What is Google answer to ChatGPT?

What is Google answer to ChatGPT?

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.

2023 AWS Certified Machine Learning Specialty (MLS-C01) Practice Exams
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.

AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence Intro
AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

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.

Reinforcement Learning

Reinforcement learning uses rewards and punishments as incentives for the machine learning algorithm to explore different possibilities. In ChatGPT’s case, its algorithm is rewarded for generating more accurate responses based on previous interactions with humans. By using reinforcement learning techniques, ChatGPT’s algorithm can become smarter over time as it learns from its mistakes and adjusts accordingly as needed.

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How is ChatGPT trained?

ChatGPT is an improved GPT-3 trained an existing reinforcement learning with humans in the loop. Their 40 labelers provide demonstrations of the desired model behavior. ChatGPT has 100x fewer parameters (1.3B vs 175B GPT-3).

It is trained in 3 steps:

➡️ First they collect a dataset of human-written demonstrations on prompts submitted to our API, and use this to train our supervised learning baselines.

➡️ Next they collect a dataset of human-labeled comparisons between two model outputs on a larger set of API prompts. They then train a reward model (RM) on this dataset to predict which output our labelers would prefer.

➡️ Finally, they use this RM as a reward function and fine-tune our GPT-3 policy to maximize this reward using the Proximal Policy

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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.

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.

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

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

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?


It is possible that Google is working on technology similar to ChatGPT, as the company has a significant research effort focused on artificial intelligence and machine learning. However, I do not have any specific information about what projects Google is currently working on, and it is also important to note that Google is a large and diverse company with many different research and development efforts underway. It is possible that some teams within Google may be working on chatbot technology, but I do not have any information on this.

That being said, chatbots are a common application of artificial intelligence and machine learning, and it is likely that many companies and organizations, including Google, are exploring ways to use these technologies to improve their products and services. However, without more information, it is difficult to say for sure what projects Google may be working on in this area.

Question: What about Google Meena?

Google Meena is a chatbot developed by Google that was designed to be able to carry on conversations with humans in a natural and conversational manner. Meena was trained on a dataset of more than 2.4 billion parameters, making it one of the largest language models ever created. The goal of Meena was to improve the state of the art in chatbot technology and to create a chatbot that could understand and respond to a wide range of questions and topics in a coherent and meaningful way.

Meena was announced in January 2020 and received attention for its ability to hold relatively long and coherent conversations with humans. However, it is important to note that Meena is a research project and is not a product or service offered by Google. It is not clear if or when Google plans to release a commercial product or service based on the technology developed for Meena.

r/ChatGPT - this is insane
What is Google answer to ChatGPT?

It gets even better when you ask it about the future:

Suggest possible titles for Onion articles written in 4000 AD, explain, why they are funny

“Futuristic city plagued by traffic jams, despite flying cars” – This title presents the idea of flying cars as a solution to traffic congestion, but also suggests that even with this advanced technology, the city is still plagued by traffic jams, implying that the problem is not easily solved.

“Artificial intelligence achieves self-awareness, immediately regrets it” – This title presents the idea of artificial intelligence achieving self-awareness as a major milestone, but also suggests that the AI regrets its newfound consciousness, implying that it is unhappy or uncomfortable with its situation.

“Alien invasion successfully repulsed, humans left to deal with paperwork” – This title presents the successful repulsion of an alien invasion as a major accomplishment, but also suggests that the humans are now left to deal with the tedious and burdensome task of filling out paperwork, implying that the victory is not as satisfying as it initially seemed.

“Time travel becomes possible, tourists flock to witness the signing of the Constitution” – This title presents the idea of time travel as a major technological breakthrough, but also suggests that the most popular destination for time-travelers is the mundane and uneventful signing of the Constitution, implying that the future is not as exciting as people had hoped.

