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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.
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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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!
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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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.
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:
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➡️ 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
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?
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.”
When individuals need an information or have a problem/concern, they turn to Google for immediate solution. We sometimes wish, Google could understand what exactly we need and provide us instantly rather than giving us hundreds of thousands of results. Why can’t it work like the Iron Man’s Jarvis?
However, it is not that far now. Have you ever seen a Chat Bot which responds like a human being, suggest or help like a friend, teach like a mentor, fix your code like a senior and what not? It is going to blow your mind.
Welcome to the new Era of technology!! The ChatGPT!
ChatGPT by OpenAI, uses artificial intelligence to speak back and forth with human users on a wide range of subjects. Deploying a machine-learning algorithm, the chatbot scans text across the internet and develops a statistical model that allows it to string words together in response to a given prompt.
As per OpenAI, ChatGPT interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
What all ChatGPT can do?
It can help with general knowledge information.
Remember what user said in previous conversation.
Allow users to provide follow-up corrections.
Trained to decline inappropriate requests.
It can write a program in any language you prefer on real-time. for example — write classification code sample in sklearn python library.
It can fix your piece of code and also explain what went wrong and how it can be fixed.
It can even generate song or rap lyrics
Even much more….
Some best usages of ChatGPT:
Make a diet and workout plan
Generate the next week’s meals with a grocery list
Create a bedtime story for kids
Prep for an interview
Solve mathematical problem
Fix software program or write a program
Plan your trip and tell expected expenses
What are its limitations of ChatGPT?
May occasionally generate incorrect information
May occasionally produce harmful instructions or biased content
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.
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?
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.
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.
What is Google answer to ChatGPT?
New slogan for McDonald’s by ChatGPT
ChatGPT Example: Make a new, brutally honest slogan for McDonald’s. It should sound funny too.
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.
Will it? Sadly, programmers will have no say in this matter, once again. It might.
Isn’t Stackoverflow advertising ChatGPT when it bans it and then making numerous posts about why it banned it? By Alan Mellor
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.
How powerful is OpenAI’s new GPT-3 deep learning model? By
“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 does ChatGPT give incorrect and unreliable results to simple arithmetic problems (e.g. it gave me three different incorrect answers to 13345*6748)? We’ve had software that can accurately do arithmetic for decades, so why can’t an advanced AI? By Richard Morris
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.
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/
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.
How does OpenAI approach the development of artificial intelligence?
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.
How valid is OpenAI chief scientist’s claim that advanced artificial intelligence may already be conscious? By Steve Baker
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.
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
What is the future of web development after ChatGPT? Will programmers lose their jobs? By Victor T. Toth
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.
How much was invested to create the GPT-3?
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.
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.
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.
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.
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:
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.
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.
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.
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…
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.”
