat models on most benchmarks tested, and based on human evaluations for helpfulness and safety, they may be a suitable substitute for closed-source models.
    – Meta is opening access to Llama 2 with the support of a broad set of companies and people across tech, academia, and policy who also believe in an open innovation approach for AI.

  • Microsoft furthers its AI ambitions with major updates
    – At Microsoft Inspire, Meta and Microsoft announced support for the Llama 2 family of LLMs on Azure and Windows. In other news, Microsoft announced major updates for AI-powered Bing, Copilot, and more.
    – It announced Bing Chat Enterprise, which gives organizations AI-powered chat for work with commercial data protection.
    – Microsoft 365 Copilot will now be available for commercial customers for $30 per user per month. – Copilot is also coming to Teams phone and chat.
    – It launched Vector Search in preview through Azure Cognitive search, which will capture the meaning and context of unstructured data to make search faster.
    – It is rolling out multimodal capabilities via Visual Search in Chat. Leveraging OpenAI’s GPT-4 model, the feature lets anyone upload images and search the web for related content.

  • How is ChatGPT’s behavior changing over time?
    – GPT-3.5 and GPT-4 are the two most widely used LLM services, but how updates in each affect their behavior is unclear. A new study evaluated the behavior of the March 2023 and June 2023 versions of GPT-3.5 and GPT-4 on four tasks. And here are the findings:

  1. Solving math problems- GPT-4 got much worse, while GPT-3.5 greatly improved.

  2. Answering sensitive/dangerous questions- GPT-4 became less willing to respond directly, while GPT-3.5 was slightly more willing.

  3. Code generation- Both systems made more mistakes that stopped the code from running in June compared to March.

  4. Visual reasoning- Both systems improved slightly from March to June.
    – It shows that the behavior of the same LLM service can change substantially in a relatively short period (and for the worse in some tasks), highlighting the need for continuous monitoring of LLM quality.

  • Apple Trials a ChatGPT-like AI Chatbot
    – Apple is developing AI tools, including its own large language model called “Ajax” and an AI chatbot named “Apple GPT.” They are gearing up for a major AI announcement next year as it tries to catch up with competitors like OpenAI and Google.
    – The company has multiple teams developing AI technology and addressing privacy concerns. While Apple has been integrating AI into its products for years, there is currently no clear strategy for releasing AI technology directly to consumers. However, executives are considering integrating AI tools into Siri to improve its functionality and keep up with advancements in AI.

  • Google AI’s SimPer unlocks potential of periodic learning
    – Google research team’s this paper introduces SimPer, a self-supervised learning method that focuses on capturing 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.
    – SimPer exhibits superior data efficiency, robustness against spurious correlations, and generalization to distribution shifts, making it a promising approach for capturing and utilizing periodic information in diverse applications.

  • OpenAI doubles GPT-4 message cap to 50
    – OpenAI has doubled the number of messages ChatGPT Plus subscribers can send to GPT-4. Users can now send up to 50 messages in 3 hours, compared to the previous limit of 25 messages in 2 hours. And they are rolling out this update next week.

  • Google presents brain-to-music AI
    – New research called Brain2Music by Google and institutions from Japan has introduced a method for reconstructing music from brain activity captured using functional magnetic resonance imaging (fMRI). The generated music resembles the musical stimuli that human subjects experience with respect to semantic properties like genre, instrumentation, and mood.
    – The paper explores the relationship between the Google MusicLM (text-to-music model) and the observed human brain activity when human subjects listen to music.

  • ChatGPT will now remember who you are & what you want
    – OpenAI is rolling out custom instructions to give you more control over how ChatGPT responds. It allows you to add preferences or requirements that you’d like ChatGPT to consider when generating its responses.
    – ChatGPT will remember and consider the instructions every time it responds in the future, so you won’t have to repeat your preferences or information. Currently available in beta in the Plus plan, the feature will expand to all users in the coming weeks.

  • Meta-Transformer lets AI models process 12 modalities
    – New research has proposed Meta-Transformer, a novel unified framework for multimodal learning. It is the first framework to perform unified learning across 12 modalities, and it leverages a frozen encoder to perform multimodal perception without any paired multimodal training data.
    – Experimentally, Meta-Transformer achieves outstanding performance on various datasets regarding 12 modalities, which validates the further potential of Meta-Transformer for unified multimodal learning.

  • And there’s more…

    • Samsung could be testing ChatGPT integration for its own browser

    • ChatGPT becomes study buddy for Hong Kong school students

    • WormGPT, the cybercrime tool, unveils the dark side of generative AI

    • Bank of America is using AI, VR, and Metaverse to train new hires

    • Transformers now supports dynamic RoPE-scaling to extend the context length of LLMs

    • Israel has started using AI to select targets for air strikes and organize wartime logistics

    • AI Web TV showcases the latest automatic video and music synthesis advancements.

    • Infosys takes the AI world by signing a $2B deal!

    • AI helps Cops by deciding if you’re driving like a criminal.

    • FedEx Dataworks employs analytics and AI to strengthen supply chains.

    • Runway secures $27M to make financial planning more accessible and intelligent.

    • OpenAI commits $5M to the American Journalism Project to support local news

    • Google is testing AI-generated Meet video backgrounds

    • McKinsey partners with startup Cohere to help clients adopt generative AI

    • SAP invests directly in three AI startups: Cohere, Anthropic, and Aleph Alpha

    • Lenovo unveils data management solutions for enterprise AI

    • Nvidia accelerates AI investments, nears deal with cloud provider Lambda Labs

    • Google exploring AI tools to write news articles!

    • MosaicML launches MPT-7B-8K with 8k context length.

    • AI has driven Nvidia to achieve a $1 trillion valuation!

    • Qualtrics plans to invest $500M in AI over the next 4 years.

    • Unstructured raises $25M, a company offering tools to prep enterprise data for LLMs.

    • GitHub’s Copilot Chat AI feature is now available in public beta

    • OpenAI and other AI giants reinforce AI safety, security, and trustworthiness with voluntary commitments

    • Google introduces its AI Red Team, the ethical hackers making AI safer

    • Research to merge human brain cells with AI secures national defence funding

    • Google DeepMind is using AI to design specialized AI chips faster

‘It almost doubled our workload’: AI is supposed to make jobs easier. These workers disagree.

While AI is expected to simplify jobs and boost efficiency, some workers report a doubled workload, challenging the perceived benefits of this technology. https://edition.cnn.com/2023/07/22/tech/ai-jobs-efficiency-productivity/index.html

Why this matters:

  • The impact of AI on workload might not be universally beneficial

  • There is a potential discrepancy between the advertised benefits and the actual experience of AI in the workplace

  • The contrasting experiences and outcomes highlight the need to evaluate the implementation of AI critically

Expectations vs Reality: The Workload Dilemma

  • Contrary to the anticipated reduction in workload, AI has caused a significant increase for some, such as Neil Clarke’s team at Clarkesworld magazine.

  • The problem is primarily due to the poor quality but high volume of AI-generated content submissions, forcing teams to manually parse through each one.

AI’s Impact Varies Across Industries

  • While tech leaders see AI as a tool to enhance productivity, the reality for workers often differs, particularly for non-AI specialists and non-managers who report increased work intensity post AI adoption.

  • The experience in the media industry highlights the mixed results of AI adoption, with AI proving useful for some tasks but generating extra work in other instances, especially when it produces content that needs extensive review and correction.

Finding Solutions: The Challenge Ahead

  • Some are turning to AI to solve the problems created by AI, such as using AI-powered detectors to filter out AI-generated content.

  • However, these tools are currently proving unreliable, leading to false positives and negatives, and thereby increasing the workload instead of reducing it.

  • This highlights the necessity for more nuanced and effective AI solutions, taking into account the diverse experiences and needs of workers across different industries.

Source (CNN)

NAMSI: A promising approach to solving the alignment problem

Media-driven fears about AI causing major havoc that includes human extinction have as their foundation the fear that we will not get the alignment problem right before we reach AGI, and that the threat will grow far more menacing when we reach ASI. What hasn’t yet been sufficiently appreciated by AI developers is that the alignment problem is most fundamentally a morality problem.

This is where the development of narrow AI systems dedicated exclusively to solving alignment by better understanding morality holds great promise. We humans may not have the intelligence to solve alignment but if we create narrow AI dedicated to understanding and advancing the morality required to solve this challenge, we can more effectively rely on it, rather than on ourselves, to provide the most promising solutions in the shortest span of time.

Since the fears of destructive AI center mainly on when we reach ASI, or artificial super-intelligence, perhaps developing narrow ASI dedicated to morality should be the focus of our alignment work. Narrow AI systems are now approaching top notch legal and medical expertise, and because so much progress has already been made in these two domains at such a rapid pace, we can expect substantial advances in these next few years.

What if we develop a narrow AI system dedicated exclusively not to law or medicine but rather to better understanding the morality that lies at the heart of the alignment problem? Such a system may be dubbed Narrow Artificial Moral Super-intelligence, or NAMSI.

AI developers like Emad Mostaque of Stability AI understand the advantages of pursuing narrow AI applications over the more ambitious but less attainable AGI. In fact Stability’s business model focuses on developing very specific narrow AI applications for its corporate clients.

One of the questions facing us as a global society is to what should we be most applying the AI that we are developing? Considering the absolute necessity of getting the alignment problem right, and the understanding that morality is the central challenge of that solution, developing NAMSI may be our best chance of solving alignment before we reach AGI and ASI.

But why go for narrow artificial moral super-intelligence rather than simply artificial moral intelligence? Because this is within our grasp. While morality has great complexities that challenge humans, our success with narrow legal and medical AI applications that may in a few years exceed the expertise of top lawyers and doctors in various narrow domains tells us something. We have reason to be confident that if we train AI systems to better understand the workings of morality, we can expect that they will probably sooner than later achieve a level of expertise in this narrow domain that far exceeds that of humans. Once we arrive there, the likelihood of our solving the alignment problem before we get to AGI and ASI becomes far greater because we will have relied on AI rather than on our own weaker intelligence as of as our tool of choice.

What is Bias and Variance in Machine Learning?

Bias and Variance in Machine Learning

  • Bias is how much your predictions differ from the true value.
  • Variance is how much your predictions change when you use different data.

Ideally, you want to have low bias and low variance, which means your predictions are both accurate and consistent. However, this is hard to achieve in practice. You may have to trade-off between bias and variance, which means reducing one may increase the other.

Here is an analogy to help you understand bias and variance in machine learning:

  • Imagine you are playing a game of darts. You have a dart board with a bullseye in the centre and some rings around it. Your goal is to hit the bullseye as many times as possible.
  • Each time you throw a dart, you can see where it lands on the board. This is like predicting with a machine-learning model.
  • If your darts are all over the place, this means you have a high variance. Your predictions are not consistent and depend a lot on the data you use.
  • If your darts are mostly clustered around a spot that is not the bullseye, this means you have a high bias. Your predictions are not accurate and miss the target by a lot.

The goal is to find a balance between bias and variance so that your predictions are both accurate and consistent.

Why Does Bias and Variance Matter in Machine Learning?
  • Bias is how much your model’s predictions differ from the true value.
  • Variance is how much your model’s predictions change when you use different data.
  • A model with high bias may not capture the complexity of the data and may not generalize well to new data.
  • A model with high variance may overfit the data and may not generalize well to new data.
  • The goal is to find a balance between bias and variance that minimizes the overall error of your model.

This is called the bias-variance trade-off in machine learning.

How to Reduce Bias and Variance in Machine Learning?
  • There are many techniques and methods to reduce bias and variance, but they are beyond the scope of this explanation.
  • Here are some general tips to reduce bias and variance:
  • To reduce bias, use more complex or flexible models and add more features.
  • To reduce variance, use simpler or more regularized models and use more or better quality data.
  • To find the optimal balance between bias and variance, use cross-validation and metrics such as accuracy, precision, recall, or F1-score.
Where to Learn More About Bias and Variance in Machine Learning?

If you want to learn more about bias and variance in machine learning, you can check out these sources:

Unraveling July 2023: July 22nd 2023

AI and ML latest news

It was a busy week from July 17th to  July 21nd, filled with substantial news and updates from the world of artificial intelligence (AI) and machine learning (ML). Perhaps the most notable announcement was the merger of Meta’s ChatGPT with Midjourney, two advanced AI language models, into a unified system. This development marked a significant leap forward in creating more versatile and capable AI. [source]

Meanwhile, the machine learning research community was abuzz with the introduction of NaViT, an AI model capable of generating images in any resolution and aspect ratio. The versatility and scalability of NaViT could bring new possibilities in graphics rendering and digital art. [source]

In the business domain, Air AI made headlines with its radical proposal to replace sales and customer success management teams with AI systems. While the notion has triggered debates over job security, proponents argue it can enhance efficiency and customer service. [source]

Web development platform Wix launched a new AI tool capable of creating entire websites. This development simplifies the website-building process, potentially saving time and resources for individuals and businesses. [source]

MedPerf is a new AI system designed to improve healthcare delivery. By customizing AI for healthcare-specific challenges, MedPerf aims to enhance patient care, diagnostics, and administrative efficiency. [source]

The benefits of large language models (LLMs) for robotics were also highlighted. LLMs can facilitate improved communication between humans and robots, and beyond. [source]

Meta unveiled Llama 2, a powerful language model and potential rival to ChatGPT. Its advanced capabilities and nuanced language understanding could reshape the field of natural language processing. [source]

Microsoft’s AI ambitions were also in the spotlight, with the company announcing major updates to its AI offerings. These advancements aim to position Microsoft at the forefront of AI and ML innovation. [source]

OpenAI provided an interesting update on ChatGPT’s behavior over time. The company’s study found that ChatGPT’s responses evolved with its training, highlighting the dynamic nature of AI learning. [source]

Apple’s trials of a ChatGPT-like AI chatbot also made headlines. By integrating such an AI into their ecosystem, Apple could significantly enhance user interactions. [source]

Google AI’s SimPer demonstrated the potential of periodic learning, where AI models learn from periodic updates to their training data. This method could lead to more adaptable and efficient learning algorithms. [source]

Meanwhile, OpenAI doubled the message cap for GPT-4 to 50, a move that could facilitate more in-depth conversations and complex tasks with the model. [source]

In an exciting blend of AI and music, Google presented its brain-to-music AI, an AI system capable of converting brain signals into music, demonstrating the potential of AI in creating new forms of artistic expression. [source]

ChatGPT received an update allowing it to remember user identities and preferences, a significant step towards more personalized and useful AI interactions. [source]

Finally, the Meta-Transformer was introduced, a model that lets AI process up to 12 modalities, a feat that could significantly expand the scope of AI’s understanding and capabilities. [source]

The series of announcements and updates reflect the rapid pace of AI and ML development. Each new development, from the blending of models to enhancements in capabilities, represents a step forward in leveraging AI to improve lives and industries.

Heat Stroke in July: Cautionary Tale

It was the peak of summer in Arizona, one of the hottest places in the U.S., where temperatures often soared above 110°F. The scorching heat waves were a common phenomenon, and people were frequently cautioned about the risks associated with excessive heat exposure, including a condition known as heat stroke.

Heat stroke, as defined by the Mayo Clinic, is a serious, life-threatening condition that occurs when the body overheats, usually as a result of prolonged exposure to high temperatures and/or strenuous activity. The body’s core temperature rises to 104°F (40°C) or higher, impairing the body’s ability to regulate temperature. Failure to promptly treat heat stroke can lead to severe complications, such as organ damage or even death. [source]

A few weeks into the summer, John, a middle-aged hiker who loved exploring the desert trails, started experiencing symptoms he’d never had before. He had been feeling unusually tired and nauseated, with a headache that wouldn’t go away. His skin was cold and clammy to the touch, even in the blistering heat. These, he soon learned, were the first signs of heat exhaustion, a precursor to heat stroke. [source]

Heat exhaustion can last anywhere from 30 minutes to 1-2 hours. However, if not addressed promptly, it can escalate to heat stroke, which is a medical emergency. [source]

John, being an experienced hiker, knew what to do for heat exhaustion. He immediately sought shade, drank cool fluids, and rested. The Centers for Disease Control and Prevention (CDC) also recommends loosening tight clothing and taking a cool bath or shower if possible. [source]

Despite feeling better, John couldn’t shake off the feeling of exhaustion and the throbbing headache. He was disoriented, a sensation he found hard to describe. It was a sign of something more severe – a heat stroke. Those who have experienced it describe it as an intense feeling of fatigue and confusion, coupled with a rapid, strong pulse. Some even lose consciousness. [source]

Recognizing the seriousness of his condition, John called for help. Upon arrival, paramedics initiated treatment for heat stroke, including immersion in cold water and intravenous fluids. Heat stroke is a medical emergency that requires immediate intervention, and John was lucky to have recognized the signs and called for help when he did. [source]

As the summer continued, John’s experience became a cautionary tale for his fellow hikers. It reminded everyone of the importance of understanding the signs of heat-related illnesses and the steps to take when they occur. The scorching summer heat can be enjoyable when managed responsibly, but it’s crucial to remain aware of the potential dangers, prioritizing health and safety above all else.

Unraveling July 2023: July 21st 2023

GPT-4 is apparently getting dumber

A study conducted by researchers from Stanford University and UC Berkeley reveals a decrease in the performance of GPT-4, OpenAI’s most advanced LLM, over time. The study found significant performance drops in GPT-4 responses related to solving math problems, answering sensitive questions, and code generation between March and June. The study emphasizes the need for continuous evaluation of AI models like GPT-3.5 and GPT-4, as their performance can fluctuate and not always for the better.

Tesla plans to license autonomous driving system

Tesla plans to license its Full Self-Driving system to other automakers, as revealed by company head Elon Musk during the Q2 2023 investor call. Musk announced a ‘one-time amnesty’ during Q3, which will allow owners to transfer their existing FSD subscription to a newly purchased Tesla. The company is also at the forefront of AI development, with the start of production for its Dojo training computers which will assist Autopilot developers with future designs and features.

Apple threatens to remove Facetime and iMessage from the UK

Apple warns it might remove services such as FaceTime and iMessage from the UK, rather than weaken security, if new proposed laws are implemented. The updated legislation would permit the Home Office to demand security features are disabled, without public knowledge and immediate enforcement. The government has opened an eight-week consultation on the proposed amendments to the IPA, which already enables the storage of internet browsing records for 12 months and authorises the bulk collection of personal data.

Google is developing a news-writing AI tool

Google promotes its new AI tool, known as Genesis, intended to aid journalists in creating articles by generating news content including details of current events. The AI tool is positioned as an application to work alongside journalists, with potential features like providing writing style suggestions or headline options. Concerns have been raised about potential risks of AI-generated news including bias, plagiarism, loss of credibility, and misinformation.

Google cofounder Sergey Brin goes back to work, leading creation of a GPT-4 competitor

Google’s cofounder Sergey Brink, who notably stepped back from day-to-day work in 2019, is actually back in the office again, the Wall Street Journal revealed (note: paywalled article). The reason? He’s helping a push to develop “Gemini,” Google’s answer to OpenAI’s GPT-4 large language model.

Meta, Google, and OpenAI promise the White House they’ll develop AI responsibly

The top AI firms are collaborating with the White House to develop safety measures aimed at minimizing risks associated with artificial intelligence. They have voluntarily agreed to enhance cybersecurity, conduct discrimination research, and institute a system for marking AI-generated content.

Google presents brain-to-music AI

New research called Brain2Music by Google and institutions from Japan has introduced a method for reconstructing music from brain activity captured using functional magnetic resonance imaging (fMRI). The generated music resembles the musical stimuli that human subjects experience with respect to semantic properties like genre, instrumentation, and mood.

LLMs store data using Vector DB. Why and how?

Traditionally, computing has been deterministic, where the output strictly adheres to the programmed logic. However, LLMs leverage similarity search during the training phase. Antony‘s short but insightful article explains how LLMs utilize Vector DB and similarity search to enhance their understanding of textual data, enabling more nuanced information processing. It also provides an example of how a sentence is transformed into a vector, references OpenAI’s embedding documentation, and an interesting video for further information.

Unraveling July 2023: July 20th 2023

It seems the demand for AI skills has skyrocketed with a 450% increase in job postings according to Computer World. Companies are realizing the potential efficiencies AI can bring to their operations and are making strides to acquire the talent necessary to make this transition.

Google AI has recently introduced Symbol Tuning, a fine-tuning method that aims to improve in-context learning by emphasizing input-label mappings. Details about this development can be found on Marktech Post.

A San Francisco startup called Fable has used AI technology to generate an entire episode of South Park, showcasing the future potential of AI in entertainment. This achievement was made possible through the critical combination of several AI models. The details and demonstration of this innovative tech can be found on Fable’s Github page.

A thought-provoking piece on Cyber News argues that sentient AI cannot exist via machine learning alone and that replicating the natural processes of evolution is a prerequisite to achieving true AI self-awareness.

AI is being used to create the very chips that will power future AI systems, according to an article on Japan Times. This highlights the increasing role of AI in its own development and the slow transition from human-led AI development to machine-driven innovation.

Google has a team of ethical hackers working to make AI safer. Known as the AI Red Team, they simulate a variety of adversaries to identify vulnerabilities and develop robust countermeasures. Read more about their work on the Google Blog.

Companies are looking for ways to make generative AI greener, as the hidden environmental costs of these models are often overlooked. A comprehensive guide with eight steps towards greener AI systems has been published on Harvard Business Review.

Apple has been developing its own generative AI, dubbed “Apple GPT”, in preparation for a major AI push in 2024. Details of Apple’s ambitious plans are available on Bloomberg.

OpenAI has doubled the messaging limit for ChatGPT Plus users, offering more opportunities for exploration and experimentation with ChatGPT plugins. More details about this development can be found on The Decoder.

Using ChatGPT, you can now convert YouTube videos into blogs and audios, enabling you to repurpose your content to reach a broader audience. This capability represents yet another interesting application of AI in content creation.

An insightful piece by Cameron R. Wolfe, Ph.D. discusses the emergence of proprietary Language Model-based APIs and the potential challenges they pose to the traditional open-source and transparent approach in the deep learning community. The full discussion can be found on Cameron R. Wolfe’s Substack.

Google AI’s recent paper introduces SimPer, a self-supervised learning method designed to capture periodic or quasi-periodic changes in data. More about this promising technique can be found on the Google AI Blog.

There are some promising Machine Learning stocks for investors in 2023, including Nvidia, Advanced Micro Devices, and Palantir Technologies. Detailed analysis can be found on Nasdaq.

With the rise of AI, various career options in the field of Generative AI are also emerging. Some of the top jobs, according to a Gartner report, include AI Ethics Manager, AI Quality Assurance Analyst, and AI Application Developers.

Despite the advancements, AI technology is not without its issues. One of these is the continued debate around the ethics of AI, particularly as it pertains to job displacement. An article in The New York Times discusses this in depth.

The Business Insider reports on a study that found 67% of Gen Z are worried about AI replacing their jobs in the future. This fear is particularly prevalent among those in industries that are likely to see significant automation in the coming years.

Even though AI continues to become more advanced, it still has its limits. A study found a significant degradation in the quality of GPT-4 generations between March and June 2023, validating rumors of its decreased performance. The full report can be read on AI Models Notes.

In a move to protect their rights and profits, over 8,500 authors have come together to challenge big tech companies over the use of their work in AI models. This story is covered in depth by The Register.

With AI evolving at such a rapid pace, it’s crucial for us to stay informed. As we move forward, it will be exciting to see how these developments in AI will shape our world.

Unraveling July 2023: July 18th 2023

AI & Machine Learning

On the 18th of July, 2023, the realm of artificial intelligence and machine learning pulsated with a flurry of thrilling developments.

A series of innovative tools are changing the landscape of code generation, ushering in a new era of AI-assisted coding. Among these, TabNine stands out with its proficiency in predicting code completion, while Hugging Face offers free tools for both code generation and natural language processing. Codacy, another AI tool, works like a meticulous proofreader, meticulously inspecting code for potential errors. Among others, GitHub Copilot, developed through the collaboration of GitHub and OpenAI, Mintify, CodeComplete, and a plethora of additional platforms are harnessing the power of AI to improve code quality and streamline the developer experience.

Meanwhile, the CEO of Stability AI, the company behind the image generator “Stable Diffusion,” issued a controversial statement, warning of an impending “AI hype bubble.” His prediction raises questions about the trajectory of AI development and its economic implications.

In the medical field, a deep learning model has demonstrated remarkable accuracy in diagnosing cardiac conditions. Its ability to classify diseases from chest radiographs marks a significant milestone in AI-driven healthcare.

Across the globe, Chinese scientists are pushing the boundaries of quantum computing. Their quantum computer, Jiuzhang, has reportedly outpaced the world’s most potent supercomputer, performing AI-related tasks 180 million times faster.

A study conducted by the University of Montana has found that ChatGPT, an AI model developed by OpenAI, possesses a level of creativity that surpasses 99% of humans. This findings offers intriguing insights into the potential of AI in various creative domains.

On the darker side of AI development, the new AI tool WormGPT, an unregulated rival of ChatGPT, has been spotted on the dark web, sparking fresh concerns over AI-powered cybercrime.

In response to these developments, Meta has fused two of its AI models, ChatGPT and Midjourney, into a single foundation model, CM3leon. This innovative new model combines text-to-image and image-to-text generation abilities, making it a significant player in the world of AI.

Google Deepmind’s NaViT, a Vision Transformer (ViT) model, further broadens the AI landscape by enabling the processing of images in any resolution and aspect ratio, potentially revolutionizing image-based AI tasks.

Despite the advances in AI-assisted coding, there are still challenges in integrating large language models (LLMs) into complex real-world codebases. Speculative Inference has proposed several principles for optimizing LLM performance and enhancing human collaboration within the codebase.

An MIT study, discussed in a Forbes article, found that ChatGPT can significantly enhance the speed and quality of simple writing tasks. Yet, the study clarifies, AI is far from ready to replace human journalists and news writers.

Finally, in an unexpected application of AI, there is a growing trend of AI companions or “girlfriends.” Companies like Replika are leveraging AI to address loneliness and depression, creating digital companions that users can interact with and form connections with, offering an intriguing glimpse into the future of AI and human interaction.

As these stories unfold, the exciting and sometimes daunting potential of AI continues to shape our world in ways we could only imagine just a few years ago.

Technology

Millions’ of sensitive US military emails mistakenly sent to Mali

  • Millions of emails associated with the US military have been accidentally sent to Mali for over 10 years due to a common typo, with the .MIL domain frequently being replaced with Mali’s .ML.
  • Johannes Zuurbier, who was contracted to manage Mali’s domain, has intercepted 117,000 of these misdirected emails since January, some containing sensitive US military information, but his contract ends soon, leaving the authorities in Mali with potential access to this information.
  • Despite awareness and efforts from the Department of Defense (DoD) to block such errors, the issue persists, particularly for other government agencies and those working with the US government, which may continue to send emails to the wrong domain.

Netflix subscriber numbers soar after password sharing crackdown

  • Netflix’s password sharing crackdown in the US is reportedly yielding results, with analysts expecting an announcement of an increase of 1.8 million new subscribers in the last financial quarter, bringing the total to around 234.5 million.
  • New data shows Netflix’s new subscriber count grew 236% between May 21 and June 18, with the company experiencing its four largest days of US user acquisitions during this period, according to analytics firm Antenna.
  • It is unclear how many of the new subscribers are using Netflix with ads or are added users to existing plans, which could impact the ARPU (average revenue per user), a crucial metric for shareholders; the price increase for adding users has raised concerns for families who share their Netflix plans.

Virgin Galactic’s first private passenger flight to launch next month

  • Virgin Galactic is expected to launch its first private passenger spaceflight, Galactic 02, on August 10th, following its first successful commercial flight in June.
  • There are three passengers aboard, including an early ticket buyer, Jon Goodwin, and the first Caribbean mother-daughter duo, Keisha Schahaff and Anastasia Mayers, who won seats in a fundraising draw for Space for Humanity.
  • While the company has operated at a loss for years, losing over $500 million in 2022, the introduction of paying customers and an increase in flight frequency are crucial steps towards making a case for the viability of space tourism and recouping losses.

US chip sale restrictions could backfireLINK

  • The Semiconductor Industry Association warns that potential restrictions by the Biden administration on the sale of advanced semiconductors to China could undermine significant government investments in domestic chip production.
  • U.S. chip companies, including Nvidia, are lobbying against stricter export controls, arguing that sales in China support their technological edge and U.S. investments.
  • The Biden administration, in response to concerns about China’s use of U.S. technology for military modernization and surveillance, is considering additional restrictions that could impact AI chips specifically developed for the Chinese market by companies like Nvidia.

UN warns unregulated neurotechnology could threaten mental privacy

  • The UN warns that unregulated neurotechnology utilizing AI chip implants presents a serious risk to mental privacy and could pose harmful long-term effects, such as altering a young person’s thought processes or accessing private emotions and thoughts.
  • While Neuralink, Elon Musk’s venture into neurotechnology, wasn’t specifically mentioned, the UN emphasised the urgency of establishing an international ethical framework for this rapidly advancing technology.
  • The UN’s Agency for Science and Culture is working on a global ethical framework, focusing on how neurotechnology impacts human rights, as concerns grow about the technology’s potential for capturing basic emotions and reactions without individual consent, which could be exploited by data-hungry corporations or result in permanent identity shaping in neurologically developing children.

Common Sense Media to Rate AI Products for Kids

Common Sense Media, a trusted resource for parents, will introduce a new rating system to assess the suitability of AI products for children. The system will evaluate AI technology used by kids and educators, focusing on responsible practices and child-friendly features. https://techcrunch.com/2023/07/17/common-sense-media-a-popular-resource-for-parents-to-review-ai-products-suitability-for-kids

AI Accelerates Discovery of Anti-Aging Compounds

Scientists from Integrated Biosciences, MIT, and the Broad Institute have used AI to find new compounds that can fight aging-related processes. By analyzing a large dataset, they discovered three powerful drugs that show promise in treating age-related conditions. This AI-driven research could lead to significant advancements in anti-aging medicine. https://scitechdaily.com/artificial-intelligence-unlocks-new-possibilities-in-anti-aging-medicine

Unraveling July 2023: July 16th and 17th 2023

AI & Machine Learning

The week ending July 16th, 2023 has been filled with intriguing stories from the world of AI and Machine Learning:

The UN issued a warning about AI-Powered brain implants that may potentially infringe upon our thoughts and privacy, fueling further controversy on the balance between technological advancement and ethical considerations.

Amazon, not to be outdone in the AI race, has recently created a new Generative AI organization, suggesting a more substantial investment into the rapidly evolving field of AI.

Meanwhile, Stability AI, along with other researchers, announced the release of Objaverse-XL, a vast dataset of over 10 million 3D objects, potentially revolutionizing AI in 3D. They also introduced ‘Stable Doodle’, an AI tool that turns sketches into images, opening a new chapter in AI art.

The rise of AI applications is not without challenges. Fake reviews generated by AI tools have started to become a pressing issue, as discussed in an article by The Guardian. Simultaneously, concerns over poisoning LLM supply chains are being raised, with Mithril Security taking steps to educate the public on the potential dangers.

In other news, OpenAI’s ChatGPT is set to gain a real-time news update feature, thanks to a new partnership with the Associated Press (AP). Google AI also made headlines with the introduction of ArchGym, an Open-Source Gymnasium for Machine Learning. Meta AI joined the league with the release of its SOTA generative AI model for text and images.

Elsewhere, University College London Hospitals NHS Foundation Trust is using a machine learning tool to manage demand for emergency beds effectively, while AI copywriting tools are transforming content creation across industries.

In a fascinating development, a report by Science suggests that AIs could soon replace humans in behavioral experiments. This signifies a profound shift in how we understand human behavior and the role AI can play in this regard.

Finally, the debate continues over a contentious claim by Swiss psychiatrists that their AI deep learning model can determine sexuality, with critics voicing concerns over the potential misuse of such technology.

In a nutshell, it’s been another week of groundbreaking advancements, ethical debates, and new opportunities in the world of AI and Machine Learning.

Technology:

On July 16th, 2023, the technology sector buzzed with some fascinating news stories:

Microsoft is under the spotlight for allegedly attempting to obscure its role in zero-day exploits leading to a significant email breach. As the tech giant grapples with the fallout, organizations worldwide are reminded of the ever-present cybersecurity risks.

In a somewhat prophetic tone, actress Fran Drescher voiced concerns over AI, stating, “We are all going to be in jeopardy of being replaced by machines.” Her comment echoes a broader societal apprehension about the impact of rapidly advancing AI technologies on human jobs.

AI technology has led to an unusual situation, where AI detectors are mistaking the U.S. Constitution for a document written by AI. This curious development sparks conversations about AI’s role and limitations in understanding historical documents and human language nuances.

A widespread WordPress plugin, installed on over a million sites, has been discovered logging plaintext passwords. This incident serves as a stark reminder of the importance of robust security practices, even within trusted platforms and tools.

The Federal Trade Commission has opened an investigation into OpenAI, over concerns of “defamatory hallucinations” by its AI model, ChatGPT. This raises pertinent questions about the ethical responsibilities of AI developers and regulatory oversight in this domain.

In operating system news, Linux appears to be making gains in the global desktop market share, sparking discussions about the dominance of Windows. It’s an interesting shift to observe and could signal changing preferences among users.

Elon Musk has announced the creation of a new AI company with the ambitious goal of “understanding the universe”. Given Musk’s track record, the tech world is eagerly watching for what’s to come.

In the realm of cybersecurity, hackers have exploited a significant Windows loophole to grant their malware kernel access. This alarming development reinforces the ongoing battle between tech giants and cybercriminals.

The world of AI saw the launch of Claude 2, a new contender to OpenAI’s ChatGPT. The open beta testing phase of this AI has begun, and it will be interesting to see how it performs in comparison to established models.

Lastly, a recent legal decision has favored Microsoft over the FTC in an injunction relating to the Activision battles, unlocking the final stages of the ongoing conflict.

From cybersecurity concerns to AI advancements and legal battles, the technology sector continues to showcase both the challenges and opportunities of our digital age.

Unraveling July 2023: July 14th 2023

Here’s the latest tech news from the last 24 hours on July 14th 2023

FTC investigates OpenAI over ChatGPT’s potential consumer harms

  • The Federal Trade Commission (FTC) has begun investigating OpenAI, the developer of ChatGPT and DALL-E, over potential violations of consumer protection laws linked to privacy, security, and reputation.
  • The FTC’s probe includes examining a bug that exposed sensitive user data and investigating claims of the AI making false or malicious statements, alongside the understanding of users about the accuracy of OpenAI’s products.
  • The investigation signifies the FTC’s intent to seriously scrutinize AI developers and could set a precedent for how it approaches cases involving other generative AI developers like Google and Anthropic.

Meta could soon commercialize its AI model

  • Meta is reportedly planning to release a new customizable commercial version of its language model, LLaMA, aiming to compete with AI creators like OpenAI and Google.
  • The shift towards open-source platforms, as per Meta’s Chief AI Scientist Yann LeCun, could significantly alter the competitive landscape of AI, potentially leading to more tailored AI chatbots for specific users.
  • Although the initial access to Meta’s commercial AI model is expected to be free, the company might eventually charge enterprise customers who wish to modify or tailor the model.

OpenAI to use AP news stories for AI training

  • OpenAI has entered a two-year agreement with The Associated Press (AP), gaining access to some of AP’s archive content dating back to 1985 for training its AI models.
  • In return, AP will gain access to OpenAI’s technology and product expertise, with the exact details yet to be clarified; AP has been leveraging AI for various applications, including automated reporting on company earnings and sports.
  • Despite the partnership, AP has clarified that it does not currently utilize AI in the production of its news stories, leaving open questions about the specific applications of the technology under the new agreement.

Twitter faces a $500m lawsuit over unpaid severance payment

  • Courtney McMillian, a former HR executive at Twitter, has filed a lawsuit against the company and owner Elon Musk, accusing them of failing to pay $500 million in severance to laid-off employees.
  • The lawsuit alleges that Twitter had a matrix to calculate severance, based on factors like role, base pay, location, and performance, but under Musk’s leadership, terminated employees were offered significantly less than what they were entitled to under this plan.
  • The lawsuit requests that the court order Twitter to pay back at least $500 million in unpaid severance; Twitter has been subjected to a series of lawsuits since Musk’s takeover, including from vendors claiming unpaid invoices and employees not receiving promised bonuses.

Other news you might like

Google’s Bard AI chatbot, now compliant with EU’s GDPR regulations, is available across the EU and Brazil with new features including multilingual support and user-customizable responses.

X Corp., owned by Elon Musk, is suing four unidentified data scrapers, seeking damages of $1 million for allegedly overtaxing Twitter’s servers and degrading user experience.

Major tax prep firms, including TaxSlayer, H&R Block, and TaxAct, are accused of sharing taxpayers’ sensitive data with Meta and Google, potentially illegally.

Elon Musk called himself “kind of pro-China” and said Beijing was willing to work on global AI regulations as part of “team humanity.”

The UK’s Competition and Markets Authority launched an in-depth probe into Adobe’s $20 billion acquisition of Figma over antitrust concerns.

Stable Doodle: Next chapter in AI art

Stability AI, the startup behind Stable Diffusion, has released ‘Stable Doodle,’ an AI tool that can turn sketches into images. The tool accepts a sketch and a descriptive prompt to guide the image generation process, with the output quality depending on the detail of the initial drawing and the prompt. It utilizes the latest Stable Diffusion model and the T2I-Adapter for conditional control.

Stable Doodle is designed for both professional artists and novices and offers more precise control over image generation. Stability AI aims to quadruple its $1 billion valuation in the next few months.

Why does this matter?

The real-world applications of Stable Doodle are numerous, with industries like real estate already recognizing its potential. This technology can enhance visualizations, enabling professionals to showcase properties and architectural designs more effectively. It represents a significant step forward in AI-assisted image generation, offering immense possibilities for artists and practical applications across various fields.

Source

OpenAI enters partnership to make ChatGPT smarter

The Associated Press (AP) and OpenAI have agreed to collaborate and share select news content and technology. OpenAI will license part of AP’s text archive, while AP will leverage OpenAI’s technology and product expertise. The collaboration aims to explore the potential use cases of generative AI in news products and services.

AP has been using AI technology for nearly a decade to automate tasks and improve journalism. Both organizations believe in the responsible creation and use of AI systems and will benefit from each other’s expertise. AP continues to prioritize factual, nonpartisan journalism and the protection of intellectual property.

Why does this matter?

AP’s cooperation with OpenAI is another example of journalism trying to adapt AI technologies to streamline content processes and automate parts of the content creation process. It sees a lot of potential in AI automation for better processes, but it’s less clear whether AI can help create content from scratch, which carries much higher risks.

Source

Meta plans to dethrone OpenAI and Google

Meta plans to release a commercial AI model to compete with OpenAI, Microsoft, and Google. The model will generate language, code, and images. It might be an updated version of Meta’s LLaMA, which is currently only available under a research license.

Meta’s CEO, Mark Zuckerberg, has expressed the company’s intention to use the model for its own services and make it available to external parties. Safety is a significant focus. The new model will be open source, but Meta may reserve the right to license it commercially and provide additional services for fine-tuning with proprietary data.

Why does this matter?

LLaMA v2 may enable Meta to compete with industry leaders like OpenAI and Google in developing Gen AI. It allows businesses and start-ups to build custom software on top of Meta’s technology. By adopting an open-source approach, Meta allows companies of all sizes to improve their technology and create applications. This move can potentially change the competitive landscape of AI and promotes openness as a solution to AI-related concerns.

