GPT-4 Tutorial: Unveiling Infinite AI Possibilities GPT-4实战教程：探索下一代人工智能的无限可能

yeah so thank you Ted um my name is so I'm a graduate student in the digital Humanities at McGill I study literature and computer science and Linguistics in the same breath and I've published some research over the last couple of years exploring what is possible with language models and culture in particular and my half or whatever of the presentation is to describe to you what is GPT that's really difficult to explain in 15 minutes and there are even a lot of things that we don't know but a good way to approach that is to First consider all the things that people call GPT by or descriptors so you can call them large language models you can call them Universal approximators from computer science you can say that that it is a generative AI we know that they are neural networks we know that it is an artificial intelligence to some it's a simulator of culture to others it just predicts text it's also a writing assistant if you've ever used chatpt you can plug in a bit of your essay get some feedback it's amazing for that it's a Content generator people use it to do copywriting jasper.ai pseudorite Etc it's an agent so the really hot thing right now if you might have seen it on Twitter Auto GPT baby AGI people are giving these things tools and letting them run a little bit free in the wild to interact with the world computers etc we use them as chat Bots obviously and the actual architecture is a Transformer so there's lots of ways to describe GPT and any other one of them is a really perfectly adequate way to begin the conversation but for our purposes we can think of it as a large language model and more specifically a language model and a language model is a model of language to if you allow me the tautology but really what it does is it produces a probability distribution over some vocabulary so let us imagine that we had the task of predicting the next word of the sequence I am so if I give a neural network the words I am what of all words in English is the next most likely word to follow that at its very core is what GPT is trained to answer and how it does it is it has a vocabulary of 50 000 words and it knows roughly given the entire internet which words are likely to follow other words of those 50 000 in some sequence up to two thousand words up to four thousand up to eight thousand and now up to thirty two thousand three pt4 so you give it a sequence here I am and over the vocabulary of fifty thousand words it gives you the likelihood of every single word that follows so here it's I am perhaps the word happy is fairly frequent so we'll get that high probability if we look at all words all utterances of English it might be I am sad maybe that's a little bit less probable I am school that really should be at the end because I don't think anybody would ever say that I am Bjork that's a little bit it's not very probable but it's less probable than happy sad but there's still some probability attached to it and when we say it's probable that's literally a percentage that's like happy follows I am maybe like five percent of the time sad photos I am maybe two percent of the time or whatever so for every word that we give GPT it tries to predict what the next word is across 50 000 words and it gives every single one of those fifty thousand words uh number that reflects how probable it is and the Really magical thing that happens is you can generate new text so if you give GPT I am and it protects happy as being the most most probable word over 50 000 you can then append it to I am so now you say I am happy and you feed it into the model again and you sample another word you feed it into the model again and again and again and again and there's lots of different ways that I am happy I am sad can go and you add a little bit of Randomness and all of a sudden you have a language model that can write essays that can talk and a whole lot of things which is really unexpected and something that we didn't predict more even five years ago so this is all relevant and if we move on as we scale up the model and we give it more compute in 2012 Alex that came out and we figured out we can give the model uh we can run the model on gpus so we can speed up the process we can give the model lots of information downloaded from the internet and it learns more and more and more and the frequent the probabilities that it gives you get better as it sees more examples of English on the internet so we have to train the model to be really large really wide and we have to train it for a really long time and as we do that the model gets more and more better and expressive and capable and it also gets a little bit intelligent and for reasons we don't understand so but the also the issue is that because it learns to replicate the internet it knows how to speak in a lot of different genres of text and a lot of different registers if you begin the conversation like chat GPT can you explain the moon landing to a six-year-old in a few sentences gpt3 this is an example drawn from the instruction BT paper from openai gpt3 would have just been like okay so you're giving me an example like explain the moon landing to a six-year-old I'm going to give you a whole bunch of similar things because those seem very likely to come in a sequence it doesn't necessarily understand that it's being asked a question has to respond with an answer gpt3 did not have that apparatus that interface for responding to questions and the scientists at openai came up with the solution and that's let's give it a whole bunch of examples of question and answers such that we first train it on the internet and then we train it with a whole bunch of questions and answers such that it has the knowledge of the internet but really knows that it has to be answering questions and that is when chat GPT was born and that's when it gained 100 million users in one month I think it'd be tick tock's record at 20 million in one month it was a huge thing and for a lot of people they went oh this thing is intelligent I can answer I can ask it questions it answers back we can work together to come to a solution and that's because it's still predicting words it's still a language model but it knows to protect words in the framework of a question and answer so that's what a prompt is that's what instruction tuning is that's a key word that's what rlhf is if you've ever seen that acronym reinforcement alignment with human feedback and all those combined means that the models that are coming out today the types of language predictors that are coming out today work to operate in a q a form gpt4 exclusively only has the Align model available and this is a really great solid foundation to build on because you can do all sorts of things you can ask Chachi PT can you do this for me can you do that for me you might have seen that open AI has allowed plug-in access to chat CPT so it can access Wolfram it can search the web it can search it can do instacart for you it can look up recipes once the model knows that not only it has to predict language but that it has to solve a problem and the problem here being give me a good answer to my question it's suddenly able to interface with the world in a really solid way and from there on there's been all sorts of tools that I build on this q a form that chatgpt uses you have Auto GPT you have Lang chain you have uh react there was a react paper where a lot of these come from and turning the model into an agent with which to achieve any ambiguous goal is where the future is going and this is all thanks to instruction tuning and with that I think I will hand it off to Ted

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Keywords: GPT-4, AI tutorial, Next-gen AI, Artificial Intelligence, Language models, Text generation, Writing assistance, Content creation, Neural networks, AI technology, AI capabilities, ChatGPT, AI principles, AI applications, AI integration, AI tools, AI advancements, AI exploration, AI learning, AI in daily life, AI future, AI strategies, AI education, AI writing, AI techniques, AI creativity, AI research, AI performance, AI optimization, AI assistance

Id: x-h3HPe8p5Y

Length: 7min 43sec (463 seconds)

Published: Sun May 07 2023