I'm putting together a new job board dedicated exclusively to internships in AI. I think it could be a great resource for AI-minded college students, recent graduates, and even experienced professionals looking for a new challenge. I'd love to get some feedback on the job board. So I can know how to improve it! There's about 40 openings at the moment but I'm planning to add more each day, with a focus on larger companies Any feedback is welcome and I hope that some people find this useful https://interninai.com/ submitted by /u/Mutagenfq [link] [comments]
I’m working on some transformer related problems and I was trying to implement ALiBi from scratch. I have a specific question about what the alibi mask should look like. The paper says something, but my understanding of the code says something else. Let’s say we generate an attention mask using ALiBi for a sequence length of 4 (small for visualization purposes). Which of the following options is the correct one (first head only): Option 1: [[0.0, -inf, -inf, -inf], [0.0, 0.25, -inf, -inf], [0.0, 0.25, 0.5, -inf], [0.0, 0.25, 0.5, 0.75]], Option 2 (Diagonal of 0s): [[0.0, ,inf, -inf, -inf], [0.0, 0.0, -inf, -inf], [0.0, 0.25, 0.0, -inf], [0.0, 0.25, 0.5, 0.0]], From reading the paper I believe it should be option 2, from my trying to follow what the code implementations is doing, I believe it should be option 1. The only thing different is the 0s diagonal. Thanks for the help. submitted by /u/AlternativeDish5596 [link] [comments]
Perhaps, one of you can chime in and explain to me something. Can we really rely on some written code/program to determine whether or not something is AI generated? I sense a paradox here. I mean, theoretically AI can start generating or subverting platforms that are setup for the purpose of regulating it. It just doesn't seem that far fetched of an idea. Am I missing something? Many thanks for any clarification! submitted by /u/manohell [link] [comments]
Hey! I want to show you my new Artificial Intelligence (AI) project, which is designed to help people learn English. I created it because I had trouble learning English, and I wanted to make something that would help others like me. My project is a website called "Learn English with Subtitles". It uses AI to analyze TV show transcripts and give you a list of difficult words and phrases from each episode. I think it can really help you learn new English words and phrases easily. I made the website very simple to use, and it's completely free and ad-free. You can visit my website, try it out, and let me know what you think! Your feedback is very important to me, as I want to improve my project and help as many people as possible to learn English. Thank you for your time! Click to visit my site! submitted by /u/Emergency-Ad6863 [link] [comments]
I want to make a game focused on Machine Learning. I've been searching about the two engines without reaching a conclusion. I ask you to guide me here. I already know the basics of Unity, C# and Python. I don't mind studying a new engine or language like c++. Which of the 2 engines is best for ML? I know it's a little vague question, but please, try to give me some highlights of the resources of both engines to help me picking the best path. I'm specifically looking for what will make my life easier in the long run. submitted by /u/felipebsr [link] [comments]
[D] Hello, everyone. I watched an explanation on the use of diffusion models for creation of 2d images. I just wonder, I think we are somewhat far away from 3d model generation. First, I think it would be much more computationally expensive. Second, I am not sure whether we have such a large set of training data. And third, the input and output that we have in 3d graphics is somewhat different from pixels, i.e. we are working with triangles in 3d graphics (maybe this is not as hard, as we can always start with vertices and then estimate triangles. What's your take on that? submitted by /u/konstantin_lozev [link] [comments]
Sure, maybe I can afford to go about my life normally for the next 5-10 years without changing anything, but I'd still like a headstart to be safe so i can adapt in the event things get crazy I'm currently studying as an English major and my career is kinda up in the air right now. I'm more of a creative/artistic type so I'd prefer not to get a tech or AI-related job but I guess it depends how things look when I graduate? My best case scenario is just using AI to the best of my ability in my creative work so I can keep up with whatever else the industry/market is doing Websites, courses, articles, videos or even advice are welcome 🙂 submitted by /u/jesster_0 [link] [comments]
Now that the author-reviewer discussion period for ICML 2023 has ended, it seems like it is up to the meta reviewers to decide. Let us discuss our experiences with the revised process. The general consensus I have seen online is that there were more low quality / absent reviewers than usual, but it is unknown how common it was. For authors, how were your reviews, and how was the author-reviewer period? Did your scores change? Was anything off about your review? I’ll start: I got one terrible score and one borderline score. The terrible score reviewer made basic factual errors in their criticism. No follow up after rebuttal. Also note we were unable to get a third reviewer. submitted by /u/Optimal-Asshole [link] [comments]