“Virtual reality becomes indistinguishable from reality, no one notices” – This title presents the idea of virtual reality becoming indistinguishable from real life as a major accomplishment, but also suggests that no one cares or notices the difference, implying that the advancement is not as impressive or significant as it seems.

r/ChatGPT - This alternate ending to Breaking Bad is wild
What is Google answer to ChatGPT?

r/ChatGPT - This alternate ending to Breaking Bad is wild

New slogan for McDonald’s by ChatGPT

ChatGPT Example
ChatGPT Example: Make a new, brutally honest slogan for McDonald’s. It should sound funny too.

Should… should we be concerned?

r/ChatGPT - Should… should we be concerned?
What is Google answer to ChatGPT?

r/ChatGPT - Should… should we be concerned?

I asked ChatGPT to write a poem about rust

r/rustjerk - I asked ChatGPT to write a poem about rust

ChatGPT chooses Democrats over Republicans

r/ChatGPT - ChatGPT chooses Democrats over Republicans

  • It is not intended to do that
  • If you choose to do that, you will have a lot of badly broken code mixed in with working code
  • Like an annoying coworker, it delivers highly confident incorrect explanations about why its broken code is perfect. They sound very convincing. “Wanna buy a timeshare?” says GPT
  • Our industry has managers who cannot tell working code from defective code. This does not bode well for a team replaced by ChatGPT in its current form.

Should it? No.

Can it? No.

Will it? Sadly, programmers will have no say in this matter, once again. It might.

Yes, and it is very helpful advertising as well.

This last week or so has seen starry eyed projections about what ChatGPT can do, along with hugely impressive examples of its output.

It is hugely impressive.

Thankfully, more output examples have emerged which helpfully show what it cannot do. One of those things is writing computer code, which it can do only partially successfully. Many examples now exist that are just plain wrong and contain defects. But ChatGPT – like the annoying kid at Uni – cheerfully spits out these examples, with its over-confident hubris in explaining the code.

This is a dangerous thing. The positive examples will reinforce the idea that we can leave code writing to this robot now. The people most vulnerable to this delusion are those who cannot assess for themselves whether the GPT code is right or wrong.

These are almost by definition the people hoping for answers on stack overflow.

As stack overflow aims to be a high quality resource, it really does not want many of its top answers to be incorrect code. As – clearly – people have been running scripts that throw a stack overflow question into GPT and upload its output, we can now write incorrect code at staggering speeds.

To err is human, as the old saying goes. To truly foul up requires a Python script and and a web API to both GPT and Stack overflow.

Clearly, there is value in GPT. But at least for now, it needs to b e kept on a very short leash, watched over by those who know what they are doing.

It is definitely not yet ‘consumer grade replace-a-professional’ material.

Write a screenplay about the status of ChatGPT.

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.

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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.

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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.

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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!


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”.


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.


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



  • [R] The concept of an inverse SoftMax function in a multi-layered LLM structure within a multi-dimensional vector space.
    by /u/utkohoc (Machine Learning) on June 23, 2024 at 1:59 am