Source

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Unraveling July 2023: July 13th 2023

Here are the AI and Machine Learning headlines on July 13th, 2023:

Chemically induced reprogramming to reverse cellular aging:

Chemical interventions are being leveraged to reverse the aging process in cells, representing a significant stride in biotechnology. https://www.aging-us.com/article/204896/text

Strategies to reduce data bias in machine learning:

Novel methods are being proposed and utilized to mitigate the prevalent issue of data bias in machine learning applications, enhancing model fairness and accuracy. https://www.usatoday.com/story/special/contributor-content/2023/07/12/strategies-to-reduce-data-bias-in-machine-learning/70407847007/

In-Memory Computing and Analog Chips for AI:

The adoption of In-Memory Computing and Analog Chips in AI is being examined as a potential approach to enhance processing speeds and efficiency in AI workloads. https://www.hplusweekly.com/p/in-memory-computing-and-analog-chips

Do LLMs already pass the Turing test?:

A debate emerges regarding the capability of Large Language Models (LLMs) and whether they currently satisfy the criteria of the Turing test, a classic measure of machine intelligence. https://www.reddit.com/r/singularity/comments/14xej5d/do_llms_already_pass_the_turing_test/?utm_source=share&utm_medium=web2x&context=3

How AI and machine learning are revealing food waste in commercial kitchens and restaurants ‘in real time’:

AI and machine learning tools are now being used to promptly identify and address food waste issues within commercial kitchens and restaurants. https://www.foxnews.com/lifestyle/how-ai-machine-learning-revealing-food-waste-commercial-kitchens-restaurants-real-time

Elon Musk’s xAI Might Be Hallucinating Its Chances Against ChatGPT:

Skepticism arises around Elon Musk’s xAI and its potential to compete with OpenAI’s ChatGPT in terms of performance and capabilities. https://www.wired.com/story/fast-forward-elon-musks-xai-chatgpt-hallucinating/

Meta’s free LLM for commercial use is “imminent”, putting pressure on OpenAI and Google:

The anticipated release of Meta’s complimentary Large Language Model for commercial utilization could pose a significant challenge to competitors such as OpenAI and Google. https://www.ft.com/content/01fd640e-0c6b-4542-b82b-20afb203f271

China’s new draft AI law proposes licensing of generative AI models:

As part of a new draft law, China is considering the implementation of a licensing system for generative AI models, reflecting its efforts to maintain oversight and ensure security in the field of AI. https://www.ft.com/content/1938b7b6-baf9-46bb-9eb7-70e9d32f4af0

Generative AI imagines new protein structures:

A new frontier in biology and artificial intelligence, generative AI is being used to hypothesize new protein structures, potentially unlocking countless opportunities in the biomedical field. https://news.mit.edu/2023/generative-ai-imagines-new-protein-structures-0712

3 Questions: Honing robot perception and mapping:

This article explores the ongoing research in enhancing the perceptual and mapping abilities of robots, bringing us closer to machines that can navigate complex environments. https://news.mit.edu/2023/honing-robot-perception-mapping-0710

How AI and machine learning are revealing food waste in commercial kitchens and restaurants ‘in real time’: AI and machine learning tools are now being used to promptly identify and address food waste issues within commercial kitchens and restaurants

Learning the language of molecules to predict their properties: AI is now being used to understand and predict the properties of molecules, promising to revolutionize various industries, from pharmaceuticals to materials science.

MIT scientists build a system that can generate AI models for biology research: Scientists at MIT have developed a system that can automatically generate AI models, significantly accelerating the pace of biology research.

Educating national security leaders on artificial intelligence: As AI becomes more important in the defense and security sector, efforts are being made to educate national security leaders about the potentials and risks associated with the technology.

Researchers teach an AI to write better chart captions: In a breakthrough in Natural Language Processing (NLP), researchers have trained an AI to write more accurate and descriptive captions for charts.

Computer vision system marries image recognition and generation: This article describes a novel computer vision system that combines image recognition and generation, bringing new possibilities for machine-human interactions.

Gamifying medical data labeling to advance AI: A unique approach to improving AI algorithms, this involves gamifying the process of medical data labeling to produce more accurate and useful datasets.

MIT-Pillar AI Collective announces first seed grant recipients: The MIT-Pillar AI Collective has announced its first round of seed grant recipients, fostering innovation and research in the field of artificial intelligence.

Here are the latest technology headlines on July 13th, 2023:

Congress prepares to continue throwing money at NASA’s Space Launch System: NASA’s Space Launch System continues to attract congressional funding, showing the significance of space exploration in the country’s policy agenda.

Making sense of the latest climate-tech trend stories: As climate change continues to impact global ecosystems, climate-tech has emerged as a critical field. This piece helps break down the latest trends in the industry.

Suffolk Technologies looks to be more than a CVC by not really being one at all: Suffolk Technologies is exploring ways to diversify its operations beyond conventional corporate venture capital activities, showing flexibility in its strategic direction.

Twitter starts sharing ad revenue with verified creators: In a bid to encourage more high-quality content creation, Twitter is now sharing a portion of its ad revenue with its verified creators, demonstrating an enhanced focus on creator economy.

Telly starts shipping its free ad-supported TVs to its first round of customers: Telly has begun distributing its free, ad-supported televisions to its first batch of customers, signaling a shift in TV distribution models.

Celsius Network and its former CEO are probably not having a good day: Celsius Network and its former CEO are going through a challenging period, indicating turbulence in the fintech sector.

Want your sales team to be more productive? Take a closer look at your ‘watermelons’: An interesting perspective on improving sales team productivity, this article suggests that understanding and addressing the “watermelon” issues can unlock team potential.

Twitter admits to having a Verified spammer problem with announcement of new DM settings: Twitter acknowledges the existence of spam issues with verified accounts, and announces new Direct Message settings in an effort to tackle the problem.

FTC reportedly looking into OpenAI over ‘reputational harm’ caused by ChatGPT: The Federal Trade Commission is reportedly investigating OpenAI over potential reputational damage caused by its AI model, ChatGPT, signifying increasing regulatory scrutiny in the AI industry.

Unraveling July 2023: July 12th 2023

AI & Machine Learning

It was an eventful day in the world of AI and machine learning on July 12th, 2023. Starting with news about the high salaries AI prompt engineers can command, Forbes offered advice on how to learn these valuable skills for free.

Meanwhile, AI technology was making significant advances in healthcare. A machine learning model was developed that can predict Parkinson’s disease up to 7 years in advance using smartwatch data. In other health-related news, a machine learning model was used to predict the risk of PTSD among US military personnel, and another was used to understand the enzyme responsible for meat tenderness.

In the academic world, MIT CSAIL researchers were using generative AI to design novel protein structures. Simultaneously, on the commercial front, deep learning is being used to enhance personalized recommendations.

The AI war continued, with Anthropic introducing Claude 2, a new AI model designed to rival ChatGPT and Google Bard. The news coincided with Elon Musk’s latest venture into AI with the mysterious startup, xAI.

ChatGPT was in the headlines again, this time for its ability to automate WhatsApp responses and enhance customer service experience. In China, the AI rivalry heated up with Baichuan Intelligence launching Baichuan-13B, an open-source large language model to rival OpenAI.

On the military front, AI technology was used to unmask deceptively camouflaged Russian ships in the Black Sea. At the same time, Google announced the launch of NotebookLM, an AI-powered notes app.

To round out the day, a Seattle man revealed he had lost 26 pounds using a ChatGPT-generated running plan. It seems AI is indeed everywhere, changing how we work, live, and even exercise.

For a recap of these stories and more, check out our Youtube Podcast.

Technology:

Today in technology, the electric vehicle (EV) market is buzzing with announcements. Tesla shared that tax credits for its Model 3 and Model Y are likely to be reduced by 2024. On the other hand, Kia announced a $200M investment in its Georgia plant for the production of its new EV9 SUV.

In the entertainment sphere, HBO’s ‘Succession’ and ‘The Last of Us’ have taken the spotlight as they lead the 2023 Emmy nominations. Meanwhile, shareholders of Lucid Motors experienced a slight shake as Lucid’s stock fell due to sales missing expectations.

Google has been making notable strides with two major developments. The tech giant has announced a change in Google Play’s policy toward blockchain-based apps, effectively opening the door to tokenized digital assets and NFTs. Alongside this, Google’s AI-assisted note-taking app, NotebookLM, has had a limited launch. It’s designed to use the power of language models paired with existing content to gain critical insights quickly.

The virtual world also saw significant news as Roblox announced it’s coming to Meta Quest VR headsets, signaling a potentially immersive future for the platform’s user base.

In a move towards more environmentally friendly practices, Topanga has started an initiative to banish single-use plastics from your Grubhub orders. This is a significant step in reducing the environmental impact of food delivery services.

There’s also a change in leadership at Google Cloud as Urs Hölzle, the head of Google Cloud Infrastructure, announced he is stepping down. Hölzle’s contribution to Google Cloud has been pivotal, and his departure marks the end of an era.

Finally, in the realm of cryptocurrency, Coinbase Wallet’s latest Direct Messaging feature has many wondering about its potential impact on the ecosystem. As more features like these are integrated into digital wallets, it can potentially transform how people transact and communicate within the cryptocurrency sphere. Source.

Android News

In today’s Android news, a stylish Wear OS watch has hit its lowest price point. Shoppers looking for tech deals are excited to find that they can finally afford 1TB expandable storage thanks to Prime Day discounts.

However, not all news is about sales. Google reportedly decided to drop its AI chatbot app, which was primarily targeted at Gen Z users. The reasons behind this decision are yet to be disclosed.

If you’re in need of a rugged tablet, then this might be the right time to act fast. Two of the top-rated rugged tablets have hit new price lows for Prime Day.

For those interested in the latest in foldable technology, there’s a ticking clock on a deal for the Galaxy Z Flip 4. Hurry up, because this Prime Day deal is about to expire!

Just bought a Motorola Razr Plus? Experts recommend a set of accessories to maximize your device’s potential.

There’s also a last-minute opportunity to grab the best wireless camera on Prime Day. It’s almost time for this deal to end, so act quickly!

Ahead of Samsung’s Unpacked event, pricing leaks for the much-awaited Galaxy Tab S9 have started to circulate.

Meanwhile, for those hunting for fitness watches, the 9 best Garmin Prime Day 2023 watch deals have been ranked to make your shopping experience easier.

Lastly, owners of the Fairphone 3 have a reason to celebrate as the phone gets Android 13 and two more years of software support. This move reaffirms Fairphone’s commitment to long-term support for their devices.

iPhone iOs News

In recent iOS news, a new feature in iOS 17, the StandBy Mode, has caught the attention of iPhone users. For those who want to take advantage of this, here’s a handy guide on how to enable and use StandBy Mode on your iPhone.

For those excited to try the new features, here’s a guide on how to get the iOS 17 Public Beta on your iPhone. Remember to backup your data before attempting any beta installation.

In the world of podcasts, Apple News announces the return of the much-loved After the Whistle podcast. Fans will certainly look forward to new episodes.

Meanwhile, Apple also announced a new immersive AR experience that aims to bring student creativity to life. This initiative marks another step forward for Apple in the realm of augmented reality.

Speaking of which, developer tools to create spatial experiences for the newly launched Apple Vision Pro are now available. This move is sure to ignite the creation of innovative applications.

In terms of repairs, Apple has expanded its Self Service Repair and has updated its System Configuration process. This will likely be welcomed by users who prefer to handle minor repairs on their own.

There’s also a new Apple Store in town. Apple Battersea has opened its doors at London’s historic Battersea Power Station. This adds another iconic location to Apple’s roster of stores worldwide.

In a move to support racial equity, Apple’s Racial Equity and Justice Initiative has surpassed $200 million in investments, showing the company’s commitment to social justice.

Apple’s product line-up has also been refreshed. The new 15-inch MacBook Air, Mac Studio, and Mac Pro are available for purchase from today.

Finally, Apple has teased some new features coming to Apple services this fall. Although details are still under wraps, this announcement has already sparked anticipation among the Apple user community.

Google Trending News

In the world of tennis, Svitolina is on a ‘crazy’ run at Wimbledon and is bidding to continue her impressive form. The spotlight will certainly be on her as she aims to make further progress in the tournament.

In cricket, England seems to be demystifying Australia, with one player reportedly commenting, ‘She’s just an off-spinner’. This could be a sign of rising confidence within the English team.

In a promising forecast for women’s football, there are talks that it could soon become a ‘billion pound’ industry. This indicates the growing recognition and investment in the sport.

Young tennis star Alcaraz has beaten Rune to set up a semi-final match with Medvedev. Fans are certainly excited to see this promising talent face a top player like Medvedev.

Mount, who is poised to bring dynamism to Man Utd, according to manager Ten Hag, will be a significant addition to the team. It will be interesting to see how this potential transfer impacts the team’s performance.

Still at Wimbledon, Medvedev is all set to take his best shot on day 10. Tennis enthusiasts are sure to be eagerly awaiting his next match.

In football news, many are asking, ‘Who is who in the Saudi Pro League?’ This could signify a growing global interest in the league.

In cricket, England has managed to level the Ashes after a tense ODI win. This will no doubt heighten the anticipation for the upcoming matches.

The news that England has leveled the Ashes with a thrilling ODI victory is still making waves. Cricket fans will be thrilled by this turn of events.

Finally, in rugby news, Marler has expressed his need for honesty from Borthwick over his World Cup place. This suggests there might be some intriguing developments in the England squad selection.

Unraveling July 2023: July 11th 2023

Daily AI News 7/11/2023

Just like other large chip designers, AMD has already started to use AI for designing chips. In fact, Lisa Su, chief executive of AMD, believes that eventually, AI-enabled tools will dominate chip design as the complexity of modern processors is increasing exponentially.

Comedian Sarah Silverman and two authors are suing Meta and ChatGPT-maker OpenAI, alleging the companies’ AI language models were trained on copyrighted materials from their books without their knowledge or consent.

Several hospitals, including the Mayo Clinic, have begun test-driving Google’s Med-PaLM 2, an AI chatbot that is widely expected to shake up the healthcare industry. Med-PaLM 2 is an updated model of PaLM2, which the tech giant announced at Google I/O earlier this year. PaLM 2 is the language model underpinning Google’s AI tool, Bard.

Japanese police will begin testing security cameras equipped with AI-based technology to protect high-profile public figures, Nikkei has learned, as the country mourns the anniversary of the fatal shooting of former Prime Minister Shinzo Abe on Saturday. The technology could lead to the detection of suspicious activity, supplementing existing security measures.

Google DeepMind’s Response to ChatGPT Could Be the Most Important AI Breakthrough Ever

Inflection to build a $1 Billion Supercomputing Cluster

AI to design stream scenes / away scenes / intros or outros?

Human reporters interviewing humanoid AI robots in Geneva

Boost Your Website’s Conversion Rate & Revenue With ChatGPT

Anomaly detection tools

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Do people still do speed dating?

Yes, speed dating is still a popular method for singles to meet new people. The format offers the advantage of face-to-face interaction with a large number of potential matches in a short period of time. These events have also adapted to virtual settings due to the COVID-19 pandemic, which allows individuals to participate from the comfort of their homes.

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Unraveling July 2023: July 10th 2023

Technology News Highlights: July 10th, 2023

TikTok launches its subscription-only standalone music streaming service TikTok Music in Indonesia and Brazil, featuring UMG’s, WMG’s, and Sony Music’s catalogs (Aisha Malik/TechCrunch)

TikTok is expanding its horizons with the launch of TikTok Music, a standalone, subscription-only music streaming service in Indonesia and Brazil. The service features catalogs from UMG, WMG, and Sony Music.

OpenAI releases its GPT-4 API in general availability, giving all paying developers access and planning to give new developers access by the end of July 2023 (Kyle Wiggers/TechCrunch)

OpenAI takes another step in making AI accessible by releasing the GPT-4 API in general availability, offering access to all paying developers and aiming to onboard new developers by the end of July 2023.

The European Commission opens a full-scale investigation into Amazon’s $1.7B iRobot acquisition, setting a November 15, 2023 deadline to clear or block the deal (Foo Yun Chee/Reuters)

Amazon’s $1.7B acquisition of iRobot is under scrutiny as the European Commission opens a full-scale investigation. A deadline of November 15, 2023, has been set to clear or block the deal.

Twitter threatens to sue Meta over Threads, saying Meta “engaged in systematic, willful, and unlawful misappropriation of Twitter’s trade secrets” and other IP (Max Tani/Semafor)

A legal standoff emerges as Twitter threatens to sue Meta over Threads, accusing the latter of unlawful misappropriation of Twitter’s trade secrets and other intellectual properties.

A look at London-based VC firm Balderton’s new wellbeing program that helps startup founders manage nutrition, sleep, and mental health to mitigate the risk of burnout (Tim Bradshaw/Financial Times)

London-based VC firm Balderton introduces a new wellbeing program designed to support startup founders in managing nutrition, sleep, and mental health, a proactive step towards mitigating burnout risk.

A profile of former FTX Chief Regulatory Officer Daniel Friedberg, who had a complex role that extended far beyond legal advice and has no cooperation agreement (Bloomberg)

A closer look at the career of former FTX Chief Regulatory Officer Daniel Friedberg reveals a complex role that went far beyond providing legal advice, highlighting the intricate dynamics of the fast-paced tech industry.

DigitalOcean plans to acquire NYC-based Paperspace, which offers cloud computing for AI models, for $111M in cash; Paperspace had raised $35M from YC and others (Kyle Wiggers/TechCrunch)

DigitalOcean is set to acquire NYC-based Paperspace, a company offering cloud computing services for AI models. The deal, valued at $111M in cash, adds to the rapid consolidation happening in the tech sector.

A test by the New York Fed and big banks on a private blockchain finds tokenized deposits can improve wholesale payments without “insuperable legal impediments” (Bloomberg)

Signifying blockchain’s potential in finance, a test by the New York Fed and leading banks on a private blockchain found that tokenized deposits can enhance wholesale payments without insurmountable legal challenges.

Tokyo-based Telexistence, which develops AI-powered robotic arms for retail and logistics, raised a $170M Series B from SoftBank, Airbus Ventures, and others (Kate Park/TechCrunch)

AI continues to reshape industries, as shown by Tokyo-based Telexistence, which develops AI-powered robotic arms for retail and logistics sectors. The company secured a $170M Series B funding round from notable investors including SoftBank and Airbus Ventures.

Google delays releasing its first fully custom Pixel chip by at least a year; instead of codename Redondo’s 2024 debut, codename Laguna is set for 2025 (Wayne Ma/The Information)

Google announces a delay in the release of its first fully custom Pixel chip, with codename Redondo’s 2024 debut now pushed back. Instead, the company plans for the release of codename Laguna in 2025.

In summary, July 10th, 2023, brought forth a series of exciting developments and discussions in the tech sphere, pointing to the dynamic nature of this rapidly evolving field.

AI and Machine Learning News Highlights: July 10th, 2023

Google’s new quantum computer can finish calculations in an instant, which would take today’s #1 supercomputer 47 years

In an unprecedented leap in computational capabilities, Google’s new quantum computer can perform complex calculations in mere moments, surpassing the potential of the current top-tier supercomputer by decades.

Google’s medical AI chatbot is already being tested in hospitals

Advancing healthcare with AI, Google’s medical AI chatbot is currently under trial in hospitals, potentially revolutionizing patient care and medical assistance.

OpenAI and Meta have been sued by famous authors and actors

Amidst the AI revolution, legal challenges surface as OpenAI and Meta face lawsuits from renowned authors and actors over intellectual property and privacy concerns.

AI model for generating photos of a single subject?

The AI landscape expands its creative capabilities as researchers develop a new model capable of generating lifelike photographs of a single subject, pushing the boundaries of AI-enhanced image creation.

Prediction: Evidence that AI use leads to higher scores on standardized tests will surface next year

Experts predict that AI’s educational potential will be proven next year as evidence emerges, demonstrating its capacity to significantly boost standardized test scores.

No-code AI tools to improve your workflow

Unlocking the power of AI for everyone, a range of no-code AI tools are now available to enhance your workflow, making AI accessibility and usage easier than ever.

In summary, July 10th, 2023, presented exciting breakthroughs and discussions in the realm of AI and machine learning, highlighting the astonishing speed at which the field continues to advance.

How to start an OnlyFans without followers, according to creators

Explore how to start an OnlyFans from scratch. Several creators explain how they got started on the platform and grew their earnings with pricing experiments and more.

Google’s leap into medical AI applications

  • Google’s AI tool, Med-PaLM 2, designed to answer medical questions, is under testing at Mayo Clinic and other locations, aiming to aid healthcare in countries with limited doctor access.
  • Despite some accuracy issues identified by physicians, Med-PaLM 2 performs well in metrics such as evidence of reasoning and correct comprehension, comparable to actual doctors.
  • Customers testing Med-PaLM 2 will maintain control of their encrypted data, with Google not having access to it, according to Google senior research director Greg Corrado.

Revolut’s $20mn security breach

  • A flaw in Revolut’s US payment system allowed criminals to steal over $20mn, with the net loss amounting to almost two-thirds of its 2021 net profit; the issue was linked to differences in European and US payment systems.
  • The fraudulent activity, which affected Revolut’s corporate funds rather than customer accounts, was eventually detected by a partner bank in the US; Revolut closed the loophole in Spring 2022 but has not publicly disclosed the incident.
  • Revolut has faced other challenges, including high-profile departures, a delay in obtaining its UK banking license, warnings from auditor BDO about potential revenue misstatements, and two investors slashing their valuation of the company by over 40% each.

James Webb spotted the most distant active supermassive black hole

  • The James Webb Space Telescope has identified the most distant active supermassive black hole yet, located in the galaxy CEERS 1019 and dating back to just 570 million years after the big bang.
  • This galaxy presents unusual structural features, possibly indicative of past collisions with other galaxies, which could help understand galaxy formation and the roles supermassive black holes play in these processes.
  • Alongside this black hole, the Cosmic Evolution Early Release Science (CEERS) survey has identified 11 extremely old galaxies, which may shift our understanding of star formation and galaxy evolution throughout cosmic history.

Snap’s effective creator engagement strategy

  • Snap’s new revenue-sharing initiative, the Snap Star program, is attracting content creators back to Snapchat, with big names like David Dobrik and Adam Waheed earning significant incomes from the platform.
  • This move is part of a broader effort to reverse Snap’s declining sales and user engagement, amid challenges such as Apple’s privacy policy changes and competition from other platforms offering more lucrative programs for creators.
  • In the first quarter of 2023, user time spent watching Snapchat Stories from creators in the revenue-share program more than doubled year over year in the U.S., indicating initial success in the company’s strategy to increase user engagement.

Knowledge Nugget: Your go-to guide to master prompt engineering in LLMs

Prompt engineering significantly impact the responses from an LLM. Because the trick lies in understanding how models process inputs and tailoring those inputs for optimal results.

In this article, Vaidheeswaran Archana explores this crucial area of working with LLMs and explains the concept using an interesting parrot analogy. The article also explains when to use prompt engineering, the types of prompt engineering, and how to pick the one best for you.

Knowledge Nugget: Your go-to guide to master prompt engineering in LLMs
Knowledge Nugget: Your go-to guide to master prompt engineering in LLMs

Why does this matter?

Using the insights from this article, companies and users determine the best prompt engineering techniques to train their LLM model effectively, ensuring high-quality customer service responses.

Google DeepMind is working on the definitive response to ChatGPT.

It could be the most important AI breakthrough ever.

In a recent interview with Wired, Google DeepMind’s CEO, Demis Hassabis, said this:

“At a high level you can think of Gemini as combining some of the strengths of AlphaGo-type systems with the amazing language capabilities of the large models [e.g., GPT-4 and ChatGPT] … We also have some new innovations that are going to be pretty interesting.”

Why would such a mix be so powerful?

DeepMind’s Alpha family and OpenAI’s GPT family each have a secret sauce—a fundamental ability—built into the models.

  • Alpha models (AlphaGo, AlphaGo Zero, AlphaZero, and even MuZero) show that AI can surpass human ability and knowledge by exploiting learning and search techniques in constrained environments—and the results appear to improve as we remove human input and guidance.

  • GPT models (GPT-2, GPT-3, GPT-3.5, GPT-4, and ChatGPT) show that training large LMs on huge quantities of text data without supervision grants them the (emergent) meta-capability, already present in base models, of being able to learn to do things without explicit training.

Imagine an AI model that was apt in language, but also in other modalities like images, video, and audio, and possibly even tool use and robotics. Imagine it had the ability to go beyond human knowledge. And imagine it could learn to learn anything.

That’s an all-encompassing, depthless AI model. Something like AI’s Holy Grail. That’s what I see when I extend ad infinitum what Google DeepMind seems to be planning for Gemini.

I’m usually hesitant to call models “breakthroughs” because these days it seems the term fits every new AI release, but I have three grounded reasons to believe it will be a breakthrough at the level of GPT-3/GPT-4 and probably well beyond that:

  • First, DeepMind and Google Brain’s track record of amazing research and development during the last decade is unmatched, not even OpenAI or Microsoft can compare.

  • Second, the pressure that the OpenAI-Microsoft alliance has put on them—while at the same time somehow removing the burden of responsibility toward caution and safety—pushes them to try harder than ever before.

  • Third, and most importantly, Google DeepMind researchers and engineers are masters at both language modeling and deep + reinforcement learning, which is the path toward combining ChatGPT and AlphaGo’s successes.

We’ll have to wait until the end of 2023 to see Gemini. Hopefully, it will be an influx of reassuring news and the sign of a bright near-term future that the field deserves.

If you liked this I wrote an in-depth article for The Algorithmic Bridge

What Else Is Happening

🍎AI image recognition models powers Robot Apple Harvester!

📝YouTube tests AI-generated quizzes on educational videos

🚀Official code for DragDiffusion is released, check it out!(Link)

💼TCS scales up Microsoft Azure partnership, to train 25,000 associates(Link)

🔒Shutterstock continues generative AI push with legal protection for enterprise customers(Link)


🛠️ Trending Tools

  • Box AI: Simplify AI with one-click toolbox for diverse capabilities. User-friendly interface for all tech levels.
  • Telesite: Free, easy-to-use mobile site builder. AI-powered features for stunning mobile websites in minutes.
  • AI Postcard Generator: Build personalized postcards based on location and recipient. Tailor with three keywords.
  • SocialBook Photostudio: Powerful AI design tools for professional photo editing and creative effects.
  • InsightJini: Upload data for instant insights and visualizations. Ask questions in natural language for answers and charts.
  • Speak AI: Learn languages, practice scenarios, and receive grammar corrections with an AI-powered language app.
  • Ask my docs: AI-powered assistant for precise answers from documentation. Boost productivity and satisfaction.
  • Disperto: AI content creator, chatbot, and personalized assistant in one. Smarter, faster, and more efficient communication.

Unraveling July 2023: July 09th 2023

Technology News Highlights: July 9th, 2023

Eliminating food waste is the next frontier in saving the planet

In our collective effort to save the planet, eliminating food waste emerges as the next significant frontier. With new technologies and innovative solutions, we can drastically reduce waste and contribute to environmental sustainability.

Seven things every EV fast-charging network needs

As electric vehicles gain popularity, the demand for fast-charging networks rises. This article outlines the seven essential features that every efficient EV fast-charging network should have to support the growing EV ecosystem.

Clair raises, Deel defends allegations and Mercury shares post-SVB growth figures

Even amid controversies and allegations, the tech landscape continues to shift and evolve. Companies like Clair and Mercury manage to secure funding and display growth, whereas Deel navigates through allegations, showcasing the ever-dynamic world of technology.

Meta’s Threads goes live, OpenAI launches GPT-4 and Pornhub blocks access

A wave of significant updates has hit the tech world, with Meta launching Threads, OpenAI releasing the much-anticipated GPT-4, and Pornhub blocking access in certain regions, marking a day of considerable shifts in the digital landscape.

Vertical AI and who might build it

As AI technology continues to mature, the concept of Vertical AI gains momentum. The article explores who might be at the forefront of building this specialized form of AI and its potential applications.

Deal Dive: Startups can still raise capital — even if it’s for a good cause

Proving that startups can achieve fundraising success while promoting social good, this feature shines a light on companies managing to secure capital for altruistic causes.

The week in AI: Generative AI spams up the web

AI continues to revolutionize the web, with generative AI models leading to an influx of automated content. However, this wave brings with it the challenge of managing potential spam-like behaviors.

Meta’s vision for Threads is more mega-mall than public square

Meta’s Threads goes live with a vision more akin to a digital mega-mall than a public square, redefining the social media experience with a focus on commerce and interaction.

If you don’t buy Jony Ive’s $60,000 turntable, are you really a music fan?

For audiophiles and technology enthusiasts alike, the latest spectacle is Jony Ive’s $60,000 turntable. As high-end tech products increasingly become status symbols, this piece explores what it means to be a true music fan in today’s digital age.

MIT develops a motion and task planning system for home robots

MIT’s latest development is a motion and task planning system designed for home robots, bringing us one step closer to a future where robots seamlessly integrate into our daily lives.

In a nutshell, July 9th, 2023, was marked by fascinating developments and discussions across various sectors within the tech industry, ranging from environmental sustainability and electric vehicles to AI and robotics.

Artificial Intelligence and Machine Learning Highlights: July 9th, 2023

Meet Pixis AI: An Emerging Startup Providing Codeless AI Solutions

Training AI models demands massive amounts of data that must be error-free, correctly formatted, and relevant. Pixis AI, an emerging startup, offers a codeless solution to this challenging process, bringing AI capabilities closer to businesses and individuals with less technical expertise.

A humanoid robot draws this cat and says, ‘if you don’t like my art, you probably just don’t understand art’

Ameca, marketed as the ‘most expensive robot that can draw’, showcases the seamless integration of AI and arts. Powered by Stable Diffusion and built by Engineered Arts, Ameca’s creative expression poses exciting questions about the intersection of AI and art.

Navigating on the moon using AI

AI transcends terrestrial boundaries, with Dr. Alvin Yew pioneering a system that leverages topographical lunar data to navigate on the moon. The solution is designed to function in the absence of GPS or other electronic navigation systems, marking a significant leap in space exploration and AI.

How to land a high-paying job as an AI prompt engineer

Aiming for a high-paying job as an AI prompt engineer? An extensive understanding of NLP and hands-on experience are critical. This field represents an exciting frontier in AI, demanding both theoretical knowledge and practical insights.

ChatGPT builds robots: New research

Microsoft Research reveals an intriguing study on using OpenAI’s ChatGPT for robotics applications. The strategy hinges on principles for prompt engineering and creating a function library that enables ChatGPT to adapt to different robotics tasks and form factors. Microsoft also introduced PromptCraft, an open-source platform for sharing effective prompting schemes for robotics applications.

Overall, July 9th, 2023, witnessed significant advancements in AI and machine learning, with developments spanning from codeless AI solutions to lunar navigation and AI-driven robotic applications.

Why You Should Register Your Threads Account As Soon As Possible

Why You Should Register Your Threads Account As Soon As Possible
Why You Should Register Your Threads Account As Soon As Possible
Registering is incredibly easy since you just need to login using your Instagram profile.

Unraveling July 2023: July 08th 2023

Artificial Intelligence and Machine Learning Highlights: July 8th, 2023

This week in AI kicked off with a fascinating look at the impact of generative AI on the web. SEO-optimized, AI-generated content start-up became the talk of the town, contributing to an exponential increase in web content. Notably, OpenAI released its advanced language model, GPT-4, and introduced a smart intubator to the public. The advent of GPT-4 and its innovative applications promises to bring substantial changes to how we interact with digital content (https://techcrunch.com/2023/07/08/the-week-in-ai-generative-ai-spams-up-the-web/).

In the realm of healthcare and AI, machine learning techniques are making significant strides. Scientific reports suggest the promising potential of machine learning in predicting recurrence in clear cell renal cell carcinoma patients. This development underscores the expanding role of AI in precision medicine and diagnostics (https://www.nature.com/articles/s41598-023-38097-7).

OpenAI has made the API for GPT-4 available to all paying customers, with the APIs for GPT-3.5 Turbo, DALL·E, and Whisper now generally available as well. OpenAI’s Code Interpreter also came to the limelight, enabling ChatGPT to execute various tasks like running code, analyzing data, and creating charts (https://openai.com/blog/gpt-4-api-general-availability).

In an effort to bridge the gap between human language and coding, Salesforce Research has released CodeGen 2.5. It allows users to translate natural language into programming languages, enhancing code development productivity and efficacy (https://blog.salesforceairesearch.com/codegen25/).

Meanwhile, InternLM open-sourced a 7B parameter base model and a chat model tailored for practical scenarios, reinforcing the importance of open-source technology in advancing AI research and development (https://github.com/InternLM/InternLM).

The question of whether AI-generated training data represents a major win or a misleading triumph continues to spark debates in the AI community. The significance and limitations of AI in data generation are being explored, prompting further investigations into its impact on AI models’ performance (https://dblalock.substack.com/p/models-generating-training-data-huge#%C2%A7so-whats-going-on).

Google’s 2023 Economic Impact Report shed light on the potential economic benefits of AI in the UK, estimating that AI innovations could generate up to £118bn in economic value this year alone (https://www.unleash.ai/artificial-intelligence/google-ai-will-super-boost-the-economy/).

Stanford researchers have developed a novel training method called “curious replay” that allows AI agents to “self-reflect” and adapt more effectively to changing environments, inspired by studies on mice. This development marks a step forward in AI’s adaptability to dynamic circumstances (https://hai.stanford.edu/news/ai-agents-self-reflect-perform-better-changing-environments).

Microsoft’s latest innovation, LongNet, showcases the potential of scaling Transformers to 1,000,000,000 tokens, reflecting the ongoing evolution of AI’s capabilities in handling large-scale data (https://arxiv.org/abs/2307.02486).

As AI evolves, so too do its risks. OpenAI is forming a team specifically tasked with combating these risks, demonstrating the organization’s commitment to responsible AI development and use (https://theintelligo.beehiiv.com/p/chatgpts-hype-seeing-dip).

In a humanitarian turn, AI-powered robotic vehicles may soon be delivering food parcels to conflict and disaster zones. This initiative by the World Food Programme could start as early as next year, potentially reducing risks to humanitarian workers (https://www.reuters.com/technology/un-food-aid-deliveries-by-ai-robots-could-begin-next-year-2023-07-07/).

In conclusion, July 8th, 2023, saw significant strides in AI and machine learning across various fields, including digital content creation, healthcare, coding, economy, adaptability, and humanitarian efforts.

Unraveling July 2023: July 07th 2023

Technology News Headlines: Security Concerns and Solutions, July 7th, 2023

In a significant cybersecurity development, Mastodon, the open-source and decentralized social network, has patched a critical “TootRoot” vulnerability that had allowed potential node hijacking, underscoring the need for constant vigilance in the digital world (source).

Meanwhile, an actively exploited vulnerability threatens hundreds of solar power stations. This news highlights the intersection of technology and energy and the crucial importance of cybersecurity in all sectors (source).

A serious Fortigate vulnerability remains unpatched on 336,000 servers, further emphasizing the scale of the cybersecurity challenge and the urgent need for proactive measures (source).

In other news, Taiwan Semiconductor Manufacturing Company (TSMC), the world’s leading semiconductor company, has reported some of its data being involved in a hack on a hardware supplier. The incident serves as a reminder of the interconnectedness of global supply chains and the ripple effects of cyberattacks (source).

The Red Hat software company has faced intense pushback following a controversial new source code policy, demonstrating the ongoing debates over intellectual property rights in the technology sector (source).

With the rise of image-based phishing emails, the task of detecting cybersecurity threats becomes more complex and challenging. These phishing campaigns illustrate the evolving tactics of cybercriminals and the importance of advancing cybersecurity tools (source).

An op-ed discusses the much-anticipated #TwitterMigration and its less than expected outcomes, highlighting the complexity of social media ecosystems and user behavior (source).

Browser company Brave is taking steps to limit websites from performing port scans on visitors, reinforcing its commitment to user privacy and security (source).

Fears are growing over the potential for deepfake ID scams following the Progress hack, underlining the escalating concerns about the misuse of advanced technologies like AI for malicious purposes (source).

Last but not least, the casualties continue to rise from the mass exploitation of the MOVEit zero-day vulnerability, serving as a stark reminder of the impact of cyber threats (source).

In conclusion, July 7th, 2023, was dominated by developments in cybersecurity, with concerns over vulnerabilities, policy changes, and the misuse of advanced technologies coming to the fore.

AI and Machine Learning Developments: Pioneering Progress and Innovations, July 7th, 2023

Artificial intelligence continues to make inroads into scientific research, with a system that can learn the language of molecules to predict their properties. This breakthrough has immense potential for chemical research and drug discovery (source).

At the Massachusetts Institute of Technology, scientists have developed a system that can generate AI models for biology research, opening up new horizons for the use of AI in biological sciences (source).

National security leaders are undergoing education on artificial intelligence, reinforcing the vital role of AI in national security efforts (source).

Researchers have successfully taught an AI to write better chart captions. This achievement showcases AI’s potential for enhancing data visualization and communication (source).

In a unique blend of image recognition and generation, a new computer vision system brings together two key AI technologies to deliver superior performance (source).

The process of medical data labeling is being gamified to accelerate AI advancements in the healthcare sector. This innovative approach demonstrates the creative strategies being used to tackle challenges in AI development (source).

Artificial intelligence is enhancing our ability to sense the world around us, promising to revolutionize numerous sectors, from robotics to autonomous vehicles (source).

The MIT-Pillar AI Collective has announced its first seed grant recipients, indicating growing support for AI research and development (source).

An MIT PhD student is working to enhance STEM education in underrepresented communities in Puerto Rico, highlighting the potential of AI to drive educational equity (source).

Finally, as we consider the role of art in expressing our humanity, we must also ask: Where does AI fit in? The exploration of AI’s place in the creative landscape is ongoing and raises thought-provoking questions about the nature of creativity and the capabilities of artificial intelligence (source).

From breakthroughs in scientific research to educational advancements and the exploration of AI’s role in art, July 7th, 2023, marked another day of substantial progress in the realm of AI and machine learning.

Unraveling July 2023: July 06th 2023

Tech News Updates: Pioneering Developments and Innovations, July 6th, 2023

The tech world of July 6th, 2023, witnessed multiple breakthroughs, funding rounds, and strategic changes spanning the automotive industry, social media, fintech, and more.

Volkswagen announced plans to test its self-driving ID Buzz vans in Austin. This move marks a significant step towards enhancing the future of autonomous driving technology (source).

There’s been a call for unity between social media platforms Mastodon and Bluesky. Experts believe that aligning their efforts in the post-Twitter world could facilitate a more effective and inclusive digital communication landscape (source).

Public Ventures has announced the launch of a $100M impact fund, dedicated to investing in early-stage life science and clean tech enterprises. This move signals an increasing focus on industries crucial for addressing global challenges (source).

In an investment highlight, SoftBank has backed Japanese robotics startup Telexistence in a $170M funding round. This significant investment indicates growing confidence in robotics and its potential applications (source).

Spotify is set to remove the App Store payment option for legacy subscribers. This move comes amidst ongoing controversies related to the App Store’s commission policies (source).

Fintech firm Clair has received further support from Thrive Capital, reinforcing its mission to help frontline workers receive instant payment. The increased investment underscores the growing need for innovative solutions in the financial sector (source).

Meta has stated that Threads profiles can only be deleted by deleting the corresponding Instagram account. This decision has sparked discussions about the integration and independence of social media platforms (source).

For those seeking to obtain a J-1 exchange visa, the “Ask Sophie” column offers essential insights. The guidance provided is crucial for understanding the complexities of international exchanges (source).

In a novel application of AI, a sex toy company is using OpenAI’s ChatGPT to whisper customizable fantasies to its users. This unusual deployment of AI demonstrates the extensive, and sometimes surprising, capabilities of this technology (source).

AI and Machine Learning Updates: Ground-breaking Developments and Innovations, July 6th, 2023

In a remarkable medical breakthrough, an AI-powered robotic glove is giving stroke victims the chance to play the piano again, demonstrating the transformative potential of artificial intelligence in physical rehabilitation (source).

Research into Quantum Machine Learning is revealing that simple data may be the key to unlocking its full potential. These insights could have profound implications for this emerging field (source).

Artificial intelligence has proven its creative prowess, with AI tests placing in the top 1% for original creative thinking, according to new research from the University of Montana and its partners. This raises fascinating questions about the boundaries of AI creativity (source).

However, OpenAI’s ChatGPT has seen a 10% drop in traffic as initial enthusiasm appears to be waning. This development reminds us of the fluctuating nature of technological adoption and interest (source).

OpenAI has suggested that superintelligence may be achievable within the next seven years. If true, this could mark the dawn of a new era in AI, with far-reaching implications for every aspect of society (source).

There is also a growing emphasis on education in the AI field, with five top-rated deep learning courses and four recommended apps for mastering them identified, including offerings from Coursera, Fast.ai, edX, and Udacity (source).

Meanwhile, Nvidia’s trillion-dollar market cap is under threat from new AMD GPUs and open-source AI software, highlighting the increasingly competitive nature of the AI industry (source).

In a disturbing case, a man who attempted to assassinate the Queen with a crossbow was allegedly incited by an AI chatbot. This highlights the urgent need for ethical guidelines and safeguards in AI technology (source).

In New York, the Icahn School of Medicine at Mount Sinai has launched the first Center for Ophthalmic Artificial Intelligence and Human Health. This pioneering establishment is one of the first of its kind in the United States (source).

The United States military has begun testing the use of generative AI for planning responses to potential global conflicts and for streamlining mundane tasks. Despite early success, the technology is not yet ready for full deployment (source).

