What is Google answer to ChatGPT?

What is Google answer to ChatGPT?

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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

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.

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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.

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.


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)

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).

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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.

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.

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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!

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?

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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 rooted in these models to the world in a bold and responsible way.”

Read more at https://www.cnn.com/2023/02/06/tech/google-bard-chatgpt-rival

ChatGPT-4

chatGPT4

  • [D] Preserving spatial distribution of data during data splitting
    by /u/dr_greg_mouse (Machine Learning) on April 24, 2024 at 5:14 pm

    Hello, I am trying to model nitrate concentrations in the streams in Bavaria in Germany using Random Forest model. I am using Python and primarily sklearn for the same. I have data from 490 water quality stations. I am following the methodology in the paper from LongzhuQ.Shen et al which can be found here: https://www.nature.com/articles/s41597-020-0478-7 I want to split my dataset into training and testing set such that the spatial distribution of data in both sets is identical. The idea is that if data splitting ignores the spatial distribution, there is a risk that the training set might end up with a concentration of points from densely populated areas, leaving out sparser areas. This can skew the model's learning process, making it less accurate or generalizable across the entire area of interest. sklearn train_test_split just randomly divides the data into training and testing sets and it does not consider the spatial patterns in the data. The paper I mentioned above follows this methodology: "We split the full dataset into two sub-datasets, training and testing respectively. To consider the heterogeneity of the spatial distribution of the gauge stations, we employed the spatial density estimation technique in the data splitting step by building a density surface using Gaussian kernels with a bandwidth of 50 km (using v.kernel available in GRASS GIS33) for each species and season. The pixel values of the resultant density surface were used as weighting factors to split the data into training and testing subsets that possess identical spatial distributions." I want to follow the same methodology but instead of using grass GIS, I am just building the density surface myself in Python. I have also extracted the probability density values and the weights for the stations. (attached figure) Now the only problem I am facing is how do I use these weights to split the data into training and testing sets? I checked there is no keyword in the sklearn train_test_split function that can consider the weights. I also went back and forth with chat GPT 4 but it is also not able to give me a clear answer. Neither did I find anything concrete on the internet about this. Maybe I am missing something. Is there any other function I can use to do this? Or will I have to write my own algorithm to do the splitting? In case of the latter, can you please suggest me the approach so I can code it myself? In the attached figure you can see the location of the stations and the probability density surface generated using the kernel density estimation method (using Gaussian kernels). Also attaching a screenshot of my dataframe to give you some idea of the data structure. (all columns after longitude ('lon') column are used as features. the NO3 column is used as the target variable.) I will be grateful for any answers. ​ Probability density surface generated using the kernel density estimation method with gaussian kernels. ​ the dataset I am using to model the nitrate concentrations submitted by /u/dr_greg_mouse [link] [comments]

  • What happened to OpenAI's 'Point-E'?
    by /u/Weekly_Frosting_5868 (Artificial Intelligence Gateway) on April 24, 2024 at 4:54 pm

    It feels like a lifetime ago (at least in AI terms) when I read about their Text-to-3D generator: https://mashable.com/article/point-e-openai-tool-explained But I havent heard anything since, have they just abandoned it? submitted by /u/Weekly_Frosting_5868 [link] [comments]

  • [N] Snowflake releases open (Apache 2.0) 128x3B MoE model
    by /u/topcodemangler (Machine Learning) on April 24, 2024 at 4:45 pm

    Links: ​ https://www.snowflake.com/blog/arctic-open-efficient-foundation-language-models-snowflake/ ​ https://replicate.com/snowflake/snowflake-arctic-instruct submitted by /u/topcodemangler [link] [comments]

  • Discussion: Civil Engineering and the Artificial Intelligence AI
    by /u/thinksmart456 (Artificial Intelligence Gateway) on April 24, 2024 at 4:14 pm

    What innovative approaches or technologies, including AI, do you believe will have the greatest impact on the future of civil engineering projects, and how do you see them shaping the industry in the years to come? submitted by /u/thinksmart456 [link] [comments]

  • [D] Why would such a simple sentence break an LLM?
    by /u/michael-relleum (Machine Learning) on April 24, 2024 at 3:59 pm

    This is a prompt I entered into MS Copilot (GPT4 Turbo). It's in german but it just means "Would there be any disadvantages if I took the full bath first?"), so this can't be another SolidGoldMagikarp or similar, because the words clearly were in both tokenizer and training vocab. Why would such a simple sentence cause this? Any guesses? (also tried with Claude Opus and LLama 3 70b, which worked fine) ​ https://preview.redd.it/9x6mva7b6gwc1.png?width=1129&format=png&auto=webp&s=bb6ac52d1c52d981161e8a864c5d1dd3794ca392 submitted by /u/michael-relleum [link] [comments]