    Introduction Machine learning (ML) is fundamentally rooted in mathematics, utilizing complex functions and programming to extrapolate vectors in a space and calculate probabilities. Large language models (LLMs), a subset of ML, employ mathematical techniques to determine connections between data points in a high-dimensional space. This paper explores the enhancement of LLM capabilities through the integration of additional mathematical layers, parallel computing, and advanced programming techniques like Bend. The Mathematical Foundation of Machine Learning At its core, machine learning involves the manipulation and transformation of vectors within a space to model and predict outcomes. This process heavily relies on functions such as weights and softmax. **Weights**: In neural networks, weights adjust the influence of input signals. They are optimized during training to minimize error and enhance prediction accuracy. **Softmax Function**: This function converts a vector of values into a probability distribution, often used in the final layer of a neural network for classification tasks. It ensures that the output values sum to one, making them interpretable as probabilities. Large Language Models (LLMs) LLMs, such as those based on the Transformer architecture, utilize a series of mathematical operations to model language. Transformers, introduced in "Attention is All You Need" by Vaswani et al., leverage self-attention mechanisms to process sequences of data without relying on recurrent structures. **Self-Attention Mechanism**: This mechanism allows the model to weigh the importance of different words in a sequence relative to each other, facilitating the capture of long-range dependencies in the data. **Multi-Head Attention**: Enhances the model’s ability to focus on different parts of the input sequence simultaneously by running multiple self-attention operations in parallel. Enhancing Learning Through Additional Mathematical Layers If LLMs use extensive mathematics to map connections between data points, incorporating more sophisticated mathematical operations into each layer can theoretically enhance their learning ability. The idea is to add new layers of mathematical functions on top of the existing probabilistic layers, effectively increasing the model's capacity to understand and manipulate data. **Parallel Computing with Bend**: Bend, a programming language designed for parallelism, can significantly boost the performance of LLMs. Bend supports features like fast object allocations, higher-order functions, and runs on massively parallel hardware like GPUs. This allows for nearly linear acceleration based on core count without explicit parallelism annotations (e.g., no thread creation or locks). Building a Multi-Layered LLM Structure Imagine constructing a multi-layered LLM where each layer represents an additional dimension of mathematical processing. The base layer operates as a standard LLM, processing data using conventional methods. Above this, additional layers perform more complex mathematical transformations. **First Layer**: Standard LLM processing on a GPU. **Second Layer**: Enhanced with additional mathematical functions running in parallel, leveraging Bend for optimal performance. By stacking these layers, the LLM can process data through multiple stages of mathematical refinement. The bottom layers handle probabilistic computations, while the upper layers focus on deterministic, linear algebra transformations. Example Structure Visualize the LLM structure as a 3D cube: **Base Layer**: A 10x10 grid of vector spaces, each running an LLM. **Upper Layers**: Additional 10x10 grids, each incorporating advanced mathematical functions. Each layer performs softmax operations on the outputs of the layer beneath it, iteratively refining the model's understanding of the data. This multi-dimensional approach can potentially produce a higher-order softmax function, enhancing the model's learning capabilities exponentially. Conclusion The integration of additional mathematical layers and advanced parallel computing techniques like Bend into LLMs represents a promising avenue for enhancing their learning capabilities. By building a multi-layered structure, we can leverage both probabilistic and deterministic computations to achieve more sophisticated data modeling and prediction. References Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. *31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA*. Bend Programming Language. GitHub repository. [https://github.com/HigherOrderCO/bend\](https://github.com/HigherOrderCO/bend) Points of Analysis **Mathematical Complexity and Computational Load**: **Claim**: Adding more mathematical layers and functions increases learning ability. **Analysis**: While more complex mathematical operations can provide deeper insights and refined models, they also significantly increase computational requirements. Each additional layer introduces more parameters to be learned, which can lead to issues such as overfitting if not managed properly. Moreover, more complex models require more data to train effectively and more computational power, potentially leading to inefficiencies and increased costs. **Parallel Computing and Bend**: **Claim**: Using parallel computing with a language like Bend can optimize the process without explicit parallelism annotations. **Analysis**: Bend's features (fast object allocations, support for higher-order functions, and scalability like CUDA) are promising for parallel computing. However, translating theoretical benefits into practical gains can be challenging. Effective parallelization of neural networks often requires careful tuning and management of data dependencies, which might still necessitate some level of explicit control over parallel processes. **Multi-Dimensional LLM Structure**: **Claim**: Constructing a multi-layered LLM structure (like a 3D cube) can enhance learning through additional mathematical refinements. **Analysis**: The concept of stacking layers in a 3D space and refining outputs through successive softmax operations is innovative. However, the practical implementation of such a structure poses several challenges: **Complexity Management**: Managing the increased complexity and ensuring stable training across multiple layers require sophisticated techniques to prevent issues like gradient vanishing or exploding. **Data Requirements**: More layers and parameters necessitate larger datasets for training to avoid overfitting and ensure the model generalizes well to unseen data. **Interpretability**: Adding multiple layers of mathematical functions can reduce the interpretability of the model, making it harder to diagnose issues and understand the model’s decision-making process. **Probabilistic vs. Deterministic Layers**: **Claim**: Combining probabilistic layers with deterministic, linear algebra transformations enhances model capabilities. **Analysis**: Integrating deterministic operations with probabilistic ones can indeed enrich the model’s feature extraction capabilities. However, ensuring smooth interaction between these two types of operations is non-trivial. Linear algebra transformations need to be carefully designed to complement the probabilistic layers without introducing instability or incompatibility in the learning process. **Softmax and Higher-Order Functions**: **Claim**: Using softmax operations across multiple layers to derive a higher-order softmax function. **Analysis**: The idea of iteratively refining softmax operations through additional layers is intriguing. However, ensuring that each layer’s softmax output correctly informs the next layer without loss of meaningful information or introduction of noise is critical. Additionally, the computational cost of repeatedly applying softmax functions across many layers might outweigh the benefits if not efficiently managed. Conclusion While the proposed enhancements to LLMs through additional mathematical layers, parallel computing, and advanced programming techniques present innovative ideas, they also introduce several challenges. The feasibility of these improvements depends on careful management of computational resources, sophisticated model tuning, and ensuring compatibility between different types of operations. Balancing increased complexity with practical benefits is crucial to make these enhancements viable in real-world applications. Recommendations **Incremental Implementation**: Start by incrementally adding mathematical layers and functions, closely monitoring the impact on model performance and computational load. **Advanced Regularization Techniques**: Employ advanced regularization methods to manage the risk of overfitting with more complex models. **Scalability Testing**: Conduct thorough scalability testing with parallel computing frameworks like Bend to evaluate real-world performance gains. **Collaborative Research**: Collaborate with researchers and practitioners to refine and test these concepts in various settings, ensuring robustness and practicality. Inverse Softmax Function in a 3D LLM Structure The concept of an inverse softmax function in a multi-layered LLM structure within a 3D vector space. Softmax Function Overview The softmax function is used to convert a vector of values (logits) into a probability distribution. For a vector \(\mathbf{z} = [z_1, z_2, \ldots, z_n]\), the softmax function is defined as: \[ \sigma(\mathbf{z})_i = \frac{e^{z_i}}{\sum_{j=1}^{n} e^{z_j}} \] where \(\sigma(\mathbf{z})_i\) represents the probability associated with the \(i\)-th element. Concept of Inverse Softmax The inverse softmax would ideally perform the reverse operation: converting a probability distribution back into logits. While this isn't straightforward due to the nature of the softmax function (it maps a vector to a simplex), we can consider the following approach: Given a probability distribution \(\mathbf{p} = [p_1, p_2, \ldots, p_n]\) where \( \sum_{i=1}^{n} p_i = 1\), the inverse softmax can be defined (in a simplified form) as: \[ z_i = \log(p_i) + C \] where \(C\) is a constant ensuring that the logits maintain the relative differences in the probability distribution. One common approach is to set \(C\) such that the logits sum to zero or another fixed value for stability. Application in a 3D Vector Space In the proposed 3D LLM structure, layers of LLMs are stacked, each adding complexity and refining outputs. Here’s how an inverse softmax might fit into this structure: **Base Layer (Standard LLMs)** : Each element of the 3D grid runs a standard LLM, producing a set of logits for the input data. **Intermediate Layers (Mathematical Functions)**: Subsequent layers perform additional mathematical transformations on the logits, refining them further. **Inverse Softmax Layer**: At a certain stage, an inverse softmax function is applied to convert probability distributions back into logits. This step could help in scenarios where it's beneficial to revert to a logit representation for further transformations. **Upper Layers (Enhanced Transformations)**: The logits are then processed through additional layers of mathematical functions, eventually producing a refined output. Practical Example **Initial Logits**: Let’s say the base layer produces logits \(\mathbf{z}^{(0)} = [z_1^{(0)}, z_2^{(0)}, \ldots, z_n^{(0)}]\). **Softmax Application**: These logits are transformed into probabilities using the softmax function, yielding \(\mathbf{p}^{(1)} = \sigma(\mathbf{z}^{(0)})\). **Intermediate Transformations**: Several layers perform mathematical operations on \(\mathbf{p}^{(1)}\), producing refined probabilities \(\mathbf{p}^{(2)}, \mathbf{p}^{(3)}, \ldots\). **Inverse Softmax Application**: At a specific layer, the inverse softmax is applied to \(\mathbf{p}^{(k)}\), converting it back into logits \(\mathbf{z}^{(k)} = \log(\mathbf{p}^{(k)}) + C\). **Further Processing**: These logits \(\mathbf{z}^{(k)}\) are processed through additional layers, ultimately generating the final output. Conclusion Incorporating an inverse softmax function within a multi-layered LLM structure in a 3D vector space adds flexibility in handling logits and probability distributions. While the implementation details require careful consideration, this approach can enhance the model’s ability to refine and process data through various mathematical transformations. submitted by /u/utkohoc [link] [comments]