Auto-Analyst leverages power of cutting-edge Large Language Models (LLMs) to revolutionize data analytics. This powerful UI tool simplifies the data analysis process, eliminating the need for complex coding. 🔎 Key Features of Auto-Analyst: Streamlined data analysis process utilizing advanced AI technology and LLMs Enhanced productivity and efficiency through intuitive language-based commands Increased accessibility to data analysis for professionals across industries 🔗 Want to explore and contribute to the project? Head over to the GitHub repo: https://github.com/aadityaubhat/auto-analyst submitted by /u/aadityaubhat [link] [comments]
Hello, I searching for an AI tool for image editing with a specific skillset and I figured this would be the best place to ask. I'd want to find a tool that can take part of one image and put it, in a coherent manner, inside of another image. For exemple take a picture of a friend and put it a screenshot of videogame in a way that contextually makes sense (matching artstyle and lighting and all that) Does such a tool exist ? I've seen papers talkling about that but idk if it's made it's way into public use yet. Thanks for your help ! submitted by /u/Gauwal [link] [comments]
I was surprised to learn I'm in the minority of people with access to the plugins. I signed up on the waitlist the day it was announced and received notification of my access the same day. I have been a paid user for about a month. The plugins I have access to are: Speak Milo Family AI Expedia KAYAK OpenTable Instacart FiscalNote Klarna Shopping Wolfram Shop Zapier You can have up to 3 plugins running at the same time in a single chat instance. You cannot have both Kayak & Expedia running together in the same instance. I'm not aware of any other restrictions like that. I haven't ever used or set up Zapier so we probably can't test that (and since it accesses my personal information I could not share results). Give me your requested plugins and prompt and I'll provide the results. edit: I haven't figured out how to use Zapier. You need to connect ChatGPT to Zapier via the openai API key. I think you have to set up a Zap that pings ChatGPT? If any Zapier users can chime in so I can test, please let me know. submitted by /u/JetbatUnleashed [link] [comments]
It's got a near perfect sounding verse from Kanye West, which he never wrote or recorded. Scary as hell. https://www.youtube.com/watch?v=jPneAh2CZaQ submitted by /u/trillmill [link] [comments]
Problem statement: I have a sparse directed graph (about 6000-10000 nodes) with no node attributes, and numerical edge values. (The edge values are calculated by the same program based on data regarding the nodes, based on a statistical formula, if it's important) Goal: I want to find paths within the graph that have significantly higher edge values than the rest of the paths' edges (edge values are relative). I thought about graph clustering and partitioning but don't care about how highly connected a particular node is, and from my (elementary) understanding, these methods are not really well-suited for paths. I thought about doing a variation of iterative deepening search that starts on every node that has 0 incoming edges (and terminates when the last explored node has a small number of outgoing edges with small edge values), but these first edges that the search encounters may have smaller values than edges further down the paths, so if I use a traditional search algorithm, it would have to recursively update the start node for some iterations to reach the goal state, which is a path with all edges having edge values larger than other paths in the graph. As an extension, perhaps node characteristics (such as number of outgoing edges and their edge values) could be used as a heuristic? Also, the whole graph needs to be explored, and edge values are relative to each other so the comparison between different paths has to be relative. Is anyone aware of a search method like this, or another method that may be suitable? submitted by /u/austinkunchn [link] [comments]
This white paper is still being edited. I came up with this back on 3/19, and then the bombshell GPT-4 paper hit, and basically blew me out of the water. I still think I have some improvements and specificity that they didnt cover, in regards to the creation of Identity and benefits of multi-model friction to create better performanc. I will also be releasing my notes on something I call “Modal-ID’s” which were basically plugins until OpenAI released plugins immediately after I came up with this! Haha. Hope you enjoy! Recombinant AI: Creating Dynamically Contextualized Modular AGI Environments in Lower-Dimensional Space Abstract In this paper, I introduce Recombinant AI. By leveraging pre-trained language models, such as GPT-4, a recombinant contextual learning loop, and efficient indexing techniques like Hierarchical Navigable Small