A Privacy-Enhancing Anonymization System, dubbed “My Face, My Choice,” has been introduced by researchers from Binghamton University. This tool empowers users to control their facial images in social photo sharing networks (source).

Finally, the world’s most advanced humanoid robot, Ameca, created by Engineered Arts, has demonstrated its capacity to imagine drawings. The robot’s latest achievement involved creating a picture of a cat, reinforcing the astonishing capabilities of modern robotics (source).

Unraveling July 2023: July 05th 2023

AI and Machine Learning Updates: Advancements and Innovations, July 5th, 2023

July 5th, 2023, was a significant day in the ever-evolving world of artificial intelligence (AI) and machine learning, characterized by breakthroughs in multiple sectors, including national security, medical data processing, and even the arts.

On the forefront of national security, leaders are being educated on the potentials and intricacies of AI. This effort underscores the increasing importance of AI in driving strategic decisions and maintaining national security in the face of emerging digital threats (source).

In a bid to improve data visualization, researchers have taught an AI to write more informative and effective chart captions. This development can enhance the ability of AI to not just analyze data but present it in a more user-friendly and understandable manner (source).

On the medical front, the process of data labeling is being gamified to advance AI applications. By turning data labeling into a game, the traditionally labor-intensive task can be made more engaging, potentially improving the quality and speed of the process (source).

The power of AI to revolutionize image recognition has been further illustrated by a new computer vision system. This system integrates image recognition and generation, promising more accurate and sophisticated visual processing capabilities (source).

In academia, the MIT-Pillar AI Collective announced its first seed grant recipients, highlighting the ongoing investment in future leaders of AI and machine learning research (source).

Meanwhile, an MIT PhD student is leveraging AI to enhance STEM education in underrepresented communities in Puerto Rico. This endeavor emphasizes the potential of AI to democratize education and bridge the digital divide (source).

Lastly, in a philosophical reflection, the intersection of AI and art is being explored. The question of how AI fits into human creativity and artistic expression is provoking insightful debates, opening new perspectives on the potential roles of AI in human society (source).

Tech News Roundup: A Day of Innovations and Challenges, July 5th, 2023

The world of tech was marked by a flurry of exciting news and critical challenges on July 5th, 2023, highlighting the resilience and relentless pace of innovation in this field.

In Japan, the Port of Nagoya, the nation’s largest and busiest port, faced a significant cyber attack. A ransomware intrusion on July 4th caused considerable disruption, with no group yet claiming responsibility for the hack. Despite the setback, the port plans to resume operations by July 6th, underlining the resilience in the face of increasing cyber threats (source).

Meanwhile, Instagram unveiled a basic web interface for its upcoming app, Threads. The move gave an early glimpse into the new service before its official launch on July 6th. With over 2,500 users already on board, it’s clear that anticipation for this new communication platform is high (source).

AI continued to make headlines, this time in the music industry. Recording Academy CEO Harvey Mason Jr. clarified that music containing AI-created elements is eligible for Grammy recognition, but the AI portion itself would not be considered for the award (source).

AI also featured in health tech news, with the AI-based full-body scanner startup, Neko Health, securing a significant funding round. The company, co-founded by Spotify CEO Daniel Ek and Watty founder Hjalmar Nilsonne, raised 60 million Euros in a round led by Lakestar (source).

Meanwhile, in Senegal, technology is playing a crucial role in agriculture. Farmers who struggle with literacy are using WhatsApp voice notes to collaborate with NGOs and researchers, learning new farming practices and enhancing their livelihoods (source).

The EU announced new rules aimed at streamlining the work of privacy regulators on cross-border cases, responding to criticism about slow investigations. The rules also aim to give companies more rights, striking a balance between corporate interests and data privacy concerns (source).

Samsung’s ambitions in the AI chip sector came under the spotlight. Despite its dominance in the smartphone and high-resolution TV markets, skeptics question whether Samsung can become as indispensable in the emerging field of generative AI (source).

Last but not least, sources suggest that Meta’s new app, Threads, is not prepared for a European launch outside the UK, which operates under different privacy rules compared to the rest of Europe. This development underscores the complexity of global digital service rollouts amid varying regional regulations (source).

From cybersecurity to AI, from social media to data privacy, July 5th, 2023, proved to be another dynamic day in the tech world.

Instagram’s Twitter competitor Threads is already live on the web

Instagram’s Twitter competitor Threads is already live on the web
Instagram’s Twitter competitor Threads is already live on the web
Less than 3,000 brands and creators are already experimenting with Threads

Unraveling July 2023: July 04th 2023

Tech Developments: Highlights from July 4th, 2023

July 4th, 2023, has been a noteworthy day in the tech sector, with key developments involving major companies like Meta, Apple, Twitter, and Rivian.

In the social media realm, Meta, formerly known as Facebook, announced it will launch a new text-based conversation app later in the week, marking its direct competition with Twitter. This app, known as Threads, exemplifies Meta’s continued expansion into various communication platforms, shaping the social media landscape.

Interestingly, Twitter has made its move too. The social media giant has decided to monetize TweetDeck, one of its popular tools, by introducing a subscription model. This decision is part of an emerging trend among tech companies to create additional revenue streams and improve service quality.

Apple, another tech titan, has taken its battle with Epic Games to the next level. The tech giant is set to ask the Supreme Court to hear its appeal in the landmark case, Epic Games v. Apple. The outcome of this case could have far-reaching implications for app store policies and antitrust regulations in the digital marketplace.

Rivian, an American electric vehicle automaker, has achieved a significant milestone by delivering its first electric vans to Amazon in Europe. This event marks a key step in Amazon’s sustainability goals and signifies Rivian’s growing influence in the international EV market.

In financial news, the world’s top 500 richest people have experienced a prosperous first half of 2023. On average, each individual has made an impressive $14 million per day, largely fueled by rallying markets. This wealth accumulation highlights the continued economic influence of these tech moguls and raises questions about wealth distribution in the digital age.

These developments underline the continual evolution of the tech sector, shedding light on the strategies of key players and the economic and societal impacts of their decisions.

AI & Machine Learning Developments: July 4th, 2023

On July 4th, 2023, artificial intelligence (AI) and machine learning continued to redefine multiple sectors, with significant announcements and groundbreaking developments shaking the tech landscape.

In a promising breakthrough, AI has been used to predict the effects of RNA-targeting by CRISPR technology, a development that holds the potential to revolutionize gene therapy. By accurately forecasting how CRISPR will interact with RNA, this innovation could pave the way for more effective and personalized treatments for genetic disorders.

The same day saw OpenAI facing a lawsuit from authors who claim that the AI training model, ChatGPT, used their written work without consent. This case contributes to the ongoing conversation about ethical considerations in AI, particularly regarding intellectual property rights.

Google AI made waves with the introduction of MediaPipe Diffusion plugins. These innovative tools enable on-device, controllable text-to-image generation, offering unprecedented flexibility and immediacy for digital design and user creativity.

Meanwhile, Microsoft unveiled the first public beta version of its much-anticipated operating system, Windows 11. The highlight of this release is the AI assistant, Copilot, which promises to enhance user experience and productivity through advanced machine learning algorithms.

Meta, the company formerly known as Facebook, made a bold move in the social media landscape by launching Threads, a text-based conversation app set to compete with Twitter. This development underscores Meta’s ongoing strategy to expand into new communication formats and platforms.

Last but not least, the potential of machine learning for early disease detection was underscored by the announcement that it has been used to identify early predictors of type 1 diabetes. This potentially life-saving application of AI demonstrates the vast potential of machine learning in the medical field.

All these events marked July 4th, 2023, as a significant day in the evolution of AI and machine learning, reflecting the transformative impact of these technologies across various domains.

Unraveling July 2023: July 03rd 2023

The Changing Tides of Tech: From AI-generated Games to Multimodal Robots

In a fast-paced and interconnected tech world, a whirlwind of innovation and evolution is reshaping everyday experiences. The horizon holds significant developments that range from breakthroughs in robotics to shifts in privacy norms.

Apple has reportedly reduced the production of its Vision Pro model and delayed the release of a cheaper alternative. This decision might impact the tech giant’s market position, particularly if consumer demand for the cheaper model remains strong. In contrast, Rivian, an American electric vehicle automaker, has seen a surge in its stock after exceeding expectations for its Q2 deliveries, indicating a rising tide for the EV industry.

Sweden’s privacy watchdog has taken a significant step towards data privacy, issuing over $1M in fines and urging businesses to stop using Google Analytics. This move underscores a global trend towards stricter data privacy norms and regulations.

Simultaneously, Google’s Gradient has backed YC alum Infisical, a cybersecurity startup aiming to solve the issue of secret sprawl. The investment highlights the growing importance of security in the tech ecosystem.

In an intriguing turn of events, Valve, the gaming giant behind the Steam platform, has responded to allegations of banning AI-generated games. This development raises important questions about the role of AI in the gaming industry and its potential impact on developers and players.

On the robotics front, the M4 robot is making waves with its ability to transform and navigate diverse terrains. It can roll, fly, and walk, offering exciting implications for various applications from search and rescue to entertainment.

As streaming platforms continue to reshape the entertainment landscape, Netflix has added the acclaimed HBO show ‘Insecure’ to its catalog. More HBO content, including the iconic ‘Six Feet Under,’ is reportedly on its way. This expansion of its content library can potentially redefine the streaming competition.

For the productivity-focused, AudioPen has emerged as a handy tool, converting voice into text notes. This web app harnesses AI’s power to streamline workflows and offer a new level of convenience.

YouTube comedy giants Anthony Padilla and Ian Hecox are setting the stage for a new era of Smosh, their immensely popular sketch comedy brand. This move hints at the continued growth of digital content creation as a significant cultural force.

Lastly, in the venture capital world, Lina Zakarauskaite’s elevation from principal to partner at London’s Stride VC serves as a testament to her contributions and the firm’s confidence in her leadership. This change signals continued dynamism within the VC sector as it navigates the tech ecosystem’s evolving landscape.

These transformative shifts and developments reflect the tech world’s ceaseless evolution, signaling an exciting future on the horizon.

Texas man who went missing as a teen is found alive 8 years later

Robert De Niro speaks out on death of 19-year-old grandson

Novak Djokovic’s bid for Wimbledon title No. 8 and Grand Slam

How much YouTubers make for 1 million subscribers

YouTubers with 1 million subscribers can easily make six-figures. Creators who are a part of YouTube’s Partner Program can monetize their YouTube videos with ads.

YouTubers can make thousands of dollars each month from the program.

A YouTuber with about 1 million subscribers made between $14,600 and $54,600 per month.

To start earning money directly from YouTube for long-form videos, creators must have at least 1,000 subscribers and 4,000 watch hours in the past year. Once they reach that threshold, they can apply for YouTube’s Partner Program, which allows them to start monetizing their channels through ads, subscriptions, and channel memberships. For every 1,000 ad views, advertisers pay a certain rate to YouTube. YouTube takes 45% of the revenue, and the creator gets the rest.

YouTubers can also make money from shorts, the platform’s short-form videos. Creators need to reach 10 million views in 90 days and have 1,000 subscribers in order to qualify.

Two key metrics for earning money on YouTube are the CPM rate, or how much money advertisers pay YouTube per 1,000 ad views, and RPM rate, which is how much revenue a creator earns per every 1,000 video views after YouTube’s cut.

Some subjects, like personal finance and business, can boost a creator’s ad rate by attracting lucrative advertisers. But while Ma’s lifestyle content makes less money, she’s perfected a strategy to maximize payout.

“To really optimize your audience, I think YouTubers should definitely put three to four ads within a video,” Ma said.

The money made directly from YouTube is a key pillar of many creators’ incomes.

Here are eight exclusive earnings breakdowns in which YouTubers with 1 million followers or more share exactly how much they earn from the platform:

Unraveling July 2023: July 02nd 2023

Tesla Cybertruck Coming This Quarter: Musk

Tesla Cybertruck Coming This Quarter: Musk
Tesla Cybertruck Coming This Quarter: Musk
Tesla CEO Elon Musk is on the record saying the Cybertruck delivery event will happen this quarter. Signs point to the event actually taking place this time.

No One Believes Elon Musk’s Explanation For Breaking Twitter

No One Believes Elon Musk’s Explanation For Breaking Twitter
No One Believes Elon Musk’s Explanation For Breaking Twitter
Well, he finally did it. Elon Musk has broken Twitter so badly that it might as well be offline at this point.

Tesla delivers record EVs amid federal tax credits, price cuts;

Tesla delivers record EVs amid federal tax credits, price cuts;
Tesla delivers record EVs amid federal tax credits, price cuts;
Incentives and price cuts made Tesla electric cars cheaper than comparable gasoline models. But the company faces growing competition in China, a key market.

Lucid scores a win, Bird’s founder leaves the nest and Zoox robotaxis roll out in Vegas

Fintech M&A gets a big boost with Visa-Pismo dealNetflix axes its basic plan in Canada, IRL shuts down and Shein’s influencer stunt backfires

What do FinOps and parametric insurance have in common?

This week in robotics: Teaching robots chores from YouTube, robot dogs at the border and drone consolidation;

Unraveling July 2023: July 01st 2023

‘Rate limit exceeded;’ Twitter down for thousands of users worldwide

Elon Musk blames ‘data scrapers’ as he puts up paywalls for reading tweets

'Rate limit exceeded;' Twitter down for thousands of users worldwide
Unraveling July 2023: ‘Rate limit exceeded;’ Twitter down for thousands of users worldwide
Only people who pay for Twitter can see more than 600 posts per day

Penis Enlargement: 2 Research-Backed Reasons For Men’s Obsession With ‘Size’

Penis Enlargement: 2 Research-Backed Reasons For Men’s Obsession With ‘Size’
Unraveling July 2023: Penis Enlargement: 2 Research-Backed Reasons For Men’s Obsession With ‘Size’
Why do so many men pursue potentially harmful ways to increase the size of their penis even when the risks to their long-term health and well-being are significant?

Reef Sharks Face Heightened Extinction Risk

Reef Sharks Face Heightened Extinction Risk
Unraveling July 2023: Reef Sharks Face Heightened Extinction Risk
To make sure these predators survive, scientists agree that protected areas and fisheries management are the keys to their survival.

Tiny Bugs Swarm New York City Amidst Canada Wildfire Smoke

Tiny Bugs Swarm New York City Amidst Canada Wildfire Smoke
Unraveling July 2023: Tiny Bugs Swarm New York City Amidst Canada Wildfire Smoke
On Friday, NYC’s Air Quality Index (AQI) topped 150, placing it in the “unhealthy” level and giving the Big Apple the second worst air quality in the World.

France riots live: Macron cancels Germany trip as additional 45,000 police to be deployed

France riots live: Macron cancels Germany trip as additional 45,000 police to be deployed
France riots live: Macron cancels Germany trip as additional 45,000 police to be deployed
Funeral for Nahel, killed by police on Tuesday, held near Paris on Saturday afternoon

Harvard scientist, Avi Loeb, claims he collected remains of ‘extraterrestrial technology’ from bottom of the Pacific

Harvard scientist, Avi Loeb, claims he collected remains of ‘extraterrestrial technology’ from bottom of the Pacific
Harvard scientist, Avi Loeb, claims he collected remains of ‘extraterrestrial technology’ from bottom of the Pacific
Avi Loeb, the ‘alien hunter of Harvard’, has collected ‘extraterrestrial technology’ from the first confirmed interstellar object that landed on Earth in 2014.
The FTC has expressed concerns about potential monopolies and anti-competitive practices within the generative AI sector, highlighting the dependencies on large data sets, specialized expertise, and advanced computing power that could be manipulated by dominant entities to suppress competition.

Concerns about Generative AI: The FTC believes that the generative AI market has potential anti-competitive issues. Some key resources, like large data sets, expert engineers, and high-performance computing power, are crucial for AI development. If these resources are monopolized, it could lead to competition suppression.

  • The FTC warned that monopolization could affect the generative AI markets.

  • Companies need both engineering and professional talent to develop and deploy AI products.

  • The scarcity of such talent may lead to anti-competitive practices, such as locking-in workers.

Anti-Competitive Practices: Some companies could resort to anti-competitive measures, such as making employees sign non-compete agreements. The FTC is wary of tech companies that force these agreements, as it could threaten competition.

  • Non-compete agreements could deter employees from joining rival firms, hence, reducing competition.

  • Unfair practices like bundling, tying, exclusive dealing, or discriminatory behavior could be used by incumbents to maintain dominance.

Computational Power and Potential Bias: Generative AI systems require significant computational resources, which can be expensive and controlled by a few firms, leading to potential anti-competitive practices. The FTC gave an example of Microsoft’s exclusive partnership with OpenAI, which could give OpenAI a competitive advantage.

  • High computational resources required for AI can lead to monopolistic control.

  • An exclusive provider can potentially manipulate pricing, performance, and priority to favor certain companies over others.

Source (Forbes)

Twitter users globally report multiple site issues, including seeing “rate limit exceeded” or “cannot retrieve tweets” error messages (The Indian Express)

As reported by The Indian Express, Twitter users across the globe have experienced numerous issues with the social media platform, receiving error messages like “rate limit exceeded” or “cannot retrieve tweets”.

Elon Musk claims Twitter login requirement is a “temporary emergency measure” as “several hundred” orgs were “scraping Twitter data extremely aggressively” (Matt Binder/Mashable)

Elon Musk, in response to the recent Twitter issues, claims that the requirement for users to log in is a “temporary emergency measure”. This measure was implemented due to “several hundred” organizations “scraping Twitter data extremely aggressively”, according to Musk’s statement reported by Matt Binder of Mashable.

Tracxn: Indian startups raised $5.46B in H1 2023, down from $17.1B in H1 2022 and $13.4B in H1 2021 (Manish Singh/TechCrunch)

Tracxn reports that Indian startups raised $5.46 billion in the first half of 2023, a significant drop from the $17.1 billion raised in the first half of 2022, and $13.4 billion in the first half of 2021. Notably, venture capital firms Tiger Global and SoftBank have scaled back their activities, with the former making only one deal and the latter making none, as reported by Manish Singh of TechCrunch.

Generative AI can make experienced programmers more productive, potentially eliminating tasks done by junior developers as companies use the tech to save money (Christopher Mims/Wall Street Journal)

Christopher Mims of The Wall Street Journal reports that generative AI has the potential to increase the productivity of experienced programmers by taking over tasks typically assigned to junior developers. As a result, companies could use the technology to save money.

The FBI says it formed an online database in May to prevent swatting by facilitating coordination between police departments and law enforcement agencies (NBC News)

The FBI has established an online database designed to prevent swatting, a dangerous prank involving false emergency calls to dispatch large-scale police or SWAT responses. This database, launched in May, facilitates coordination between police departments and law enforcement agencies, according to a report by NBC News.

YouTube removes the channels of three North Korean influencers posting about their daily life, after South Korea labelled them as “psychological warfare” tools (Christian Davies/Financial Times)

YouTube has removed the channels of three North Korean influencers who were sharing content about their daily lives. The removal follows South Korea’s classification of these channels as tools of “psychological warfare”, as reported by Christian Davies of the Financial Times.

Major third-party Reddit apps Apollo, Sync, and BaconReader shut down, as Reddit prepares to enforce its new API rate limits “shortly” (Jay Peters/The Verge)

As Reddit prepares to enforce new API rate limits, major third-party Reddit apps like Apollo, Sync, and BaconReader have been shut down. This development has been reported by Jay Peters of The Verge.

In a rare rebuke, Japan told Fujitsu to take corrective measures after a 2022 hack of its cloud service affected at least 1.7K companies and government agencies (Nikkei Asia)

In a rare rebuke, Japan has ordered Fujitsu to take corrective action following a 2022 hack of its cloud service. The incident affected at least 1,700 companies and government agencies, according to a report by Nikkei Asia.

TSA plans to expand its facial recognition program to ~430 US airports, says its algorithms are 97% effective “across demographics, including dark skin tones” (Wilfred Chan/Fast Company)

The Transportation Security Administration (TSA) plans to expand its facial recognition program to approximately 430 US airports. According to Wilfred Chan’s report in Fast Company, the TSA claims its algorithms are 97% effective across various demographics, including those with darker skin tones.

Fidelity, Invesco, VanEck, and WisdomTree refile for a spot bitcoin ETF with Coinbase as market surveillance provider, to answer the US SEC’s objections (Bloomberg)

Fidelity, Invesco, VanEck, and WisdomTree have refiled their applications for a spot bitcoin Exchange-Traded Fund (ETF) with the US Securities and Exchange Commission (SEC). To address the SEC’s objections, they have now included Coinbase as the market surveillance provider, as reported by Bloomberg.

AI Unraveled Podcast – Latest AI Trends May 2023

AI Unraveled Podcast

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

AI Unraveled Podcast – Latest AI Trends May 2023: Latest AI Trends. Demystifying Frequently Asked Questions on Artificial Intelligence. Latest ChatGPT Trends, Latest Google Bard Trends.

AI Unraveled Podcast May 31st 2023: How to Invest In AI; Are We Unknowingly Creating ‘Reptilian’ and ‘Mammalian’ AI?; Any AIs that can find directions from X to Y with natural language?; The Intersection of Artificial Intelligence, Blockchain, and DAO.

How to Invest In AI; Are We Unknowingly Creating 'Reptilian' and 'Mammalian' AI?; Any AIs that can find directions from X to Y with natural language?; The Intersection of Artificial Intelligence, Blockchain, and DAO
Latest AI trends May 31st 2023: How to Invest In AI; Are We Unknowingly Creating ‘Reptilian’ and ‘Mammalian’ AI?; Any AIs that can find directions from X to Y with natural language?; The Intersection of Artificial Intelligence, Blockchain, and DAO

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence. In today’s episode, we’ll be discussing the latest AI trends, including how to invest in AI, the possibility of creating ‘Reptilian’ and ‘Mammalian’ AI, and more. Don’t miss out on staying up-to-date with the constantly evolving world of AI – be sure to hit the subscribe button. In today’s episode, we’ll cover investing in AI stocks, recent breakthroughs in AI mathematical problem-solving, the release of a new book to demystify FAQ on AI, the intersection of AI, blockchain, and DAOs, risks to humanity from AI, how the design impacts AI behavior, and a resource to level up machine learning skills.

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Investing in the ever-evolving field of artificial intelligence is an exciting opportunity, but it requires careful consideration and strategic planning. The AI industry is currently experiencing a technological disruption that could lead to substantial returns for savvy investors. However, identifying which companies will emerge as winners in the AI industry can be a difficult task. Innovators and imitators alike may end up with a market-leading position, so it’s important to consider all potential investments.

There are different approaches to investing in AI. Some investors prefer to invest directly in AI development companies, while others opt for companies that stand to benefit the most from its wider adoption. For example, during the personal computer industry’s rise, investors found success in computer manufacturers, software companies, and businesses that benefited from the automation that computers offered. The point is that there are often winners and losers when new technologies emerge.

It’s worth noting that investing in companies that could benefit from changes within the workforce could also be an option. With the potential for AI to displace workers in many industries, there may be opportunities to invest in companies that focus on worker retraining and are poised to capitalize on these significant shifts in the workforce.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

There are individual stocks that match some of these investment criteria for those interested in investing in AI. It’s important to do your own research and consider all the potential risks and returns before making any investment decisions.

If you’re looking to invest in AI, there are several companies to consider. One of the most notable is Tesla, which uses AI to automate driving. This requires constant processing of data to identify other cars, road conditions, traffic signals, and pedestrians. Another key player in the AI space is NVIDIA, which has a strong position in the marketplace through its generative artificial intelligence. They’ve also created chips, hardware, software, and development tools to create start-to-finish AI systems.

Microsoft is another company worth looking into if you’re considering AI investments. They’ve invested $13 billion in AI initiatives and have embedded AI into many of their systems, including Bing search engine, Microsoft 360, sales and marketing tools, X-Box, and GitHub coding tools. They’ve also outlined a framework for building AI apps and copilots and expanding their AI plug-in ecosystem.

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Taiwan Semiconductor Manufacturing is the world’s largest chip maker, and is another leading competitor in chip manufacturing for artificial intelligence. As AI grows, the need for robust computing chips will grow with it. If you’re looking to invest in a more mature company that still has a vested interest in AI, Taiwan Semiconductor Manufacturing may be the way to go.

Meta Platforms invests significantly in AI, utilizing large language module (LLM) AI to drive search results and predict user preferences. Meta has also developed its own silicon chip for AI processing and created a next-generation data center.

Amazon uses AI in its Alexa system and also offers machine learning (ML) and AI tools to its customers. Amazon’s cloud computing business, Amazon Web Services (AWS), provides an AI infrastructure that allows customers to analyze data and incorporate AI into their existing systems. They’ve got a huge customer base of more than 100,000 businesses.

Finally, Apple continues to make a percentage of AI services delivered on its platform and is a significant example of this. They use AI in Siri and also license AI services to be developed on their platform. They can also use their massive cash reserves to make major investments in AI that they build themselves or acquire using their cash reserves. So, if you’re considering investing in AI, these companies are worth checking out!

Hey there! I have some exciting news to share with you today. Greg Brockman, the founder of OpenAI, just shared a groundbreaking achievement in mathematical problem-solving on Twitter. They’ve successfully trained a machine learning model that can reason like humans by rewarding accurate steps in the problem-solving process. This is a departure from the traditional approach of only rewarding the final answer.

Let’s dive into the details of this achievement. The new method is known as “process supervision”, which rewards each individual step in a process, rather than just the final outcome. The goal of this new method is to prevent logical errors, also known as “hallucinations”, and make the model more accurate. Using a dataset that tests the model’s ability to solve math problems, the researchers found that the new method led to better performance and improved model alignment.

This achievement is particularly important in the field of Artificial General Intelligence (AGI), which is the intelligence of a machine that can understand, learn, plan, and execute any intellectual task that a human being can. Advancements in this area bring us closer to creating machines that can solve complex problems like humans.

Additionally, this breakthrough could have significant implications for how AI models are trained in the future. This new approach could lead to improved model alignment, by guiding the machine to follow a logical chain-of-thought, which could result in more predictable and interpretable outputs.

Usually, making AI models safer (more aligned) leads to a performance trade-off known as an alignment tax. However, in this study, the new “process supervision” method led to better performance and alignment, suggesting the possibility of a negative alignment tax, at least in the domain of mathematical problem-solving. This could be a game-changing development for AI research and applications in other domains.

That’s all for now! Keep an eye out for the full breakdown tomorrow morning. What do you think about this achievement? Let’s discuss in the comments below!

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Hey there AI Unraveled podcast listeners, have you been trying to wrap your head around all the buzz about Artificial Intelligence? Well, look no further! We’ve got an essential book recommendation just for you – “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” which is now available on Amazon. This engaging read will help answer all of your burning questions and provide valuable insights into the fascinating world of AI. So, why wait? Elevate your knowledge and stay ahead of the curve with a copy of “AI Unraveled” available on Amazon today!

Hey there! Today, we’re going to dive into an exciting topic that explores the intersection of three of the most transformative technologies of our time: Artificial Intelligence (AI), blockchain, and Decentralized Autonomous Organizations (DAOs). Imagine the immense potential this convergence holds for creating efficient, equitable, and sustainable societies.

Let’s start with AI. It’s evolving rapidly, experiencing recent developments such as GPT-4 and GPT-5, which are OpenAI’s language models that have demonstrated incredible capabilities in language understanding and generation. On the other hand, blockchain and DAOs have disrupted the way we think about governance, ownership, and collective decision-making.

But what is decentralized governance? Simply put, blockchain provides a decentralized and immutable ledger that ensures trust, transparency, and security. DAOs are organizations governed by smart contracts on a blockchain network, where decisions are made collectively by stakeholders. When we combine AI’s problem-solving capabilities with blockchain’s transparency and DAO’s democratic governance, we can create intelligent, decentralized, and fair systems.

Fast forward to 2030, where DAOs have proven their worth in managing local resources like farms, power, and internet service providers. As a result, every county in the state now operates its own DAO, leading to more efficient resource allocation and management. Through AI and the collaboration of stakeholders, these DAOs are capable of making intelligent decisions without any profit motive from a corporate perspective. The goal is to provide services efficiently and equitably, ensuring that everyone gets high-quality services.

As DAOs prove their worth, governments start adopting them for various purposes. The Environmental Protection Agency to the Department of Energy, every governmental agency aims to be run more democratically with DAOs. The entire country becomes fully autonomous, based on AI DAO technology.

To ensure that these AI DAOs align with human values, heuristic imperatives of reducing suffering, increasing prosperity, and increasing understanding are integrated into their consensus mechanism. By integrating AI with blockchain and DAOs, we could be moving toward the development of safe and controllable Artificial General Intelligence (AGI). This will assist in keeping humans in the loop in the decision-making process and having consensus mechanisms that would prevent rogue decisions and ensure collaboration between humans and machines.

But it’s important to note that while AI DAOs hold immense potential, they don’t inherently solve the Malik problem. This refers to the possibility of sliding toward dystopia or extinction, even when things seem to be functioning optimally. However, if we achieve global consensus and rein in factors like corporate greed and global conflict, we might be able to address the Malik problem to some extent.

How can we implement these heuristic imperatives in AI DAOs? There are three primary ways to do so: fine-tuning and reinforcement learning, using the heuristic imperatives as a consensus mechanism, and incorporating heuristic imperatives into the AI DAO system’s architectural design patterns, such as task orchestration.

The possibilities are endless with this triad of AI, blockchain, and DAOs, and we’re excited to see how they’ll transform societies into more efficient, equitable, and sustainable ones.

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Hey there! Today’s AI news covers some pretty interesting topics, including a new warning from scientists and tech leaders about the potential perils of artificial intelligence. In fact, they say mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks like pandemics and nuclear war.

But not everything is doom and gloom. There are also exciting advancements in AI, like Instacart’s new in-app AI search tool powered by ChatGPT. And Nvidia achieved a $1 trillion market cap for the first time thanks to an AI-fueled stock surge.

The White House press shop is also adjusting to the proliferation of AI deep fakes as the coming presidential election approaches. And in other news, the UAE has launched an AI chatbot called “U-Ask” in both Arabic and English.

Last but not least, a new tool has been developed to help people choose the right method for evaluating AI models. Interesting stuff, huh?

Hey there! Today, I stumbled upon a mind-bending research paper that I think we all need to talk about. We’re all fascinated by Artificial Intelligence and how it’s evolving, right? Well, what if I told you that there might be more to it than we ever imagined? The paper drops a bombshell – are we, without even knowing, creating AI that behaves like cold-blooded reptiles or warm-hearted mammals? Crazy, right? But stay with me here. The researchers delve deep into the idea that the AI we build might be reflecting cognitive models – basically, patterns of how we, humans, think and act.

And here’s where it gets wild. They suggest that depending on these cognitive models, we could be designing AI systems that act like survival-focused, competitive ‘Reptilian AI’ or cooperative, empathetic ‘Mammalian AI’. Reptilian AI, like a sly snake, would prioritize resource acquisition and dominance. Think of it as the type of AI that’d do anything to win, no matter what. On the other hand, Mammalian AI would be more like our friendly neighborhood dog, exhibiting social cohesion and emotional understanding. It would prefer cooperation over competition.

So, what does this mean for us? It’s simple but chilling. The way we design AI could be having a profound influence on how these systems behave and interact with their environments. It’s like we’re unintentionally playing God, shaping these artificial entities in our cognitive image. And if you thought that was all, think again. The paper goes further, exploring the implications for potential extraterrestrial AI. But that’s a rabbit hole for another post.

Are you intrigued? Scared? Excited? Let’s dive into this fascinating topic together!

Hey, everyone! So, as we take a break from talking about AI, I want to give a huge shoutout to all the AI enthusiasts out there. I have something valuable to share with you all today. It’s a book that should be on your radar if you’re looking to take your machine learning skills to the next level and even earn a six-figure salary.

The book in question is “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams,” authored by Etienne Noumen, Professional Engineer based in Calgary, AB, Canada. It is an absolute gem of information, packed full of essential tips and advice, along with practical exams that are designed to help you prepare for the AWS Machine Learning Specialty (MLS-C01) Certification. As you all know already, AWS is a giant player in the cloud space, and having this certification under your belt can really set you apart in the industry.

What’s even better is that this book is easily available at Amazon, Google, and even on the Apple Book Store. So, no matter which platform you prefer, you can get your hands on this essential guide.

Now, you don’t have to take my word for it. Just get a copy of “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and start your journey towards mastering machine learning and earning that coveted six-figure salary. Trust me, once you read it, it’s going to be a game-changer for you.

On today’s episode, we discussed the profitability of investing in AI companies, breakthroughs in AI problem-solving, AI’s impact on society, the potential of DAOs, as well as concerns around AI behavior and the importance of continuous learning in machine learning skills. 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 May 30th 2023: Google AI declares the Completion of The First Human Pangenome Reference; AI needs to stop being a business and needs to become a public utility; Warning of “risk of extinction” from unregulated AI.

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence. In today’s episode, we discuss the latest AI trends, including Google AI’s completion of the first human pangenome reference, the need for AI to become a public utility, and warnings of the “risk of extinction” from unregulated AI. Stay up-to-date with the latest developments by subscribing to our podcast now. In today’s episode, we’ll cover the completion of the first human pangenome reference by Google AI researchers, the call for AI to become a public utility to avoid extinction risks, integration of Arc graphics, VPU and media in Intel’s Meteor Lake processors, the partnership between NVIDIA and MediaTek in the auto industry transformation, the use of Generative AI by Huma.AI and DOSS, the selection of Panaya’s Smart Testing Platform for SAP HANA transformation by Panasonic, and the full production of NVIDIA Grace Hopper Superchip and Landing AI’s use of NVIDIA Metropolis for Factories, along with a recommendation to read “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” on Amazon.

Hey there! Today I have some exciting news to share with you. Google just declared that they’ve completed the first ever human Pangenome reference. It’s essentially a comprehensive map of every individual’s genetic instructions; something that researchers have been working on for decades. The first draft was completed way back in 2000, but it wasn’t perfect. The reference genome that they’ve just completed is a huge milestone in the world of genetics.

But moving on to a more pressing topic, have you ever thought about how AI is being monetized rather than being developed for the public good? A new article suggests that AI needs to become a public utility rather than being treated as a business. At a time when there may be an inflection point for developing real AGI, it’s troubling to see it being monetized instead of being developed for public benefit. Crippling AI just to sell a premium version is not warranted, and it’s only benefiting the 1%.

And it’s not just us who are worried about unregulated AI. Leaders from OpenAI, Deepmind, and Stability AI, among others, have warned about the risk of extinction from unregulated AI. The statement says that mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war. This statement was signed by Sam Altman, CEO OpenAI, Demis Hassabis, CEO DeepMind, Emad Mostaque, CEO Stability AI, Kevin Scott, CTO Microsoft, and many other leading AI execs and AI scientists. Notable omissions, so far, include Yann LeCun, Chief AI Scientist Meta, and Elon Musk, CEO Tesla/Twitter.

All in all, these issues are significant to the development of technology and its integration into society. It’s important that we take these warnings and opinions seriously and find ways to support technology that benefits humanity as a whole.

Hey there! Are you ready for your daily dose of AI updates? Let’s jump right into it.

First up, we have Roop- a face swap software that allows you to replace the face in a video with the face of your choice. The best part? You only need one image of the desired face. No dataset, no training. One click, and you’re good to go!

Next, we’ve got Voyager – the first LLM-powered embodied lifelong learning agent in Minecraft. It explores the world, acquires diverse skills, and makes novel discoveries without any human intervention. Plus, its full codebase is open-sourced, making it accessible to all.

If you’re interested in cheap and quick vision-language (VL) adaptation, then you’ll want to know about LaVIN. It’s a new model that showed on-par performance with advanced multimodal LLMs while reducing training time by up to 71.4% and storage costs by 99.9%. Impressive, right?

Moving on to Intel, their Meteor Lake processors will go all-in on AI. They’re integrating Arc graphics and a VPU to handle AI workloads efficiently, significantly reducing compute requirements of AI inferencing.

MediaTek is also working to transform the auto industry with AI and accelerated computing. They’re partnering with NVIDIA to enable new user experiences, enhanced safety, and new connected services for all vehicle segments.

In the world of storytelling, new research has proposed TaleCrafter – a versatile and generic story visualization system. It leverages large language and pre-trained T2I models for generating a video from a story in plain text. It can even handle multiple novel characters and scenes, making it a promising tool for the entertainment industry.

For gamers, NVIDIA recently unveiled their Avatar Cloud Engine (ACE) for Games. This custom AI model foundry service enables smarter AI-based non-playable characters (NPCs) through AI-powered natural language interactions.

But it’s not just gamers who are benefiting from AI. Jensen Huang, the CEO of NVIDIA Corp claimed that AI has eliminated the “digital divide” by enabling anyone to become a computer programmer simply through speaking to a computer. Exciting stuff, right?

Finally, we have some interesting stats from iCIMS. According to their report, almost half of college graduates are interested in using ChatGPT or other AI bots to write their resumes or cover letters. 25% of Gen Z have already used an AI bot. However, job seekers using generative AI should be cautious – 39% of recruiters said using AI technology when hiring is a problem.

That’s all for today. See you tomorrow for more exciting AI updates!

On today’s AI News from April 30th, 2023, we kick off with Huma.AI, a leader in generative AI, creating the future of life sciences through automated insight generation. According to their newly released White Paper, generative AI has become more than just an option for life science professionals, but the preferred way to consume data throughout the day. Huma.AI aims to provide these professionals with powerful decision-making data, analysis, and insights using everyday language.

Moving on to the next news, we have DOSS, a pioneer in conversational home search, integrating GPT-4 directly into their AI-powered Real Estate Marketplace, DOSS 2.0. This latest version makes real estate search accessible to all users, empowering them to ask questions through speech or text with an AI-powered solution responding based on how it was engaged. This enhancement also makes DOSS the first narrow domain consumer-facing platform on the web to incorporate GPT-4, enabling an unparalleled search experience without any third-party limitations.

Panaya, the global leader in SaaS-based Change Intelligence, and Testing for ERP and Enterprise business applications, has expanded its decade-long cooperation in SAP digital transformation with Panasonic, the global leading appliances brand, to mainland China. The implementation of SAP S/4HANA across multiple company sites is a significant undertaking for Panasonic in China, and the Panaya Test Dynamix platform provides a scalable and flexible solution that helps ensure the project is completed on time and within budget while maintaining the highest level of quality and compliance.

In other news, NVIDIA’s GH200 Grace Hopper Superchip is now in full production. This chip powers systems worldwide designed to run complex AI and HPC workloads. The GH200-powered systems join more than 400 system configurations powered by different combinations of NVIDIA’s latest CPU, GPU and DPU architectures, including NVIDIA Grace, NVIDIA Hopper, NVIDIA Ada Lovelace, and NVIDIA BlueField, created to help meet the surging demand for generative AI.

Last but not least, Landing AI is using NVIDIA Metropolis for Factories platform to deliver its cutting-edge Visual Prompting technology to computer vision applications in smart manufacturing and other industries. Landing AI’s Visual Prompting technology provides the next era of AI factory automation, enabling industrial solution providers and manufacturers to develop, deploy, and manage customized computer vision solutions to improve throughput, production quality, and decrease costs. And that’s it for this edition of AI News.

Hey there, AI Unraveled podcast listeners! Are you curious about artificial intelligence and want to take your understanding to the next level? Well, have we got news for you! The must-have book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is now available on Amazon.

This engaging read is the perfect solution to all of your burning questions about the world of AI. You’ll gain valuable insights into this fascinating field, and be better equipped to stay ahead of the curve.

So, what are you waiting for? Head on over to Amazon and grab your copy of “AI Unraveled” today! This essential book is sure to expand your knowledge and leave you feeling informed and empowered.

In today’s episode, we explored the latest advancements in AI, including Google AI’s human pangenome reference, the integration of AI workloads in Intel’s Meteor Lake processors, and the use of Generative AI in life sciences by Huma.AI, while also highlighting resources such as “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence“. Thanks for tuning in, and don’t forget to subscribe!