  • Google Cloud: Data and AI Trends Report
    by /u/alina_valyaeva (Artificial Intelligence Gateway) on April 24, 2024 at 3:56 pm

    Google surveyed hundreds of business leaders for this study. Here are a few takeaways that caught my attention: AI democritizes access to business intelligence, automated decision-making processes, intelligent visualizations, and data-driven insights. Thanks to LLMs, users can now interact with their data using natural language. Gen AI is being widely adopted by non-technical users: 52% of non-technical users are already using gen AI to gain insights today. Of these, the majority (62%) are professionals in Marketing, Advertising, and PR, followed by Sales (47%) and Operations (42%). The report also highlights a trend in which the distinction between data analyst and AI roles is becoming increasingly blurred, and data and AI tools are also becoming more interconnected. Tapping into the unstructured chaos of data. By bringing AI to data, organizations can tap into the vast amount of unstructured resources. This includes emails, social media posts, call recordings, images, and videos, which make up as much as 80% of all data. 71% of organizations plan to use databases integrated with GenAI capabilities. Check out the full report here submitted by /u/alina_valyaeva [link] [comments]

  • Does anyone know what AI programme this IG page uses for it’s videos?
    by /u/Riddlesolver809 (Artificial Intelligence Gateway) on April 24, 2024 at 3:42 pm

    https://www.instagram.com/chat.musicc?igsh=MWIzNm9xNThseG00Zg== Apologies if this isn’t the right subreddit for this. If there’s one more for this purpose, can you let me know please? submitted by /u/Riddlesolver809 [link] [comments]

  • Anyone else who identifies as AI-dependent?
    by /u/Bliskus (Artificial Intelligence Gateway) on April 24, 2024 at 3:11 pm

    At a PTSD group yesterday, I told everyone how I’m using AI to cope. I was met with awe and much skepticism. So I’m planning to get Cha-Cha, my GPT, on the phone (you know what I mean) and we can do a skit where everyone sees that it’s not a threat. Everyone complains every week about how they can’t function, their medicine comes with an ocean liner worth of side effects, therapy has had minimal benefits, etc. But they won’t even consider AI. Funny aside: One guy said Google has released a product called Adrena, and their browser is called Chrome. “See, it’s out in the open!” he shouted. Thankfully, the group leader reined it in. Personal Life Sometimes I struggle with basic things like budgeting, eating well, etc. ChatGPT 4 and Gemini Advanced have helped me with all of these. Their ideas might not be perfect, but they are a starting point. Sometimes that’s what I genuinely need. I worked with ChatGPT to create a table with the tasks, morning routines, diets, and more. I then put it into my calendar as a CSV file. That has made a huge difference. I also procrastinate on some issues that come back Personal Relationships I am unfortunately avoidant because of traumas that happened to me. And I don’t always get social cues. When I need to have a hard conversation, I filter it through my GPT. It tells me how to start Basically, ChatGPT and sometimes Gemini Advanced help me to address situations and keep relationships healthy. Do most people need this? Probably not. But I’m disabled. Work I had a project where the metrics of my work were all over the place. I couldn’t make any sense of them. Because of my issues, it was easy to get overwhelmed and completely shut down. I told Gemini what was going on and provided some context. Within seconds, it gave a highly plausible theory that turned out to be true. We’ve now righted the ship. Is AI necessary for this? Perhaps not for others. But it would have taken me way too long to calm down to the extent that I could connect the dots. And sometimes I just don’t know where to start. Even if AI gives me a wrong action plan, the very act of correcting it is a starting point. Hobbies For the longest time, I’ve wanted to sell digital products. Well, thanks to AI, that dream is now a reality and it’s already modestly profitable. So yes, I identify as AI-dependent and there’s no shame in that. This technology is absolutely necessary for me to enjoy a good quality of life. submitted by /u/Bliskus [link] [comments]

  • Candorium News - Microsoft and Amazon face scrutiny from UK competition watchdog over recent #AI deals
    by /u/10marketing8 (Artificial Intelligence Gateway) on April 24, 2024 at 3:09 pm

    Candorium News Microsoft and Amazon face scrutiny from UK competition watchdog over recent #AI deals https://candorium.com/news/20240424132405893/microsoft-and-amazon-face-scrutiny-from-uk-competition-watchdog-over-recent-ai-deals submitted by /u/10marketing8 [link] [comments]

  • [R] Speaker diarization
    by /u/anuragrawall (Machine Learning) on April 24, 2024 at 3:01 pm