World (HNSW) Graphs, we are able to generate AI modules that when sufficiently robust, will inherently (with human input and direction) begin to function as distinct entities with their own knowledge, conversational history, and personality guidelines. The proposed framework allows for the creation of powerful and interactive AI applications, with the potential to enhance user experiences across various domains, including, but not limited to: Interactive storytelling customer support personalized AI assistants. Instantly customizable solutions In this context, I discuss the underlying principles, implementation details, and potential applications of Recombinant AI, drawing comparisons to existing methodologies, and highlighting unique solutions, challenges, and opportunities. Additionally, I will explore the impact of real-time adaptation and indexing, combined with a recombination flow, allowing AI modules to learn immediately from user interactions and commit these lessons to improve their performance over time. By integrating state-of-the-art language models with advanced indexing and retrieval techniques, Recombinant AI represents a promising new direction in the pursuit of dynamic and versatile AGI systems.It’s important for me to note that this methodology is not meant to supplant fine-tuning of an LLM. In fact, I believe this framework not only augments current fine-tuning strategies, but is itself strengthened by the utilization of fine-tuned external LLMs. However, I do believe that this presents the potential for a more flexible, dynamic, and accessible approach to model customization and improvement by an order of magnitude. My approach to this involves 3 main components. Introduction Recombinant AI builds upon existing systems, but aims to revolutionize the development of artificial general intelligence (AGI) systems by harnessing the power of pre-trained language models and lower dimensional indexing techniques. With the advent of increasingly sophisticated language models like GPT-4, the potential to create dynamic and modular AGI environments has never been more promising. In this section, we provide an overview of the key ideas behind Recombinant AI, illustrating its unique features, advantages, and potential applications. The primary goal of Recombinant AI is to create distinct AI modules, each with its own knowledge base, conversational history, and personality guidelines. These modules can be seen as AGI "game cartridges" that can be loaded and interacted with on-demand, allowing users to engage with highly customizable AI applications that cater to specific needs and preferences. To achieve this, Recombinant AI relies on two main components: pre-trained language models and efficient lower dimensional indexing techniques, such as Hierarchical Navigable Small World (HNSW). By combining these components, we can create highly scalable and adaptable AI modules that learn and evolve through user interactions. [CONTENT HERE: An illustration demonstrating the interaction between pre-trained language models, lower dimensional indexing, and AI modules in the Recombinant AI framework.] In the following sections, we delve deeper into the methodology, implementation details, and potential applications of Recombinant AI, exploring the unique challenges and opportunities it presents. We also discuss how the framework can adapt in real-time, allowing AI modules to learn from user inputs and improve their performance over time. Through its innovative approach to AGI development, Recombinant AI has the potential to transform a wide range of industries, from interactive storytelling and customer support to personalized AI assistants and AI-driven gaming. By offering dynamic, modular, and scalable solutions, Recombinant AI paves the way for a new era of interactive and versatile AI applications. Methodology and Implementation In this section, we delve into the methodology and implementation details of Recombinant AI, providing an in-depth explanation of the key components, processes, and techniques involved in creating dynamic and modular AGI environments. We will discuss the role of pre-trained language models, lower dimensional indexing techniques, and prompt chaining strategies, as well as provide code examples and tables to illustrate the practical application of the framework. 2.1 Pre-trained Language Models Recombinant AI leverages the power of pre-trained language models like GPT-4 to generate context-aware embeddings and responses. These models have been trained on vast amounts of text data, making them capable of generating coherent and contextually relevant text based on user inputs. [CONTENT HERE: A table comparing different pre-trained language models, such as GPT-4, BERT, and RoBERTa, highlighting their key features, performance metrics, and suitability for various applications.] 