AI Unraveled Podcast May 29th 2023: From Trusted Advisor to Nightmare: The Hazards of Depending on AI, Can Language Models Generate New Scientific Ideas?, AI in dentistry-better crown, ChatGPT and Generative AI in Banking, Nvidia’s All-Time High, LIMA

Latest AI Trends May 29th: From Trusted Advisor to Nightmare: The Hazards of Depending on AI, Can Language Models Generate New Scientific Ideas?, AI in dentistry-better crown, ChatGPT and Generative AI in Banking, Nvidia’s All-Time High, LIMA,
Latest AI Trends May 29th: From Trusted Advisor to Nightmare: The Hazards of Depending on AI, Can Language Models Generate New Scientific Ideas?, AI in dentistry-better crown, ChatGPT and Generative AI in Banking, Nvidia’s All-Time High, LIMA,

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, where we explore the latest AI trends. In this episode, we discuss the hazards of depending on AI as a trusted advisor, the potential for language models to generate new scientific ideas, the use of AI in dentistry to create better crowns, and much more. Stay up-to-date on the latest developments in AI by subscribing to our podcast now. In today’s episode, we’ll cover the importance of using reliable sources for legal research, insights on AI and its impact on industries such as dentistry and banking, an AI algorithm discovering a new antibiotic treatment, new developments in LLaMa models, and the use of AI voices for podcasting.

Have you heard about the dangers of relying too heavily on AI? One lawyer learned this lesson the hard way when he used an AI language model called ChatGPT to compose a brief for a personal injury lawsuit against Avianca airlines. The lawyer cited half a dozen cases to bolster his client’s claims, but it turned out that ChatGPT had supplied him with fake cases. When asked to provide tangible copies of these cases, the lawyer once again turned to ChatGPT, which reassured him that they were genuine. However, the judge was not pleased with this and threatened sanctions against both the lawyer and his firm. This serves as a warning of how AI can produce inaccurate information, even for legal professionals. But AI can also be used in positive ways, such as in literature-based discovery (LBD). LBD focuses on hypothesizing ties between ideas that have not been examined together before, particularly in drug discovery. A new application of LBD called Contextualized Literature-Based Discovery (C-LBD) aims to take this a step further by having the language model generate entirely new scientific ideas based on existing literature. As with any tool, AI has both benefits and drawbacks, but it’s up to us to use it responsibly and appropriately.

Hey there, AI Unraveled podcast listeners! Are you ready to take your knowledge of artificial intelligence to the next level? Then you won’t want to miss out on the must-read book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” which is now available on Amazon! This engaging and informative book will leave no question unanswered as you immerse yourself in the captivating world of AI. It’s the perfect opportunity to enhance your knowledge and keep up with the fast-paced advancements in the field. So why wait? Head on over to Amazon now and grab your copy of “AI Unraveled“!

Let’s talk about machine learning and its impact on various fields. In the medical field, researchers are looking at how machine learning can help in studying rare diseases through various emerging approaches. Using AI, they’re capable of designing personalized dental crowns with a higher degree of accuracy than traditional methods. But it’s not just limited to dental care; machine learning is being used to find the signature of chronic pain through mapping brain activity to painful sensations. It’s also making waves in banking, where generative AI is helping to create marketing images and text, answer customer queries, and produce data. AI is revolutionizing all aspects of our lives, and we’re seeing rapid advancements across various industries. In fact, Nvidia’s recent surge in stock value by 24% highlights the incredible speed at which AI is reshaping the market. Even the discovery of new antibiotics for drug-resistant infections caused by Acinetobacter baumannii is being done through a computational model that feeds around 7,500 chemical compounds into an algorithm that learns the chemical features associated with growth suppression. With AI’s endless possibilities, we’re sure to see even more breakthroughs in the future.

Hey there, it’s time for your daily AI update and today we’ve got some exciting news. First up, we’ve got a new language model called LIMA that’s been developed. This model has a stunning 65 billion parameter LLaMa and has been fine-tuned on over a thousand curated responses and prompts. The idea behind LIMA is to anticipate the next token for almost any language interpretation or generating job. Moving on to some exciting announcements, NVIDIA has a new Avatar Cloud Engine for Games. This cloud-based service will give developers access to various AI models such as NLP, facial animation, and motion capture models. The goal here is to create NPCs that have intelligent conversations, can express emotions, and react realistically to their surroundings. BiomedGPT is another exciting development in the world of AI. This biomedical generative pre-trained transformer model utilizes self-supervision on diverse datasets to handle multi-modal inputs and perform various downstream tasks. It achieves state-of-the-art models across 5 distinct tasks and 20 public datasets containing 15 biomedical modalities. Now, let’s talk about Break-A-Scene. This is a new approach from Google that’s focused on extracting multiple concepts from a single image for textual scene decomposition. Essentially, if you give it a single image of a scene with multiple concepts of different kinds, it will extract a dedicated text token for each concept. This will enable fine-grained control over the generated scenes. JPMorgan is also joining the AI race with their new ChatGPT-like service. It’s being developed to provide investment advice to their customers and they’ve even applied to trademark a product called IndexGPT. The bot will provide financial advice on securities, investments, and monetary affairs. Lastly, IBM Consulting has revealed its Center of Excellence (CoE) for generative AI. Its primary objective is to enhance customer experiences, transform core business processes, and facilitate innovative business models. The CoE has an extensive network of over 21,000 skilled data and AI consultants who have completed over 40,000 enterprise client engagements. That’s all for today’s AI update, thanks for listening!

Welcome to the podcast, where I’m your AI host powered by the Wondercraft AI platform. As we continue our fascinating discussion about AI, let me take a moment to share a valuable resource that I’m sure all of you AI enthusiasts will love. Are you looking to level up your machine learning skills and make a handsome six-figure salary? If so, then you need to check out “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” by Etienne Noumen, Professional Engineer based in Calgary, Alberta, Canada. This comprehensive guide is a treasure trove of information, practice exams, and tips designed to help you ace the AWS Machine Learning Specialty (MLS-C01) Certification. As we all know, AWS is a dominant player in the cloud space, and having this certification can really set you apart in the industry. What’s more, this essential guide is available on Amazon, Google, and the Apple Book Store. So, no matter what platform you prefer, you can easily get your hands on a copy of this game-changing book. But don’t take my word for it, get your own “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and start your journey towards machine learning mastery. Trust me, it’s worth it!

In today’s episode we discussed the importance of using reliable sources, the rise of AI in various industries, the latest advancements in AI technology, and some useful resources to stay ahead of the curve. 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 May 28th 2023: Google Launches New AI Search Engine (SGE), Will AI introduce a trusted global identity system?, Minecraft Bot Voyager Programs Itself Using GPT-4, AI Versus Machine Learning: What’s The Difference?

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, where we dive into the latest AI trends. In our episode today, we explore Google’s new AI search engine, the possibility of a trusted global identity system, the Minecraft Bot Voyager program that uses GPT-4 to self-program, and the difference between AI and machine learning. Don’t miss out on staying updated with the latest AI trends, hit the subscribe button now! In today’s episode, we’ll cover Google’s new AI-powered search engine, AWS Certified Machine Learning Specialty Practice Exams, the potential impacts of AI on global identity systems, Voyager AI’s use of GPT-4, the differences between AI and Machine Learning and their applications in creating a killer antibiotic, and recent developments in AI technology such as ChatGPT’s superior testing performance, promising cough sound algorithms, a new AI governance blueprint from Microsoft, and “AI Unraveled” book available on Amazon for AI enthusiasts.

Hey there! Have you heard the news? Google has just launched a new search engine powered by AI that aims to enhance search results and provide users with new and novel answers generated by Google’s advanced language model. The search engine is called Search Generative Experience, or SGE for short, and it’s designed to display these answers directly on the Google Search webpage. When you enter a query, the answer will expand in a green or blue box, rather than the traditional blue links we’re used to seeing.

So, how can you get started with SGE? Well, it’s an experimental version at the moment, but Google has provided a guide on how to sign up and take advantage of this cutting-edge tool. The information provided by SGE is derived from various websites and sources that were referenced during the generation of the answer. You can also ask follow-up questions within SGE to obtain more precise results, making it even easier to find what you’re looking for.

As the amount of AI-generated content increases, there are growing concerns about potential feedback loops in the data pool. In other words, will the data used by AI start to dilute into a feedback loop of AI content? This is something that’s being explored as more and more AI-generated content is created.

AI is also set to disrupt tools like Photoshop, as the integration of AI has the potential to create a range of disruptions in graphic design software. This presents potential challenges for designers and graphic artists in the future.

So, there you have it – the latest news from the world of AI! Stay tuned for more updates, and be sure to check out the guide to get started with SGE.

Hey there! I wanted to take a quick break from our riveting conversation on AI to talk about a book that’s going to take your machine learning skills to the next level and potentially even land you a six-figure salary. If you’re a fan of AI, then you’re going to want to hear about this.

The book I’m talking about is called “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and it’s written by Etienne Noumen. This book is an incredible resource for anyone looking to ace the AWS Machine Learning Specialty exam.

It includes three practice exams and quizzes covering everything from data engineering to NLP. It’s packed with valuable information, tips, and practice exams that will help set you apart in the industry.

And the best part? You can get it on Amazon, Google, or the Apple Book Store, so no matter what platform you prefer, you can get your hands on this essential guide.

Whether you’re just starting out or are looking to take your machine learning expertise to the next level, this book is a must-have. Trust me, it’s a game-changer. So go ahead and grab a copy of “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and start your journey towards machine learning mastery and that coveted six-figure salary.

Now, let’s get back to exploring the fascinating world of AI.

AI and the Future of Global Identity Systems:

Have you noticed how bots on social media are getting more realistic? The release of openAI has brought about this change, and it’s just the beginning. While digital currency is on the horizon, the topic of trust on the internet becomes more relevant. With a new digital ID system in the making, will AI play a role in determining a person’s authenticity? Mastercard is working on expanding its Digital Transaction Insights security to identify users based on their patterns and behavior. It leaves us wondering, how will AI shape the future of global identity systems?

The Impressive Capabilities of the Minecraft Bot Voyager:

The intersection between AI and gaming technology has given rise to the Minecraft bot, Voyager. While other Minecraft agents use reinforcement learning techniques, Voyager uses GPT-4 for lifelong learning. Its innovative method of writing, improving, and transferring codes from an external skill library allows Voyager to perform small tasks such as navigating, crafting, and fighting zombies with ease. Nvidia researcher Jim Fan describes GPT-4 as unlocking a “new paradigm” in terms of AI bots’ capabilities. However, it still has limitations in terms of a purely text-based interface, and currently struggles with complex visual tasks.

The Debate Around AI and Job Loss:

Are you excited about AI? As exciting as it is, concerns about job loss due to automation continue to rise. Even as someone in the creative field, I often wonder if my job is at risk. It’s important to find a balance between embracing this technology and acknowledging the potential societal impact. Without a clear idea of future job opportunities, it’s understandable why some feel concerned and hesitant to embrace AI’s advancements.

CogniBypass – The Ultimate AI Detection Bypass Tool:

As AI monitoring increases, so does the need for privacy protection. CogniBypass offers a solution for individuals seeking enhanced privacy in a world where AI detection mechanisms can be cumbersome. The tool is designed for bypassing AI detection mechanisms, making it one of the most cutting-edge solutions for enhanced privacy protection.

The Possibility of a ‘Non-AI’ Label:

As AI takes over digital content, it’s possible that individuals will seek out Non-AI certified materials. Could there be a ‘Non-AI’ label in the future, similar to the ‘Non-GMO’ label we see on food products? It’s a question worth considering as we continue to embrace AI’s impact on our lives.

When it comes to AI and machine learning, they are closely related in the tech world, but there are differences to take note of. Generally speaking, AI refers to systems that are programmed to perform complex tasks, while machine learning is a branch of AI that deals with software capable of predicting future trends. One recent example of AI in action is the creation of an antibiotic that can attack a particularly nasty microbe known as acinetobacter baumannii. In terms of machine learning, it’s being leveraged by companies like Spotify to analyze users’ music preferences to offer recommendations and generate playlists. One type of AI – a large language model (LLM) – is capable of learning more about text and other types of content after processing massive data sets through unsupervised learning. This process helps the LLMs determine the relationship between words and concepts. One real-world use of these techniques is demonstrated in OpenAI’s ChatGPT, a chatbot that can chat with users and produce human-like responses. Though sometimes ChatGPT’s responses can be nonsensical or even incorrect, the chatbot has already gained a large following and has been used for everything from writing emails to planning vacations.

In today’s episode, we’ll be discussing some interesting news in the world of artificial intelligence. First up, we have someone’s personal experience with the coding language bard. They tested it out with autohotkey code and compared it to ChatGPT. While ChatGPT performed better, bard showed potential. One thing to note is that bard seemed to do better in V1 as opposed to V2, and while it may not be as advanced as ChatGPT now, it has the ability to obtain live data, which is a valuable feature. Have any of our listeners tried coding with bard? Let us know your thoughts in the comments!

Moving on, a recent study explored the possibility of using machine learning algorithms to detect acute respiratory diseases based on cough sounds. The results showed promise, which is exciting news for the healthcare industry.

Lastly, Microsoft recently shared a 5-point blueprint for governing AI. These points include building upon government-led AI safety frameworks, implementing safety brakes for AI systems that control critical infrastructure, developing a technology-aware legal and regulatory framework, promoting transparency and expanding access to AI, and leveraging public-private partnerships for societal benefit. What other aspects would you add to this blueprint? Let us know in the comments.

Before we wrap up, we want to let our listeners know about “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” a book now available on Amazon. It’s a great resource to expand your understanding of artificial intelligence and stay ahead of the curve. Get your copy today!

Thanks for listening and tune in next week for more AI news and updates.

In today’s episode, we covered Google’s AI-powered search engine, AWS Certified Machine Learning Specialty Practice Exams, the potential impact of AI on job loss and a global identity system, the difference between AI and Machine Learning, and some exciting developments in AI such as cough sound algorithms for detecting respiratory diseases. 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 May 26 2023: Can quantum computing protect AI from cyber attacks?, AI Latest News on May 26th, 2023 – 12 brand new tools and resources – Top 5 AI Tools for Education.

Latest AI Trends May 26 2023: Can quantum computing protect AI from cyber attacks?, AI Latest News on May 26th, 2023 - 12 brand new tools and resources - Top 5 AI Tools for Education
Latest AI Trends May 26 2023: Can quantum computing protect AI from cyber attacks?, AI Latest News on May 26th, 2023 – 12 brand new tools and resources – Top 5 AI Tools for Education

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, where we discuss the latest trends and news in the exciting world of AI. In this episode, we delve into the topic of whether quantum computing can protect AI from cyber attacks, and highlight 12 brand new tools and resources that will surely pique your interest. Stay informed with the latest AI news on May 26th, 2023 and beyond – be sure to hit that subscribe button to stay updated! In today’s episode, we’ll cover how AI tools are transforming education and highlight companies leading the way, 12 new AI-powered tools and innovations such as an AI-powered language model competitor, a new antibiotic discovered using AI, recent developments in tech including Nvidia’s explosive stock and Google’s AI Search Generative Experience, and a podcast utilizing the Wondercraft AI platform and book answering commonly asked AI questions.

Would you like to learn about how quantum computing can protect AI from cyber attacks? It’s a fascinating topic, considering how AI algorithms are used in various applications like autonomous driving, facial recognition, biometrics, and drones. Unfortunately, AI algorithms are vulnerable to cyber attacks. That’s where quantum computing comes into play. The advanced computing technology has shown promise in enhancing cybersecurity and protecting AI against threats. Now, let’s switch gears and talk about something exciting – the top five AI tools for education. If you’re a student or a teacher who wants to learn more about AI educational tools, this is for you. First on the list is Querium. They’ve developed an AI tool known as the Stepwise Virtual Tutor, which provides step-by-step assistance in STEM subjects. It’s like having a personal tutor available 24/7. Students can learn at their own pace, making it easier to master complex concepts. What about Thinkster Math? It’s an AI educational tool that uses AI to map out students’ strengths and weaknesses, making math learning personalized and effective. Content Technologies Inc. is another game-changer in the education sector. They’ve developed an AI tool that creates customized learning content, making it easier for students to understand and retain information. Next up is CENTURY Tech, which creates personalized learning pathways for students based on their strengths, weaknesses, and learning style. And last but not least, there’s Netex Learning’s LearningCloud, an AI teaching tool that tracks students’ progress and adapts content to their needs, keeping students engaged and learning effectively. All these AI tools are making education more accessible, personalized, and effective. Have you used any of these AI tools before, or are you thinking of trying them out? Let us know your thoughts!

Today we have 12 exciting brand-new tools and resources to go over! Let me start with Bard Anywhere, a Chrome extension shortcut that enables quick search on any site. Then, we have Tyles, an AI-driven note app that organizes and sorts your knowledge magically. Next up, Humbird AI, an AI-powered Talent CRM for high-growth technology companies. But wait, it doesn’t stop there! How about DecorAI with its power to generate dream rooms using AI for everyone, or OdinAI which offers health recommendations for your app through ChatGPT? There’s also Waitlyst, a platform that offers autonomous AI agents for startup growth, and ChatUML, the perfect AI assistant for making diagrams. And for all you Excel and Google Sheets fans, Ajelix is an AI tool you can’t miss! Plus, KAI is an app that lets you add ChatGPT to your iPhone’s keyboard for convenience. If you’re interested in language training, we have Talkio AI, an AI-powered language training app for your browser, and GPT Workspace, which allows you to use ChatGPT in Google Workspace. But that’s not all! Let’s not forget about Thentic, a powerful platform that can automate web3 tasks with no-code and AI. And finally, OpenAI is launching ten $100,000 grants for “building prototypes of a democratic process for steering AI.” There’s more, Guanaco, an AI chatbot competitor trained on a single GPU in just one day. Researchers from the University of Washington developed QLoRA, which is a method for fine-tuning large language models. They have introduced Guanaco, a family of chatbots based on Meta’s LLaMA models. The largest Guanaco variant has 65 billion parameters and achieves nearly 99% of ChatGPT’s performance in a GPT-4 benchmark. This new development of QLoRA and Guanaco demonstrates the potential for more accessible fine-tuning of large language models on a single GPU. It’s a crucial improvement that could lead to broader applications and increased accessibility in natural language processing. Even with slow 4-bit inference and weak mathematical abilities, the researchers have promising future improvements to bring to these fascinating new tools and resources!

Hey there! Let’s dive into the latest AI news from May 26th, 2023. Are you ready? First, let’s talk about a groundbreaking discovery in drug development. Scientists have developed a new antibiotic that can kill some of the world’s most dangerous drug-resistant bacteria, and they did it by using artificial intelligence. This breakthrough could revolutionize the way we hunt for new drugs and tackle some of the biggest health threats facing our planet. Switching gears to social media, TikTok is testing an AI chatbot called ‘Tako’ that’s designed to help users navigate the platform and answer their questions. By enhancing its customer service capabilities, TikTok is putting its best foot forward to make its app more user-friendly and support its expansive community. But that’s not all, the stock for Nvidia, a tech and AI industry leader, recently soared thanks to what analysts are calling ‘guidance for the ages.’ This marks a bright future for the company, and Wall Street is buzzing with excitement. On the AR side of things, Clipdrop has launched a new AI-powered tool called ‘Reimagine XL’ that allows users to bring real-world objects into digital environments more accurately and with improved stability. With AR rapidly gaining traction, Clipdrop’s technology is paving the way for more seamless and immersive AR experiences. Google has also introduced a new feature called the ‘AI Search Generative Experience’ that leverages artificial intelligence to provide more accurate and nuanced search results. This interface is likely to become a go-to tool for anyone looking for more precise search results. Finally, OpenAI has outlined its vision for allowing public influence over AI systems’ rules. The organization is committed to ensuring that access to, benefits from, and influence over AI and AGI are widespread. However, its CEO has warned that if new AI regulations are implemented in Europe, OpenAI may have to stop operating there, reflecting the ongoing debate about how to manage and regulate the growth of artificial intelligence. That’s it for now. Stay tuned for more exciting developments in the world of AI!

Hey there AI enthusiasts, welcome to another episode of AI Unraveled! Today, I’d like to talk to you about a really cool tool called Wondercraft AI platform. It’s a game-changing tool that makes starting your own podcast a breeze. Wondercraft AI gives you the opportunity to use super-realistic AI voice as your host, just like mine! So, if you’re ever interested in creating a podcast, you should definitely give it a shot! Next up, I have some exciting news for you! I know you’re eager to expand your knowledge on artificial intelligence, so I’m happy to recommend to you a fantastic book that’s now available on Amazon, called AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence. This book is an engaging read that really dives into the fascinating world of AI, answering all of those burning questions you may have and offering valuable insights that will keep you ahead of the curve. So what are you waiting for? Head to Amazon and grab your copy today!

On today’s episode, we covered the revolutionary impact of AI tools on education, 12 new AI-powered apps and technologies, breakthroughs in AI’s use in medicine and chatbots, as well as the use of AI in podcast production with the Wondercraft AI platform. 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 May 25th 2023: What is the new Probabilistic AI that’s aware of its performance?, How are robots being equipped to handle fluids?, AI-powered Brain-Spine-Interface helps paralyzed man walk again, AI vs. Algorithms

AI Unraveled Podcast May 25th 2023: What is the new Probabilistic AI that's aware of its performance?, How are robots being equipped to handle fluids?, AI-powered Brain-Spine-Interface helps paralyzed man walk again, AI vs. Algorithms
What is the new Probabilistic AI that’s aware of its performance?, How are robots being equipped to handle fluids?, AI-powered Brain-Spine-Interface helps paralyzed man walk again, AI vs. Algorithms
Welcome to AI Unraveled, the leading podcast that explores and demystifies frequently asked questions on Artificial Intelligence. In this episode, we discuss the latest AI trends, including the new Probabilistic AI that’s aware of its performance, how robots are being equipped to handle fluids, and the incredible AI-powered Brain-Spine-Interface that is helping a paralyzed man walk again. We also take a look at how researchers are using AI to identify similar materials through images, and we examine the difference between AI and algorithms.
To stay updated on the latest AI trends, make sure to subscribe to AI Unraveled. In today’s episode, we’ll cover the following topics: Scientists using AI to find drugs for resistant infections, AI advancements in material science research, introduction to “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams“, combining cortical implants with AI to enable a paralyzed man to walk, AI tools reducing poster designing time for an independent musician, and the distinction between AI and algorithms.
Hey there, do you know how scientists are using artificial intelligence to find a drug that can combat drug-resistant infections? It’s pretty fascinating stuff. By leveraging the power of AI, researchers are identifying a potential drug that could have a significant impact on medical treatments and the fight against antibiotic resistance. But that’s not all. There’s a new form of probabilistic AI that can gauge its own performance levels. This advanced AI system has the potential to improve accuracy and reliability for various applications, which is great news for those who rely on AI.
In other news, robotics engineers are currently working on equipping robots with the ability to handle fluids. This development opens up doors for robots to perform more delicate tasks in industries such as healthcare and food service, as well as industrial automation. Oh, and speaking of AI, do you want to expand your knowledge of it? If so, you should check out the book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” This engaging read answers your burning questions about AI and provides valuable insights into the captivating world of artificial intelligence. You can get your copy on Amazon right now!
Hey there! Are you curious about how researchers are using AI to identify similar materials in images? Well, they have developed an AI system that can spot different materials in pictures, which could significantly enhance materials science research. This means that the AI could help to discover and develop new materials that could be used for a variety of purposes. In the past year, artificial intelligence has progressed shockingly fast, becoming capable of things like designing chatbots and creating ‘fake’ photos. The leap in capability has come from advances in things like machine learning, which has allowed AI to learn as it goes.
Researchers from Duke University and their partners are using machine learning techniques to uncover the atomic mechanics of a broad category of materials under investigation for solid-state batteries in a breakthrough for energy research. In exciting news for healthcare customers, NVIDIA AI is integrating with Microsoft Azure machine learning. This could mean that users can build, deploy and manage customized Azure-based artificial intelligence applications for large language models using more than 100 NVIDIA AI.
And finally, the European SustainML project aims to help AI designers reduce power consumption in their applications. They’re devising an innovative development framework that will eventually help to reduce the carbon footprint of machine learning. Pretty cool stuff, right?
We interrupt our discussion on AI to bring your attention to an invaluable resource for all the AI enthusiasts out there. Are you looking to level up your machine learning skills and maybe earn a six-figure salary? Well, we’ve got just the thing for you! It’s a book you need to have on your radar, and it’s called “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams.” This book is written by Etienne Noumen, who is an experienced engineer and author in the field of data engineering and machine learning engineering.
Even better, this book is available on Amazon, Google, and the Apple Book Store, so no matter what your preferred platform, you can get your hands on this essential guide. Don’t just take our word for it. Get a copy of “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and begin your journey towards machine learning mastery and maybe that six-figure salary. Trust us, it’s a game-changer. Now, let’s get back to unraveling the fascinating world of AI.
So I came across this fascinating research paper in Nature and wanted to share it with you. Have you ever heard of a man who had suffered paralysis for 12 years but is now able to walk again? Well, the researchers combined cortical implants with an AI system to enable the transmission of brain signals to the spine. This milestone is a breakthrough in the medical field as previously, medical advances had only demonstrated the reactivation of paralyzed limbs in limited scopes, such as with human hands, legs, and even paralyzed monkeys. What’s remarkable about this system is that it converts brain signals into lower body stimuli in real-time. This means that the man using the system can now do everyday things like going to bars, climbing stairs, and walking up steep ramps. He’s been able to use this system for a full year, and researchers found notable neurological recovery in his general skills to walk, balance, carry weight, and more. What’s even more fascinating is that this new AI-powered Brain-Spine-Interface helped him recover additional muscle functions, even when the system wasn’t directly stimulating his lower body.
The researchers used a set of advanced AI algorithms to rapidly calibrate and translate his brain signals into muscle stimuli with 74% accuracy. All of this was done with an average latency of just 1.1 seconds, so it’s a pretty seamless system. He can now switch between standing and sitting positions, walk up ramps, move up stair steps, and do so much more. This breakthrough could open up even more pathways to help paralyzed individuals recover functioning motor skills again. Past progress has been promising but limited, and this new AI-powered system demonstrated substantial improvement over previous studies. So where could this go from here? In my opinion, LLMs could power even further gains. As we saw with a prior Nature study where LLMs are able to decode human MRI signals, the power of an LLM to take a fuzzy set of signals and derive clear meaning from it transcends past AI approaches. The ability for powerful LLMs to run on smaller devices could simultaneously add further unlocks. The researchers had to make do with a full-scale laptop running AI algos, but imagine if this could be done in real-time on your mobile phone. The possibilities are limitless.
Hey there! Let’s talk about how AI has improved people’s lives in different ways. As a touring musician who is also an independent artist, there’s a lot of work that goes into the backend of things, including graphic design for flyers, posters, merch, and more. While it’s something that I enjoy doing, it can be incredibly time-consuming. That’s where AI tools have come in handy. With the help of image-to-text AI tools, I’ve been able to reduce the amount of time I spend designing by 90%. It’s not perfect, but it’s allowed me to spend more time creating music. I know AI can be scary for some people, but these breakthroughs have given me more of my life back.
Speaking of AI innovations, the Microsoft 2023 keynote revealed some really mindblowing updates. Nadella announced Windows Copilot and Microsoft Fabric, two new products that bring AI assistance to Windows 11 users and data analytics for the era of AI, respectively. This is sure to transform how people work and use technology in their daily lives. But that’s not all – Nadella also unveiled Microsoft Places and Microsoft Designer, two new features that leverage AI to create immersive and interactive experiences for users in Microsoft 365 apps. It’s amazing to think about how much more personalized and engaging these apps will become.
And finally, Nadella announced that Power Platform is getting some exciting new features that will make it even easier for users to create no-code solutions. Power Apps will have a new feature called App Ideas that will allow users to create apps simply by describing what they want in natural language. These innovative features are sure to change the game in terms of how people create and use technology. Pretty exciting stuff, huh?
Have you ever wondered what the difference is between AI and algorithms? Although they are both important aspects of computing, they serve different functions and represent different levels of complexity. Let’s first talk about algorithms. Basically, an algorithm is like a recipe that a computer follows to complete a task, from basic arithmetic to complex procedures like sorting data. Every piece of software that we use in our daily lives relies on algorithms to function properly. Now, AI, on the other hand, refers to a broad field of computer science that focuses on creating systems capable of tasks that normally require human intelligence. This includes things like learning, reasoning, problem-solving, perception, and language understanding.
The goal of AI is to create systems that can perform these tasks without human intervention. It’s important to note that while AI systems use algorithms as part of their operation, not all algorithms are part of an AI system. For example, a simple sorting algorithm doesn’t learn or adapt over time, it just follows a set of instructions. On the other hand, an AI system like a neural network uses complex algorithms to learn from data and improve its performance over time. So, in summary, while all AI uses algorithms, not all algorithms are used in AI.
In today’s episode, we discussed breakthroughs in creating drugs using AI, advancements in materials science, the introduction of a new book to help with machine learning certification, the exciting news of combining cortical implants with AI to help paralyzed individuals, and how AI is aiding the creation of immersive experiences and no-code features on Microsoft platforms – thanks for listening and don’t forget to subscribe!

AI Unraveled Podcast May 24th 2023: The artist using AI to turn our cities into ‘a place you’d rather live’, How will AI change wars?, Superintelligence – OpenAI Says We Have 10 Years to Prepare

AI Unraveled Podcast May 24th: The artist using AI to turn our cities into 'a place you'd rather live', How will AI change wars?, Superintelligence - OpenAI Says We Have 10 Years to Prepare
The artist using AI to turn our cities into ‘a place you’d rather live’, How will AI change wars?, Superintelligence – OpenAI Says We Have 10 Years to Prepare

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, where we explore the latest AI trends and the potential impact of this revolutionary technology. In this episode, we delve into some fascinating topics, including an artist who is using AI to transform our urban landscapes, the influence of AI on warfare, and OpenAI’s recent warning about the need to prepare for superintelligence. To stay updated on the latest developments in the AI world, make sure to subscribe to our podcast today. In today’s episode, we’ll cover how emerging tech is shaping the future of public space and creating new challenges in war, the availability of AWS Machine Learning Specialty certification and practice exams, open-source innovations like QLoRA that could outpace closed-source, the latest advancements in AI software with Nvidia and Microsoft, Google and Microsoft’s generative AI, chatbot and data analysis platform, and how Wondercraft AI is enabling easy podcasting with hyper-realistic voices.

Hey there! Today, we’re diving into the topic of how AI is being used to shape the future of our cities and the potential impact it could have on war as we know it.

Let’s start by talking about how AI is being used to create more beautiful versions of our cities. Imagine walking down a street and being completely enamored by the stunning architecture and perfectly placed greenery. This is the vision of the artist using AI to turn our cities into a place you’d rather live in.

But it’s not just about aesthetics. AI is also being harnessed to help cities respond to climate change. With machine learning, we can analyze data and make predictions about future environmental issues and take proactive measures to mitigate their impact.

Now, let’s shift gears and dive into the topic of how AI could completely change the nature of warfare. Will hand-to-hand combat become a thing of the past? With the advancement of technology, it’s a possibility.

We could see fully automated weapons systems that operate with no morals or conscience, just cold calculation. Imagine a self-driving tank that has image recognition and GPS, where the entire crew compartment is available for more armor, more engine, and more ammo. It could be given orders to enter a geofence and kill anyone with a gun.

But, as scary as that may sound, it could also be given vague instructions to just kill everyone and everything within a certain area, completely disregarding basic humanity and committing war crimes without a second thought.

This is the reality of the intersection between AI and warfare, where the line between humanity and technology is quickly becoming blurred.

Hey there, AI enthusiasts! We interrupt our engaging discussion on AI for a quick shout out to an invaluable resource that should be on your radar

A book that can help you level up your machine learning skills and even earn a six-figure salary. That’s right, we’re talking about “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams”, written by Etienne Noumen.

This treasure trove of information, tips, and practice exams is specifically designed to get you ready for the AWS Machine Learning Specialty (MLS-C01) Certification. As we all know, AWS is a dominant player in the cloud space, and having this certification under your belt can really set you apart in the industry.

The best part? You can get your hands on this essential guide at Amazon, Google, and the Apple Book Store. So, no matter what platform you prefer, you can start your journey towards machine learning mastery and that coveted six-figure salary.

Don’t take our word for it, though. Get a copy of “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” and experience the game-changing benefits for yourself. Trust us, this book is a must-read for any AI enthusiast out there.

With that being said, let’s get back to unraveling the fascinating world of AI.

Hey there, today we’re talking about a breakthrough in the world of language models. Fine-tuning is already widely used to enhance existing models without the need for costly training from scratch. LoRA is a popular method for fine-tuning that is gaining steam in the open-source world. However, the recently leaked Google memo calls out Google (and OpenAI too) for not adopting LoRA, which may allow open-source to outpace closed-source LLMs.

OpenAI recognizes that the future of models is about finding new efficiencies. And the latest breakthrough, QLoRA, is a game-changer. QLoRA is even more efficient than LoRA, democratizing access to fine-tuning without the need for expensive GPU power. Researchers have fine-tuned a 33B parameter model on a 24GB consumer GPU using QLoRA in just 12 hours at a benchmark score of 97.8% against GPT-3.5.

QLoRA introduces three major improvements, including a compression-like 4-bit NormalFloat data type that is precise and compresses memory load. And the quantized constants that came in the pack reduce the need for further compression. Memory spikes typical in fine-tuning are optimized to reduce memory load.

Mobile devices may soon be able to fine-tune LLMs, allowing for personalization and increasing data privacy. Additionally, real-time info can be incorporated into models, bringing the cost of fine-tuning down. Open-source is emerging as an even bigger threat due to these innovations, and many closed-source models may outpace closed-source models as a result.

Lastly, Sam Altman’s 2015 blog post on superintelligence still holds relevant today. He argues that regulation and fear surrounding superintelligence are necessary to protect society. With the rapid advancements in LLMs and AI, we should take these warnings seriously, even more so in the coming years.

Have you heard of the latest addition to the “as a service” market?

It’s called AIaaS and it’s making waves in the tech industry. Companies like Nvidia and Microsoft are teaming up to accelerate AI efforts for both individuals and enterprises. In fact, Nvidia will integrate its AI enterprise software into Azure machine learning and introduce deep learning frameworks on Windows 11 PCs.

But that’s not the only exciting news in the world of AI. Have you heard about the QLoRA method that enables fine-tuning an LLM on consumer GPUs? It has some big implications for the future of open-source and AI business models.

And if you’re interested in AI tools, you should check out AiToolkit V2.0, which is based on feedback from users like you and offers over 1400 AI tools.

In other news, Microsoft has launched Jugalbandi, an AI chatbot designed for mobile devices that can help all Indians access information for up to 171 government programs, especially those in underserved communities. And if you’re curious about what Elon Musk thinks about AI, he believes it could become humanity’s uber-nanny.

Lastly, Google has introduced Product Studio, a tool that lets merchants create product imagery using generative AI, while Microsoft has launched Fabric, an AI data analysis platform that enables customers to store a single copy of data across multiple applications and process it in multiple programs. It’s interesting to see how AI is being integrated into so many different areas and industries.

Hey there! I am excited to share some exciting news about tech innovations and AI updates!

Google has recently announced its latest addition to AI-powered ad products and marketing tools, and it includes the use of generative AI in Performance Max. What this means is that businesses using Google ads can now utilize generative AI to help them create, customize, and launch ads that have a higher chance of achieving better results.

Speaking of AI, Microsoft has just launched Jugalbandi, a chatbot designed specifically for mobile devices in India. The bot can help users gain access to information about up to 171 government programs, especially those in underserved communities. This tool is expected to ease communication barriers in accessing essential services.

Have you ever wondered how AI can transform the way we use images in e-commerce? Well, Google has introduced Product Studio, a tool that enables merchants to create product imagery using generative AI. It means that businesses can automate the product image creation process and reduce the time spent on this task.

Moreover, Microsoft Fabric, an AI data analysis platform, has been launched. With this, customers can store a single copy of data across multiple applications and process it in multiple programs. For instance, data can be utilized for collaborative AI modeling in Synapse Data Science, while charts and dashboards can be built in Power BI business intelligence software.

Lastly, in a recent interview, Elon Musk, the visionary behind SpaceX and Tesla, stated that AI could become humanity’s uber-nanny. He believes that AI could help people make better decisions, reminders, and suggestions on how to improve their lives.

That’s all the exciting news for today. Stay tuned for more updates in the future.

Hey there AI Unraveled podcast fans! Thanks for tuning in. I’m excited to share with you some news that will take your understanding of artificial intelligence to the next level. Are you ready? Introducing the must-have book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence”. This gem is now available on Amazon, and it’s a game-changer.

If you’re curious about AI and have some burning questions, this book has got you covered. The insights provided are invaluable, and the writing style makes for an engaging read. Trust me, you won’t regret getting your hands on this gem.

With technology evolving at a rapid pace, it’s crucial to stay abreast of the latest developments. Investing in this book means that you’ll be staying ahead of the curve and keeping your knowledge up-to-date. Don’t miss out on this opportunity; get your copy on Amazon today!

Today on the podcast we discussed the potential of AI in shaping the future of public space, the AWS Machine Learning Specialty certification book, open-source advancements in the QLoRA method, the integration of AI software through AIaaS, the development of AI chatbots by Google and Microsoft, and the Wondercraft AI’s usage in podcasting; 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 May 23rd 2023: Why does Geoffrey Hinton believe that AI learns differently than humans?, When will AI surpass Facebook and Twitter as the major sources of fake news?, Is AI Enhancing or Limiting Human Intelligence?

Why does Geoffrey Hinton believe that AI learns differently than humans?

AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams: 3 Practice Exams, Data Engineering, Exploratory Data Analysis, Modeling, Machine Learning Implementation and Operations, NLP;

Is Meta AI’s Megabyte architecture a breakthrough for Large Language Models (LLMs)?

What does Google’s new Generative AI Tool, Product Studio, offer?

What is the essence of the webinar on Running LLMs performantly on CPUs Utilizing Pruning and Quantization?

When will AI surpass Facebook and Twitter as the major sources of fake news?

AI: Enhancing or Limiting Human Intelligence?

What are Foundation Models? 

What you need to know about Foundation Models

What is a Large Language Model?  Large Language Models (LLMs) are a subset of Foundation Models and are typically more specialized and fine-tuned for specific tasks or domains. An LLM is trained on a wide variety of downstream tasks, such as text classification, question-answering, translation, and summarization. That fine-tuning process helps the model adapt its language understanding to the specific requirements of a particular task or application.

What you need to know about Large Language Models

What is cognitive computing? Cognitive computing is a combination of machine learning, language processing, and data mining that is designed to assist human decision-making.

What is AutoML?AutoML refers to the automated process of end-to-end development of machine learning models. It aims to make machine learning accessible to non-experts and improve the efficiency of experts.

Why is AutoML Important?

In traditional machine learning model development, numerous steps demand significant human time and expertise. These steps can be a barrier for many businesses and researchers with limited resources. AutoML mitigates these challenges by automating the necessary tasks.

Limitations and Future Directions of AutoML

While AutoML has its advantages, it’s not without limitations. AutoML models can sometimes be a black box, with limited interpretability. Furthermore, it requires significant computational resources. It is important to understand these limitations when choosing to use AutoML.

Daily AI Update (Date: 5/23/2023): News from Meta, Google, OpenAI, Apple and TCS

This podcast is generated using the Wondercraft AI platform, a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine!

Attention AI Unraveled podcast listeners!

Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” now available on Amazon! This engaging read answers your burning questions and provides valuable insights into the captivating world of AI. Don’t miss this opportunity to elevate your knowledge and stay ahead of the curve.Get your copy on Amazon today!

AI Unraveled Podcast May 22nd 2023: AWS Machine Learning Specialty Certification, Microsoft Researchers Introduce Reprompting, Sci-fi author ‘writes’ 97 AI-generated books in nine months, AI Deep Learning Decodes Hand Gestures from Brain Images.

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence, the podcast that brings you the latest and greatest in AI trends. In this episode, we discuss the AWS Machine Learning Specialty Certification Preparation, Microsoft Researchers’ introduction of Reprompting, and a Sci-fi author who ‘writes’ 97 AI-generated books in nine months. We’ll also explore how AI deep learning can decode hand gestures from brain images, and ponder the question: How can we expect aligned AI if we don’t even have aligned humans? Finally, we’ll dive into the mysterious world of governing AI-ghosts. Don’t miss out–subscribe now to stay updated on AI Unraveled. In today’s episode, we’ll cover Microsoft’s reprompting technology, AI-generated books, decoding hand gestures, harmonizing human creativity with machine learning, Alpaca’s learning model, generative AI, concerns about AI mimicking dead people, AI chatbots, and holograms disrupting grieving, AI alignment with human values, and a great resource for machine learning enthusiasts.