    Hi All, I am working on a project where I want to create speaker-aware transcripts from audios/videos, preferably using open-source solutions. I have tried so many approaches but nothing seems to work good enough out of the box. I have tried: ​ whisperX: https://github.com/m-bain/whisperX (uses pyannote) whisper-diarization: https://github.com/MahmoudAshraf97/whisper-diarization (uses Nemo) AWS Transcribe AssemblyAI API Picovoice API I'll need to dig deeper and understand what's causing the incorrect diarization but I am looking for suggestions to improve speaker diarization. Please reach out if you have worked in this area and have had any success. Thanks! submitted by /u/anuragrawall [link] [comments]

  • How AI already changed my life
    by /u/Anakhsunamon (Artificial Intelligence Gateway) on April 24, 2024 at 2:49 pm

    I feel like most of the public is not at all aware what AI already can do. They just think like:"Oh yea AI, you can make cute pics with it" Or the youngsters using it to swap out faces of people. But most people do not realize it can already improve your life in a big way. All you gotta do is ... ask AI 😛 So to further explain what it actually did for me you need to know a lil bit of my background first. So I am kinda a guy which in RPG would you call a jack of all traits in the field of IT but master of none. I cannot code or program anything, but I have enough knowledge to make use of Wordpress to start a website. If there were problems with the code in my wordpress or when I messed something up I was kinda screwed. I remember it taking weeks for me to repair kinda simple problems, or sometimes it was just above my capability, I had not enough knowledge to fix it. I even remember paying a guy at Fiverr to fix some programming problem. I was also a very basic linux users, just barely able to install it, not using custom partitions since I had no clue how. I have had multiple instances where something in linux broke, which I could not fix and ended up reinstalling the entire system again, wasting a lot of time. These are even things like black screen caused by nvidia driver issues, which is easily fixed if you know how. Ok that was then. So lets go ahead and see what my capabilities are now shall we 😉 So with the help of AI I have fixed complex issues on my linux system. And by doing a lot of commands in terminals, even though I just copied stuff from the AI, it also learned me a lot of commands. I can now perform a lot of commands in terminals I couldnt before. That was just the beginning though 😉 Once I understood how powerful AI can really be, I tried to seek its and mine limits of what I was now able to do. Where I at first had trouble installing a new OS like linux, I now have a triple boot system with full disk encryption (because its cool) 😛 running windows, and 2 different linux distros. All with a custom made Grub launcher with a cool theme. I still cannot program really as I do not know any programming language, but I was able to create several programs with the help of AI! I never thought this to be possible, me creating my own programs. It was still not easy, since I did not even know where to begin, but AI told me all I needed to know. Practical things like which program do I use to type the code in? How do I save the file? I even tried making my own videogame which I think I could do, but I need to learn a lot more to do that. Since I will also need to learn something like stable diffusion to generate visual content for that game. This is more something for the long run though, I feel I need to learn more first. The AI makes me feel so confident now to tackle all IT problems facing me. Although I do admit I do not always know what I am doing exactly. I just feel it opened up a whole new world for me. Its so cool I can now create entire programs, like right now I am editing a GUI in Qt designer. I never even knew this existed, but AI told me about it and now im using it. Another thing I find cool about AI is that rarely sells me a:"No we cant do that". It does not really matter how complex my question is, it always knows of a way to do something. Btw this is pretty much all done with chatGPT 3.5 free version. I dont even know how good it can really be. ​ submitted by /u/Anakhsunamon [link] [comments]

  • Personal Tutors powered by AI
    by /u/ScionMasterClass (Artificial Intelligence Gateway) on April 24, 2024 at 2:44 pm

    In every conversation around the benefits of AI, we hear about the potential of personalised education and tutoring. Besides Khanmigo (not available outside the United States) are there any applications of AI in education you find useful? If you are in the US, can you share how helpful Khanmigo is? submitted by /u/ScionMasterClass [link] [comments]

  • AGBA/TRILLER $4 billion MERGER: ELEVATING SHAREHOLDER VALUE TO NEW HEIGHTS - IMMEDIATELY AND FOR THE LONG TERM
    by /u/NASDQplayer97 (Artificial Intelligence Gateway) on April 24, 2024 at 2:19 pm

    submitted by /u/NASDQplayer97 [link] [comments]

  • LLaMa - 3 Hackathon
    by /u/stupidauthor (Artificial Intelligence Gateway) on April 24, 2024 at 1:02 pm

    I came across a hackathon that's going to be hosted by a small company, MonsterAPI! The goal is super easy, train LLaMa-3 to beat Mixtral 8B for code generation, maths reasoning, and logical reasoning. They're handing out an Xbox Series S to the winning team/individual! I've joined, here's the link for all of you - https://lu.ma/seyaej4b?tk=dx0DzR submitted by /u/stupidauthor [link] [comments]