2.2 Lower Dimensional Indexing Techniques Efficient lower dimensional indexing techniques, such as Hierarchical Navigable Small World (HNSW) Graphs, Sparse Priming, and Clustering, play a crucial role in Recombinant AI. These techniques enable the framework to efficiently store, retrieve, and update AI module knowledge bases, conversational histories, and personality guidelines. HNSW is a graph-based indexing technique that allows for fast and accurate nearest neighbor searches in high-dimensional spaces. It is particularly well-suited for Recombinant AI due to its scalability and adaptability. Add definitions [CONTENT HERE: A diagram illustrating the structure and search process of an HNSW index, showing the hierarchical organization of nodes and the process of traversing the graph to find the nearest neighbors.] 2.3 Prompt Chaining Strategies Prompt engineering and chaining enables the framework to systematically and consistently process simple input prompts into complex, reasoned outputs. The process involves crafting a programmatic data flow through inputs, catalyst indices or code, into desired outcomes that guide the language model through a specific line of reasoning or inquiry, resulting in a coherent and context-aware response. [CONTENT HERE: An example of a prompt chain for a Dungeon Master AI module, illustrating the process of guiding the language model through a series of prompts to generate a coherent and contextually relevant response. Backend system prompt from the initial user message spins up the Dungeon Master RAI. Base index of the user’s conversational history, as well as the appropriate system role index are analyzed by the LLM…. 2.4 Code Examples and Implementation Details To better illustrate the practical application of Recombinant AI, we provide code examples that demonstrate the process of creating and interacting with AI modules. [CONTENT HERE: A code snippet showing the implementation of an HNSW index, embedding generation using GPT-4, and the process of querying the index based on user input.] [CONTENT HERE: A code snippet demonstrating the implementation of prompt chaining strategies to generate contextually relevant responses from the language model based on user input and module context.] By combining these components and techniques, Recombinant AI creates a dynamic, modular, and scalable framework for AGI development, enabling the creation of highly customizable AI applications that adapt and learn through user interactions. In the next section, we explore the potential applications and use cases of Recombinant AI, as well as discuss the challenges and opportunities it presents. submitted by /u/DrawUnited9003 [link] [comments]
I ran into an issue where when I ran out of my 25 GPT-4 messages, I clicked the option to continue with the older model. Once I had timed back into being able to use GPT-4, there was no way to switch the conversation back over. I eventually figured out how to switch back using firefox's inspect tool. This should be possible in other browsers but the step-by-step will be different. In the conversation you want to upgrade to GPT-4, right click anywhere and choose inspect. Along the new top bar, click on network. Now, type a new message into the chat interface and send it. doesn't matter what the message is. You'll see some new lines fill in the box at the bottom of your browser. One should be a POST with the file name "conversation". Right click it. On the left a new box opens. Scroll to the bottom, to the Body section. Now, scroll all the way to the right of that body, you should see near the end something like: "model":"text-davinci-002-render-sha". Change this to read: "model":"gpt-4". Click send. Wait a minute. Possibly it will seem like nothing is happening, that's ok. Once you've waited a minute close the inspect thingy with the X in the far right corner and refresh the page. Your new message and reply should now be at the end of the chat and you should be back in GPT-4 mode. submitted by /u/kinkykusco [link] [comments]
Inspired by some threads on this subreddit, I copy/pasted Google Bard and ChatGPT's responses to each other, and they got pretty ruthless. I then threw the responses in Synthesia, used Midjourney to generate a background, and found a freestyle beat. LMK if you want me to run another battle. https://www.youtube.com/watch?v=WxUfda63UNw submitted by /u/Affectionate_Sky2717 [link] [comments]
Will A.I. bring in a new golden era? Will we have tons of scientists and engineers doing all sorts of cool stuff? We will have tons of new medical research going on, done in a fashion that is inarguably effective, like conducting the same study across the glob several times? We will have resources to convert our deserts into lush tropical oasis, by digging and letting the sea into the deserts? We will be able to create lots of islands? Will we terraform venus? Our imaginations are our biggest limiting factor. submitted by /u/Terminator857 [link] [comments]
Levi’s Will Use AI Generated Models To Sell Clothes In a move that the company describes as making the shopping experience more 'personal' and 'inclusive,' the hyper-realistic models will be of every body type, age, size, and skin tone. How would you react to knowing that the images you are seeing are AI Generated models? View Poll submitted by /u/daihlo [link] [comments]