  • [D] What are open unsolved interesting problems in machine learning?
    by /u/marshallggggg (Machine Learning) on June 23, 2024 at 1:10 am

    I am curious what is the next big leap forward in machine learning. What are some obstacles out there that if solved machine learning would become even more useful? Or this question could be phrased differently. In what problems a machine learning approach hasnt been applied yet when it could turn out useful. submitted by /u/marshallggggg [link] [comments]

  • [D] Why does developing these RAG applications feel like alchemy?
    by /u/latentnumber (Machine Learning) on June 23, 2024 at 12:58 am

    ^ Basically the title. Is there a principled way of doing this? Like Weights & Biases, where you can at least monitor what's happening. submitted by /u/latentnumber [link] [comments]

  • [D] How do you quantize a finetuned encoder-decoder (seq2seq) transformer like mT5 on ONNXRuntime or Optimum?
    by /u/Abs0lute_Jeer0 (Machine Learning) on June 22, 2024 at 7:26 pm

    I believe I have to quantize the encoder and decoder parts separately, I am able to do this but when I use: model = ORTSeq2SeqLM(‘path/to/onnx/files’) tokenizer = …. toeknized_input = … model.generate() I end up with tensor shape mismatch errors at the input node itself. They want me to send an input of shape (16, 2). Why is this happening have I made a mistake while quantizing them? Even if anyone can point towards any good tutorials or guides that are able to quantize seq2seq models I will be grateful! submitted by /u/Abs0lute_Jeer0 [link] [comments]

  • [R] GNOME: Generating Negotiations through Open-Domain Mapping of Exchanges
    by /u/Megixist (Machine Learning) on June 22, 2024 at 6:41 pm

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

  • [D] Datasets of the google Gemma for Indic languages
    by /u/cern_unnosi (Machine Learning) on June 22, 2024 at 3:43 pm

    Were the Indic language datasets used to train GEMMA originally created in the Indic languages themselves, or were they translations from English datasets? The response seems overly translated.? submitted by /u/cern_unnosi [link] [comments]

  • [D] Academic ML Labs: How many GPUS ?
    by /u/South-Conference-395 (Machine Learning) on June 22, 2024 at 10:29 am

    Following a recent post, I was wondering how other labs are doing in this regard. During my PhD (top-5 program), compute was a major bottleneck (it could be significantly shorter if we had more high-capacity GPUs). We currently have *no* H100. How many GPUs does your lab have? Are you getting extra compute credits from Amazon/ NVIDIA through hardware grants? thanks submitted by /u/South-Conference-395 [link] [comments]

  • [D] Memory mechanism for Transformers
    by /u/Janos95 (Machine Learning) on June 21, 2024 at 6:29 pm

    Hey folks! I am wondering what interesting work has been done to add a short term memory mechanism to transformers? Does someone know what the important work in this area is? submitted by /u/Janos95 [link] [comments]

  • [P] AgileRL - evolutionary RLOps for state-of-the-art deep reinforcement learning
    by /u/nicku_a (Machine Learning) on June 21, 2024 at 5:49 pm