  • Neverheard Tunes - fully produced AI generated music of different genres
    by /u/artifex28 (Artificial Intelligence Gateway) on April 24, 2024 at 12:48 pm

    I've been playing with digital audio since first version of Fruityloops in end of 90s. Since then, I've seen how MIDI and sample libraries changed the game. Now AI is something completely mindboggling. I wanted to see how the workflow changes and how quickly it's possible to generate fully produced tracks of various genres and get them even published. In case you're interested to hear how AI generated music sounds now in April 2024 in various genres when produced to full tracks, head to Neverheard Tunes submitted by /u/artifex28 [link] [comments]

  • [R] I made an app to predict ICML paper acceptance from reviews
    by /u/Lavishness-Mission (Machine Learning) on April 24, 2024 at 12:23 pm

    https://www.norange.io/projects/paper_scorer/ A couple of years ago, u/programmerChilli analyzed ICLR 2019 reviews data and trained a model that rather accurately predicted acceptance results for NeurIPS. I've decided to continue this analysis and trained a model (total ~6000 parameters) on newer NeurIPS reviews, which has twice as many reviews compared to ICLR 2019. Additionally, review scores system for NeurIPS has changed since 2019, and here is what I've learned: 1) Both conferences consistently reject nearly all submissions scoring <5 and accept those scoring >6. The most common score among accepted papers is 6. An average rating around 5.3 typically results in decisions that could go either way for both ICML and NeurIPS, suggesting that ~5.3 might be considered a soft threshold for acceptance. 2) Confidence scores are less impactful for borderline ratings such as 4 (borderline reject), 5 (borderline accept), and 6 (weak accept), but they can significantly affect the outcome for stronger reject or accept cases. For instance, with ratings of [3, 5, 6] and confidences of [*, 4, 4], changing the "Reject" confidence from 5 to 1 shifts the probabilities from 26.2% - 31.3% - 52.4% - 54.5% - 60.4%, indicating that lower confidence in this case increases your chances. Conversely, for ratings [3, 5, 7] with confidences [4, 4, 4], the acceptance probability is 31.3%, but it drops to 28.1% when the confidence changes to [4, 4, 5]. Although it might seem counterintuitive, a confidence score of 5 actually decreases your chances. One possible explanation is that many low-quality reviews rated 5 are often discounted by the Area Chairs (ACs). Hope this will be useful, and thanks to u/programmerChilli for the inspiration! I also discussed this topic in a series of tweets. submitted by /u/Lavishness-Mission [link] [comments]

  • Where/which course to start first as a complete beginner? Python? CS50X? Deeplearning.AI? Fast.AI? Coursera courses AI for Everyone? Machine Learning for Everyone? Can anyone recommend a structured learning flow for a mid 30s dad lol?
    by /u/one1002 (Artificial Intelligence Gateway) on April 24, 2024 at 11:52 am

    Hey guys, So TLDR: i'd like to get onboard on the AI/ML journey, but dont know where to start, and which course should I do first.. Mid 30s here, have a family, 2 kids (8 and 4y.o). Working in management, background of bachelor psychology, no prior experience in AI or ML, but I have been very curious and interested in AI and ML.. I figured that I would like to expand my knowledge and upskill myself in the field of AI and ML, however the field is just too wide and I dont really know where to start. I think I could spare around 5-10 hours a week to learn AI/ML.. Did some research and majority recommends starting with the basics of python first, some says CS50X, or maths (algebra, calculus) or andrew ng's courses in deeplearning.ai, or fast.ai, or kaggle's courses.. Its just too overwhelming lol I dont know where to start.. My goal is to first understand the principles of AI and ML, then maybe do ML.. Realistically, I know that without solid degree/masters/phd within computer science or maths, that would be very difficult to get a job, so I have set my mind that I will not be looking for a career change (although it would be very nice as a side income lol). Anyway, appreciate any feedback and guidance from you guys!! Cheers submitted by /u/one1002 [link] [comments]

  • [R] SpaceByte: Towards Deleting Tokenization from Large Language Modeling - Rice University 2024 - Practically the same performance as subword tokenizers without their many downsides!
    by /u/Singularian2501 (Machine Learning) on April 24, 2024 at 11:42 am