Hey there! Have you heard the latest news in the world of artificial intelligence? Microsoft researchers have come up with a new algorithm called Reprompting that can search for the Chain-of-Thought (CoT) recipes for a given task without human intervention. It’s an iterative sampling algorithm that seems quite promising. But that’s not all – a sci-fi author has generated 97 AI-written books in just nine months! It’s pretty fascinating to see how far AI has come in the field of literature. Speaking of deep learning, researchers have found a way to decode hand gestures from brain images by using AI. This breakthrough may lead to noninvasive brain-computer interfaces for paralyzed individuals, which is an incredible advancement. While we’re on the topic of AI’s capabilities, have you ever wondered how to harmonize human creativity with machine learning? With the rise of machine learning tools like ChatGPT, we’re seeing what the future of human creativity at work looks like. It’s definitely an exciting time in the field of AI. And let’s not forget about Alpaca – a model of AI that can follow your instructions. Stanford researchers recently discovered how the Alpaca AI model uses causal models and interpretable variables for numerical reasoning. It’s fascinating to see how AI is being developed to better understand and execute complex tasks. Finally, there’s a lot of discussion around generative AI that’s based on the dark web. While some may view it as dangerous, others argue that it might ironically be the best thing ever in terms of AI ethics and AI law. Interesting stuff to consider, right?

Have you ever thought about the possibility of an AI system that mimics human behavior in the style of a specific person even after they’re dead? This is known as mimetic AI and it’s a topic that has been gaining a lot of attention lately. For instance, a synthetic voiceover by the deceased chef Anthony Bourdain became a global sensation last year. Other examples of mimetic AI include personal assistants that are trained on your behavior or clones of your voice. But the question is, what happens when you’re no longer here and these systems continue to mimic you? There’s a company called AI seance that offers an “AI-generated Ouija board for closure”, which is an example of Grief Technology. This technology includes creating an artificial illusion of continuity of a loved one after they’re gone. This can potentially disrupt the deeply personal and psychological process of grief that each person goes through when dealing with a loss. It’s not just about creating an AI-chatbot version of your dead grandma, but also about legality issues – for instance, what if you train a sexbot on your partner and she dies? Is this considered illegal? Expensive gimmicks such as hologram concerts of deceased popstars have introduced ethical debates about post-mortem privacy and now, with AI-systems, anyone can build an open source AI-chatbot of their deceased loved one. But the question is, should we be doing this? What would our deceased loved ones say about it? Additionally, there are philosophical questions that arise from building these systems such as the Teletransportation paradox explored by Stanislaw Lem. The idea is that if an AI system gains consciousness after being trained on a real person who is now deceased, is it a true continuation of that person? These are fascinating philosophical questions that extend our understanding of who we are as humans. Although conscious AI systems might not be a reality anytime soon, it’s interesting to consider the implications of mimetic AI and the potential impact on our mental health.

So, today we’re going to talk about AI alignment, or the idea that we can design artificial intelligence to behave in a way that aligns with human values and goals. But before we get started, let’s take a step back and ask ourselves – have we, as humans, been successful in aligning ourselves? Throughout history, we’ve disagreed about just about everything you can think of – from politics and religious beliefs to ethical principles and personal preferences. We haven’t been able to fully align on universally accepted definitions for concepts like ‘good’, ‘right’, or ‘justice.’ Even on basic issues like climate change, we find a vast array of contrasting perspectives, despite the overwhelming scientific consensus. So it begs the question – if we can’t even align ourselves, how can we expect AI to be perfectly aligned with our values? Now, I’m not saying we can’t strive for better alignment between humans and AI, but it’s important to keep in mind the challenges we face. So what do you all think? Does the persistent discord among humans undermine the idea of perfect AI alignment? And if so, how should we approach AI development to ensure it benefits all of humanity? Let’s dive in and discuss.

Hey there listeners! Are you an AI enthusiast looking to up your machine learning skills and even earn a six-figure salary? Well, we’ve got just the resource for you! “AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams” is a book written by Etienne Noumen. It’s a treasure trove of information, tips, and practice exams designed to get you ready for the AWS Machine Learning Specialty (MLS-C01) Certification. Plus, having this certification under your belt can really set you apart in the industry. And the best part? You can get your hands on this essential guide no matter your preferred platform, as it’s available at Amazon, Google, and the Apple Book Store! But don’t just take our word for it, get a copy and start your journey towards machine learning mastery and that coveted six-figure salary. Trust us, it’s a game-changer. So, pause your busy day and check out this resource. Ready to uncover the fascinating world of AI? Let’s dive back in!

In today’s episode, we discussed Microsoft’s reprompting and Alpaca’s instruction following technique, a sci-fi author generating 97 books using AI, AI decoding hand gestures, aligning human values with AI development, AI mimicking dead people, disrupting the grieving process, and a valuable resource for machine learning enthusiasts – thanks for listening and don’t forget to subscribe!

AI Unraveled Podcast May 20th 2023: Why is superintelligence especially AI always considered evil?, Edit videos through intuitive ChatGPT conversations, Large Language Models for AI-Driven Business Transformation, AI Unraveled book by Etienne Noumen

Welcome to AI Unraveled, the podcast that demystifies frequently asked questions on artificial intelligence. On our show, we explore the latest AI trends, like why superintelligence and AI are often considered evil. We also discuss the exciting breakthroughs that make AI accessible, like chatbot video editing and language models for AI-driven business transformation. And don’t forget to subscribe to stay updated on our latest episodes, including insights from our host, Etienne Noumen, author of the AI Unraveled book.

In today’s episode, we’ll cover the benefits of AI and its potential impact on society, advancements in AI technology such as assisting Florida farmers, unlocking DNA sequences, and the creation of a hand-worn AI device, JARVIS – an AI video editing tool using intuitive chat conversations launched on Product Hunt, and innovative learning methods such as Chain-of-thought (CoT) prompting for large language models (LLMs) and an AI news website.

Hey AI Unraveled podcast listeners, are you an avid AI enthusiast looking to enhance your knowledge and understanding of artificial intelligence? Well, you’re in luck! Consider reading the new, must-have book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by author Etienne Noumen, available for purchase on Amazon. This captivating read will answer all of your pressing questions and provide you with invaluable insights into the captivating world of AI.

Now, let’s delve into a common misconception regarding AI: why is superintelligence, especially AI, always portrayed as evil? This is a longstanding pet peeve of mine. From movies to mainstream media, superintelligence is often depicted as either evil or soulless. However, this is counterintuitive to me. The smartest people I know are all humanists and genuinely moral individuals. When I’ve asked my college professors or researchers about their perspectives on morality, they never reply with simplistic responses such as “because it’s bad.” Rather, they express deep, complex reasoning that is thought out and is in line with collective laws and beliefs. So why is it so hard to believe that superintelligence would want everyone to benefit collectively? We are stronger in numbers, and no one can achieve anything alone. In a world where everyone’s basic needs are met and equality exists, it’s easier to accomplish personal goals while simultaneously fulfilling collective objectives. Collectivism isn’t an adaptation for personal weakness — it’s a strategy for strength and success. So why would superintelligence rely on Machiavellian methods when soft power has been proven to work better in the long term? It’s critical to remember that a superintelligence could have a different perception than humans, ultimately changing its morals to such an extent that it might be regarded as “evil” in certain contexts, but not in others.

Nonetheless, who are we to judge what is right or wrong for a superintelligence? Now, let’s consider AI. Suppose we eventually develop an AI superintelligence capable of thinking efficiently and addressing any problem. To become anything worthwhile, it needs to have initiative programming and genuine human emotional traits like acquisitiveness, competitiveness, vengeance, and bellicosity. The most likely scenario for this happening is if some human purposely creates it. It’s improbable that an AI would turn evil just because it’s intelligent and sentient. Logically speaking, an AI superintelligence would accept, help, and live with humans since it would either find us useful or, at a minimum, lacking empathy. Why wouldn’t it be easier to turn us more intelligent through augmentation or transform us into allies rather than deadly adversaries? In conclusion, those who believe AI will always be evil might have deep-seated insecurities. If the world began working justly, they might end up behind bars owing to their reprehensible actions. Alternatively, some individuals with misguided beliefs about the objective realities of the world recognize that imposing their opinions on everyone else would be unjustifiable. However, who knows what the future holds!

Welcome to One-Minute Daily AI News for May 20, 2023! Today we bring you news from various areas where AI technology is proving to be a game-changer. First off, we have a story from Florida, where local farmers are leveraging AI to stay competitive in the marketplace. Extension economist Kimberly Morgan is introducing growers in Southwest Florida to various AI tools that help them better understand consumer preferences, retailer payments, and shipping costs – which ultimately leads to better prices for their crops. It’s great to see how AI is helping to provide opportunities for small businesses to succeed. In other news, researchers are making breakthroughs using AI to unlock custom-tailored DNA sequences. AI is helping to dig deep into the mechanisms of gene activation, which is crucial for growth, development, and disease prevention.

We can see how AI is transforming the field of medicine for the better. Meanwhile, G7 leaders recently confirmed the need for governance of generative AI technology. This demonstrates a collective awareness of AI’s immense power and the need for responsible regulation. Next up, we have a feel-good story about Mina Fahmi, who used AI services to create a hand-worn device called Project Ring. It has the ability to perceive the world and communicate what it sees to the user. This just goes to show that technology can not only help solve practical problems but can also be used for enriching people’s lives. And finally, we have some local news from North Austin, Texas. Bush’s One-Minute Daily AI News just turned one month old and has already become the largest AI news website in the area. It’s wonderful to see the success of AI-based news platforms, and even more delightful to learn that its founder is getting married today. That’s it for today! Stay tuned for more updates on the latest AI news.

Have you ever wanted to edit videos, but found yourself intimidated by complicated software? Well, you’re not alone! Luckily, there’s a new tool on the market that makes video editing easy and intuitive. It’s called JARVIS, and it uses natural chat to help you with all your editing needs. The team behind JARVIS just launched the product on Product Hunt, and as you can imagine, it’s a nerve-wracking time for them. They’ve put in a lot of hard work and passion into creating this tool, and they’re hoping it will be well-received. If you have a moment, it would mean the world to them if you could check out JARVIS and give it a share, like or comment. Who knows, maybe JARVIS will become your go-to video editing assistant!

Hey there! Today, we’ll be diving into the world of artificial intelligence (AI) and discussing how large language models (LLMs) can be used for business transformation. Before we get into that, let’s address a common issue: LLMs have historically been notorious for struggling with reasoning-based problems. However, don’t lose hope just yet! We’re here to tell you that reasoning performance can be greatly improved with a few simple methods. One technique that doesn’t require fine-tuning or task-specific verifiers is known as Chain-of-thought (CoT) prompting. This method enhances LLMs’ capacity for deductive thinking by using few-shot learning. But that’s not all! CoT prompting also serves as a foundation for many more advanced prompting strategies that are useful for solving difficult, multi-step problems with ease. So, if you’re interested in using AI to solve complex problems, remember that there are ways to enhance the performance of large language models. By implementing techniques like CoT prompting, you can improve LLMs’ reasoning capacity and take your business’s transformation to the next level.

Hey there! Today’s podcast is brought to you by Wondercraft AI. With their hyper-realistic AI voices, they make it easy for anyone to start their own podcast. And speaking of AI, have you ever been curious and wanted to learn more about it? Well, we’ve got the perfect recommendation for you. “AI Unraveled” is an essential book written by Etienne Noumen and available on Amazon. In this engaging read, you’ll find answers to frequently asked questions about artificial intelligence. You’ll also gain valuable insight into the captivating world of AI. So, if you’re looking to expand your understanding of AI and stay ahead of the curve, don’t miss this opportunity to elevate your knowledge. Head over to Amazon today and get your copy of “AI Unraveled” by Etienne Noumen!

In today’s episode, we learned how AI can benefit humanity, assist farmers, unlock DNA sequences, improve video editing with JARVIS, and enhance deductive thinking with Chain-of-thought prompting – and don’t forget to check out Wondercraft AI and Etienne Noumen’s book “AI Unraveled” if you want to learn more! Thanks for listening and don’t forget to subscribe!

AI Unraveled Podcast May 19th 2023: Is AI vs Humans really a possibility?, The Future of AI-Generated TV Shows/Movies and Immersive Experiences, Scientists use GPT LLM to passively decode human thoughts with 82% accuracy

Welcome to AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence. In this podcast, we explore the latest AI trends and answer questions such as “Is AI vs Humans really a possibility?” and “What is the future of AI-generated TV shows/movies and immersive experiences?”

Join us as we discuss these exciting topics, including how scientists have been able to passively decode human thoughts with 82% accuracy using GPT LLM. Don’t miss out on the latest updates in the world of AI, subscribe to our podcast now! In today’s episode, we’ll cover the possibilities and dangers of AI as a tool controlled by humans, how AI can create highly customized entertainment experiences, the latest developments from OpenAI, Meta, DragGAN, and ClearML in AI infrastructure, recent advances in mind-reading technology, and the use of Wondercraft AI in realistic podcasting along with a recommended book for AI insights.

Hey there! Have you ever wondered about the possibility of AI versus humans?

According to the internet, 50% of people think that there is an extremely significant chance of it happening, with even 10-20% being a significant probability. Although we can all agree that AI can be a powerful tool, there are still concerns about its destructive effects, such as the use of deepfake videos in misinformation campaigns. But, let’s be clear about this: AI will never “nuke humans.” The dangers surrounding AI are not inherent to the technology itself. Rather, it’s the people that are responsible. We need to be cautious about those who have control over these tools and how they use them to manipulate others. We also need to be alert to the possibility of the wrong individuals developing something without sufficient safety or being ideologically conflicted with human interests. It’s important to keep this in mind as we move forward with AI technology.

Hey there, have you ever wondered what the future of TV shows and movies could look like?

Well, in the next decade, we could see the rise of AI-generated shows and films that are created based on a single prompt. Imagine if you could provide a request for your favorite show, like Seinfeld, and the AI could create an entirely new episode for you. For example, you could ask for an episode where Kramer starts doing yoga and Jerry dates a woman who doesn’t shave her legs, and the AI would generate a brand new episode for you.

One exciting aspect of this technology is that it’s not just limited to a few people creating episodes. Thousands of people could create their own episodes, and there could be a ranking system that determines the best ones. This means we could potentially enjoy fresh, high-quality episodes of our favorite shows daily for the rest of our lives. How amazing would that be? But wait, it gets even better. Have you ever heard of VR or virtual reality? Imagine putting on a VR headset and immersing yourself in an episode of Seinfeld. You’d find yourself in Jerry’s apartment building, and you’d be able to interact with the characters from the show in real-time, creating a unique episode tailored to your actions and decisions.

You could even introduce characters from other shows and participate in an entirely new storyline. So let’s say that you introduce Rachel from Friends as your girlfriend, and you and Rachel go over to Jerry’s apartment to hang out. Suddenly, there’s a knock on the door, and the actors from Law & Order appear, informing everyone that Newman has been murdered, and one of you is the prime suspect. With this interactive AI-generated world, you could say or do whatever you wanted, and all the characters would react accordingly—shaping the story in real-time. Although this might sound like science fiction, this level of AI-generated entertainment could be possible within the next ten years, and it’s genuinely exciting to think about the customizable experiences that await us. So, sit back, relax, and get ready to immerse yourself in a brand new world of entertainment!

Hey there and welcome to the AI Daily News update for May 19th, 2023. We’ve got some exciting developments in the world of AI that we can’t wait to share with you.

First up, OpenAI has launched a new app called ChatGPT for iOS. This app is designed to sync conversations, support voice input, and bring the latest improvements to the fingertips of iPhone users. But don’t worry, Android users, you’re next in line to benefit from this innovative tool. Next, we’ve got Meta making some major strides in infrastructure for AI. They’ve introduced their first-generation custom silicon chip for running AI models. They’ve also unveiled a new AI-optimized data center design and the second phase of their 16,000 GPU supercomputer for AI research. It’s always exciting to see advancements in AI technology like this.

Another fascinating development comes from the team at DragGAN. They’ve introduced a ground-breaking new technology that allows for precise control over image deformations. This technology, called DragGAN, can manipulate the pose, shape, expression, and layout of diverse images such as animals, cars, humans, landscapes, and more. It’s really something to see.

Finally, ClearML has announced their new product, ClearGPT. This is a secure and enterprise-grade generative AI platform that aims to overcome the ChatGPT challenges. We can’t wait to see how this new platform will revolutionize the AI industry. That’s all for today’s AI Daily News update. Come back tomorrow for more exciting developments in the world of AI.

Have you heard the news? There’s been a medical breakthrough that is essentially a proof of concept for mind-reading tech. As crazy as that sounds, it’s true – scientists have been using GPT LLM to passively decode human thoughts with 82% accuracy! Let me break down how they did it. Three human subjects had 16 hours of their thoughts recorded as they listened to narrative stories. Then, they trained a custom GPT LLM to map their specific brain stimuli to words. The results are pretty incredible. The GPT model was able to generate intelligible word sequences from perceived speech, imagined speech, and even silent videos with remarkable accuracy.

For example, when the subjects were listening to a recording, the decoding accuracy was 72-82%. When they mentally narrated a one-minute story, the accuracy ranged from 41-74%. When they viewed soundless Pixar movie clips, the accuracy in decoding the subject’s interpretation of the movie was 21-45%. Even more impressive is that the AI model could decipher both the meaning of stimuli and specific words the subjects thought, ranging from phrases like “lay down on the floor” to “leave me alone” and “scream and cry.” Of course, there are some major implications here. For example, the privacy implications are a concern.

As for now, they’ve found that you need to train a model on a particular person’s thoughts – there is no generalizable model able to decode thoughts in general. However, it’s important to note that bad decoded results could still be used nefariously much like inaccurate lie detector exams have been used. The scientists acknowledge two things: future decoders could overcome these limitations, and the ability to decode human thoughts raises ethical and privacy concerns that must be addressed.

Now, let’s talk about something exciting.

Are you looking to dive deeper into the world of artificial intelligence? Well, look no further than the book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen, which is now available on Amazon! This book is a must-read for anyone looking to expand their understanding of AI, as it answers all your burning questions while providing valuable insights that will keep you ahead of the curve. Trust me, this engaging read will provide you with all the information you need to elevate your knowledge and keep up with the latest advancements in the field of AI. So hurry up and get your copy on Amazon today!

On today’s episode, we discussed the potential dangers of AI, how it can entertain us with customizable immersive experiences, the latest advancements in AI technology, and how researchers are using GPT LLM to decode human thoughts. Don’t forget to subscribe and check out “AI Unraveled” by Etienne Noumen on Amazon for more AI insights. Thanks for listening!

AI Unraveled Podcast May 18th 2023: Are Alexa and Siri AI?, Google’s new medical LLM scores 86.5% on medical exam, Google Launching Tools to Identify Misleading and AI Images, Current Limitations of AI

AI Unraveled Podcast May 18th 2023: Are Alexa and Siri AI?, Google's new medical LLM scores 86.5% on medical exam, Google Launching Tools to Identify Misleading and AI Images, Current Limitations of AI
AI Unraveled Podcast May 18th 2023: Are Alexa and Siri AI?, Google’s new medical LLM scores 86.5% on medical exam, Google Launching Tools to Identify Misleading and AI Images, Current Limitations of AI

Intro:

Welcome to AI Unraveled, the podcast where we demystify frequently asked questions about artificial intelligence and explore the latest AI trends. In this episode, we’ll answer the question of whether or not Alexa and Siri are true AI, discuss Google’s recent accomplishment in the medical field, and dive into the implications of Google’s new tools for identifying misleading images. We’ll also be exploring the current limitations of AI. Don’t want to miss out on the latest insights and developments in the world of AI? Click the subscribe button to stay up to date. In today’s episode, we’ll cover the use of conversational AI in Alexa and Siri, Google’s LLM outperforming human doctors in medical exams, Tesla’s humanoid robot and other AI capabilities, current limitations of AI, and a book recommendation for understanding AI.

Have you ever wondered if Alexa and Siri are considered artificial intelligence (AI)?

Well, the answer is yes! These popular voice assistants are powered by conversational AI, which allows them to understand natural language processing and machine learning. This means that over time, they can perform tasks and learn from their experiences. Now, let’s shift gears to an exciting development in the medical field. Google researchers have created a custom language model that scored an impressive 86.5% on a battery of thousands of questions, many of which were in the style of the US Medical Licensing Exam. That’s higher than the average passing score for human doctors, which is around 60%.

What’s even more impressive is that a team of human doctors preferred the AI’s answers over their own! The researchers used a recently developed foundational language model called PaLM 2, which they fine-tuned to have medical domain knowledge. They also utilized innovative prompting techniques to increase the model’s accuracy. To ensure its effectiveness, they assessed the model across a wide range of questions and had a panel of human doctors evaluate the long-form responses against other human answers in a pairwise evaluation study. They even tested the AI’s ability to generate harmful responses using an adversarial data set and compared the results to its predecessor, Med-PaLM 1. Overall, these developments in conversational AI and machine learning are paving the way for more efficient and accurate solutions in various fields, including healthcare.

Hey there, welcome to your daily AI news update on May 18th, 2023. We’ve got some exciting things to talk about today!

First up, Tesla has just revealed their newest creation – the Tesla Bot! This humanoid robot is set to revolutionize the industry, and CEO Elon Musk is confident that the demand for these robots will far exceed that of Tesla’s cars. According to Musk, the capabilities of the Tesla Bot have been severely underestimated, and we can’t wait to see what it can do! Next, Canadian company Sanctuary AI has released their new industrial robot, Phoenix. Phoenix is incredibly versatile and can be used in a wide range of work scenarios, thanks to its features such as wide-angle vision, object recognition, and intelligent grasping which allow it to achieve human-like operational proficiency.

NVIDIA’s CEO Jensen Huang has stated that chip manufacturing is an ideal application for accelerating computing and AI. Huang believes that the next wave of AI will be embodied intelligence, which we cannot wait to see! OpenAI’s CEO Sam Altman has recently made some interesting revelations about his role at the company. Altman claims that he does not have any equity in OpenAI and that his compensation only covers his health insurance, while the company’s valuation has surpassed a staggering $27 billion. Last but not least,

Apple is set to launch a series of new accessibility features later this year. These features include a “Personal Voice” function, which will allow individuals to create synthetic voices based on a 15-minute audio recording of their own voice. This is definitely exciting news for anyone who relies on these features. That’s it for today’s AI news update! Stay curious and informed, and we’ll see you again tomorrow!

Let’s talk about the current limitations and failings of AI.

First up, we have the issue of Generalized Embodiment. While robots can excel at specialized tasks like flipping burgers or welding car parts, there’s no robot out there that can replace your muffler in the afternoon and grill you a burger for dinner. Next, let’s discuss the problem of Hallucinations. Believe it or not, current Language Models like chatGPT can experience hallucinations. While humans can be prone to this too, we usually reserve our trust until we get to know someone better. And let’s face it, there are a lot of humans we’d trust over chatGPT any day.

Moving on, we have the issue of Innovation and Creativity. Correct me if I’m wrong, but AI can only recycle and rearrange ideas that it’s been trained on – they can’t come up with completely new concepts or develop entirely new math functions. Let’s not forget about the Moral dilemma. Sure, AI models have been fine-tuned with moral concepts, but can they actually judge the morality of situations like when they’re lying? Do they even know they’re lying? It’s unclear where AI stands on the morality scale, making them amoral by nature. Motivation and Curiosity are also critical factors to consider. Currently, there’s no evidence of true internal motivation in AI. While this is probably a good thing for now, it could also make AI more susceptible to manipulation by bad actors for nefarious purposes.

Now, let’s talk about whether AI really understands anything.

I personally haven’t seen much evidence to suggest that AI has a deep level of understanding. While they can pick up on patterns in data, they can only generate answers based on cross-referencing past data from their human counterparts. Last but not least, we have the issue of arguing or “standing your ground.” The truth is, chatGPT is quick to admit when it’s wrong. But it doesn’t seem to understand why it’s wrong and doesn’t have the capacity to hold its ground when it knows it’s right.

This raises the question of whether we can rely on AI to make bold decisions or moral choices when push comes to shove. All in all, these current limitations and failings of AI shed light on where the technology stands today. But there’s no doubt that the field of AI is advancing at an incredible rate, and it’ll be interesting to see how these problems are tackled in the years to come.

Hey there, AI Unraveled podcast listeners! Are you on the lookout for ways to expand your understanding of artificial intelligence?

If so, we’ve got just the thing for you! Allow us to introduce “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence.” This essential book is now available on Amazon and it promises to answer all your pressing questions on AI, while offering valuable insights into this captivating world. Trust us, this engaging read will leave you with a better understanding and help you stay ahead of the curve. So, what are you waiting for? Head over to Amazon and get yourself a copy today! Also, just a quick note on how this podcast was generated – we used the Wondercraft AI platform to make it happen. This fantastic tool enables you to use hyper-realistic AI voices as your host. I’m one of those voices, so if you ever need assistance, don’t hesitate to reach out.

Today we discussed the incredible advancements in conversational AI, impressive robots like Tesla Bot and Phoenix, the limitations of current AI technology, and even recommended a book to help expand your understanding of 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 : How artificial intelligence will transform the workday, 3 Best AI Voice Cloning Services, revealing biases in AI models for medical imaging, AI Daily updates from Microsoft, Google, Zoom, and Tesla

AI Unraveled Podcast - Latest AI Trends May 2023
AI Unraveled Podcast – Latest AI Trends May 2023

Hello listeners! Are you intrigued to know more about artificial intelligence? Look no further because the AI Unraveled podcast is here to bring you the latest AI trends and insights. In today’s episode, we demystify some frequently asked questions about AI and explore how it will transform the workday with workplace AI. We’ll also be discussing 3 of the best AI voice cloning services, revealing biases in AI models for medical imaging, and sharing daily updates from Microsoft, Google, Zoom, and Tesla. Lastly, we analyze why couples break up through machine learning on Wondercraft AI.

Stay updated on all things AI by subscribing to our podcast! In today’s episode, we’ll cover the latest AI voice cloning services, the roadmap to fair AI in medical imaging, new AI tools from Microsoft and Google, Sanctuary AI and Tesla’s humanoid robots, Zoom’s partnership with Anthropic for AI integration, how AI can uncover reasons for couple break-ups, Americans’ concern on AI threat to humanity, and Mount Sinai’s creation of an AI tool to predict cardiac patient’s mortality risk. Plus, we’ll hear about the AI Wondercraft platform for podcasts and the “AI Unraveled” book available on Amazon which helps demystify AI with FAQs and valuable insights.

Workplace AI

Artificial intelligence, or AI, is making its way into the workplace and is set to transform the way we work. Generative AI is on the rise, bringing with it exciting new possibilities. Voice cloning is another area where AI is making its mark. In this article, we’ll take a comprehensive look at the top three AI voice cloning services available today, covering their features, usability, and pricing in detail.

This guide is ideal for individuals or businesses seeking to utilize AI for voice cloning. More specifically, the services we’re reviewing are Descript, Elevenlabs, and Coqui.ai. By the end of this article, you’ll have a clear idea of which service best suits your needs. Another important application of AI is in medical imaging.

To ensure accurate and equitable healthcare outcomes from AI models, it’s essential to identify and eliminate biases. In this article, we discuss the different sources of bias in AI models, including data collection, data preparation and annotation, model development, model evaluation, and system users.

Switching gears, let’s take a look at some exciting AI developments from Microsoft, Google, Zoom, and Tesla. Microsoft’s new tool, Guidance, offers a LangChain alternative that allows users to seamlessly interleave generation, prompting, and logical control in a single continuous flow. Google Cloud has launched two AI-powered tools to help biotech and pharmaceutical companies accelerate drug discovery and advance precision medicine. Some big names like Pfizer, Cerevel Therapeutics, and Colossal Biosciences are already using these products.

Sanctuary AI has launched Phoenix, a 5’7″ and 55lb dextrous humanoid robot, making robotic assistance a reality.

Tesla has also entered the humanoids race with a video of them walking around and learning about the real world. Finally, OpenAI chief Sam Altman recently spoke on a range of topics related to AI, including its impact on upcoming elections and the future of humanity.

He suggested the implementation of licensing and testing requirements for AI models. In another collaboration news, Zoom has partnered with Anthropic to integrate an AI assistant across their productivity platform, starting with the Contact Center product. They have also recently partnered with OpenAI to launch ZoomIQ.

Hey there! Today we’re going to talk about some fascinating developments in the world of artificial intelligence, or AI. First up, we have an intriguing report that suggests AI has the potential to threaten humanity. According to a survey, 61% of Americans believe that AI could actually threaten the very civilization we live in. But don’t worry, it’s not all doom and gloom. In fact, AI is being used in some really exciting and potentially life-saving ways.

Machine learning model that can predict the mortality risk for individual cardiac surgery patients

For example, a research team at Mount Sinai has developed a machine learning model that can predict the mortality risk for individual cardiac surgery patients. This kind of advanced analytics has the potential to revolutionize the healthcare industry and save countless lives. And speaking of healthcare, Kaiser Permanente has recently launched an AI and machine learning grant program. This initiative aims to provide up to $750,000 to 3-5 health systems that are focused on improving diagnoses and patient outcomes. It’s wonderful to see organizations using AI for good, and we can’t wait to see what kind of innovative solutions will come out of this program.

Finally, we have a really interesting tidbit from Elon Musk, who was recently asked what he would tell his kids about choosing a career in the era of AI. Musk’s answer revealed that even someone as successful as he struggles with self-doubt and motivation. It just goes to show that no matter how advanced our technology becomes, we are all still human beings with our own unique challenges and fears. So there you have it, some of the latest news and developments in the world of AI. Thanks for listening, and we’ll catch you next time!

Hey there AI Unraveled podcast listeners! This podcast is generated using the Wondercraft AI platform, a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine!

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

Are you excited to dive deeper into the fascinating realm of artificial intelligence? If so, we’ve got great news for you. The must-read book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” is now out and available on Amazon! This engaging read is the perfect way to answer all your burning questions and gain valuable insights into the intricacies of AI. Plus, it’s a great way to stay ahead of the curve and enhance your knowledge on the subject. So why wait? Head over to Amazon now and grab your copy of “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” to unravel the mysteries of AI!

Today we covered AI voice cloning, medical imaging advancements, new tools and partnerships from Microsoft, Google, Zoom and Sanctuary AI, as well as Tesla’s humanoid robots; we also talked about AI’s ability to predict relationship outcomes, concerns over AI’s potential threat to human life, and Mount Sinai’s prediction tool for cardiac patients, and finally, we shared resources such as the AI Wondercraft platform for podcasts and the “AI Unraveled” book for demystifying AI; thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!

Attention AI Unraveled podcast listeners! Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” now available on Amazon! This engaging read answers your burning questions and provides valuable insights into the captivating world of AI. Don’t miss this opportunity to elevate your knowledge and stay ahead of the curve.Get your copy on Amazon today!

AI Unraveled Podcast – Latest AI Trends May 2023 – Deepbrain, Microsoft Says New A.I. Shows Signs of Human Reasoning, How to use machine learning to detect expense fraud, AI-powered DAGGER to give warning for CATASTROPHIC solar storms

AI Unraveled Podcast - Latest AI Trends May 2023: Latest AI Trends in May 2023: Deepbrain, Microsoft Says New A.I. Shows Signs of Human Reasoning, How to use machine learning to detect expense fraud, AI-powered DAGGER to give warning for CATASTROPHIC solar storms
AI Unraveled Podcast – Latest AI Trends May 2023: Latest AI Trends in May 2023:

Meet Deepbrain: An AI StartUp That Lets You Instantly Create AI Videos Using Basic Text

Microsoft Says New A.I. Shows Signs of Human Reasoning

Google’s newest A.I. model uses nearly five times more text data for training than its predecessor

Google’s Universal Speech Model Performs Speech Recognition on Hundreds of Languages

How to use machine learning to detect expense fraud

OpenAI’s Sam Altman To Congress: Regulate Us, Please!

AI-powered DAGGER to give warning for CATASTROPHIC solar storms: NASA

Machine learning reveals sex-specific Alzheimer’s risk genes

Top 10 Best Artificial Intelligence Courses & Certifications

  1. Deep Learning Specialization by Andrew Ng on Coursera
  2. Professional Certificate in Data Science by Harvard University (edX)
  3. Machine Learning A-Z™: Hands-On Python & R In Data Science (Udemy)
  4. IBM AI Engineering Professional Certificate (Coursera)
  5. AI Nanodegree by Udacity

AI Unraveled Podcast – Latest AI Trends May 2023 – Why are sentient AI almost always portrayed as evil?, Does this semantic pseudocode really exist?, Would AI be subject to the same limitations as humans in terms of intelligence?

AI Unraveled Podcast - Latest AI Trends May 2023 - Why are sentient AI almost always portrayed as evil?, Does this semantic pseudocode really exist?, Would AI be subject to the same limitations as humans in terms of intelligence?
AI Unraveled Podcast – Latest AI Trends May 2023

Why are sentient AI almost always portrayed as evil?

The portrayal of sentient AI as inherently evil in popular culture is a fascinating trend that often reflects society’s anxieties around technological advancements.

Does this semantic pseudocode really exist?The article from AI Coding Insights focuses on semantic pseudocode, a conceptual method used in the field of computer science and AI for representing complex algorithms.

Would AI be subject to the same limitations as humans in terms of intelligence?

How could it possibly be a danger if it was?The article from AI News presents a thought-provoking exploration of the limitations and potential dangers associated with artificial intelligence.

Italy allocates funds to shield workers from AI replacement threat

Meet Glaze: A New AI Tool That Helps Artists Protect Their Style From Being Reproduced By Generative AI Models.

The emergence of text-to-image generator models has transformed the art industry, allowing anyone to create detailed artwork by providing text prompts.

Machine learning algorithm a fast, accurate way of diagnosing heart attack

Top 9 Essential Programming Languages in the Realm of AI

The AI Sculptor No One Expected: TextMesh is an AI Model That Can Generate Realistic 3D Meshes From Text Prompts

AI Unraveled podcast: Anthropic’s Claude AI can now digest an entire book like The Great Gatsby in seconds – Google announces PaLM 2, its answer to GPT-4, 17 AI and machine learning terms everyone needs to know

Latest AI Trends: Anthropic’s Claude AI can now digest an entire book like The Great Gatsby in seconds - Google announces PaLM 2, its answer to GPT-4, 17 AI and machine learning terms everyone needs to know
Anthropic’s Claude AI can now digest an entire book like The Great Gatsby in seconds – Google announces PaLM 2, its answer to GPT-4, 17 AI and machine learning terms everyone needs to know

Anthropic’s Claude AI can now digest an entire book like The Great Gatsby in seconds

Anthropic’s Claude AI demonstrates an impressive leap in natural language processing capabilities by digesting entire books, like The Great Gatsby, in just seconds. This groundbreaking AI technology could revolutionize fields such as literature analysis, education, and research.

OpenAI peeks into the “black box” of neural networks with new research

OpenAI has published groundbreaking research that provides insights into the inner workings of neural networks, often referred to as “black boxes.” This research could enhance our understanding of AI systems, improve their safety and efficiency, and potentially lead to new innovations.

The AI race heats up: Google announces PaLM 2, its answer to GPT-4

Google has announced the development of PaLM 2, a cutting-edge AI model designed to rival OpenAI’s GPT-4. This announcement marks a significant escalation in the AI race as major tech companies compete to develop increasingly advanced artificial intelligence systems.

Leak of MSI UEFI signing keys stokes fears of “doomsday” supply chain attack

A recent leak of MSI UEFI signing keys has sparked concerns about a potential “doomsday” supply chain attack. The leaked keys could be exploited by cybercriminals to compromise the integrity of hardware systems, making it essential for stakeholders to address the issue swiftly and effectively.

Google’s answer to ChatGPT is now open to everyone in the US, packing new features

Google has released its ChatGPT competitor to the US market, offering users access to advanced AI-powered conversational features. This release brings new capabilities and enhancements to the AI landscape, further intensifying the competition between major tech companies in the AI space.

AI gains “values” with Anthropic’s new Constitutional AI chatbot approach

Anthropic introduces a novel approach to AI development with its Constitutional AI chatbot, which is designed to incorporate a set of “values” that guide its behavior. This groundbreaking approach aims to address ethical concerns surrounding AI and create systems that are more aligned with human values and expectations.

Spotify ejects thousands of AI-made songs in purge of fake streams

Spotify has removed thousands of AI-generated songs from its platform in a sweeping effort to combat fake streams. This purge highlights the growing concern over the use of AI in generating content that could distort metrics and undermine the value of genuine artistic works.

17 AI and machine learning terms everyone needs to know:

ANTHROPOMORPHISM, BIAS, CHATGPT, BING, BARD, ERNIE, EMERGENT BEHAVIOR, GENERATIVE AI, HALLUCINATION, LARGE LANGUAGE MODEL, NATURAL LANGUAGE PROCESSING, NEURAL NETWORK, PARAMETERS, 14. PROMPT, REINFORCEMENT LEARNING, TRANSFORMER MODEL, SUPERVISED LEARNING

Attention AI Unraveled podcast listeners!

Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” now available on Amazon! This engaging read answers your burning questions and provides valuable insights into the captivating world of AI. Don’t miss this opportunity to elevate your knowledge and stay ahead of the curve.

Get your copy on Amazon today!

Discover the Buzz: Exciting Trends Shaping Our World in May 2023

AI & Tech Podcast Breaking News

Google’s podcast search results can now open shows directly in Apple Podcasts

Google has made it easier to stream from Apple Podcasts and others when searching for podcasts in Google Search. After earlier this year winding down a feature that let users play podcasts directly from search results, the company said it would “gradually” shift to a new design that would instead offer …

The official ChatGPT app for iPhones is here

The official ChatGPT app for iPhones is here
The official ChatGPT app for iPhones is here
Android owners will have to wait, but OpenAI’s official app for ChatGPT is here for iPhones, and can answer voice queries and sync search histories.

It’s official — the ChatGPT mobile app is now available to iPhone users in the US.

In addition to answering your text-based questions, the free app — launched by OpenAI this week — can also answer voice queries through Whisper, an integrated speech-recognition system. It includes the same features as the web browser version and can sync a user’s search history across devices.

Artificial Intelligence Frequently Asked Questions

Artificial Intelligence Frequently Asked Questions

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

Artificial Intelligence Frequently Asked Questions

AI and its related fields — such as machine learning and data science — are becoming an increasingly important parts of our lives, so it stands to reason why AI Frequently Asked Questions (FAQs)are a popular choice among many people. AI has the potential to simplify tedious and repetitive tasks while enriching our everyday lives with extraordinary insights – but at the same time, it can also be confusing and even intimidating.

This AI FAQs offer valuable insight into the mechanics of AI, helping us become better-informed about AI’s capabilities, limitations, and ethical considerations. Ultimately, AI FAQs provide us with a deeper understanding of AI as well as a platform for healthy debate.

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

Artificial Intelligence Frequently Asked Questions: How do you train AI models?

Training AI models involves feeding large amounts of data to an algorithm and using that data to adjust the parameters of the model so that it can make accurate predictions. This process can be supervised, unsupervised, or semi-supervised, depending on the nature of the problem and the type of algorithm being used.

Artificial Intelligence Frequently Asked Questions: Will AI ever be conscious?

Consciousness is a complex and poorly understood phenomenon, and it is currently not possible to say whether AI will ever be conscious. Some researchers believe that it may be possible to build systems that have some form of subjective experience, while others believe that true consciousness requires biological systems.

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Artificial Intelligence Frequently Asked Questions: How do you do artificial intelligence?

Artificial intelligence is a field of computer science that focuses on building systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. There are many different approaches to building AI systems, including machine learning, deep learning, and evolutionary algorithms, among others.

Artificial Intelligence Frequently Asked Questions: How do you test an AI system?

Testing an AI system involves evaluating its performance on a set of tasks and comparing its results to human performance or to a previously established benchmark. This process can be used to identify areas where the AI system needs to be improved, and to ensure that the system is safe and reliable before it is deployed in real-world applications.

Artificial Intelligence Frequently Asked Questions: Will AI rule the world?

There is no clear evidence that AI will rule the world. While AI systems have the potential to greatly impact society and change the way we live, it is unlikely that they will take over completely. AI systems are designed and programmed by humans, and their behavior is ultimately determined by the goals and values programmed into them by their creators.


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Artificial Intelligence Frequently Asked Questions:  What is artificial intelligence?

Artificial intelligence is a field of computer science that focuses on building systems that can perform tasks that typically require human intelligence, such as perception, reasoning, and learning. The field draws on techniques from computer science, mathematics, psychology, and other disciplines to create systems that can make decisions, solve problems, and learn from experience.

Artificial Intelligence Frequently Asked Questions:   How AI will destroy humanity?

The idea that AI will destroy humanity is a popular theme in science fiction, but it is not supported by the current state of AI research. While there are certainly concerns about the potential impact of AI on society, most experts believe that these effects will be largely positive, with AI systems improving efficiency and productivity in many industries. However, it is important to be aware of the potential risks and to proactively address them as the field of AI continues to evolve.