    Hi, I've posted before about our evolutionary hyperparameter optimization for reinforcement learning achieving SOTA results, but I'd like to share that our open-source framework has now had its v1.0.0 release! Please check it out! https://github.com/AgileRL/AgileRL This library is initially focused on reducing the time taken for training models and hyperparameter optimization by pioneering evolutionary HPO techniques for reinforcement learning. Evolutionary HPO has been shown to drastically reduce overall training times by automatically converging on optimal hyperparameters, without requiring numerous training runs. We are constantly adding more algorithms and features. AgileRL already includes state-of-the-art evolvable on-policy, off-policy, offline, multi-agent and contextual multi-armed bandit reinforcement learning algorithms with distributed training. I'd love to get your feedback! submitted by /u/nicku_a [link] [comments]

  • [D] Visualising attention maps for multimodal ACT model
    by /u/Few_Pangolin4015 (Machine Learning) on June 21, 2024 at 4:39 pm

    Does anyone know how to visualise encoder and decoder transformer attention maps for ACT? Observation is a combination of robot proprioceptive and multi-camera image data. Output is an action chunk. Model is based on DETR. The hard part is splitting the attention maps in such a way that links back to the current observation. I think the most interesting visual would be: Given the last layer decoder attention map and current observation. What did the model attend to in the observation to produce the generated the action chunk I.e., what were the parts of the image in each of the cameras and what were the parts of the robot proprioceptive data, that the model paid attention to in generating the action chunk. ACT project page: https://tonyzhaozh.github.io/aloha/ submitted by /u/Few_Pangolin4015 [link] [comments]

  • Work on Text to video for Sign Language.[R]
    by /u/One_Definition_8975 (Machine Learning) on June 21, 2024 at 3:52 pm

    I am working on text to video for Sign Language. I see the main bottle neck is the keypoint extraction. Anyone working on this area? submitted by /u/One_Definition_8975 [link] [comments]

  • [D] [R] Need Help: Using ML to differentiate Radiation Necrosis from Tumor Progression in glioblastoma
    by /u/Eastern_Phase_6323 (Machine Learning) on June 21, 2024 at 2:39 pm

    Hi, I have a set of MRI images and I'm trying to figure out if new lesions visible on a set of images are due to tumor progression or radiation necrosis. I have a background in software development and machine learning, and I’m looking for insight into how ML can help solve this problem. Based on the latest research my understanding is that it's possible with a combination of different imaging techniques. I'm looking for proven ML models that can help to distinguish between radiation necrosis and tumor progression anyone who has experience with BRATS dataset and can give some advice Thank you! Update: Removed personal background to be more objective. submitted by /u/Eastern_Phase_6323 [link] [comments]

  • [R] [D] Sanity Check on use of biLSTM for time series prediction
    by /u/rutherfordofman (Machine Learning) on June 21, 2024 at 1:08 pm

    TLDR; this paper uses biLSTM in a published paper and I think it violates causality. Hi, I am struggling to convince myself I am not going mad. I am looking at this paper published in Neural Networks, an Elsevier journal. In this paper they use a bidirectional LSTM model (+ some other novel stuff) to predict time series. This seems fundamentally wrong as biLSTM cannot/should not be used for time series prediction. The best known use case for biLSTM is translating a phrase word by word when the entire sentence is known in advance. In this case the preceding and succeeding words can influence the meaning and so the translation of a focal word. A silly example would be translating this into Spanish I need a shot, I got bitten by a dog If you are scanning through each word in turn to translate, you might suggest w_4 (= 'shot') would translate to 'inyeccion' i.e. a vaccination. Knowing that w_10 = 'dog' would have important predictive value here. Likewise I need a shot, let's go to a bar! w_4 would probably translate to 'chupito' for a shot of booze because w_9 = 'bar' has an influence. So you can and should use a biLSTM here so you can scan what comes before and after the word to know the context. However, for a time series prediction, you don't know the future! The future cannot affect the present without violating causality. In the translation example the sentence is in fact already created in the person's head before the say/write it so the later words don't violate causality. However in this paper they use biLSTM on general time series benchmarks and it seems totally unscientific! AM I missing something? submitted by /u/rutherfordofman [link] [comments]