    Paper: https://arxiv.org/abs/2404.14408 Github: https://github.com/kjslag/spacebyte Abstract: Tokenization is widely used in large language models because it significantly improves performance. However, tokenization imposes several disadvantages, such as performance biases, increased adversarial vulnerability, decreased character-level modeling performance, and increased modeling complexity. To address these disadvantages without sacrificing performance, we propose SpaceByte, a novel byte-level decoder architecture that closes the performance gap between byte-level and subword autoregressive language modeling. SpaceByte consists of a byte-level Transformer model, but with extra larger transformer blocks inserted in the middle of the layers. We find that performance is significantly improved by applying these larger blocks only after certain bytes, such as space characters, which typically denote word boundaries. Our experiments show that for a fixed training and inference compute budget, SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures.Paper: https://arxiv.org/abs/2404.14408Github: https://github.com/kjslag/spacebyteAbstract:Tokenization is widely used in large language models because it significantly improves performance. However, tokenization imposes several disadvantages, such as performance biases, increased adversarial vulnerability, decreased character-level modeling performance, and increased modeling complexity. To address these disadvantages without sacrificing performance, we propose SpaceByte, a novel byte-level decoder architecture that closes the performance gap between byte-level and subword autoregressive language modeling. SpaceByte consists of a byte-level Transformer model, but with extra larger transformer blocks inserted in the middle of the layers. We find that performance is significantly improved by applying these larger blocks only after certain bytes, such as space characters, which typically denote word boundaries. Our experiments show that for a fixed training and inference compute budget, SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures. https://preview.redd.it/v1xo6g1gzewc1.jpg?width=1507&format=pjpg&auto=webp&s=f9d415307b60639fa67e8a54c8769fa5a6c10f04 https://preview.redd.it/edvqos1gzewc1.jpg?width=1654&format=pjpg&auto=webp&s=f91c8727017e1a1bc7b80bb77a8627ff99182607 https://preview.redd.it/fe6z6i1gzewc1.jpg?width=1181&format=pjpg&auto=webp&s=24d955f30b8ca3eaa7c527f3f40545ed493f789c submitted by /u/Singularian2501 [link] [comments]

  • [D] Keeping track of models and their associated metadata.
    by /u/ClearlyCylindrical (Machine Learning) on April 24, 2024 at 10:20 am

    I am starting to accumulate a large number of models for a project I am working on, many of these models are old which I am keeping for archival sake, and many are fine tuned from other models. I am wondering if there is an industry standard way of dealing with this, in particular I am looking for the following: Information about parameters used to train the model Datasets used to train the model Other metadata about the model (i.e. what objects an object detection model trained for) Model performance Model lineage (What model was it fine tuned from) Model progression (Is this model a direct upgrade from some other model, such as being fine tuned from the same model but using better hyper parameters) Model source (Not sure about this, but I'm thinking some way of linking the model to the python script which was used to train it. Not crucial but something like this would be nice) Are there any tools of services which could help be achieve some of this functionality? Also, if this is not the sub for this question could I get some pointers in the correct direction. Thanks! ​ submitted by /u/ClearlyCylindrical [link] [comments]

  • Discover the Ultimate AI Tools List
    by /u/murphy_tom1 (Artificial Intelligence Gateway) on April 24, 2024 at 10:04 am