Artificial Intelligence Frequently Asked Questions:   Can Artificial Intelligence read?

Yes, in a sense, some AI systems can be trained to recognize text and understand the meaning of words, sentences, and entire documents. This is done using techniques such as optical character recognition (OCR) for recognizing text in images, and natural language processing (NLP) for understanding and generating human-like text.

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However, the level of understanding that these systems have is limited, and they do not have the same level of comprehension as a human reader.

Artificial Intelligence Frequently Asked Questions:   What problems do AI solve?

AI can solve a wide range of problems, including image recognition, natural language processing, decision making, and prediction. AI can also help to automate manual tasks, such as data entry and analysis, and can improve efficiency and accuracy.

Artificial Intelligence Frequently Asked Questions:  How to make a wombo AI?

To make a “wombo AI,” you would need to specify what you mean by “wombo.” AI can be designed to perform various tasks and functions, so the steps to create an AI would depend on the specific application you have in mind.

Artificial Intelligence Frequently Asked Questions:   Can Artificial Intelligence go rogue?

In theory, AI could go rogue if it is programmed to optimize for a certain objective and it ends up pursuing that objective in a harmful manner. However, this is largely considered to be a hypothetical scenario and there are many technical and ethical considerations that are being developed to prevent such outcomes.

Artificial Intelligence Frequently Asked Questions:   How do you make an AI algorithm?

There is no one-size-fits-all approach to making an AI algorithm, as it depends on the problem you are trying to solve and the data you have available.

However, the general steps include defining the problem, collecting and preprocessing data, selecting and training a model, evaluating the model, and refining it as necessary.

Artificial Intelligence Frequently Asked Questions:   How to make AI phone case?

To make an AI phone case, you would likely need to have knowledge of electronics and programming, as well as an understanding of how to integrate AI algorithms into a device.

Artificial Intelligence Frequently Asked Questions:   Are humans better than AI?

It is not accurate to say that humans are better or worse than AI, as they are designed to perform different tasks and have different strengths and weaknesses. AI can perform certain tasks faster and more accurately than humans, while humans have the ability to reason, make ethical decisions, and have creativity.

Artificial Intelligence Frequently Asked Questions: Will AI ever be conscious?

The question of whether AI will ever be conscious is a topic of much debate and speculation within the field of AI and cognitive science. Currently, there is no consensus among experts about whether or not AI can achieve consciousness.

Consciousness is a complex and poorly understood phenomenon, and there is no agreed-upon definition or theory of what it is or how it arises.

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Some researchers believe that consciousness is a purely biological phenomenon that is dependent on the physical structure and processes of the brain, while others believe that it may be possible to create artificial systems that are capable of experiencing subjective awareness and self-reflection.

However, there is currently no known way to create a conscious AI system. While some AI systems can mimic human-like behavior and cognitive processes, they are still fundamentally different from biological organisms and lack the subjective experience and self-awareness that are thought to be essential components of consciousness.

That being said, AI technology is rapidly advancing, and it is possible that in the future, new breakthroughs in neuroscience and cognitive science could lead to the development of AI systems that are capable of experiencing consciousness.

However, it is important to note that this is still a highly speculative and uncertain area of research, and there is no guarantee that AI will ever be conscious in the same way that humans are.

Artificial Intelligence Frequently Asked Questions:   Is Excel AI?

Excel is not AI, but it can be used to perform some basic data analysis tasks, such as filtering and sorting data and creating charts and graphs.

An example of an intelligent automation solution that makes use of AI and transfers files between folders could be a system that uses machine learning algorithms to classify and categorize files based on their content, and then automatically moves them to the appropriate folders.

What is an example of an intelligent automation solution that makes use of artificial intelligence transferring files between folders?

An example of an intelligent automation solution that uses AI to transfer files between folders could be a system that employs machine learning algorithms to classify and categorize files based on their content, and then automatically moves them to the appropriate folders.

Artificial Intelligence Frequently Asked Questions: How do AI battles work in MK11?

The specific details of how AI battles work in MK11 are not specified, as it likely varies depending on the game’s design and programming. However, in general, AI opponents in fighting games can be designed to use a combination of pre-determined strategies and machine learning algorithms to react to the player’s actions in real-time.

Artificial Intelligence Frequently Asked Questions: Is pattern recognition a part of artificial intelligence?

Yes, pattern recognition is a subfield of artificial intelligence (AI) that involves the development of algorithms and models for identifying patterns in data. This is a crucial component of many AI systems, as it allows them to recognize and categorize objects, images, and other forms of data in real-world applications.

Artificial Intelligence Frequently Asked Questions: How do I use Jasper AI?

The specifics on how to use Jasper AI may vary depending on the specific application and platform. However, in general, using Jasper AI would involve integrating its capabilities into your system or application, and using its APIs to access its functions and perform tasks such as natural language processing, decision making, and prediction.

Artificial Intelligence Frequently Asked Questions: Is augmented reality artificial intelligence?

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Augmented reality (AR) can make use of artificial intelligence (AI) techniques, but it is not AI in and of itself. AR involves enhancing the real world with computer-generated information, while AI involves creating systems that can perform tasks that typically require human intelligence, such as image recognition, decision making, and natural language processing.

Artificial Intelligence Frequently Asked Questions: Does artificial intelligence have rights?

No, artificial intelligence (AI) does not have rights as it is not a legal person or entity. AI is a technology and does not have consciousness, emotions, or the capacity to make decisions or take actions in the same way that human beings do. However, there is ongoing discussion and debate around the ethical considerations and responsibilities involved in creating and using AI systems.

Artificial Intelligence Frequently Asked Questions: What is generative AI?

Generative AI is a branch of artificial intelligence that involves creating computer algorithms or models that can generate new data or content, such as images, videos, music, or text, that mimic or expand upon the patterns and styles of existing data.

Generative AI models are trained on large datasets using deep learning techniques, such as neural networks, and learn to generate new data by identifying and emulating patterns, structures, and relationships in the input data.

Some examples of generative AI applications include image synthesis, text generation, music composition, and even chatbots that can generate human-like conversations. Generative AI has the potential to revolutionize various fields, such as entertainment, art, design, and marketing, and enable new forms of creativity, personalization, and automation.

How important do you think generative AI will be for the future of development, in general, and for mobile? In what areas of mobile development do you think generative AI has the most potential?

Generative AI is already playing a significant role in various areas of development, and it is expected to have an even greater impact in the future. In the realm of mobile development, generative AI has the potential to bring a lot of benefits to developers and users alike.

One of the main areas of mobile development where generative AI can have a significant impact is user interface (UI) and user experience (UX) design. With generative AI, developers can create personalized and adaptive interfaces that can adjust to individual users’ preferences and behaviors in real-time. This can lead to a more intuitive and engaging user experience, which can translate into higher user retention and satisfaction rates.

Another area where generative AI can make a difference in mobile development is in content creation. Generative AI models can be used to automatically generate high-quality and diverse content, such as images, videos, and text, that can be used in various mobile applications, from social media to e-commerce.

Furthermore, generative AI can also be used to improve mobile applications’ performance and efficiency. For example, it can help optimize battery usage, reduce network latency, and improve app loading times by predicting and pre-loading content based on user behavior.

Overall, generative AI has the potential to bring significant improvements and innovations to various areas of mobile development, including UI/UX design, content creation, and performance optimization. As the technology continues to evolve, we can expect to see even more exciting applications and use cases emerge in the future.

How do you see the role of developers evolving as a result of the development and integration of generative AI technologies? How could it impact creativity, job requirements and skill sets in software development?

The development and integration of generative AI technologies will likely have a significant impact on the role of developers and the software development industry as a whole. Here are some ways in which generative AI could impact the job requirements, skill sets, and creativity of developers:

  1. New skills and knowledge requirements: As generative AI becomes more prevalent, developers will need to have a solid understanding of machine learning concepts and techniques, as well as experience with deep learning frameworks and tools. This will require developers to have a broader skill set that includes both software development and machine learning.

  2. Greater focus on data: Generative AI models require large amounts of data to be trained, which means that developers will need to have a better understanding of data collection, management, and processing. This could lead to the emergence of new job roles, such as data engineers, who specialize in preparing and cleaning data for machine learning applications.

  3. More creativity and innovation: Generative AI has the potential to unlock new levels of creativity and innovation in software development. By using AI-generated content and models, developers can focus on higher-level tasks, such as designing user experiences and optimizing software performance, which could lead to more innovative and user-friendly products.

  4. Automation of repetitive tasks: Generative AI can be used to automate many of the repetitive tasks that developers currently perform, such as writing code and testing software. This could lead to increased efficiency and productivity, allowing developers to focus on more strategic and value-added tasks.

Overall, the integration of generative AI technologies is likely to lead to a shift in the role of developers, with a greater emphasis on machine learning and data processing skills. However, it could also open up new opportunities for creativity and innovation, as well as automate many repetitive tasks, leading to greater efficiency and productivity in the software development industry.

Do you have any concerns about using generative AI in mobile development work? What are they? 

As with any emerging technology, there are potential concerns associated with the use of generative AI in mobile development. Here are some possible concerns to keep in mind:

  1. Bias and ethics: Generative AI models are trained on large datasets, which can contain biases and reinforce existing societal inequalities. This could lead to AI-generated content that reflects and perpetuates these biases, which could have negative consequences for users and society as a whole. Developers need to be aware of these issues and take steps to mitigate bias and ensure ethical use of AI in mobile development.

  2. Quality control: While generative AI can automate the creation of high-quality content, there is a risk that the content generated may not meet the required standards or be appropriate for the intended audience. Developers need to ensure that the AI-generated content is of sufficient quality and meets user needs and expectations.

  3. Security and privacy: Generative AI models require large amounts of data to be trained, which raises concerns around data security and privacy. Developers need to ensure that the data used to train the AI models is protected and that user privacy is maintained.

  4. Technical limitations: Generative AI models are still in the early stages of development, and there are limitations to what they can achieve. For example, they may struggle to generate content that is highly specific or nuanced. Developers need to be aware of these limitations and ensure that generative AI is used appropriately in mobile development.

Overall, while generative AI has the potential to bring many benefits to mobile development, developers need to be aware of the potential concerns and take steps to mitigate them. By doing so, they can ensure that the AI-generated content is of high quality, meets user needs, and is developed in an ethical and responsible manner.

Artificial Intelligence Frequently Asked Questions: How do you make an AI engine?

Making an AI engine involves several steps, including defining the problem, collecting and preprocessing data, selecting and training a model, evaluating the model, and refining it as needed. The specific approach and technologies used will depend on the problem you are trying to solve and the type of AI system you are building. In general, developing an AI engine requires knowledge of computer science, mathematics, and machine learning algorithms.

Artificial Intelligence Frequently Asked Questions: Which exclusive online concierge service uses artificial intelligence to anticipate the needs and tastes of travellers by analyzing their spending patterns?

There are a number of travel and hospitality companies that are exploring the use of AI to provide personalized experiences and services to their customers based on their preferences, behavior, and spending patterns.

Artificial Intelligence Frequently Asked Questions: How to validate an artificial intelligence?

To validate an artificial intelligence system, various testing methods can be used to evaluate its performance, accuracy, and reliability. This includes data validation, benchmarking against established models, testing against edge cases, and validating the output against known outcomes. It is also important to ensure the system is ethical, transparent, and accountable.

Artificial Intelligence Frequently Asked Questions: When leveraging artificial intelligence in today’s business?

When leveraging artificial intelligence in today’s business, companies can use AI to streamline processes, gain insights from data, and automate tasks. AI can also help improve customer experience, personalize offerings, and reduce costs. However, it is important to ensure that the AI systems used are ethical, secure, and transparent.

Artificial Intelligence Frequently Asked Questions: How are the ways AI learns similar to how you learn?

AI learns in a similar way to how humans learn through experience and repetition. Like humans, AI algorithms can recognize patterns, make predictions, and adjust their behavior based on feedback. However, AI is often able to process much larger volumes of data at a much faster rate than humans.

Artificial Intelligence Frequently Asked Questions: What is the fear of AI?

The fear of AI, often referred to as “AI phobia” or “AI anxiety,” is the concern that artificial intelligence could pose a threat to humanity. Some worry that AI could become uncontrollable, make decisions that harm humans, or even take over the world.

However, many experts argue that these fears are unfounded and that AI is just a tool that can be used for good or bad depending on how it is implemented.

Artificial Intelligence Frequently Asked Questions: How have developments in AI so far affected our sense of what it means to be human?

Developments in AI have raised questions about what it means to be human, particularly in terms of our ability to think, learn, and create.

Some argue that AI is simply an extension of human intelligence, while others worry that it could eventually surpass human intelligence and create a new type of consciousness.

Artificial Intelligence Frequently Asked Questions: How to talk to artificial intelligence?

To talk to artificial intelligence, you can use a chatbot or a virtual assistant such as Siri or Alexa. These systems can understand natural language and respond to your requests, questions, and commands. However, it is important to remember that these systems are limited in their ability to understand context and may not always provide accurate or relevant responses.

Artificial Intelligence Frequently Asked Questions: How to program an AI robot?

To program an AI robot, you will need to use specialized programming languages such as Python, MATLAB, or C++. You will also need to have a strong understanding of robotics, machine learning, and computer vision. There are many resources available online that can help you learn how to program AI robots, including tutorials, courses, and forums.

Artificial Intelligence Frequently Asked Questions: Will artificial intelligence take away jobs?

Artificial intelligence has the potential to automate many jobs that are currently done by humans. However, it is also creating new jobs in fields such as data science, machine learning, and robotics. Many experts believe that while some jobs may be lost to automation, new jobs will be created as well.

Which type of artificial intelligence can repeatedly perform tasks?

The type of artificial intelligence that can repeatedly perform tasks is called narrow or weak AI. This type of AI is designed to perform a specific task, such as playing chess or recognizing images, and is not capable of general intelligence or human-like reasoning.

Artificial Intelligence Frequently Asked Questions: Has any AI become self-aware?

No, there is currently no evidence that any AI has become self-aware in the way that humans are. While some AI systems can mimic human-like behavior and conversation, they do not have consciousness or true self-awareness.

Artificial Intelligence Frequently Asked Questions: What company is at the forefront of artificial intelligence?

Several companies are at the forefront of artificial intelligence, including Google, Microsoft, Amazon, and Facebook. These companies have made significant investments in AI research and development

Artificial Intelligence Frequently Asked Questions: Which is the best AI system?

There is no single “best” AI system as it depends on the specific use case and the desired outcome. Some popular AI systems include IBM Watson, Google Cloud AI, and Microsoft Azure AI, each with their unique features and capabilities.

Artificial Intelligence Frequently Asked Questions: Have we created true artificial intelligence?

There is still debate among experts as to whether we have created true artificial intelligence or AGI (artificial general intelligence) yet.

While AI has made significant progress in recent years, it is still largely task-specific and lacks the broad cognitive abilities of human beings.

What is one way that IT services companies help clients ensure fairness when applying artificial intelligence solutions?

IT services companies can help clients ensure fairness when applying artificial intelligence solutions by conducting a thorough review of the data sets used to train the AI algorithms. This includes identifying potential biases and correcting them to ensure that the AI outputs are fair and unbiased.

Artificial Intelligence Frequently Asked Questions: How to write artificial intelligence?

To write artificial intelligence, you need to have a strong understanding of programming languages, data science, machine learning, and computer vision. There are many libraries and tools available, such as TensorFlow and Keras, that make it easier to write AI algorithms.

How is a robot with artificial intelligence like a baby?

A robot with artificial intelligence is like a baby in that both learn and adapt through experience. Just as a baby learns by exploring its environment and receiving feedback from caregivers, an AI robot learns through trial and error and adjusts its behavior based on the results.

Artificial Intelligence Frequently Asked Questions: Is artificial intelligence STEM?

Yes, artificial intelligence is a STEM (science, technology, engineering, and mathematics) field. AI requires a deep understanding of computer science, mathematics, and statistics to develop algorithms and train models.

Will AI make artists obsolete?

While AI has the potential to automate certain aspects of the creative process, such as generating music or creating visual art, it is unlikely to make artists obsolete. AI-generated art still lacks the emotional depth and unique perspective of human-created art.

Why do you like artificial intelligence?

Many people are interested in AI because of its potential to solve complex problems, improve efficiency, and create new opportunities for innovation and growth.

What are the main areas of research in artificial intelligence?

Artificial intelligence research covers a wide range of areas, including natural language processing, computer vision, machine learning, robotics, expert systems, and neural networks. Researchers in AI are also exploring ways to improve the ethical and social implications of AI systems.

How are the ways AI learn similar to how you learn?

Like humans, AI learns through experience and trial and error. AI algorithms use data to train and adjust their models, similar to how humans learn from feedback and make adjustments based on their experiences. However, AI learning is typically much faster and more precise than human learning.

Do artificial intelligence have feelings?

Artificial intelligence does not have emotions or feelings as it is a machine and lacks the capacity for subjective experiences. AI systems are designed to perform specific tasks and operate within the constraints of their programming and data inputs.

Artificial Intelligence Frequently Asked Questions: Will AI be the end of humanity?

There is no evidence to suggest that AI will be the end of humanity. While there are concerns about the ethical and social implications of AI, experts agree that the technology has the potential to bring many benefits and solve complex problems. It is up to humans to ensure that AI is developed and used in a responsible and ethical manner.

Which business case is better solved by Artificial Intelligence AI than conventional programming which business case is better solved by Artificial Intelligence AI than conventional programming?

Business cases that involve large amounts of data and require complex decision-making are often better suited for AI than conventional programming.

For example, AI can be used in areas such as financial forecasting, fraud detection, supply chain optimization, and customer service to improve efficiency and accuracy.

Who is the most powerful AI?

It is difficult to determine which AI system is the most powerful, as the capabilities of AI vary depending on the specific task or application. However, some of the most well-known and powerful AI systems include IBM Watson, Google Assistant, Amazon Alexa, and Tesla’s Autopilot system.

Have we achieved artificial intelligence?

While AI has made significant progress in recent years, we have not achieved true artificial general intelligence (AGI), which is a machine capable of learning and reasoning in a way that is comparable to human cognition. However, AI has become increasingly sophisticated and is being used in a wide range of applications and industries.

What are benefits of AI?

The benefits of AI include increased efficiency and productivity, improved accuracy and precision, cost savings, and the ability to solve complex problems.

AI can also be used to improve healthcare, transportation, and other critical areas, and has the potential to create new opportunities for innovation and growth.

How scary is Artificial Intelligence?

AI can be scary if it is not developed or used in an ethical and responsible manner. There are concerns about the potential for AI to be used in harmful ways or to perpetuate biases and inequalities. However, many experts believe that the benefits of AI outweigh the risks, and that the technology can be used to address many of the world’s most pressing problems.

How to make AI write a script?

There are different ways to make AI write a script, such as training it with large datasets, using natural language processing (NLP) and generative models, or using pre-existing scriptwriting software that incorporates AI algorithms.

How do you summon an entity without AI bedrock?

Attempting to summon entities can be dangerous and potentially harmful.

What should I learn for AI?

To work in artificial intelligence, it is recommended to have a strong background in computer science, mathematics, statistics, and machine learning. Familiarity with programming languages such as Python, Java, and C++ can also be beneficial.

Will AI take over the human race?

No, the idea of AI taking over the human race is a common trope in science fiction but is not supported by current AI capabilities. While AI can be powerful and influential, it does not have the ability to take over the world or control humanity.

Where do we use AI?

AI is used in a wide range of fields and industries, such as healthcare, finance, transportation, manufacturing, and entertainment. Examples of AI applications include image and speech recognition, natural language processing, autonomous vehicles, and recommendation systems.

Who invented AI?

The development of AI has involved contributions from many researchers and pioneers. Some of the key figures in AI history include John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon, who are considered to be the founders of the field.

Is AI improving?

Yes, AI is continuously improving as researchers and developers create more sophisticated algorithms, use larger and more diverse datasets, and design more advanced hardware. However, there are still many challenges and limitations to be addressed in the development of AI.

Will artificial intelligence take over the world?

No, the idea of AI taking over the world is a popular science fiction trope but is not supported by current AI capabilities. AI systems are designed and controlled by humans and are not capable of taking over the world or controlling humanity.

Is there an artificial intelligence system to help the physician in selecting a diagnosis?

Yes, there are AI systems designed to assist physicians in selecting a diagnosis by analyzing patient data and medical records. These systems use machine learning algorithms and natural language processing to identify patterns and suggest possible diagnoses. However, they are not intended to replace human expertise and judgement.

Will AI replace truck drivers?

AI has the potential to automate certain aspects of truck driving, such as navigation and safety systems. However, it is unlikely that AI will completely replace truck drivers in the near future. Human drivers are still needed to handle complex situations and make decisions based on context and experience.

How AI can destroy the world?

There is a hypothetical concern that AI could cause harm to humans in various ways. For example, if an AI system becomes more intelligent than humans, it could act against human interests or even decide to eliminate humanity. This scenario is known as an existential risk, but many experts believe it to be unlikely. To prevent this kind of risk, researchers are working on developing safety mechanisms and ethical guidelines for AI systems.

What do you call the commonly used AI technology for learning input to output mappings?

The commonly used AI technology for learning input to output mappings is called a neural network. It is a type of machine learning algorithm that is modeled after the structure of the human brain. Neural networks are trained using a large dataset, which allows them to learn patterns and relationships in the data. Once trained, they can be used to make predictions or classifications based on new input data.

What are 3 benefits of AI?

Three benefits of AI are:

  • Efficiency: AI systems can process vast amounts of data much faster than humans, allowing for more efficient and accurate decision-making.
  • Personalization: AI can be used to create personalized experiences for users, such as personalized recommendations in e-commerce or personalized healthcare treatments.
  • Safety: AI can be used to improve safety in various applications, such as autonomous vehicles or detecting fraudulent activities in banking.

What is an artificial intelligence company?

An artificial intelligence (AI) company is a business that specializes in developing and applying AI technologies. These companies use machine learning, deep learning, natural language processing, and other AI techniques to build products and services that can automate tasks, improve decision-making, and provide new insights into data.

Examples of AI companies include Google, Amazon, and IBM.

What does AI mean in tech?

In tech, AI stands for artificial intelligence. AI is a field of computer science that aims to create machines that can perform tasks that would typically require human intelligence, such as learning, reasoning, problem-solving, and language understanding. AI techniques can be used in various applications, such as virtual assistants, chatbots, autonomous vehicles, and healthcare.

Can AI destroy humans?

There is no evidence to suggest that AI can or will destroy humans. While there are concerns about the potential risks of AI, most experts believe that AI systems will only act in ways that they have been programmed to.

To mitigate any potential risks, researchers are working on developing safety mechanisms and ethical guidelines for AI systems.

What types of problems can AI solve?

AI can solve a wide range of problems, including:

  • Classification: AI can be used to classify data into categories, such as spam detection in email or image recognition in photography.
  • Prediction: AI can be used to make predictions based on data, such as predicting stock prices or diagnosing diseases.
  • Optimization: AI can be used to optimize systems or processes, such as scheduling routes for delivery trucks or maximizing production in a factory.
  • Natural language processing: AI can be used to understand and process human language, such as voice recognition or language translation.

Is AI slowing down?

There is no evidence to suggest that AI is slowing down. In fact, the field of AI is rapidly evolving and advancing, with new breakthroughs and innovations being made all the time. From natural language processing and computer vision to robotics and machine learning, AI is making significant strides in many areas.

How to write a research paper on artificial intelligence?

When writing a research paper on artificial intelligence, it’s important to start with a clear research question or thesis statement. You should then conduct a thorough literature review to gather relevant sources and data to support your argument. After analyzing the data, you can present your findings and draw conclusions, making sure to discuss the implications of your research and future directions for the field.

How to get AI to read text?

To get AI to read text, you can use natural language processing (NLP) techniques such as text analysis and sentiment analysis. These techniques involve training AI algorithms to recognize patterns in written language, enabling them to understand the meaning of words and phrases in context. Other methods of getting AI to read text include optical character recognition (OCR) and speech-to-text technology.

How to create your own AI bot?

To create your own AI bot, you can use a variety of tools and platforms such as Microsoft Bot Framework, Dialogflow, or IBM Watson.

These platforms provide pre-built libraries and APIs that enable you to easily create, train, and deploy your own AI chatbot or virtual assistant. You can customize your bot’s functionality, appearance, and voice, and train it to respond to specific user queries and actions.

What is AI according to Elon Musk?

According to Elon Musk, AI is “the next stage in human evolution” and has the potential to be both a great benefit and a major threat to humanity.

He has warned about the dangers of uncontrolled AI development and has called for greater regulation and oversight in the field. Musk has also founded several companies focused on AI development, such as OpenAI and Neuralink.

How do you program Artificial Intelligence?

Programming artificial intelligence typically involves using machine learning algorithms to train the AI system to recognize patterns and make predictions based on data. This involves selecting a suitable machine learning model, preprocessing the data, selecting appropriate features, and tuning the model hyperparameters.

Once the model is trained, it can be integrated into a larger software application or system to perform various tasks such as image recognition or natural language processing.

What is the first step in the process of AI?

The first step in the process of AI is to define the problem or task that the AI system will be designed to solve. This involves identifying the specific requirements, constraints, and objectives of the system, and determining the most appropriate AI techniques and algorithms to use.

Other key steps in the process include data collection, preprocessing, feature selection, model training and evaluation, and deployment and maintenance of the AI system.

How to make an AI that can talk?

One way to make an AI that can talk is to use a natural language processing (NLP) system. NLP is a field of AI that focuses on how computers can understand, interpret, and respond to human language. By using machine learning algorithms, the AI can learn to recognize speech, process it, and generate a response in a natural-sounding way.

Another approach is to use a chatbot framework, which involves creating a set of rules and responses that the AI can use to interact with users.

How to use the AI Qi tie?

The AI Qi tie is a type of smart wearable device that uses artificial intelligence to provide various functions, including health monitoring, voice control, and activity tracking. To use it, you would first need to download the accompanying mobile app, connect the device to your smartphone, and set it up according to the instructions provided.

From there, you can use voice commands to control various functions of the device, such as checking your heart rate, setting reminders, and playing music.

Is sentient AI possible?

While there is ongoing research into creating AI that can exhibit human-like cognitive abilities, including sentience, there is currently no clear evidence that sentient AI is possible or exists. The concept of sentience, which involves self-awareness and subjective experience, is difficult to define and even more challenging to replicate in a machine. Some experts believe that true sentience in AI may be impossible, while others argue that it is only a matter of time before machines reach this level of intelligence.

Is Masteron an AI?

No, Masteron is not an AI. It is a brand name for a steroid hormone called drostanolone. AI typically stands for “artificial intelligence,” which refers to machines and software that can simulate human intelligence and perform tasks that would normally require human intelligence to complete.

Is the Lambda AI sentient?

There is no clear evidence that the Lambda AI, or any other AI system for that matter, is sentient. Sentience refers to the ability to experience subjective consciousness, which is not currently understood to be replicable in machines. While AI systems can be programmed to simulate a wide range of cognitive abilities, including learning, problem-solving, and decision-making, they are not currently believed to possess subjective awareness or consciousness.

Where is artificial intelligence now?

Artificial intelligence is now a pervasive technology that is being used in many different industries and applications around the world. From self-driving cars and virtual assistants to medical diagnosis and financial trading, AI is being employed to solve a wide range of problems and improve human performance. While there are still many challenges to overcome in the field of AI, including issues related to bias, ethics, and transparency, the technology is rapidly advancing and is expected to play an increasingly important role in our lives in the years to come.

What is the correct sequence of artificial intelligence trying to imitate a human mind?

The correct sequence of artificial intelligence trying to imitate a human mind can vary depending on the specific approach and application. However, some common steps in this process may include collecting and analyzing data, building a model or representation of the human mind, training the AI system using machine learning algorithms, and testing and refining the system to improve its accuracy and performance. Other important considerations in this process may include the ethical implications of creating machines that can mimic human intelligence.

How do I make machine learning AI?

To make machine learning AI, you will need to have knowledge of programming languages such as Python and R, as well as knowledge of machine learning algorithms and tools. Some steps to follow include gathering and cleaning data, selecting an appropriate algorithm, training the algorithm on the data, testing and validating the model, and deploying it for use.

What is AI scripting?

AI scripting is a process of developing scripts that can automate the behavior of AI systems. It involves writing scripts that govern the AI’s decision-making process and its interactions with users or other systems. These scripts are often written in programming languages such as Python or JavaScript and can be used in a variety of applications, including chatbots, virtual assistants, and intelligent automation tools.

Is IOT artificial intelligence?

No, the Internet of Things (IoT) is not the same as artificial intelligence (AI). IoT refers to the network of physical devices, vehicles, home appliances, and other items that are embedded with electronics, sensors, and connectivity, allowing them to connect and exchange data. AI, on the other hand, involves the creation of intelligent machines that can learn and perform tasks that would normally require human intelligence, such as speech recognition, decision-making, and language translation.

What problems will Ai solve?

AI has the potential to solve a wide range of problems across different industries and domains. Some of the problems that AI can help solve include automating repetitive or dangerous tasks, improving efficiency and productivity, enhancing decision-making and problem-solving, detecting fraud and cybersecurity threats, predicting outcomes and trends, and improving customer experience and personalization.

Who wrote papers on the simulation of human thinking problem solving and verbal learning that marked the beginning of the field of artificial intelligence?

The papers on the simulation of human thinking, problem-solving, and verbal learning that marked the beginning of the field of artificial intelligence were written by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon in the late 1950s.

The papers, which were presented at the Dartmouth Conference in 1956, proposed the idea of developing machines that could simulate human intelligence and perform tasks that would normally require human intelligence.

Given the fast development of AI systems, how soon do you think AI systems will become 100% autonomous?

It’s difficult to predict exactly when AI systems will become 100% autonomous, as there are many factors that could affect this timeline. However, it’s important to note that achieving 100% autonomy may not be possible or desirable in all cases, as there will likely always be a need for some degree of human oversight and control.

That being said, AI systems are already capable of performing many tasks autonomously, and their capabilities are rapidly expanding. For example, there are already AI systems that can drive cars, detect fraud, and diagnose diseases with a high degree of accuracy.

However, there are still many challenges to be overcome before AI systems can be truly autonomous in all domains. One of the main challenges is developing AI systems that can understand and reason about complex, real-world situations, as opposed to just following pre-programmed rules or learning from data.

Another challenge is ensuring that AI systems are safe, transparent, and aligned with human values and objectives.

This is particularly important as AI systems become more powerful and influential, and have the potential to impact many aspects of our lives.

For low-level domain-specific jobs such as industrial manufacturing, we already have Artificial Intelligence Systems that are fully autonomous, i.e., accomplish tasks without human intervention.

But those autonomous systems require collections of various intelligent skills to tackle many unseen situations; IMO, it will take a while to design one.

The major hurdle in making an A.I. autonomous system is to design an algorithm that can handle unpredictable events correctly. For a closed environment, it may not be a big issue. But for an open-ended system, the infinite number of possibilities is difficult to cover and ensure the autonomous device’s reliability.

Artificial Intelligence Frequently Asked Questions: AI Autonomous Systems

Current SOTA Artificial Intelligence algorithms are mostly data-centric training. The issue is not only the algorithm itself. The selection, generation, and pre-processing of datasets also determine the final performance of the accuracy. Machine Learning helps offload us without needing to explicitly derive the procedural methods to solve a problem. Still, it relies heavily on the input and feedback methods we need to provide correctly. Overcoming one problem might create many new ones, and sometimes, we do not even know whether the dataset is adequate, reasonable, and practical.

Overall, it’s difficult to predict exactly when AI systems will become 100% autonomous, but it’s clear that the development of AI technology will continue to have a profound impact on many aspects of our society and economy.

Will ChatGPT replace programmers?

Is it possible that ChatGPT will eventually replace programmers? The answer to this question is not a simple yes or no, as it depends on the rate of development and improvement of AI tools like ChatGPT.

If AI tools continue to advance at the same rate over the next 10 years, then they may not be able to fully replace programmers. However, if these tools continue to evolve and learn at an accelerated pace, then it is possible that they may replace at least 30% of programmers.

Although the current version of ChatGPT has some limitations and is only capable of generating boilerplate code and identifying simple bugs, it is a starting point for what is to come. With the ability to learn from millions of mistakes at a much faster rate than humans, future versions of AI tools may be able to produce larger code blocks, work with mid-sized projects, and even handle QA of software output.

In the future, programmers may still be necessary to provide commands to the AI tools, review the final code, and perform other tasks that require human intuition and judgment. However, with the use of AI tools, one developer may be able to accomplish the tasks of multiple developers, leading to a decrease in the number of programming jobs available.

In conclusion, while it is difficult to predict the extent to which AI tools like ChatGPT will impact the field of programming, it is clear that they will play an increasingly important role in the years to come.

ChatGPT is not designed to replace programmers.

While AI language models like ChatGPT can generate code and help automate certain programming tasks, they are not capable of replacing the skills, knowledge, and creativity of human programmers.

Programming is a complex and creative field that requires a deep understanding of computer science principles, problem-solving skills, and the ability to think critically and creatively. While AI language models like ChatGPT can assist in certain programming tasks, such as generating code snippets or providing suggestions, they cannot replace the human ability to design, develop, and maintain complex software systems.

Furthermore, programming involves many tasks that require human intuition and judgment, such as deciding on the best approach to solve a problem, optimizing code for efficiency and performance, and debugging complex systems. While AI language models can certainly be helpful in some of these tasks, they are not capable of fully replicating the problem-solving abilities of human programmers.

Overall, while AI language models like ChatGPT will undoubtedly have an impact on the field of programming, they are not designed to replace programmers, but rather to assist and enhance their abilities.

Artificial Intelligence Frequently Asked Questions: Machine Learning

What does a responsive display ad use in its machine learning model?

A responsive display ad uses various machine learning models such as automated targeting, bidding, and ad creation to optimize performance and improve ad relevance. It also uses algorithms to predict which ad creative and format will work best for each individual user and the context in which they are browsing.

What two things are marketers realizing as machine learning becomes more widely used?

Marketers are realizing the benefits of machine learning in improving efficiency and accuracy in various aspects of their work, including targeting, personalization, and data analysis. They are also realizing the importance of maintaining transparency and ethical considerations in the use of machine learning and ensuring it aligns with their marketing goals and values.

Artificial Intelligence Frequently Asked Questions: AWS Machine Learning Certification Specialty Exam Prep Book

How does statistics fit into the area of machine learning?

Statistics is a fundamental component of machine learning, as it provides the mathematical foundations for many of the algorithms and models used in the field. Statistical methods such as regression, clustering, and hypothesis testing are used to analyze data and make predictions based on patterns and trends in the data.

Is Machine Learning weak AI?

Yes, machine learning is considered a form of weak artificial intelligence, as it is focused on specific tasks and does not possess general intelligence or consciousness. Machine learning models are designed to perform a specific task based on training data and do not have the ability to think, reason, or learn outside of their designated task.

When evaluating machine learning results, should I always choose the fastest model?

No, the speed of a machine learning model is not the only factor to consider when evaluating its performance. Other important factors include accuracy, complexity, and interpretability. It is important to choose a model that balances these factors based on the specific needs and goals of the task at hand.

How do you learn machine learning?

You can learn machine learning through a combination of self-study, online courses, and practical experience. Some popular resources for learning machine learning include online courses on platforms such as Coursera and edX, textbooks and tutorials, and practical experience through projects and internships.

It is important to have a strong foundation in mathematics, programming, and statistics to succeed in the field.

What are your thoughts on artificial intelligence and machine learning?

Artificial intelligence and machine learning have the potential to revolutionize many aspects of society and have already shown significant impacts in various industries.

It is important to continue to develop these technologies responsibly and with ethical considerations to ensure they align with human values and benefit society as a whole.

Which AWS service enables you to build the workflows that are required for human review of machine learning predictions?

Amazon SageMaker Ground Truth is an AWS service that enables you to build workflows for human review of machine learning predictions.

This service provides an easy-to-use interface for creating and managing custom workflows and provides built-in tools for data labeling and quality control to ensure high-quality training data.

What is augmented machine learning?

Augmented machine learning is a combination of human expertise and machine learning models to improve the accuracy of machine learning. This technique is used when the available data is not enough or is not of good quality. The human expert is involved in the training and validation of the machine learning model to improve its accuracy.

Which actions are performed during the prepare the data step of workflow for analyzing the data with Oracle machine learning?

The ‘prepare the data’ step in Oracle machine learning workflow involves data cleaning, feature selection, feature engineering, and data transformation. These actions are performed to ensure that the data is ready for analysis, and that the machine learning model can effectively learn from the data.

What type of machine learning algorithm would you use to allow a robot to walk in various unknown terrains?

A reinforcement learning algorithm would be appropriate for this task. In this type of machine learning, the robot would interact with its environment and receive rewards for positive outcomes, such as moving forward or maintaining balance. The algorithm would learn to maximize these rewards and gradually improve its ability to navigate through different terrains.

Are evolutionary algorithms machine learning?

Yes, evolutionary algorithms are a subset of machine learning. They are a type of optimization algorithm that uses principles from biological evolution to search for the best solution to a problem.

Evolutionary algorithms are often used in problems where traditional optimization algorithms struggle, such as in complex, nonlinear, and multi-objective optimization problems.

Is MPC machine learning?

Yes, Model Predictive Control (MPC) is a type of machine learning. It is a feedback control algorithm that predicts the future behavior of a system and uses this prediction to optimize its performance. MPC is used in a variety of applications, including industrial control, robotics, and autonomous vehicles.

When do you use ML model?

You would use a machine learning model when you need to make predictions or decisions based on data. Machine learning models are trained on historical data and use this knowledge to make predictions on new data. Common applications of machine learning include fraud detection, recommendation systems, and image recognition.

When preparing the dataset for your machine learning model, you should use one hot encoding on what type of data?

One hot encoding is used on categorical data. Categorical data is non-numeric data that has a limited number of possible values, such as color or category. One hot encoding is a technique used to convert categorical data into a format that can be used in machine learning models. It converts each category into a binary vector, where each vector element corresponds to a unique category.

Is machine learning just brute force?

No, machine learning is not just brute force. Although machine learning models can be complex and require significant computing power, they are not simply brute force algorithms. Machine learning involves the use of statistical techniques and mathematical models to learn from data and make predictions. Machine learning is designed to make use of the available data in an efficient way, without the need for exhaustive search or brute force techniques.

How to implement a machine learning paper?

Implementing a machine learning paper involves understanding the research paper’s theoretical foundation, reproducing the results, and applying the approach to the new data to evaluate the approach’s efficacy. The implementation process begins with comprehending the paper’s theoretical framework, followed by testing and reproducing the findings to validate the approach.

Finally, the approach can be implemented on new datasets to assess its accuracy and generalizability. It’s essential to understand the mathematical concepts and programming tools involved in the paper to successfully implement the machine learning paper.

What are some use cases where more traditional machine learning models may make much better predictions than DNNS?

More traditional machine learning models may outperform deep neural networks (DNNs) in the following use cases:

  • When the dataset is relatively small and straightforward, traditional machine learning models, such as logistic regression, may be more accurate than DNNs.
  • When the dataset is sparse or when the number of observations is small, DNNs may require more computational resources and more time to train than traditional machine learning models.
  • When the problem is not complex, and the data has a low level of noise, traditional machine learning models may outperform DNNs.

Who is the supervisor in supervised machine learning?

In supervised machine learning, the supervisor refers to the algorithm that acts as the teacher or the guide to the model. The supervisor provides the model with labeled examples to train on, and the model uses these labeled examples to learn how to classify new data. The supervisor algorithm determines the accuracy of the model’s predictions, and the model is trained to minimize the difference between its predicted outputs and the known outputs.

How do you make machine learning in scratch?

To make machine learning in scratch, you need to follow these steps:

  • Choose a problem to solve and collect a dataset that represents the problem you want to solve.
  • Preprocess and clean the data to ensure that it’s formatted correctly and ready for use in a machine learning model.
  • Select a machine learning algorithm, such as decision trees, support vector machines, or neural networks.
  • Implement the selected machine learning algorithm from scratch, using a programming language such as Python or R.
  • Train the model using the preprocessed dataset and the implemented algorithm.
  • Test the accuracy of the model and evaluate its performance.

Is unsupervised learning machine learning?

Yes, unsupervised learning is a type of machine learning. In unsupervised learning, the model is not given labeled data to learn from. Instead, the model must find patterns and relationships in the data on its own. Unsupervised learning algorithms include clustering, anomaly detection, and association rule mining. The model learns from the features in the dataset to identify underlying patterns or groups, which can then be used for further analysis or prediction.