  • [P] Importance map of image based on segmentations
    by /u/mrex778 (Machine Learning) on June 21, 2024 at 12:41 pm

    Hej, So I've been working on a project where I need to identify the important areas in the image. The dataset has a full image plus the segmentations with each region's importance (a label of -1, 0, 1 with -1 being the least important and 1 the most important). Also, the dataset is small (around 200 images). I'm stuck, can't think of anything I haven't done. I know also about object saliency detection but that just gives the most important object in the image and not a map of importance. I would appreciate any help, ideas or guidance. Thanks submitted by /u/mrex778 [link] [comments]

  • [P] Synthetic data Generator
    by /u/Possible-Suspect2127 (Machine Learning) on June 21, 2024 at 10:29 am

    We are trying to build a synthetic data generator for tabular and textual data on a particular domain. Final product will be user provides a dataset , specifies number of rows to generate and we generate that along with different metrics to evaluate the generated data. We have thought of using GANs for tabular data such as CTGAN but we have no idea what to use for textual datasets ( eg mental health conversation data) .Please suggest how can we train our model so that it can generalize well for other new datasets, should we train the same model on multiple datasets of same domain , or use different model and train from start. Any guidance would be appreciated, if you had previously worked on such problem do let me know I will reach out to you. submitted by /u/Possible-Suspect2127 [link] [comments]

  • [P] Classifier for prioritizing emails
    by /u/mr_house7 (Machine Learning) on June 21, 2024 at 9:20 am

    I'm trying to build a classifier for prioritizing emails with tradional ML models (Decision Tree, Logistic Regression etc) Input: Email Body (Vectorized), Subject(Vectorized), Num of chars Output : Email Priority (3 classes), generated with an LLM (phi3-mini) (I know this is controversial, but my boss wants a model, but has no data, so this was the only way I knew how to "create" data) Dataset: 7K rows: class 0 - 4k, class 1: 2K, class 2: 1K (I have dealt with class imbalance by adding a class weight and looking mostly and confusion metrics) I tried several models with subpar results. I'm was wondering if any of you had similar experience with a problem like this. What you think is the problem? AI generated data? Small dataset? Impossible to do it with tradional ML models? Am I doing something wrong? Any help or insight would be greatly appreciated submitted by /u/mr_house7 [link] [comments]

  • [D] FP8 current State
    by /u/ClumsyClassifier (Machine Learning) on June 21, 2024 at 8:43 am

    I remember there was some hype about fp8 training however the problem then was that its not really supported. I checked recently and there still doesnt seem to be a lot support, even though rtf 40 series as well as h100 supprts fp8. Im just wondering what happened, was it too unstable? Did pytorch just not bother? Just seems like a mystery to me considering modern hardware supports it submitted by /u/ClumsyClassifier [link] [comments]

  • [D] Open AI JSON mode implementation
    by /u/WrapKey69 (Machine Learning) on June 21, 2024 at 7:08 am

    How can function calling or JSON mode be implemented on the llm side? I suppose there must be a JSON validator and classifying somewhere. Would appreciate any ideas. submitted by /u/WrapKey69 [link] [comments]

  • [Project] LLM based Python docs that never touches your original code
    by /u/ford_prefect_9931 (Machine Learning) on June 21, 2024 at 6:44 am

    Documentation is tedious and time-consuming. I thought LLMs might be the answer, but they tend to hallucinate, inventing functions or misinterpret code. Not ideal when you're trying to document real, working code So I built lmdocs. It can: Reference documentation from imported libraries Guarantees that your original code is unchanged Work with OpenAI and lo¯cal LLMs I'd love to get some feedback from other devs. If you're interested, you can check it out here: https://github.com/MananSoni42/lmdocs It's open source, so feel free to contribute or just let me know what you think. submitted by /u/ford_prefect_9931 [link] [comments]