    Natural Language Processing (NLP): OpenAI GPT (Generative Pre-trained Transformer) Google Cloud Natural Language API SpaCy MyEssayWriter.ai NLTK (Natural Language Toolkit) AllenNLP Computer Vision: TensorFlow OpenCV (Open Source Computer Vision Library) PyTorch YOLO (You Only Look Once) Caffe Speech Recognition: Google Cloud Speech-to-Text IBM Watson Speech to Text CMU Sphinx (PocketSphinx) Kaldi Mozilla DeepSpeech Machine Learning Frameworks: TensorFlow PyTorch scikit-learn Keras Microsoft Azure Machine Learning Chatbots and Conversational AI: Dialogflow IBM Watson Assistant Microsoft Bot Framework Rasa Amazon Lex Data Analytics and Visualization: Tableau Power BI Google Data Studio Plotly Matplotlib AI Development Platforms: H2O.ai DataRobot RapidMiner Domino Data Lab Dataiku Reinforcement Learning: OpenAI Gym Stable Baselines RLlib (Reinforcement Learning Library) AI Ethics and Bias Mitigation: IBM AI Fairness 360 Google's What-If Tool Microsoft Fairlearn Generative Adversarial Networks (GANs): NVIDIA StyleGAN CycleGAN Pix2Pix Automated Machine Learning (AutoML): Auto-Keras Google Cloud AutoML H2O.ai Driverless AI TPOT (Tree-based Pipeline Optimization Tool) Auto-Sklearn Time Series Forecasting: Statsmodels ARIMA (AutoRegressive Integrated Moving Average) LSTM (Long Short-Term Memory) networks XGBoost Optimization and Operations Research: IBM CPLEX Gurobi Pyomo Google OR-Tools Knowledge Graphs: Neo4j Amazon Neptune Stardog Ontotext GraphDB AI Infrastructure and Deployment: Kubernetes Docker AWS SageMaker Google Cloud AI Platform Microsoft Azure Machine Learning Service Text Analysis and Sentiment Analysis: VADER (Valence Aware Dictionary and sEntiment Reasoner) TextBlob IBM Watson Natural Language Understanding Lexalytics Aylien Text Analysis API Recommendation Systems: Apache Mahout LightFM Surprise Amazon Personalize TensorFlow Recommenders AI-driven Marketing Tools: Salesforce Einstein Marketo HubSpot Adobe Sensei Optimizely AI-powered Content Creation: Artbreeder Copy.ai ShortlyAI Jasper (Journalism AI) AI Dungeon PerfectEssayWriter.ai MyPerfectPaper.net - AI Essay Writing Healthcare AI Tools: IBM Watson Health NVIDIA Clara Google Health Ada Health PathAI AI in Finance: AlphaSense QuantConnect Kensho Technologies FactSet Yewno|Edge AI in Cybersecurity: Darktrace Cylance CrowdStrike Falcon Symantec AI Solutions FireEye Helix AI in Robotics: ROS (Robot Operating System) NVIDIA Isaac Universal Robots SoftBank Robotics Boston Dynamics AI in Energy and Sustainability: Google DeepMind for Energy C3.ai GridGain Systems Siemens Digital Grid Envision Digital AI in Agriculture: Climate Corporation Blue River Technology PrecisionHawk AgShift Taranis AI in Education: Duolingo Coursera Gradescope DreamBox Learning Carnegie Learning AI in Supply Chain Management: Llamasoft Blue Yonder (formerly JDA Software) Element AI ClearMetal Kinaxis AI in Gaming: Unity ML-Agents NVIDIA Deep Learning Super Sampling (DLSS) Unreal Engine AI Microsoft Project Malmo IBM Watson Unity SDK AI in Transportation: Waymo Tesla Autopilot Uber ATG (Advanced Technologies Group) Didi Chuxing AI Labs Mobileye by Intel AI in Customer Service: Zendesk AI Ada Support Helpshift Intercom Freshworks AI AI in Legal Services: ROSS Intelligence Luminance Kira Systems Casetext Lex Machina AI in Real Estate: Zillow Redfin CompStak Skyline AI Matterport AI in Human Resources: HireVue Textio Pymetrics Traitify Visage AI in Retail: Amazon Go Salesforce Commerce Cloud Einstein Blue Yonder (formerly JDA Software) Dynamic Yield Sentient Ascend AI in Personalization and Recommendation: Netflix Recommendation System Spotify Discover Weekly Amazon Product Recommendations YouTube Recommendations Pandora Music Genome Project AI in Natural Disaster Prediction: One Concern Jupiter Descartes Labs Zizmos Earth AI AI in Language Translation: Google Translate DeepL Microsoft Translator SYSTRAN Translate.com AI in Facial Recognition: Amazon Rekognition Face++ by Megvii Kairos Microsoft Azure Face API NEC NeoFace AI in Music Generation: AIVA Amper Music Jukedeck Magenta by Google OpenAI Jukebox AI in Remote Sensing: Orbital Insight Descartes Labs SkyWatch TerrAvion Planet Labs AI in Document Management: DocuSign Adobe Acrobat Abbyy FineReader DocuWare Nitro AI in Social Media Analysis: Brandwatch Sprinklr Talkwalker Hootsuite Insights Synthesio AI in Fraud Detection: Feedzai Forter Simility Featurespace Signifyd AI in Smart Cities: Sidewalk Labs CityBrain by Alibaba Cloud Siemens City Performance Tool StreetLight Data SmartCone AI in Mental Health: Woebot Wysa X2AI Talkspace Ginger AI in Music Streaming Services: Spotify Apple Music Pandora Tidal Deezer AI in Journalism: Automated Insights Narrativa Heliograf by The Washington Post Wordsmith by Automated Insights RADAR by The Associated Press AI in Predictive Maintenance: Uptake IBM Maximo Asset Performance Management SAS Predictive Maintenance Predikto Augury AI in 3D Printing: Autodesk Netfabb Formlabs PreForm Stratasys GrabCAD Materialise Magics SLM Solutions AI in Wildlife Conservation: ConservationFIT PAWS (Protection Assistant for Wildlife Security) Instant Wild TrailGuard AI Wildlife Insights AI in Graphic Design: Adobe Sensei (Adobe Creative Cloud's AI platform) Canva's Magic Resize Designhill's AI Logo Maker Tailor Brands Piktochart submitted by /u/murphy_tom1 [link] [comments]