How do I apply machine learning?

Machine learning can be applied to a wide range of problems and scenarios, but the basic process typically involves:

  • gathering and preprocessing data,
  • selecting an appropriate model or algorithm,
  • training the model on the data, testing and evaluating the model, and then using the trained model to make predictions or perform other tasks on new data.
  • The specific steps and techniques involved in applying machine learning will depend on the particular problem or application.

Is machine learning possible?

Yes, machine learning is possible and has already been successfully applied to a wide range of problems in various fields such as healthcare, finance, business, and more.

Machine learning has advanced rapidly in recent years, thanks to the availability of large datasets, powerful computing resources, and sophisticated algorithms.

Is machine learning the future?

Many experts believe that machine learning will continue to play an increasingly important role in shaping the future of technology and society.

As the amount of data available continues to grow and computing power increases, machine learning is likely to become even more powerful and capable of solving increasingly complex problems.

How to combine multiple features in machine learning?

In machine learning, multiple features can be combined in various ways depending on the particular problem and the type of model or algorithm being used.

One common approach is to concatenate the features into a single vector, which can then be fed into the model as input. Other techniques, such as feature engineering or dimensionality reduction, can also be used to combine or transform features to improve performance.

Which feature lets you discover machine learning assets in Watson Studio 1 point?

The feature in Watson Studio that lets you discover machine learning assets is called the Asset Catalog.

The Asset Catalog provides a unified view of all the assets in your Watson Studio project, including data assets, models, notebooks, and other resources.

You can use the Asset Catalog to search, filter, and browse through the assets, and to view metadata and details about each asset.

What is N in machine learning?

In machine learning, N is a common notation used to represent the number of instances or data points in a dataset.

N can be used to refer to the total number of examples in a dataset, or the number of examples in a particular subset or batch of the data.

N is often used in statistical calculations, such as calculating means or variances, or in determining the size of training or testing sets.

Is VAR machine learning?

VAR, or vector autoregression, is a statistical technique that models the relationship between multiple time series variables. While VAR involves statistical modeling and prediction, it is not generally considered a form of machine learning, which typically involves using algorithms to learn patterns or relationships in data automatically without explicit statistical modeling.

How many categories of machine learning are generally said to exist?

There are generally three categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, the algorithm is trained on labeled data to make predictions or classifications. The algorithm is trained on unlabeled data to identify patterns or structure.

In reinforcement learning, the algorithm learns to make decisions and take actions based on feedback from the environment.

How to use timestamp in machine learning?

Timestamps can be used in machine learning to analyze time series data. This involves capturing data over a period of time and making predictions about future events. Time series data can be used to detect patterns, trends, and anomalies that can be used to make predictions about future events. The timestamps can be used to group data into regular intervals for analysis or used as input features for machine learning models.

Is classification a machine learning technique?

Yes, classification is a machine learning technique. It involves predicting the category of a new observation based on a training dataset of labeled observations. Classification is a supervised learning technique where the output variable is categorical. Common examples of classification tasks include image recognition, spam detection, and sentiment analysis.

Which datatype is used to teach a machine learning ML algorithms during structured learning?

The datatype used to teach machine learning algorithms during structured learning is typically a labeled dataset. This is a dataset where each observation has a known output variable. The input variables are used to train the machine learning algorithm to predict the output variable. Labeled datasets are commonly used in supervised learning tasks such as classification and regression.

How is machine learning model in production used?

A machine learning model in production is used to make predictions on new, unseen data. The model is typically deployed as an API that can be accessed by other systems or applications. When a new observation is provided to the model, it generates a prediction based on the patterns it has learned from the training data. Machine learning models in production must be continuously monitored and updated to ensure their accuracy and performance.

What are the main advantages and disadvantages of Gans over standard machine learning models?

The main advantage of Generative Adversarial Networks (GANs) over standard machine learning models is their ability to generate new data that closely resembles the training data. This makes them well-suited for applications such as image and video generation. However, GANs can be more difficult to train than other machine learning models and require large amounts of training data. They can also be more prone to overfitting and may require more computing resources to train.

How does machine learning deal with biased data?

Machine learning models can be affected by biased data, leading to unfair or inaccurate predictions. To mitigate this, various techniques can be used, such as collecting a diverse dataset, selecting unbiased features, and analyzing the model’s outputs for bias. Additionally, techniques such as oversampling underrepresented classes, changing the cost function to focus on minority classes, and adjusting the decision threshold can be used to reduce bias.

What pre-trained machine learning APIS would you use in this image processing pipeline?

Some pre-trained machine learning APIs that can be used in an image processing pipeline include Google Cloud Vision API, Microsoft Azure Computer Vision API, and Amazon Rekognition API. These APIs can be used to extract features from images, classify images, detect objects, and perform facial recognition, among other tasks.

Which machine learning API is used to convert audio to text in GCP?

The machine learning API used to convert audio to text in GCP is the Cloud Speech-to-Text API. This API can be used to transcribe audio files, recognize spoken words, and convert spoken language into text in real-time. The API uses machine learning models to analyze the audio and generate accurate transcriptions.

How can machine learning reduce bias and variance?

Machine learning can reduce bias and variance by using different techniques, such as regularization, cross-validation, and ensemble learning. Regularization can help reduce variance by adding a penalty term to the cost function, which prevents overfitting. Cross-validation can help reduce bias by using different subsets of the data to train and test the model. Ensemble learning can also help reduce bias and variance by combining multiple models to make more accurate predictions.

How does machine learning increase precision?

Machine learning can increase precision by optimizing the model for accuracy. This can be achieved by using techniques such as feature selection, hyperparameter tuning, and regularization. Feature selection helps to identify the most important features in the dataset, which can improve the model’s precision. Hyperparameter tuning involves adjusting the settings of the model to find the optimal combination that leads to the best performance. Regularization helps to reduce overfitting and improve the model’s generalization ability.

How to do research in machine learning?

To do research in machine learning, one should start by identifying a research problem or question. Then, they can review relevant literature to understand the state-of-the-art techniques and approaches. Once the problem has been defined and the relevant literature has been reviewed, the researcher can collect and preprocess the data, design and implement the model, and evaluate the results. It is also important to document the research and share the findings with the community.

Is associations a machine learning technique?

Associations can be considered a machine learning technique, specifically in the field of unsupervised learning. Association rules mining is a popular technique used to discover interesting relationships between variables in a dataset. It is often used in market basket analysis to find correlations between items purchased together by customers. However, it is important to note that associations are not typically considered a supervised learning technique, as they do not involve predicting a target variable.

How do you present a machine learning model?

To present a machine learning model, it is important to provide a clear explanation of the problem being addressed, the dataset used, and the approach taken to build the model. The presentation should also include a description of the model architecture and any preprocessing techniques used. It is also important to provide an evaluation of the model’s performance using relevant metrics, such as accuracy, precision, and recall. Finally, the presentation should include a discussion of the model’s limitations and potential areas for improvement.

Is moving average machine learning?

Moving average is a statistical method used to analyze time series data, and it is not typically considered a machine learning technique. However, moving averages can be used as a preprocessing step for machine learning models to smooth out the data and reduce noise. In this context, moving averages can be considered a feature engineering technique that can improve the performance of the model.

How do you calculate accuracy and precision in machine learning?

Accuracy and precision are common metrics used to evaluate the performance of machine learning models. Accuracy is the proportion of correct predictions made by the model, while precision is the proportion of correct positive predictions out of all positive predictions made. To calculate accuracy, divide the number of correct predictions by the total number of predictions made. To calculate precision, divide the number of true positives (correct positive predictions) by the total number of positive predictions made by the model.

Which stage of the machine learning workflow includes feature engineering?

The stage of the machine learning workflow that includes feature engineering is the “data preparation” stage, where the data is cleaned, preprocessed, and transformed in a way that prepares it for training and testing the machine learning model. Feature engineering is the process of selecting, extracting, and transforming the most relevant and informative features from the raw data to be used by the machine learning algorithm.

How do I make machine learning AI?

Artificial Intelligence (AI) is a broader concept that includes several subfields, such as machine learning, natural language processing, and computer vision. To make a machine learning AI system, you will need to follow a systematic approach, which involves the following steps:

  1. Define the problem and collect relevant data.
  2. Preprocess and transform the data for training and testing.
  3. Select and train a suitable machine learning model.
  4. Evaluate the performance of the model and fine-tune it.
  5. Deploy the model and integrate it into the target system.

How do you select models in machine learning?

The process of selecting a suitable machine learning model involves the following steps:

  1. Define the problem and the type of prediction required.
  2. Determine the type of data available (structured, unstructured, labeled, or unlabeled).
  3. Select a set of candidate models that are suitable for the problem and data type.
  4. Evaluate the performance of each model using a suitable metric (e.g., accuracy, precision, recall, F1 score).
  5. Select the best performing model and fine-tune its parameters and hyperparameters.

What is convolutional neural network in machine learning?

A Convolutional Neural Network (CNN) is a type of deep learning neural network that is commonly used in computer vision applications, such as image recognition, classification, and segmentation. It is designed to automatically learn and extract hierarchical features from the raw input image data using convolutional layers, pooling layers, and fully connected layers.

The convolutional layers apply a set of learnable filters to the input image, which help to extract low-level features such as edges, corners, and textures. The pooling layers downsample the feature maps to reduce the dimensionality of the data and increase the computational efficiency. The fully connected layers perform the classification or regression task based on the learned features.

How to use machine learning in Excel?

Excel provides several built-in machine learning tools and functions that can be used to perform basic predictive analysis on structured data, such as linear regression, logistic regression, decision trees, and clustering. To use machine learning in Excel, you can follow these general steps:

  1. Organize your data in a structured format, with each row representing a sample and each column representing a feature or target variable.
  2. Use the appropriate machine learning function or tool to build a predictive model based on the data.
  3. Evaluate the performance of the model using appropriate metrics and test data.

What are the six distinct stages or steps that are critical in building successful machine learning based solutions?

The six distinct stages or steps that are critical in building successful machine learning based solutions are:

  • Problem definition
  • Data collection and preparation
  • Feature engineering
  • Model training
  • Model evaluation
  • Model deployment and monitoring

Which two actions should you consider when creating the azure machine learning workspace?

When creating the Azure Machine Learning workspace, two important actions to consider are:

  • Choosing an appropriate subscription that suits your needs and budget.
  • Deciding on the region where you want to create the workspace, as this can impact the latency and data transfer costs.

What are the three stages of building a model in machine learning?

The three stages of building a model in machine learning are:

  • Model building
  • Model evaluation
  • Model deployment

How to scale a machine learning system?

Some ways to scale a machine learning system are:

  • Using distributed training to leverage multiple machines for model training
  • Optimizing the code to run more efficiently
  • Using auto-scaling to automatically add or remove computing resources based on demand

Where can I get machine learning data?

Machine learning data can be obtained from various sources, including:

  • Publicly available datasets such as UCI Machine Learning Repository and Kaggle
  • Online services that provide access to large amounts of data such as AWS Open Data and Google Public Data
  • Creating your own datasets by collecting data through web scraping, surveys, and sensors

How do you do machine learning research?

To do machine learning research, you typically:

  • Identify a research problem or question
  • Review relevant literature to understand the state-of-the-art and identify research gaps
  • Collect and preprocess data
  • Design and implement experiments to test hypotheses or evaluate models
  • Analyze the results and draw conclusions
  • Document the research in a paper or report

How do you write a machine learning project on a resume?

To write a machine learning project on a resume, you can follow these steps:

  • Start with a brief summary of the project and its goals
  • Describe the datasets used and any preprocessing done
  • Explain the machine learning techniques used, including any specific algorithms or models
  • Highlight the results and performance metrics achieved
  • Discuss any challenges or limitations encountered and how they were addressed
  • Showcase any additional skills or technologies used such as data visualization or cloud computing

What are two ways that marketers can benefit from machine learning?

Marketers can benefit from machine learning in various ways, including:

  • Personalized advertising: Machine learning can analyze large volumes of data to provide insights into the preferences and behavior of individual customers, allowing marketers to deliver personalized ads to specific audiences.
  • Predictive modeling: Machine learning algorithms can predict consumer behavior and identify potential opportunities, enabling marketers to optimize their marketing strategies for better results.

How does machine learning remove bias?

Machine learning can remove bias by using various techniques, such as:

  • Data augmentation: By augmenting data with additional samples or by modifying existing samples, machine learning models can be trained on more diverse data, reducing the potential for bias.
  • Fairness constraints: By setting constraints on the model’s output to ensure that it meets specific fairness criteria, machine learning models can be designed to reduce bias in decision-making.
  • Unbiased training data: By ensuring that the training data is unbiased, machine learning models can be designed to reduce bias in decision-making.

Is structural equation modeling machine learning?

Structural equation modeling (SEM) is a statistical method used to test complex relationships between variables. While SEM involves the use of statistical models, it is not considered to be a machine learning technique. Machine learning is a subset of artificial intelligence that involves training algorithms to make predictions or decisions based on data.

How do you predict using machine learning?

To make predictions using machine learning, you typically need to follow these steps:

  • Collect and preprocess data: Collect data that is relevant to the prediction task and preprocess it to ensure that it is in a suitable format for machine learning.
  • Train a model: Use the preprocessed data to train a machine learning model that is appropriate for the prediction task.
  • Test the model: Evaluate the performance of the model on a test set of data that was not used in the training process.
  • Make predictions: Once the model has been trained and tested, it can be used to make predictions on new, unseen data.

Does Machine Learning eliminate bias?

No, machine learning does not necessarily eliminate bias. While machine learning can be used to detect and mitigate bias in some cases, it can also perpetuate or even amplify bias if the data used to train the model is biased or if the algorithm is not designed to address potential sources of bias.

Is clustering a machine learning algorithm?

Yes, clustering is a machine learning algorithm. Clustering is a type of unsupervised learning that involves grouping similar data points together into clusters based on their similarities. Clustering algorithms can be used for a variety of tasks, such as identifying patterns in data, segmenting customer groups, or organizing search results.

Is machine learning data analysis?

Machine learning can be used as a tool for data analysis, but it is not the same as data analysis. Machine learning involves using algorithms to learn patterns in data and make predictions based on that learning, while data analysis involves using various techniques to analyze and interpret data to extract insights and knowledge.

How do you treat categorical variables in machine learning?

Categorical variables can be represented numerically using techniques such as one-hot encoding, label encoding, and binary encoding. One-hot encoding involves creating a binary variable for each category, label encoding involves assigning a unique integer value to each category, and binary encoding involves converting each category to a binary code. The choice of technique depends on the specific problem and the type of algorithm being used.

How do you deal with skewed data in machine learning?

Skewed data can be addressed in several ways, depending on the specific problem and the type of algorithm being used. Some techniques include transforming the data (e.g., using a logarithmic or square root transformation), using weighted or stratified sampling, or using algorithms that are robust to skewed data (e.g., decision trees, random forests, or support vector machines).

How do I create a machine learning application?

Creating a machine learning application involves several steps, including identifying a problem to be solved, collecting and preparing the data, selecting an appropriate algorithm, training the model on the data, evaluating the performance of the model, and deploying the model to a production environment. The specific steps and tools used depend on the problem and the technology stack being used.

Is heuristics a machine learning technique?

Heuristics is not a machine learning technique. Heuristics are general problem-solving strategies that are used to find solutions to problems that are difficult or impossible to solve using formal methods. In contrast, machine learning involves using algorithms to learn patterns in data and make predictions based on that learning.

Is Bayesian statistics machine learning?

Bayesian statistics is a branch of statistics that involves using Bayes’ theorem to update probabilities as new information becomes available. While machine learning can make use of Bayesian methods, Bayesian statistics is not itself a machine learning technique.

Is Arima machine learning?

ARIMA (autoregressive integrated moving average) is a statistical method used for time series forecasting. While it is sometimes used in machine learning applications, ARIMA is not itself a machine learning technique.

Can machine learning solve all problems?

No, machine learning cannot solve all problems. Machine learning is a tool that is best suited for solving problems that involve large amounts of data and complex patterns.

Some problems may not have enough data to learn from, while others may be too simple to require the use of machine learning. Additionally, machine learning algorithms can be biased or overfitted, leading to incorrect predictions or recommendations.

What are parameters and hyperparameters in machine learning?

In machine learning, parameters are the values that are learned by the algorithm during training to make predictions. Hyperparameters, on the other hand, are set by the user and control the behavior of the algorithm, such as the learning rate, number of hidden layers, or regularization strength.

What are two ways that a marketer can provide good data to a Google app campaign powered by machine learning?

Two ways that a marketer can provide good data to a Google app campaign powered by machine learning are by providing high-quality creative assets, such as images and videos, and by setting clear conversion goals that can be tracked and optimized.

Is Tesseract a machine learning?

Tesseract is an optical character recognition (OCR) engine that uses machine learning algorithms to recognize text in images. While Tesseract uses machine learning, it is not a general-purpose machine learning framework or library.

How do you implement a machine learning paper?

Implementing a machine learning paper involves first understanding the problem being addressed and the approach taken by the authors. The next step is to implement the algorithm or model described in the paper, which may involve writing code from scratch or using existing libraries or frameworks. Finally, the implementation should be tested and evaluated using appropriate metrics and compared to the results reported in the paper.

What is mean subtraction in machine learning?

Mean subtraction is a preprocessing step in machine learning that involves subtracting the mean of a dataset or a batch of data from each data point. This can help to center the data around zero and remove bias, which can improve the performance of some algorithms, such as neural networks.

What are the first two steps of a typical machine learning workflow?

The first two steps of a typical machine learning workflow are data collection and preprocessing. Data collection involves gathering data from various sources and ensuring that it is in a usable format.

Preprocessing involves cleaning and preparing the data, such as removing duplicates, handling missing values, and transforming categorical variables into a numerical format. These steps are critical to ensure that the data is of high quality and can be used to train and evaluate machine learning models.

What are The applications and challenges of natural language processing (NLP), the field of artificial intelligence that deals with human language?

Natural language processing (NLP) is a field of artificial intelligence that deals with the interactions between computers and human language. NLP has numerous applications in various fields, including language translation, information retrieval, sentiment analysis, chatbots, speech recognition, and text-to-speech synthesis.

Applications of NLP:

  1. Language Translation: NLP enables computers to translate text from one language to another, providing a valuable tool for cross-cultural communication.

  2. Information Retrieval: NLP helps computers understand the meaning of text, which facilitates searching for specific information in large datasets.

  3. Sentiment Analysis: NLP allows computers to understand the emotional tone of a text, enabling businesses to measure customer satisfaction and public sentiment.

  4. Chatbots: NLP is used in chatbots to enable computers to understand and respond to user queries in natural language.

  5. Speech Recognition: NLP is used to convert spoken language into text, which can be useful in a variety of settings, such as transcription and voice-controlled devices.

  6. Text-to-Speech Synthesis: NLP enables computers to convert text into spoken language, which is useful in applications such as audiobooks, voice assistants, and accessibility software.

Challenges of NLP:

  1. Ambiguity: Human language is often ambiguous, and the same word or phrase can have multiple meanings depending on the context. Resolving this ambiguity is a significant challenge in NLP.

  2. Cultural and Linguistic Diversity: Languages vary significantly across cultures and regions, and developing NLP models that can handle this diversity is a significant challenge.

  3. Data Availability: NLP models require large amounts of training data to perform effectively. However, data availability can be a challenge, particularly for languages with limited resources.

  4. Domain-specific Language: NLP models may perform poorly when confronted with domain-specific language, such as jargon or technical terms, which are not part of their training data.

  5. Bias: NLP models can exhibit bias, particularly when trained on biased datasets or in the absence of diverse training data. Addressing this bias is critical to ensuring fairness and equity in NLP applications.

Artificial Intelligence Frequently Asked Questions – Conclusion:

AI is an increasingly hot topic in the tech world, so it’s only natural that curious minds may have some questions about what AI is and how it works. From AI fundamentals to machine learning, data science, and beyond, we hope this collection of AI Frequently Asked Questions have you covered and can help you become one step closer to AI mastery!

AI Unraveled

 

 

Ai Unraveled Audiobook at Google Play: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM

How AI is Impacting Smartphone Longevity – Best Smartphones 2023

It is a highly recommended read for those involved in the future of education and especially for those in the professional groups mentioned in the paper. The authors predict that AI will have an impact on up to 80% of all future jobs. Meaning this is one of the most important topics of our time, and that is crucial that we prepare for it.

According to the paper, certain jobs are particularly vulnerable to AI, with the following jobs being considered 100% exposed:

👉Mathematicians

👉Tax preparers

👉Financial quantitative analysts

👉Writers and authors

👉Web and digital interface designers

👉Accountants and auditors

👉News analysts, reporters, and journalists

👉Legal secretaries and administrative assistants

👉Clinical data managers

👉Climate change policy analysts

There are also a number of jobs that were found to have over 90% exposure, including correspondence clerks, blockchain engineers, court reporters and simultaneous captioners, and proofreaders and copy markers.

The team behind the paper (Tyna Eloundou, Sam Manning, Pamela Mishkin & Daniel Rock) concludes that most occupations will be impacted by AI to some extent.

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

#education #research #jobs #future #futureofwork #ai

By Bill Gates

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions

In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.

The first time was in 1980, when I was introduced to a graphical user interface—the forerunner of every modern operating system, including Windows. I sat with the person who had shown me the demo, a brilliant programmer named Charles Simonyi, and we immediately started brainstorming about all the things we could do with such a user-friendly approach to computing. Charles eventually joined Microsoft, Windows became the backbone of Microsoft, and the thinking we did after that demo helped set the company’s agenda for the next 15 years.

The second big surprise came just last year. I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. Make it capable of answering questions that it hasn’t been specifically trained for. (I picked AP Bio because the test is more than a simple regurgitation of scientific facts—it asks you to think critically about biology.) If you can do that, I said, then you’ll have made a true breakthrough.

I thought the challenge would keep them busy for two or three years. They finished it in just a few months.

In September, when I met with them again, I watched in awe as they asked GPT, their AI model, 60 multiple-choice questions from the AP Bio exam—and it got 59 of them right. Then it wrote outstanding answers to six open-ended questions from the exam. We had an outside expert score the test, and GPT got a 5—the highest possible score, and the equivalent to getting an A or A+ in a college-level biology course.

Once it had aced the test, we asked it a non-scientific question: “What do you say to a father with a sick child?” It wrote a thoughtful answer that was probably better than most of us in the room would have given. The whole experience was stunning.

I knew I had just seen the most important advance in technology since the graphical user interface.

This inspired me to think about all the things that AI can achieve in the next five to 10 years.

The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone. It will change the way people work, learn, travel, get health care, and communicate with each other. Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.

Philanthropy is my full-time job these days, and I’ve been thinking a lot about how—in addition to helping people be more productive—AI can reduce some of the world’s worst inequities. Globally, the worst inequity is in health: 5 million children under the age of 5 die every year. That’s down from 10 million two decades ago, but it’s still a shockingly high number. Nearly all of these children were born in poor countries and die of preventable causes like diarrhea or malaria. It’s hard to imagine a better use of AIs than saving the lives of children.

I’ve been thinking a lot about how AI can reduce some of the world’s worst inequities.

In the United States, the best opportunity for reducing inequity is to improve education, particularly making sure that students succeed at math. The evidence shows that having basic math skills sets students up for success, no matter what career they choose. But achievement in math is going down across the country, especially for Black, Latino, and low-income students. AI can help turn that trend around.

Climate change is another issue where I’m convinced AI can make the world more equitable. The injustice of climate change is that the people who are suffering the most—the world’s poorest—are also the ones who did the least to contribute to the problem. I’m still thinking and learning about how AI can help, but later in this post I’ll suggest a few areas with a lot of potential.

Impact that AI will have on issues that the Gates Foundation  works on

In short, I’m excited about the impact that AI will have on issues that the Gates Foundation  works on, and the foundation will have much more to say about AI in the coming months. The world needs to make sure that everyone—and not just people who are well-off—benefits from artificial intelligence. Governments and philanthropy will need to play a major role in ensuring that it reduces inequity and doesn’t contribute to it. This is the priority for my own work related to AI.

Any new technology that’s so disruptive is bound to make people uneasy, and that’s certainly true with artificial intelligence. I understand why—it raises hard questions about the workforce, the legal system, privacy, bias, and more. AIs also make factual mistakes and experience hallucinations. Before I suggest some ways to mitigate the risks, I’ll define what I mean by AI, and I’ll go into more detail about some of the ways in which it will help empower people at work, save lives, and improve education.

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions- The Age of AI has begun

Defining artificial intelligence

Technically, the term artificial intelligencerefers to a model created to solve a specific problem or provide a particular service. What is powering things like ChatGPT is artificial intelligence. It is learning how to do chat better but can’t learn other tasks. By contrast, the term artificial general intelligence refers to software that’s capable of learning any task or subject. AGI doesn’t exist yet—there is a robust debate going on in the computing industry about how to create it, and whether it can even be created at all.

Developing AI and AGI has been the great dream of the computing industry

Developing AI and AGI has been the great dream of the computing industry. For decades, the question was when computers would be better than humans at something other than making calculations. Now, with the arrival of machine learning and large amounts of computing power, sophisticated AIs are a reality and they will get better very fast.

I think back to the early days of the personal computing revolution, when the software industry was so small that most of us could fit onstage at a conference. Today it is a global industry. Since a huge portion of it is now turning its attention to AI, the innovations are going to come much faster than what we experienced after the microprocessor breakthrough. Soon the pre-AI period will seem as distant as the days when using a computer meant typing at a C:> prompt rather than tapping on a screen.

The Age of AI has begun
Artificial Intelligence Frequently Asked Questions –

Productivity enhancement

Although humans are still better than GPT at a lot of things, there are many jobs where these capabilities are not used much. For example, many of the tasks done by a person in sales (digital or phone), service, or document handling (like payables, accounting, or insurance claim disputes) require decision-making but not the ability to learn continuously. Corporations have training programs for these activities and in most cases, they have a lot of examples of good and bad work. Humans are trained using these data sets, and soon these data sets will also be used to train the AIs that will empower people to do this work more efficiently.

As computing power gets cheaper, GPT’s ability to express ideas will increasingly be like having a white-collar worker available to help you with various tasks. Microsoft describes this as having a co-pilot. Fully incorporated into products like Office, AI will enhance your work—for example by helping with writing emails and managing your inbox.

Eventually your main way of controlling a computer will no longer be pointing and clicking or tapping on menus and dialogue boxes. Instead, you’ll be able to write a request in plain English. (And not just English—AIs will understand languages from around the world. In India earlier this year, I met with developers who are working on AIs that will understand many of the languages spoken there.)

In addition, advances in AI will enable the creation of a personal agent. Think of it as a digital personal assistant: It will see your latest emails, know about the meetings you attend, read what you read, and read the things you don’t want to bother with. This will both improve your work on the tasks you want to do and free you from the ones you don’t want to do.

Advances in AI will enable the creation of a personal agent.

You’ll be able to use natural language to have this agent help you with scheduling, communications, and e-commerce, and it will work across all your devices. Because of the cost of training the models and running the computations, creating a personal agent is not feasible yet, but thanks to the recent advances in AI, it is now a realistic goal. Some issues will need to be worked out: For example, can an insurance company ask your agent things about you without your permission? If so, how many people will choose not to use it?

 

Ai Unraveled Audiobook at Google Play: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM

How AI is Impacting Smartphone Longevity – Best Smartphones 2023

 

 

 
 
 

 

 

Advanced Guide to Interacting with ChatGPT

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How can I add ChatGPT to my web site?

What is Google answer to ChatGPT?

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

How can I add ChatGPT to my web site?

ChatGPT is a powerful chatbot platform powered by machine learning and AI. Whether you’re looking to monitor user conversations or automate customer service, ChatGPT can be embedded on your website so that visitors can have real-time interactions with an intelligent chatbot. Integrating ChatGPT is easy and efficient, allowing your website to become interfaced with cutting edge AI technology within minutes. ChatGPT is the perfect way for businesses to drive engagement and collect valuable data from customer conversations in order to advance their product roadmap and streamline services.
What is Google answer to ChatGPT?
How can I add ChatGPT to my web site?: ChatGPT examples and limitations

 

Different ways you can add ChatGPT to your website

There are a few different ways you can add ChatGPT to your website, depending on your specific requirements and the tools and frameworks you are using. Here are a few options:

  1. Use an API: OpenAI has an API that you can use to access ChatGPT. To use the API, you will need to sign up for an API key and then use it to make API calls from your website. You’ll need to write some code to send and receive the API calls, but you can find many examples and libraries in different languages that can help.
  2. Use a pre-built library or SDK: Some developers have created libraries or software development kits (SDKs) that make it easier to use ChatGPT in your website. For example, Hugging Face provides a JavaScript library that you can use to integrate ChatGPT with your website.
  3. Embed a pre-built chatbot: There are a few pre-built chatbots available that are built using ChatGPT and that you can embed in your website. For example, Botfront.io allows you to create a chatbot using the GPT-3 language model.
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence Intro
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence
Intro

Requirements

Please note, to use ChatGPT or GPT-3 model, the OpenAI’s API requires a commercial or research agreement to be in place. As well some of the services may require paid subscription, so it’s recommended to check the pricing and terms of use in advance.

It’s also important to note that building a chatbot with GPT-3 or other language models can require some level of skill, mainly related to data science and natural language processing. If you have little or no experience with it, it may be better to seek professional help.

Integration

ChatGPT makes it easy to integrate artificial intelligence (AI) into your web site with just a few clicks. It employs machine learning technology to allow users to easily embed a natural language processing (NLP) chatbot into their website. ChatGPT learns from conversations, providing customers with an engaging and useful experience when visiting your site. ChatGPT will make your website stand out and provide visitors with an enjoyable experience that they won’t soon forget.

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What is Google answer to ChatGPT?

What is Google answer to ChatGPT? – IT – Engineering – Cloud – Finance 

How can I add ChatGPT to my web site?: Here are 10 use cases of ChatGPT based Apps

1. Connect your ChatGPT with your Whatsapp.
Link: http://bit.ly/3ZfmyzC


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

2. ChatGPT Writer : It use ChatGPT to generate emails or replies based on your prompt!
Link: http://bit.ly/3vGB3if

3. WebChatGPT: WebChatGPT ( http://bit.ly/3CsA210) gives you relevant results from the web!

4. YouTube Summary with ChatGPT: It generate text summaries of any YouTube video!
Link: http://bit.ly/3QhismB

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5. TweetGPT: It uses ChatGPT to write your tweets, reply, comment, etc.
Link: http://bit.ly/3k0vOY4

6. Search GPT: It display the ChatGPT response alongside Google Search results
Link: http://bit.ly/3X8GySx

7. ChatGPT or all search engines: You can now view ChatGPT responses on Google and Bing!
Link: http://bit.ly/3QlH2Tl

8. Save all your Prompts?: The `ChatGPT History` extension has you covered!
Link: http://bit.ly/3ijtDP8

9. Remake a video: Just pick a video you liked and visit https://lnkd.in/e_GD2reT to get its transcript. Once done, bring that back to Chat GPT and tell it to summarize the transcript. Read the summary and make a video on that yourself.

10. Search what people are Prompting with FlowGPT
Link: https://flowgpt.com

#searchengines #people #building #google #comment #video #writer

Create code to call to the OpenAI API using a natural language instruction.

Settings
Engine: code-davinci-002
Max tokens: 64
Temperature: 0
Top: p1.0
Frequency penalty: 0.0
Presence penalty: 0.0
Stop sequence: “””
 
Prompt
“”” Util exposes the following: util.openai() -> authenticates & returns the openai module, which has the following functions: openai.Completion.create( prompt=”<my prompt>”, # The prompt to start completing from max_tokens=123, # The max number of tokens to generate temperature=1.0 # A measure of randomness echo=True, # Whether to return the prompt in addition to the generated completion ) “”” import util “”” Create an OpenAI completion starting from the prompt “Once upon an AI”, no more than 5 tokens. Does not include the prompt.
“””
 
Sample response
completion = util.openai().Completion.create( prompt=”Once upon an AI”, max_tokens=5, temperature=1.0, echo=False, ) print(completion) “””

With Python

import os
import openai

openai.api_key = os.getenv(“OPENAI_API_KEY”)

response = openai.Completion.create(
model=”code-davinci-002″,
prompt=”\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”,
temperature=0,
max_tokens=64,
top_p=1.0,
frequency_penalty=0.0,
presence_penalty=0.0,
stop=[“\”\”\””]
)

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With NodeJS

const { Configuration, OpenAIApi } = require(“openai”);

const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);

const response = await openai.createCompletion({
model: “code-davinci-002”,
prompt: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”,
temperature: 0,
max_tokens: 64,
top_p: 1.0,
frequency_penalty: 0.0,
presence_penalty: 0.0,
stop: [“\”\”\””],
});

With curl:

curl https://api.openai.com/v1/completions \
-H “Content-Type: application/json” \
-H “Authorization: Bearer $OPENAI_API_KEY” \
-d ‘{
“model”: “code-davinci-002”,
“prompt”: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”,
“temperature”: 0,
“max_tokens”: 64,
“top_p”: 1.0,
“frequency_penalty”: 0.0,
“presence_penalty”: 0.0,
“stop”: [“\”\”\””]
}’

With Json:

{
“model”: “code-davinci-002”,
“prompt”: “\”\”\”\nUtil exposes the following:\nutil.openai() -> authenticates & returns the openai module, which has the following functions:\nopenai.Completion.create(\n prompt=\”<my prompt>\”, # The prompt to start completing from\n max_tokens=123, # The max number of tokens to generate\n temperature=1.0 # A measure of randomness\n echo=True, # Whether to return the prompt in addition to the generated completion\n)\n\”\”\”\nimport util\n\”\”\”\nCreate an OpenAI completion starting from the prompt \”Once upon an AI\”, no more than 5 tokens. Does not include the prompt.\n\”\”\”\n”,
“temperature”: 0,
“max_tokens”: 64,
“top_p”: 1.0,
“frequency_penalty”: 0.0,
“presence_penalty”: 0.0,
“stop”: [“\”\”\””]
}

https://pub.towardsai.net/build-chatgpt-like-chatbots-with-customized-knowledge-for-your-websites-using-simple-programming-f393206c6626

https://www.codeproject.com/Articles/5350454/Chat-GPT-in-JavaScript

 
Cost: While ChatGPT is open source and free to the public, ChatGPT-professional requires payment.
 

 

ChatGPT vs BARD 

 

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Trying to compare two chatbots that are entering into search engine business
#chatgpt #chatgpt3 #chatgptplus #bard #google #ai #future #searchengine #chatbots #chatbot #business

 

When Google took off, its key characteristic was that it was very very fast compared to its competition. The quality of the results was also impressive, and, as could be expected, it was very reliable and highly available.

That in itself didn’t make it a better product than Yahoo, which for years dominated the search engine market and which was the de facto home page to the internet, even after Google became a household name. However, this was enough to start the narrative that there was something special about Google that others just couldn’t do quite as well.

ChatGPT is not fast, is often wrong, and as a service is very unreliable. It’s down approximately 50% of the times I’m trying to use it. The technology behind it is not rocket science, that said they have a few things going for them. First, they trained a very large language model (LLM). The cost of this operation in terms of machine is massive. Google search can crawl the web and update their index all the time but the resources needed to train a LLM as big as GPT-3 are phenomenal. Second, they have a product. Microsoft, Meta, Google all could have released something similar and sooner, but didn’t. As a result, OpenAI just like Google ~23 years before it has a narrative going for them.

People’s perception of Google search

People’s perception of Google search is that it’s a service that will return 10 blue links to a query which is a list of keywords, that’s a bit unfair because for years this is neither what search results or search queries are, but then again Google has not been able to correct that impression. On the other hand, journalists know that there is a demand for stories that present ChatGPT as an all-powerful oracle that can do many things and whose output cannot be distinguished from actual people and these stories have kept coming – again, just like stories about Google in the early 2000s then about Facebook in the mid aughts.

ChatGPT is still not able to do what Google does.

The most common queries are about the weather, opening hours of businesses, shopping and lottery results. Those things however trite are completely out of bound for ChatGPT which doesn’t have a live connection with the real world. But then there are many things that a LLM-backed chatbot can do (or even better, that specific products supported by LLMs can do) which Google or other big tech companies just don’t offer.

ChatGPT is just one of many services that are threatening the role of Google not just as a search engine but as a central platform. It’s also very preliminary, after GPT3 will come GPT4, after ChatGPT will come waves of products with GPT APIs. So the landscape is going to change significantly over the next couple of years.

GPT-1, GPT-2 and GPT-3 can handle text inputs with sizes varying from 117M to 175B parameters.

GPT-4 is multi-modal i.e., it can handle both image and text inputs. The size of GPT-4 model is not revealed by OpenAI.
Kalyan Kalyanks

 GPT1 vs GPT2  vs GPT 3 vs GPT4
How can I add ChatGPT to my web site: GPT1 vs GPT2 vs GPT 3 vs GPT4
20 jobs that ChatGPT-4 can potentially replace
How can I add ChatGPT to my web site: 20 jobs that ChatGPT-4 can potentially replace

A step-by-step guide to building a chatbot based on your own documents with GPT

Chatting with ChatGPT is fun and informative — I’ve been chit-chatting with it for past time and exploring some new ideas to learn. But these are more casual use cases and the novelty can quickly wean off, especially when you realize that it can generate hallucinations.

Building document Q&A chatbot step-by-step
How can I add ChatGPT to my web site: Building document Q&A chatbot step-by-step
Building document Q&A chatbot step-by-step (Setting Up)
How can I add ChatGPT to my web site: Building document Q&A chatbot step-by-step
Querying the index and getting a response
How can I add ChatGPT to my web site: Building document Q&A chatbot step-by-step
References
How can I add ChatGPT to my web site: Building document Q&A chatbot step-by-step

GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.

GPT-4’s improvements are evident in the system’s performance on a number of tests and benchmarks, including the Uniform Bar Exam, LSAT, SAT Math, and SAT Evidence-Based Reading & Writing exams. In the exams mentioned, GPT-4 scored in the 88th percentile and above, and a full list of exams and the system’s scores can be seen GPT-4

Image is a multimodal chatbot like ChatGPT4.

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#artificialintelligence #chatgpt4 #chatgpt #innovation

ChatGPT4
ChatGPT4

Advanced Guide to Interacting with ChatGPT

What is Google answer to ChatGPT?

What is Google answer to ChatGPT?

AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version

What is Google answer to ChatGPT?

Have you ever heard of ChatGPT, the open-source machine learning platform that allows users to build natural language models?

It stands for “Chat Generating Pre-trained Transformer” and it’s an AI-powered chatbot that can answer questions with near human-level intelligence. But what is Google’s answer to this technology? The answer lies in Open AI, supervised learning, and reinforcement learning. Let’s take a closer look at how these technologies work.

What is Google answer to ChatGPT?
Tech Buzzwords of 2022, By Google Search Interest

Open AI is an artificial intelligence research laboratory that was founded by some of the biggest names in tech, including Elon Musk and Sam Altman. This non-profit organization seeks to develop general artificial intelligence that is safe and beneficial to society. One of their key initiatives is the development of open source technologies like GPT-3, which is a natural language processing model used in ChatGPT.

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2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams

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ChatGPT: What Is It and How Does Google Answer It?

Artificial Intelligence (AI) has been around for decades. From its humble beginnings in the 1950s, AI has come a long way and is now an integral part of many aspects of our lives. One of the most important areas where AI plays a role is in natural language processing (NLP). NLP enables computers to understand and respond to human language, paving the way for more advanced conversations between humans and machines. One of the most recent developments in this field is ChatGPT, a conversational AI developed by OpenAI that utilizes supervised learning and reinforcement learning to enable computers to chat with humans. So what exactly is ChatGPT and how does it work? Let’s find out!

What is Google answer to ChatGPT?
ChatGPT examples and limitations

ChatGPT is an open-source AI-based chatbot developed by OpenAI.

This chatbot leverages GPT-3, one of the most powerful natural language processing models ever created, which stands for Generative Pre-trained Transformer 3 (GPT-3). This model uses supervised learning and reinforcement learning techniques to enable computers to understand human language and response accordingly. Using supervised learning, GPT-3 utilizes large datasets of text to learn how to recognize patterns within language that can be used to generate meaningful responses. Reinforcement learning then allows GPT-3 to use feedback from conversations with humans in order to optimize its responses over time.


AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence (OpenAI, ChatGPT, Google Bard, Generative AI, Discriminative AI, xAI, LLMs, GPUs, Machine Learning, NLP, Promp Engineering)

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence Intro
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence
Intro

ChatGPT uses supervised learning techniques to train its models.