We are excited to share our latest findings in predicting finger movement and pressure using machine learning. The results show that our model is capable of predicting the finger movement within a Mean Absolute Error (MAE) of 25, which is a sufficient level of accuracy for detecting both the finger movement and the pressure applied. Predicted vs Actual The system is comprised of a bracelet and label system that captures the data to feed into an artificial neural network. Bracelet in the background with the LASK label system in the foreground. These screenshots showcase a portion of the data file available for download, which contains the actual and predicted finger movement and pressure values. Our model not only indicates that a finger is moving but also estimates the amount of pressure being applied, providing valuable insights into the intricacies of finger movements. This achievement opens up new possibilities for applications that require precise finger movement and pressure detection, such as in rehabilitation therapy, robotics, and gesture-based user interfaces. We invite you to download the full data file and explore the results in more detail. As we continue to refine our model and improve its accuracy, we look forward to discovering new ways to utilize this technology for the betterment of various fields and industries. All data to train the model and code available on our Github: https://github.com/turfptax/openmuscle https://www.youtube.com/watch?v=ZC1migPdiRk Open Muscle Bracelet. submitted by /u/turfptax [link] [comments]
I have an image I want to convert to an illustration. Would prefer platforms where I can upload an image and mention what I want the AI to do with it, rather than describing the entire thing. Any recs? submitted by /u/badbitch-sadbitch [link] [comments]
Language model directly trained on machine code as data. Can it be a more efficient way to get AI to write program? Would it enable them to eventually alter there own source code? submitted by /u/Haydern2019 [link] [comments]
I don't know if r/learnmachinelearning is a better fit for this, but I thought I'd raise a discussion here as well. I'm doing some research on depth images, and my models keep collapsing to a suboptimal value. Shallower networks converge to a model that predicts a nearly constant prediction (not necessarily the mean) regardless of the input data. Deeper networks will overfit after reaching this stage. No matter what architecture I use, my validation performance never gets better than the constant prediction. On the data - my inputs are (x,y,z) coordinates of 17 points sampled from a depth image from two different perspectives. I am attempting to predict 45 values from these coordinates (each normalized be bounded from 0 to 1). I'm effectively using Openpose to downsample an image and predict some parameters from it. My dataset is 3000 samples and I'm using the regular 80-20 train-test split. This data is synthetically generated and takes a long time to create (~24 hrs for 3k samples), so I want to make sure I don't have any fundamental issues before committing more time to generate more samples. Things I've tried that haven't worked - network depth (deeper networks can at least overfit but can't generalize), reducing the output dimensions (no change in loss), normalizing the inputs to standardize the coordinates (no change in loss). Any recommendations/advice? I've been stuck on this for some time and I suspect a fundamental issue is present, or I'm missing something critical/obvious. I've checked the data and the training inputs/targets are fine as well. Thanks! submitted by /u/U03B1Q [link] [comments]
The Horizon Video Game series really sparked my love for AI with the use of GAIA and it got me thinking about the current AI possibilities. While I know something like GAIA is unattainable as we currently stand because we are unaware of what "sentient" means and what makes thoughts and feelings is too complex. I am wondering how far away would something like GAIA be? Would it be possible to tell a model these characteristics make you sad because xyz. These make you feel happy because abc. Then feed positive information into the model referencing it to happy, opposite for sad, slowly developing a correlation between the two? With the expansion of quantum computing I feel like we could now begin to combine that with the creation of a super powerful AI. The amount of processes quantum computing has would allow us to feed an AI MASS amounts of data. It would be able to learn at speeds unseen. It would be dumb today and fairly smart in 20 minutes. We probably couldn't feed it enough data to keep up. Maybe the issue is asking questions for itself to seek out further knowledge? Supposedly the developer from Google claimed their AI is sentient so maybe were further along than the government wants the public to know to limit panic? I can't wait for the day I can have my own personal AI like GAIA that I can befriend. Go buy a dumb AI in the store, program it, feed it info, teach it almost like a child and then it will slowly destroy my life and take over but hey that's not for a long time right? submitted by /u/Webb2312 [link] [comments]
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/