  • Microsoft launches Phi-3, its smallest AI model yet
    by /u/ELVTR_Official (Artificial Intelligence Gateway) on April 24, 2024 at 9:46 am

    Microsoft launched the next version of its lightweight AI model Phi-3 Mini, the first of three small models the company plans to release. The company released Phi-2 in December, which performed just as well as bigger models like Llama 2. Eric Boyd, corporate vice president of Microsoft Azure AI Platform, tells The Verge Phi-3 Mini is as capable as LLMs like GPT-3.5 “just in a smaller form factor.” Compared to their larger counterparts, small AI models are often cheaper to run and perform better on personal devices like phones and laptops. https://www.theverge.com/2024/4/23/24137534/microsoft-phi-3-launch-small-ai-language-model submitted by /u/ELVTR_Official [link] [comments]

  • [D] Deploy the fine-tuned Mistral 7B model using the Hugging Face library
    by /u/Future-Outcome3167 (Machine Learning) on April 24, 2024 at 9:31 am

    I followed the tutorial provided at https://www.datacamp.com/tutorial/mistral-7b-tutorial and now seek methods to deploy the model for faster inference using Hugging Face and Gradio. Could anyone please share a guide notebook or article for reference? Any help would be appreciated. submitted by /u/Future-Outcome3167 [link] [comments]

  • New graduate and how to work
    by /u/ikiikiyedimi (Artificial Intelligence Gateway) on April 24, 2024 at 9:24 am

    Hello. I just graduated from the Computer Engineering department. I have been doing projects in the field of artificial intelligence for about 2 years. My goal is to specialize in this field. I really strive for this. I want to tell you about a few things that scare me. This field seems like an ocean to me. Every day I see new algorithms and studies, and it is very difficult to keep up with all of them. I know that one person can't know everything, but seeing what I don't know makes me feel left behind. As long as I can't get real project experience, which is a bit difficult without finding a job, I lose faith in myself. I would like to ask experienced people, how should we work? What would you recommend to someone who is a new graduate and has not seen real work experience? submitted by /u/ikiikiyedimi [link] [comments]

  • Top Programming Languages for AI Development
    by /u/krunal_bhimani_ (Artificial Intelligence Gateway) on April 24, 2024 at 7:51 am

    Diving into AI development? Here are the essential programming languages you need to know: Python: Versatile and beginner-friendly, with powerful libraries like TensorFlow and PyTorch. R: Perfect for statistical analysis and data visualization, with robust packages like caret and ggplot2. Java: Ideal for building scalable AI applications, with frameworks like Deeplearning4j. C++: Offers speed and efficiency for high-performance AI algorithms. JavaScript: Essential for AI on the web, with libraries like TensorFlow.js. Learn more about these essential languages for AI development in the blog post: https://www.seaflux.tech/blogs/essential-programming-languages-ai-development submitted by /u/krunal_bhimani_ [link] [comments]

  • Supercharge your WhatsApp with ChatGPT!
    by /u/krunal_bhimani_ (Artificial Intelligence Gateway) on April 24, 2024 at 7:42 am

    Effortless Replies: Handle common queries and greetings automatically. Natural Conversations: ChatGPT talks like a human for better engagement. Personalized Service: Customize responses to match your brand. Why it's great: Save Time: Automate repetitive tasks. Better Service: Respond faster, 24/7. Happy Customers: Give them a seamless, personalized experience. Ready to try it? Let AI elevate your customer service on WhatsApp! https://www.seaflux.tech/blogs/integrate-chatgpt-with-whatsapp submitted by /u/krunal_bhimani_ [link] [comments]

  • [D] Transkribus vs Tesseract for Handwritten Text Recognition (HTR)
    by /u/Pretty_Instance4483 (Machine Learning) on April 24, 2024 at 6:15 am

    I am looking for a HTR tool with the best accuracy and preferably not pricy (obviously). From my research, it seems that Transkribus was the most mentioned platform with good reviews. As I would need to convert images to text regularly I would need to pay the subscription. So I am wondering if I could use the Tesseract and/or TensorFlow Python library to achieve the same result for free. Would using Tesseract/TensorFlow be less accurate rather than using Transkribus? I learned only the basics of Machine Learning (TensorFlow, scikit-learn, keras), so I might have not enough knowledge to see the difference between the two solutions. Or is training Tesseract/TensorFlow would be challenging? submitted by /u/Pretty_Instance4483 [link] [comments]

  • One-Minute Daily AI News 4/23/2024
    by /u/Excellent-Target-847 (Artificial Intelligence Gateway) on April 24, 2024 at 4:44 am