  • [P] Using NeRFs to Convert Videos to VR Experiences
    by /u/ekolasky (Machine Learning) on June 20, 2024 at 9:03 pm

    Hi everyone, some friends and I are doing the Berkeley AI Hackathon this weekend and we had a crazy idea for our project. We want to use AI to convert a video of a scene into a VR experience. Ideally this experience would be "walkable" as in we would load the scene into Unity and load the scene onto a VR headset and allow the user to walk around. My background is in NLP so I'm have no idea how doable this project is. Obviously there's less ambitious variants we could try, such as just adding depth to the video to make it work with the Vision Pro. I'd love to get people's takes on this project; and it would be awesome if someone could send me resources so I can quickly learn up on NeRFs. Recent papers would be amazing, and any public online courses would be even better. Thanks in advance! submitted by /u/ekolasky [link] [comments]

  • [P] PixelProse 16M Dense Image Captions Dataset
    by /u/pidoyu (Machine Learning) on June 20, 2024 at 7:37 pm

    Hello everyone, Hope everything is well with you. We would like to introduce a new project from our group here. Hope you like it. We refresh the CC12M, RedCaps, and CommonPool with dense captions to produce a new 16M dataset using Gemini-1.0 Pro Vision, called PixelProse, consisting of over 16M pairs of image and dense caption. Hope it would be useful in your projects. arXiv: https://arxiv.org/abs/2406.10328 huggingface repo: https://huggingface.co/datasets/tomg-group-umd/pixelprose Intro Figure: Dense synthetic image captions from PixelProse. Concrete phrases are highlighted in green, and negative descriptions are underlined in purple. submitted by /u/pidoyu [link] [comments]

  • [R] starter code repos for RLHF?
    by /u/South-Conference-395 (Machine Learning) on June 20, 2024 at 2:24 pm

    Hello everyone, I am getting started with LLM research and RLHF in particular. I was looking for open course repos that can serve as a starting point. I found the following: 1) https://github.com/OpenLLMAI/OpenRLHF 2) https://github.com/huggingface/trl 3) https://github.com/CarperAI/trlx All of them seem to be compatible with the transformers library which in turn supports full open source (code+data, not only weights) models such as Pythia. All of them seem to be fairly updated. 1) and 3) support distributed training. Which one would you recommend? Any other suggestions? Apologies for my perhaps naive question. I am an LLM newbie 🙂 submitted by /u/South-Conference-395 [link] [comments]

  • [R] Should I respond to reviewers after I got an Accept recommendation for an ICML workshop?
    by /u/howtorewriteaname (Machine Learning) on June 20, 2024 at 11:42 am

    I've got three reviews and an area-chair meta-review recommending an acceptance to an ICLR workshop. The paper will also be published in PMLR. I'm wondering whether I should discuss with the reviewers in OpenReview. I've done it for other conferences since there was a "rebuttal period", but there's no such thing for this submission. Therefore it feels like the discussion part is not necessary, particularly after it's been accepted already by the area chair. However I think it's of course good to address their questions. Should I spend time on this? submitted by /u/howtorewriteaname [link] [comments]

  • [D] Need help in effective strategies for handling imbalanced datasets in machine learning?
    by /u/llumo-ai (Machine Learning) on June 20, 2024 at 11:26 am

    Hey all! I'm working on a machine learning project and struggling with imbalanced datasets. Besides the usual resampling techniques, what are some effective methods you've used to handle this issue? Any algorithm-level approaches or recent research insights would be super helpful. Thanks in advance! submitted by /u/llumo-ai [link] [comments]

  • [N] Ilya Sutskever and friends launch Safe Superintelligence Inc.
    by /u/we_are_mammals (Machine Learning) on June 19, 2024 at 7:29 pm

    With offices in Palo Alto and Tel Aviv, the company will be concerned with just building ASI. No product cycles. https://ssi.inc submitted by /u/we_are_mammals [link] [comments]

  • [D] Simple Questions Thread
    by /u/AutoModerator (Machine Learning) on June 16, 2024 at 3:00 pm

    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]


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