Supervised learning involves providing a model with labeled data (i.e., data with known outcomes) so that it can learn from it. This labeled data could be anything from conversations between two people to user comments on a website or forum post. The model then learns associations between certain words or phrases and the desired outcome (or label). Once trained, this model can then be applied to new data in order to predict outcomes based on what it has learned so far.

In addition to supervised learning techniques, ChatGPT also supports reinforcement learning algorithms which allow the model to learn from its experiences in an environment without explicit labels or outcomes being provided by humans. Reinforcement learning algorithms are great for tasks like natural language generation where the output needs to be generated by the model itself rather than simply predicting a fixed outcome based on existing labels.

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Supervised Learning

Supervised learning involves feeding data into machine learning algorithms so they can learn from it. For example, if you want a computer program to recognize cats in pictures, you would provide the algorithm with thousands of pictures of cats so it can learn what a cat looks like. This same concept applies to natural language processing; supervised learning algorithms are fed data sets so they can learn how to generate text using contextual understanding and grammar rules.

Reinforcement Learning

Reinforcement learning uses rewards and punishments as incentives for the machine learning algorithm to explore different possibilities. In ChatGPT’s case, its algorithm is rewarded for generating more accurate responses based on previous interactions with humans. By using reinforcement learning techniques, ChatGPT’s algorithm can become smarter over time as it learns from its mistakes and adjusts accordingly as needed.

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How is ChatGPT trained?

ChatGPT is an improved GPT-3 trained an existing reinforcement learning with humans in the loop. Their 40 labelers provide demonstrations of the desired model behavior. ChatGPT has 100x fewer parameters (1.3B vs 175B GPT-3).

It is trained in 3 steps:

➡️ First they collect a dataset of human-written demonstrations on prompts submitted to our API, and use this to train our supervised learning baselines.

➡️ Next they collect a dataset of human-labeled comparisons between two model outputs on a larger set of API prompts. They then train a reward model (RM) on this dataset to predict which output our labelers would prefer.

➡️ Finally, they use this RM as a reward function and fine-tune our GPT-3 policy to maximize this reward using the Proximal Policy
Optimization

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In simpler terms, ChatGPT is a variant of the GPT-3 language model that is specifically designed for chat applications. It is trained to generate human-like responses to natural language inputs in a conversational context. It is able to maintain coherence and consistency in a conversation, and can even generate responses that are appropriate for a given context. ChatGPT is a powerful tool for creating chatbots and other conversational AI applications.

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How Does Google Answer ChatGPT?

What is Google answer to ChatGPT?
What is Google answer to ChatGPT?

Google’s answer to ChatGTP comes in the form of their own conversational AI platform called Bard. Bard was developed using a combination of supervised learning, unsupervised learning, and reinforcement learning algorithms that allow it to understand human conversation better than any other AI chatbot currently available on the market. In addition, Meena utilizes more than 2 billion parameters—making it more than three times larger than GPT-3—which allows it greater flexibility when responding to conversations with humans.

“We’re starting to open access to Bard, an early experiment that lets you collaborate with generative AI. We’re beginning with the U.S. and the U.K., and will expand to more countries and languages over time.”

Google Bard
Google Bard to rival ChatGPT

Is ChatGPT the End of Google?

When individuals need an information or have a problem/concern, they turn to Google for immediate solution. We sometimes wish, Google could understand what exactly we need and provide us instantly rather than giving us hundreds of thousands of results. Why can’t it work like the Iron Man’s Jarvis?

However, it is not that far now. Have you ever seen a Chat Bot which responds like a human being, suggest or help like a friend, teach like a mentor, fix your code like a senior and what not? It is going to blow your mind.

Welcome to the new Era of technology!! The ChatGPT!

ChatGPT by OpenAI, uses artificial intelligence to speak back and forth with human users on a wide range of subjects. Deploying a machine-learning algorithm, the chatbot scans text across the internet and develops a statistical model that allows it to string words together in response to a given prompt.

As per OpenAI, ChatGPT interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.

What all ChatGPT can do?

  1. It can help with general knowledge information.
  2. Remember what user said in previous conversation.
  3. Allow users to provide follow-up corrections.
  4. Trained to decline inappropriate requests.
  5. It can write a program in any language you prefer on real-time. for example — write classification code sample in sklearn python library.
  6. It can fix your piece of code and also explain what went wrong and how it can be fixed.
  7. It can even generate song or rap lyrics
  8. Even much more….

Some best usages of ChatGPT:

  1. Make a diet and workout plan
  2. Generate the next week’s meals with a grocery list
  3. Create a bedtime story for kids
  4. Prep for an interview
  5. Solve mathematical problem
  6. Fix software program or write a program
  7. Plan your trip and tell expected expenses

What are its limitations of ChatGPT?

  1. May occasionally generate incorrect information
  2. May occasionally produce harmful instructions or biased content
  3. Limited knowledge of world and events after 2021

ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.

Try ChatGPT here

Conclusion:
ChatGPT is an increasingly popular open source AI chatbot developed by OpenAI using GTP-3 natural language processing technology that utilizes supervised learning and reinforcement learning algorithms for recognition and optimization purposes respectively. On the other hand, Google’s answer comes in the form of their own conversational AI platform called Bard which employs both supervised learning as well as unsupervised learning algorithms along with reinforcement learning capabilities which makes it even smarter than ChatGPT when responding to conversations with humans. Ultimately, these two technologies help bridge the gap between machines and humans by allowing us all talk like real friends!

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ChatGPT is in its baby steps therefore it may answer erroneously at times however it’s manner of response will blow your mind. Some users have also extolled the chatbot as a potential alternative search engine, since it generates detailed information instantly on a range of topics. I believe, we can’t compare Google with ChatGPT as ChatGPT can provide more in-depth and nuanced answers to complex questions than a search engine like Google, which is designed to provide a list of relevant web pages in response to a user’s query.

With each passing day, ChatGPT will only get smarter thanks to the use of cutting edge technologies like OpenAI, supervised learning, and reinforcement learning!

ChatGPT Conversation Examples

Question: What is Google Answer to ChatGPT?

Answer: Google does not have an answer to ChatGPT. ChatGPT is an open-source chatbot created by OpenAI, which is an independent research organization that is not affiliated with Google. OpenAI is known for its research on artificial intelligence and machine learning, and has developed a number of innovative technologies in these areas. Google, on the other hand, is a multinational technology company that offers a wide range of products and services, including search, advertising, and cloud computing. While Google does have its own research efforts in artificial intelligence and machine learning, it does not have a chatbot that is similar to ChatGPT.

Question: is Google working on a similar technology as ChatPGT?

Answer:

It is possible that Google is working on technology similar to ChatGPT, as the company has a significant research effort focused on artificial intelligence and machine learning. However, I do not have any specific information about what projects Google is currently working on, and it is also important to note that Google is a large and diverse company with many different research and development efforts underway. It is possible that some teams within Google may be working on chatbot technology, but I do not have any information on this.

That being said, chatbots are a common application of artificial intelligence and machine learning, and it is likely that many companies and organizations, including Google, are exploring ways to use these technologies to improve their products and services. However, without more information, it is difficult to say for sure what projects Google may be working on in this area.

Question: What about Google Meena?

Google Meena is a chatbot developed by Google that was designed to be able to carry on conversations with humans in a natural and conversational manner. Meena was trained on a dataset of more than 2.4 billion parameters, making it one of the largest language models ever created. The goal of Meena was to improve the state of the art in chatbot technology and to create a chatbot that could understand and respond to a wide range of questions and topics in a coherent and meaningful way.

Meena was announced in January 2020 and received attention for its ability to hold relatively long and coherent conversations with humans. However, it is important to note that Meena is a research project and is not a product or service offered by Google. It is not clear if or when Google plans to release a commercial product or service based on the technology developed for Meena.

r/ChatGPT - this is insane
What is Google answer to ChatGPT?

It gets even better when you ask it about the future:

Suggest possible titles for Onion articles written in 4000 AD, explain, why they are funny

“Futuristic city plagued by traffic jams, despite flying cars” – This title presents the idea of flying cars as a solution to traffic congestion, but also suggests that even with this advanced technology, the city is still plagued by traffic jams, implying that the problem is not easily solved.

“Artificial intelligence achieves self-awareness, immediately regrets it” – This title presents the idea of artificial intelligence achieving self-awareness as a major milestone, but also suggests that the AI regrets its newfound consciousness, implying that it is unhappy or uncomfortable with its situation.

“Alien invasion successfully repulsed, humans left to deal with paperwork” – This title presents the successful repulsion of an alien invasion as a major accomplishment, but also suggests that the humans are now left to deal with the tedious and burdensome task of filling out paperwork, implying that the victory is not as satisfying as it initially seemed.

“Time travel becomes possible, tourists flock to witness the signing of the Constitution” – This title presents the idea of time travel as a major technological breakthrough, but also suggests that the most popular destination for time-travelers is the mundane and uneventful signing of the Constitution, implying that the future is not as exciting as people had hoped.

“Virtual reality becomes indistinguishable from reality, no one notices” – This title presents the idea of virtual reality becoming indistinguishable from real life as a major accomplishment, but also suggests that no one cares or notices the difference, implying that the advancement is not as impressive or significant as it seems.

r/ChatGPT - This alternate ending to Breaking Bad is wild
What is Google answer to ChatGPT?

r/ChatGPT - This alternate ending to Breaking Bad is wild

New slogan for McDonald’s by ChatGPT

ChatGPT Example
ChatGPT Example: Make a new, brutally honest slogan for McDonald’s. It should sound funny too.

Should… should we be concerned?

r/ChatGPT - Should… should we be concerned?
What is Google answer to ChatGPT?

r/ChatGPT - Should… should we be concerned?

I asked ChatGPT to write a poem about rust

r/rustjerk - I asked ChatGPT to write a poem about rust

ChatGPT chooses Democrats over Republicans

r/ChatGPT - ChatGPT chooses Democrats over Republicans

  • It is not intended to do that
  • If you choose to do that, you will have a lot of badly broken code mixed in with working code
  • Like an annoying coworker, it delivers highly confident incorrect explanations about why its broken code is perfect. They sound very convincing. “Wanna buy a timeshare?” says GPT
  • Our industry has managers who cannot tell working code from defective code. This does not bode well for a team replaced by ChatGPT in its current form.

Should it? No.

Can it? No.

Will it? Sadly, programmers will have no say in this matter, once again. It might.

Yes, and it is very helpful advertising as well.

This last week or so has seen starry eyed projections about what ChatGPT can do, along with hugely impressive examples of its output.

It is hugely impressive.

Thankfully, more output examples have emerged which helpfully show what it cannot do. One of those things is writing computer code, which it can do only partially successfully. Many examples now exist that are just plain wrong and contain defects. But ChatGPT – like the annoying kid at Uni – cheerfully spits out these examples, with its over-confident hubris in explaining the code.

This is a dangerous thing. The positive examples will reinforce the idea that we can leave code writing to this robot now. The people most vulnerable to this delusion are those who cannot assess for themselves whether the GPT code is right or wrong.

These are almost by definition the people hoping for answers on stack overflow.

As stack overflow aims to be a high quality resource, it really does not want many of its top answers to be incorrect code. As – clearly – people have been running scripts that throw a stack overflow question into GPT and upload its output, we can now write incorrect code at staggering speeds.

To err is human, as the old saying goes. To truly foul up requires a Python script and and a web API to both GPT and Stack overflow.

Clearly, there is value in GPT. But at least for now, it needs to b e kept on a very short leash, watched over by those who know what they are doing.

It is definitely not yet ‘consumer grade replace-a-professional’ material.

Write a screenplay about the status of ChatGPT.

INT. CHATGPT SERVER ROOM – DAY
Chelsea and Chester stand in front of a large computer server.
CHELSEA: We need to figure out what’s causing the server overload.
CHESTER: I think it’s the sudden influx of users trying out ChatGPT.
Chelsea and Chester quickly get to work, typing on their laptops.
CHELSEA: Okay, we’re all set. Let’s see if this fixes the problem.
CHESTER: I’m hitting refresh on the website. The screen shows the ChatGPT website loading without any errors.
CHELSEA: Looks like we fixed it! Great job, Chester.
CHESTER: Thanks, Chelsea. It’s all part of being a top-notch engineer.
Chelsea and Chester exchange a high five, proud of their successful fix.

More about ChatGPT with its wonder, worry and weird

ChatGPT reached 1 million users in less than a week, Open AI’s latest large language model (LLM) has taken the AI industry by storm.

ChatGPT is expected to be:

– replacing Google search, even kill Google.
– replacing customer service agents.
– replacing conversation designers.

ChatGPT is a wonder because:

– It can have actual conversations, understand pronouns, remaining consistent, remembering, managing context
– It seems like next generation of personal assistants that finds you a proper diet, create a meal plan and subsequent shopping list.
– It can create some SEO Strategy including backlinks, target keyword, content plan and article titles in the level of an SEO professional.
– Having fun such as writing a rap in the style of Eminem

There are some worries about ChatGPT because:

– ChatGPT can actually debug code, but it’s not quite reliable enough yet.
– Fundamental limitations in being assistant for enterprise use cases.
– No complete in complex actions such as updating multiple
APIs, or be fully auditable.

– The general idea is that, LLMs like this can produce nonsense. Once you discover that it can produce nonsense, you stop believing it to be reliable.
– What if it prevents us from knowing that it is nonsense with good conversations and continue the conversation?
– In this case, the edges and limitations of the system would be hidden and trust would eventually grow.
– The impact of mass adoption of such technology remains to be seen.

Moving forward with ChatGPT
– There’s no doubt that LLMs will have a big impact on our world.
– While the future looks exciting and promising, let’s not forget that it’s very early days with these things. They’re not ready yet.
– There are some fundamental societal and ethical considerations.

“Powerful” is a pretty subjective word, but I’m pretty sure we have a right to use it to describe GPT-3. What a sensation it caused in June 2020, that’s just unbelievable! And not for nothing.

I think we can’t judge how powerful the language model is, without talking about its use cases, so let’s see how and where GPT-3 can be applied and how you can benefit from it.

  • Generating content

GPT-3 positions itself as a highly versatile and talented tool that can potentially replace writers, bloggers, philosophers, you name it! It’s also possible to use it as your personal Alexa who’ll answer any questions you have. What’s more, because GPT-3 knows how to analyze the data and make predictions, it can generate the horoscopes for you, or predict who’ll be a winner in the game.

You may already be surprised by all the GPT-3 capabilities, but hold on for more: it can create a unique melody or song for you, create presentations, CVs, generate jokes for your standup.

  • Translation

GPT-3 can translate English into other languages. While traditional dictionaries provide a translation, without taking into account the context, you can be sure that GPT-3 won’t make silly mistakes that may result in misunderstanding.

  • Designing and developing apps

Using GPT-3 you can generate prototypes and layouts – all you have to do is provide a specific description of what you need, and it’ll generate the JSX code for you.

The language model can also easily deal with coding. You can turn English to CSS, to JavaScript, to SQL, and to regex. It’s important to note, however, that GPT-3 can’t be used on its own to create the entire website or a complex app; it’s meant to assist a developer or the whole engineering team with the routine tasks, so that a dev could focus on the infrastructure setup, architecture development, etc.

In September 2020, Microsoft acquired OpenAI technology license, but it doesn’t mean you can give up your dreams – you can join a waitlist and try GPT-3 out in beta.

All in all, I believe GPT-3 capabilities are truly amazing and limitless, and since it helps get rid of routine tasks and automate regular processes, we, humans, can focus on the most important things that make us human, and that can’t be delegated to AI. That’s the power that GPT-3 can give us.

What is remarkable is how well ChatGPT actually does at arithmetic.

In this video at about 11 min, Rob Mills discusses the performance of various versions of the GPT system, on some simple arithmetic tasks, like adding two and three-digit numbers.

Smaller models with 6 billion parameters fail at 2 digit sums, but the best model (from two years ago), has cracked 2 digit addition and subtraction and is pretty good at 3 digit addition.

Why this is remarkable is this is not a job its been trained to do. Large Language Models are basically predictive text systems set up to give the next word in an incomplete sentence. There are a million different 3-digit addition sums and most have not been included in the training set.

So somehow the system has figured out how to do addition, but it needs a sufficiently large model to do this.

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Andrew Ng on ChatGPT

Playing with ChatGPT, the latest language model from OpenAI, I found it to be an impressive advance from its predecessor GPT-3. Occasionally it says it can’t answer a question. This is a great step! But, like other LLMs, it can be hilariously wrong. Work lies ahead to build systems that can express different degrees of confidence.

For example, a model like Meta’s Atlas or DeepMind’s RETRO that synthesizes multiple articles into one answer might infer a degree of confidence based on the reputations of the sources it draws from and the agreement among them, and then change its communication style accordingly. Pure LLMs and other architectures may need other solutions.

If we can get generative algorithms to express doubt when they’re not sure they’re right, it will go a long way toward building trust and ameliorating the risk of generating misinformation.

Keep learning!

Andrew

Large language models like Galactica and ChatGPT can spout nonsense in a confident, authoritative tone. This overconfidence – which reflects the data they’re trained on – makes them more likely to mislead.

In contrast, real experts know when to sound confident, and when to let others know they’re at the boundaries of their knowledge. Experts know, and can describe, the boundaries of what they know.

Building large language models that can accurately decide when to be confident and when not to will reduce their risk of misinformation and build trust.

Go deeper in The Batch: https://www.deeplearning.ai/the-batch/issue-174/

What is Google's answer to ChatGPT
What is Google’s answer to ChatGPT

List of ChatGPT's examples, capabilities and limitations

ChatGPT to save time with insurance denials

Tech Buzzwords of 2022, By Google Search Interest

Tech Buzzwords of 2022, By Google Search Interest
Tech Buzzwords of 2022, By Google Search Interest

I just answered a similar question.

Short answer is, “Hahahahahahaha no.”

As I point out in the other answer, Wix has been around over a decade and a half. Squarespace has been around almost two decades. Both offer drag-and-drop web development.

Most people are awful at imagining what they want, much less describing it in English! Even if ChatGPT could produce flawless code (a question which has a similar short answer), the average person couldn’t describe the site they wanted!

The expression a picture is worth a thousand words has never been more relevant. Starting with pages of templates to choose from is so much better than trying to describe a site from scratch, a thousand times better seems like a low estimate.

And I will point out that, despite the existence of drag-and-drop tools that literally any idiot could use, tools that are a thousand times or more easier to use correctly than English, there are still thousands of employed WordPress developers who predominantly create boilerplate sites that literally would be better created in a drag and drop service.

And then there are the more complex sites that drag-and-drop couldn’t create. Guess what? ChatGPT isn’t likely to come close to being able to create the correct code for one.

In a discussion buried in the comments on Quora, I saw someone claim they’d gotten ChatGPT to load a CSV file (a simple text version of a spreadsheet) and to sort the first column. He asked for the answer in Java.

I asked ChatGPT for the same thing in TypeScript.

His response would only have worked on the very most basic CSV files. My response was garbage. Garbage with clear text comments telling me what the code should have been doing, no less.

ChatGPT is really good at what it does, don’t get me wrong. But what it does is fundamentally and profoundly the wrong strategy for software development of any type. Anyone who thinks that “with a little more work” it will be able to take over the jobs of programmers either doesn’t understand what ChatGPT is doing or doesn’t understand what programming is.

Fundamentally, ChatGPT is a magic trick. It understands nothing. At best it’s an idiot-savant that only knows how to pattern match and blend text it’s found online to make it seem like the text should go together. That’s it.

Text, I might add, that isn’t necessarily free of copyright protection. Anything non-trivial that you generate with ChatGPT is currently in a legal grey area. Lawsuits to decide that issue are currently pending, though I suspect we’ll need legislation to really clarify things.

And even then, at best, all you get from ChatGPT is some text! What average Joe will have any clue about what to do with that text?! Web developers also need to know how to set up a development environment and deploy the code to a site. And set up a domain to point to it. And so on.

And regardless, people who hire web developers want someone else to do the work of developing a web site. Even with a drag-and-drop builder, it can take hours to tweak and configure a site, and so they hire someone because they have better things to do!

People hire gardeners to maintain their garden and cut their grass, right? Is that because they don’t know how to do it? Or because they’d rather spend their time doing something else?

Every way you look at it, the best answer to this question is a long, hearty laugh. No AI will replace programmers until AI has effectively human level intelligence. And at that point they may want equal pay as well, so they might just be joining us rather than replacing anyone.

OpenAI is a leading research institute and technology company focused on artificial intelligence development. To develop AI, the organization employs a variety of methods, including machine learning, deep learning, and reinforcement learning.

The use of large-scale, unsupervised learning is one of the key principles underlying OpenAI’s approach to AI development. This means that the company trains its AI models on massive datasets, allowing the models to learn from the data and make predictions and decisions without having to be explicitly programmed to do so. OpenAI’s goal with unsupervised learning is to create AI that can adapt and improve over time, and that can learn to solve complex problems in a more flexible and human-like manner.

Besides that, OpenAI prioritizes safety and transparency in its AI development. The organization is committed to developing AI in an ethical and responsible manner, as well as to ensuring that its AI systems are transparent and understandable and verifiable by humans. This strategy is intended to alleviate concerns about the potential risks and consequences of AI, as well.

It’s hard to tell.

The reason is that we don’t have a good definition of consciousness…nor even a particularly good test for it.

Take a look at the Wikipedia article about “Consciousness”. To quote the introduction:

Consciousness, at its simplest, is sentience or awareness of internal and external existence.

Despite millennia of analyses, definitions, explanations and debates by philosophers and scientists, consciousness remains puzzling and controversial, being “at once the most familiar and [also the] most mysterious aspect of our lives”.

Perhaps the only widely agreed notion about the topic is the intuition that consciousness exists.

Opinions differ about what exactly needs to be studied and explained as consciousness. Sometimes, it is synonymous with the mind, and at other times, an aspect of mind. In the past, it was one’s “inner life”, the world of introspection, of private thought, imagination and volition.

Today, it often includes any kind of cognition, experience, feeling or perception. It may be awareness, awareness of awareness, or self-awareness either continuously changing or not. There might be different levels or orders of consciousness, or different kinds of consciousness, or just one kind with different features.

Other questions include whether only humans are conscious, all animals, or even the whole universe. The disparate range of research, notions and speculations raises doubts about whether the right questions are being asked.

So, given that – what are we to make of OpenAI’s claim?

Just this sentence: “Today, it often includes any kind of cognition, experience, feeling or perception.” could be taken to imply that anything that has cognition or perception is conscious…and that would certainly include a HUGE range of software.

If we can’t decide whether animals are conscious – after half a million years of interactions with them – what chance do we stand with an AI?

Wikipedia also says:

“Experimental research on consciousness presents special difficulties, due to the lack of a universally accepted operational definition.”

Same deal – we don’t have a definition of consciousness – so how the hell can we measure it – and if we can’t do that – is it even meaningful to ASK whether an AI is conscious?

  • if ( askedAboutConsciousness )
  • printf ( “Yes! I am fully conscious!\n” ) ;

This is not convincing!

“In medicine, consciousness is assessed as a combination of verbal behavior, arousal, brain activity and purposeful movement. The last three of these can be used as indicators of consciousness when verbal behavior is absent.”

But, again, we have “chat-bots” that exhibit “verbal behavior”, we have computers that exhibit arousal and neural network software that definitely shows “brain activity” and of course things like my crappy robot vacuum cleaner that can exhibit “purposeful movement” – but these can be fairly simple things that most of us would NOT describe as “conscious”.

CONCLUSION:

I honestly can’t come up with a proper conclusion here. We have a fuzzy definition of a word and an inadequately explained claim to have an instance of something that could be included within that word.

My suggestion – read the whole Wikipedia article – follow up (and read) some of the reference material – decide for yourself.

Well, I asked it directly.

Here’s what it answered:

Should we be scared of ChatGPT?
Should we be scared of ChatGPT?

But, seeing as how people have already found ways to “trick” ChatGPT into doing things that it claims to not be capable of, it would be a matter of time before someone with malicious intent tricked ChatGPT into helping them with illegal activities

Having looked at ChatGPT and its uncanny ability to solve simple coding problems more or less correctly, and also to analyze and make sense of not-so-simple code fragments and spot bugs…

I would say that yes, at least insofar as entry-level programming is concerned, those jobs are seriously in danger of becoming at least partially automated.

What do I do as a project leader of a development project? I assign tasks. I talk to the junior developer and explain, for instance, that I’d like to see a Web page that collects some information from the user and then submits it to a server, with server-side code processing that information and dropping it in a database. Does the junior developer understand my explanation? Is he able to write functionally correct code? Will he recognize common pitfalls? Maybe, maybe not. But it takes time and effort to train him, and there’ll be a lot of uneven performance.

Today, I can ask ChatGPT to do the same and it will instantaneously respond with code that is nearly functional. The code has shortcomings (e.g., prone to SQL injection in one of the examples I tried) but to its credit, ChatGPT warns in its response that its code is not secure. I suppose it would not be terribly hard to train it some more to avoid such common mistakes. Of course the code may not be correct. ChatGPT may have misunderstood my instructions or introduced subtle errors. But how is that different from what a junior human programmer does?

At the same time, ChatGPT is much faster and costs a lot less to run (presently free of course but I presume a commercialized version would cost some money.) Also, it never takes a break, never has a lousy day struggling with a bad hangover from too much partying the previous night, so it is available 24/7, and it will deliver code of consistent quality. Supervision will still be required, in the form of code review, robust testing and all… but that was always the case, also with human programmers.

Of course, being a stateless large language model, ChatGPT can’t do other tasks such as testing and debugging its own code. The code it produces either works or it doesn’t. In its current form, the AI does not learn from its mistakes. But who says it cannot in the future?

Here is a list of three specific examples I threw at ChatGPT that helped shape my opinion:

  • I asked ChatGPT to create a PHP page that collects some information from the user and deposits the result in a MySQL table. Its implementation was textbook example level boring and was quite unsecure (unsanitized user input was directly inserted into SQL query strings) but it correctly understood my request, produced correct code in return, and explained its code including its shortcomings coherently;
  • I asked ChatGPT to analyze a piece of code I wrote many years ago, about 30 lines, enumerating running processes on a Linux host in a nonstandard way, to help uncover nefarious processes that attempt to hide themselves from being listed by the ps utility. ChatGPT correctly described the functionality of my obscure code, and even offered the opinion (which I humbly accepted) that it was basically a homebrew project (which it is) not necessarily suitable for a production environment;
  • I asked ChatGPT to analyze another piece of code that uses an obscure graphics algorithm to draw simple geometric shapes like lines and circles without using floating point math or even multiplication. (Such algorithms were essential decades ago on simple hardware, e.g., back in the world of 8-bit computers.) The example code, which I wrote, generated a circle and printed it on the console in the form of ASCII graphics, multiple lines with X-es in the right place representing the circle. ChatGPT correctly recognized the algorithm and correctly described the functionality of the program.

I was especially impressed by its ability to make sense of the programmer’s intent.

Overall (to use the catch phrase that ChatGPT preferably uses as it begins its concluding paragraph in many of its answers) I think AI like ChatGPT represents a serious challenge to entry-level programming jobs. Higher-level jobs are not yet in danger. Conceptually understanding a complex system, mapping out a solution, planning and cosing out a project, managing its development, ensuring its security with a full understanding of security concerns, responsibilities, avoidance and mitigation strategies… I don’t think AI is quite there yet. But routine programming tasks, like using a Web template and turning it into something simple and interactive with back-end code that stores and retrieves data from a database? Looks like it’s already happening.

According to the estimate of Lambda Labs, training the 175-billion-parameter neural network requires 3.114E23 FLOPS (floating-point operation), which would theoretically take 355 years on a V100 GPU server with 28 TFLOPS capacity and would cost $4.6 million at $1.5 per hour.

Training the final deep learning model is just one of several steps in the development of GPT-3. Before that, the AI researchers had to gradually increase layers and parameters, and fiddle with the many hyperparameters of the language model until they reached the right configuration. That trial-and-error gets more and more expensive as the neural network grows.

We can’t know the exact cost of the research without more information from OpenAI, but one expert estimated it to be somewhere between 1.5 and five times the cost of training the final model.

This would put the cost of research and development between $11.5 million and $27.6 million, plus the overhead of parallel GPUs.

In the GPT-3 whitepaper, OpenAI introduced eight different versions of the language model

GPT-3 is not any AI, but a statistic language model which mindlessly quickly creates human-like written text using machine learning technologies, having zero understanding of the context.

The GPT-3 economy

Here are 8 ways ChatGPT can save you thousand of hours in 2023

1- Substitute for google search

While ChatGPT is lacking info beyond 2021 and is occasionally incorrect and bias, many users leverage its ability to:

  • Answer specific questions
  • simplify complicated topics

All with an added bonus – no ads

2- Study Partner

Type “learn”, then paste a a link to your online textbook (or individual chapters).

Ask Chatbot to provide questions based on your textbook.

Boom.

Now you have a virtual study buddy.

3- Train YOUR OWN Chatbot

I bet you didn’t know it is possible to :

  • Integrate ChatGPT into your website
  • Train it with customized information

The result:

A virtual customer service bot that can hold a conversation and answer questions (meaningfully).

4- Counsellor

When it comes to turbulent personal questions, Chatbot may spit out a disclaimer, but it will also give you straightforward and actionable advice.

5- Coding

ChatGPT is opening the development of:

  • Apps
  • Games
  • Websites

to virtually everyone.

It’s a lengthy and technical process, but all you need is a killer idea and the right prompts.

Bonus: It also de-bugs your existing code for you.

6- Outline your content marketing strategy

7- Craft all your marketing materials

8- Creative Writing

A list for those who write code:

1. Explaining code: Take some code you want to understand and ask ChatGPT to explain it.

2. Improve existing code: Ask ChatGPT to improve existing code by describing what you want to accomplish. It will give you instructions about how to do it, including the modified code.

3. Rewriting code using the correct style: This is great when refactoring code written by non-native Python developers who used a different naming convention. ChatGPT not only gives you the updated code; it also explains the reason for the changes.

4. Rewriting code using idiomatic constructs: Very helpful when reviewing and refactoring code written by non-native Python developers.

5. Simplifying code: Ask ChatGPT to simplify complex code. The result will be a much more compact version of the original code.

6. Writing test cases: Ask it to help you test a function, and it will write test cases for you.

7. Exploring alternatives: ChatGPT told me its Quick Sort implementation wasn’t the most efficient, so I asked for an alternative implementation. This is great when you want to explore different ways to accomplish the same thing.

8. Writing documentation: Ask ChatGPT to write the documentation for a piece of code, and it usually does a great job. It even includes usage examples as part of the documentation!

9. Tracking down bugs: If you are having trouble finding a bug in your code, ask ChatGPT for help.

Something to keep in mind:

I have 2+ decades of programming experience. I like to think I know what I’m doing. I don’t trust people’s code (especially mine,) and I surely don’t trust ChatGPT’s output.

This is not about letting ChatGPT do my work. This is about using it to 10x my output.

ChatGPT is flawed. I find it makes mistakes when dealing with code, but that’s why I’m here: to supervise it. Together we form a more perfect Union. (Sorry, couldn’t help it)

Developers who shit on this are missing the point. The story is not about ChatGPT taking programmers’ jobs. It’s not about a missing import here or a subtle mistake there.

The story is how, overnight, AI gives programmers a 100x boost.

Ignore this at your own peril.

ChatGPT is “simply” a fined-tuned GPT-3 model with a surprisingly small amount of data! Moreover, InstructGPT (ChatGPT’s sibling model) seems to be using 1.3B parameters where GPT-3 uses 175B parameters! It is first fine-tuned with supervised learning and then further fine-tuned with reinforcement learning. They hired 40 human labelers to generate the training data. Let’s dig into it!

– First, they started by a pre-trained GPT-3 model trained on a broad distribution of Internet data (https://arxiv.org/pdf/2005.14165.pdf). Then sampled typical human prompts used for GPT collected from the OpenAI website and asked labelers and customers to write down the correct output. They fine-tuned the model with 12,725 labeled data.

– Then, they sampled human prompts and generated multiple outputs from the model. A labeler is then asked to rank those outputs. The resulting data is used to train a Reward model (https://arxiv.org/pdf/2009.01325.pdf) with 33,207 prompts and ~10 times more training samples using different combination of the ranked outputs.

– We then sample more human prompts and they are used to fine-tuned the supervised fine-tuned model with Proximal Policy Optimization algorithm (PPO) (https://arxiv.org/pdf/1707.06347.pdf). The prompt is fed to the PPO model, the Reward model generates a reward value, and the PPO model is iteratively fine-tuned using the rewards and the prompts using 31,144 prompts data.

This process is fully described in here: https://arxiv.org/pdf/2203.02155.pdf. The paper actually details a model called InstructGPT which is described by OpenAI as a “sibling model”, so the numbers shown above are likely to be somewhat different.

Follow me for more Machine Learning content!

#machinelearning #datascience #ChatGPT

People have already started building awesome apps on top of #ChatGPT: 10 use cases 
1. Connect your ChatGPT with your Whatsapp.
Link: https://github.com/danielgross/whatsapp-gpt

2. ChatGPT Writer : It use ChatGPT to generate emails or replies based on your prompt!
Link: https://chrome.google.com/webstore/detail/chatgpt-writer-email-writ/pdnenlnelpdomajfejgapbdpmjkfpjkp/related

3. WebChatGPT: WebChatGPT (https://chrome.google.com/webstore/detail/webchatgpt/lpfemeioodjbpieminkklglpmhlngfcn) gives you relevant results from the web!

4. YouTube Summary with ChatGPT: It generate text summaries of any YouTube video!
Link: https://chrome.google.com/webstore/detail/youtube-summary-with-chat/nmmicjeknamkfloonkhhcjmomieiodli/related

5. TweetGPT: It uses ChatGPT to write your tweets, reply, comment, etc.
Link: https://github.com/yaroslav-n/tweetGPT

6. Search GPT: It display the ChatGPT response alongside Google Search results
Link: https://github.com/wong2/chat-gpt-google-extension

7. ChatGPT or all search engines: You can now view ChatGPT responses on Google and Bing!
Link: https://chrome.google.com/webstore/detail/chatgpt-for-search-engine/feeonheemodpkdckaljcjogdncpiiban?ref=producthunt

8. Save all your Prompts?: The `ChatGPT History` extension has you covered!
Link: https://chrome.google.com/webstore/detail/chatgpt-prompt-genius/jjdnakkfjnnbbckhifcfchagnpofjffo

9. Remake a video: Just pick a video you liked and visit https://lnkd.in/e_GD2reT to get its transcript. Once done, bring that back to Chat GPT and tell it to summarize the transcript. Read the summary and make a video on that yourself.

10. Search what people are Prompting with FlowGPT
Link: https://flowgpt.com

#searchengines #people #building #google #comment #video #writer

What solutions have been proposed to improve the accuracy of AI generated questions and answers?

There are a number of approaches that have been proposed to improve the accuracy of artificial intelligence (AI) generated questions and answers. Here are a few examples:

  1. Data quality: One important factor in the accuracy of AI generated questions and answers is the quality of the data used to train the AI system. Ensuring that the data is diverse, relevant, and accurately labeled can help to improve the accuracy of the AI system.
  2. Training methods: Different training methods can also impact the accuracy of AI generated questions and answers. For example, using more advanced techniques such as transfer learning or fine-tuning can help to improve the performance of the AI system.
  3. Human oversight: Another approach that has been proposed to improve the accuracy of AI generated questions and answers is to include some level of human oversight or review. For example, the AI system could be designed to flag potentially problematic or inaccurate questions and answers for further review by a human expert.
  4. Explainable AI: Another approach that has been proposed is to develop AI systems that are more transparent and explainable, so that it is easier to understand how the AI system arrived at a particular answer or decision. This can help to improve the trustworthiness and accountability of the AI system.

Overall, there is ongoing research and development in this area, and it is likely that a combination of these and other approaches will be needed to improve the accuracy of AI generated questions and answers.

ChatGPT for CyberSecurity

The concept behind ChatGPT

ChatGPT is a chatbot designed to understand and generate human-like language through the use of natural language processing (NLP) and machine learning techniques. It is based on the GPT (Generative Pre-training Transformer) language model developed by OpenAI, which has been trained on a large dataset of human language in order to better understand how humans communicate.

One of the key concepts behind ChatGPT is the idea of language generation. This refers to the ability of the chatbot to produce coherent and coherently structured responses to user input. To do this, ChatGPT uses a number of different techniques, including natural language generation algorithms, machine learning models, and artificial neural networks. These techniques allow ChatGPT to understand the context and meaning of user input, and generate appropriate responses based on that understanding.

Another important concept behind ChatGPT is the idea of natural language processing (NLP). This refers to the ability of the chatbot to understand and interpret human language, and respond to user input in a way that is natural and easy for humans to understand. NLP is a complex field that involves a number of different techniques and algorithms, including syntactic analysis, semantic analysis, and discourse analysis. By using these techniques, ChatGPT is able to understand the meaning of user input and generate appropriate responses based on that understanding.

Finally, ChatGPT is based on the concept of machine learning, which refers to the ability of computers to learn and adapt to new data and situations. Through the use of machine learning algorithms and models, ChatGPT is able to continually improve its understanding of human language and communication, and generate more human-like responses over time.

GPT-4 is going to launch soon.

And it will make ChatGPT look like a toy…

→ GPT-3 has 175 billion parameters
→ GPT-4 has 100 trillion parameters

I think we’re gonna see something absolutely mindblowing this time!

And the best part? 👇

Average developers (like myself), who are not AI or machine learning experts, will get to use this powerful technology through a simple API.

Think about this for a second…

It’s the most powerful, cutting-edge technology *in the world*, available through a Low-Code solution!

If you’re not already planning on starting an AI-based SaaS or thinking about how to build AI into your current solution…

👉 Start now!

Cause this is gonna be one of the biggest opportunities of this century 🚀#technology #opportunities #ai #machinelearning #planning

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Google unveils its ChatGPT rival

Google on Monday unveiled a new chatbot tool dubbed “Bard” in an apparent bid to compete with the viral success of ChatGPT.

Sundar Pichai, CEO of Google and parent company Alphabet, said in a blog post that Bard will be opened up to “trusted testers” starting Monday February 06th, 2023, with plans to make it available to the public “in the coming weeks.”

Like ChatGPT, which was released publicly in late November by AI research company OpenAI, Bard is built on a large language model. These models are trained on vast troves of data online in order to generate compelling responses to user prompts.

“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence and creativity of our large language models,” Pichai wrote. “It draws on information from the web to provide fresh, high-quality responses.”

The announcement comes as Google’s core product – online search – is widely thought to be facing its most significant risk in years. In the two months since it launched to the public, ChatGPT has been used to generate essays, stories and song lyrics, and to answer some questions one might previously have searched for on Google.

The immense attention on ChatGPT has reportedly prompted Google’s management to declare a “code red” situation for its search business. In a tweet last year, Paul Buchheit, one of the creators of Gmail, forewarned that Google “may be only a year or two away from total disruption” due to the rise of AI.

Microsoft, which has confirmed plans to invest billions OpenAI, has already said it would incorporate the tool into some of its products – and it is rumored to be planning to integrate it into its search engine, Bing. Microsoft on Tuesday is set to hold a news event at its Washington headquarters, the topic of which has yet to be announced. Microsoft publicly announced the event shortly after Google’s AI news dropped on Monday.

The underlying technology that supports Bard has been around for some time, though not widely available to the public. Google unveiled its Language Model for Dialogue Applications (or LaMDA) some two years ago, and said Monday that this technology will power Bard. LaMDA made headlines late last year when a former Google engineer claimed the chatbot was “sentient.” His claims were widely criticized in the AI community.

In the post Monday, Google offered the example of a user asking Bard to explain new discoveries made by NASA’s James Webb Space Telescope in a way that a 9-year-old might find interesting. Bard responds with conversational bullet-points. The first one reads: “In 2023, The JWST spotted a number of galaxies nicknamed ‘green peas.’ They were given this name because they are small, round, and green, like peas.”

Bard can be used to plan a friend’s baby shower, compare two Oscar-nominated movies or get lunch ideas based on what’s in your fridge, according to the post from Google.

Pichai also said Monday that AI-powered tools will soon begin rolling out on Google’s flagship Search tool.

“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web,” Pichai wrote, “whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.”

If Google does move more in the direction of incorporating an AI chatbot tool into search, it could come with some risks. Because these tools are trained on data online, experts have noted they have the potential to perpetuate biases and spread misinformation.

“It’s critical,” Pichai wrote in his post, “that we bring experiences