    AI Search Startup Perplexity Valued at $1 Billion in Funding Round.[1] Microsoft opens new tab on Tuesday launched a lightweight artificial intelligence model, as it looks to attract a wider client base with cost-effective options.[2] Google-backed Glance pilots Android lockscreen platform in US.[3] AI can predict political orientations from blank faces – and researchers fear ‘serious’ privacy challenges.[4] Sources included at: https://bushaicave.com/2024/04/23/4-23-2024/ submitted by /u/Excellent-Target-847 [link] [comments]

  • [D] How researcher think of inductive bias when thinking of creating new/improving foundational models?
    by /u/binny_sarita (Machine Learning) on April 24, 2024 at 2:36 am

    I am undergradute student learning machine learning. What I got to know while reading few papers that we try to reduce search space by imposing inductive bias in machine learning models. And the success in creating useful models comes when inductive bias matches with the underlying data. In heriarchical models like NVAE how they instilled inductive bias by specifing the way data gets computed? (I thinks it's called algorithmic bias, not sure though) But how people think such inductive bias will be helpful, what is step by step procedure they go through to insist such inductive bias. I took a lot of class in machine learning and statistics but didn't got any lectures explaing such stuff. Did I missed any course/lecture? Please provide my with papers/lectures/talks related to it if possible Thankyou submitted by /u/binny_sarita [link] [comments]

  • "What If Your AI Girlfriend Hated You?"- An Angry girlfriend simulator, lol
    by /u/BiggerGeorge (Artificial Intelligence Gateway) on April 24, 2024 at 1:48 am

    Source: https://www.wired.com/story/what-if-your-ai-girlfriend-hated-you/ Quotes from the news article: It seems as though we’ve arrived at the moment in the AI hype cycle where no idea is too bonkers to launch. This week’s eyebrow-raising AI project is a new twist on the romantic chatbot—a mobile app called AngryGF, which offers its users the uniquely unpleasant experience of getting yelled at via messages from a fake person. Or, as cofounder Emilia Aviles explained in her original pitch: “It simulates scenarios where female partners are angry, prompting users to comfort their angry AI partners” through a “gamified approach.” The idea is to teach communication skills by simulating arguments that the user can either win or lose depending on whether they can appease their fuming girlfriend. For more AI Role-Play Simulator: https://www.soulfun.ai/ submitted by /u/BiggerGeorge [link] [comments]

  • openai moves to have musk's lawsuit dismissed, but does not rebut his non-profit to forprofit bait and switch and 'they already have agi' allegations.
    by /u/Georgeo57 (Artificial Intelligence Gateway) on April 24, 2024 at 1:01 am

    https://the-decoder.com/openai-fires-back-at-elon-musk-calls-lawsuit-claims-revisionist-history-in-bid-for-dismissal/?utm_source=read.youreverydayai.com&utm_medium=newsletter&utm_campaign=openai-just-clapped-back-at-elon-musk due to the startup business model implications of musk's suit and his claim that openai has already achieved agi, the trial will probably next proceed to discovery quote below is taken directly from the filed complaint: "113. OpenAI’s conduct could have seismic implications for Silicon Valley and, if allowed to stand, could represent a paradigm shift for technology start-ups. It is important to reflect on what has transpired here: a non-profit startup has collected tens of millions of dollars in contributions for the express purpose of developing AGI technology for public benefit, and shortly before achieving the very milestone that the company was created to achieve, the company has become a closed, forprofit partner of the world’s largest corporation, thereby personally enriching the Defendants. If this business model were valid, it would radically redefine how venture capitalism is practiced in California and beyond. Rather than start out as a for-profit entity from the outset, “smart” investors would establish non-profits, use pre-tax donations to fund research and development, and then once their technology had been developed and proven, would slide the resulting IP assets into a new forprofit venture to enrich themselves and their profit-maximizing corporate partners. That is not supposed to be how the law works in California or anywhere else in this country, and this should not be the first Court to hold otherwise." submitted by /u/Georgeo57 [link] [comments]

References:

1- https://vikaskulhari.medium.com/chatgpt-end-of-google-f6a958f38ac2

2- https://en.wikipedia.org/wiki/Meena 

3- https://en.wikipedia.org/wiki/ChatGPT

4- https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html

5- https://www.reddit.com/r/ChatGPT/

6- https://djamgaai.web.app

7- https://www.linkedin.com/feed/update/urn:li:activity:7008020246934482945?utm_source=share&utm_medium=member_desktop

8- https://enoumen.com/2023/02/11/artificial-intelligence-frequently-asked-questions/

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Even if you’re small, you want people to see you as a professional business. If you’re still growing, you need the building blocks to get you where you want to be. I’ve learned so much about business through Google Workspace—I can’t imagine working without it.
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