ChatGPT

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Alternatives

2023-05-17 ◦ Github Copilot and ChatGPT alternatives - The Pragmatic Engineer

– OpenAI APIs. Curiously, ChatGPT uses data entered via its web interface for training, but not when using its APIs. So an obvious workaround is to use the APIs with a wrapper, like the open source Chatbot UI. – Azure OpenAI Service. Fine-tune custom AI models with your company data and hyperparameters. – MosaicML. Train large AI models with your company data in a secure environment. Point to an AWS S3 bucket, and that’s it! – Glean. AI-powered workplace search across the company’s apps, powered by deep learning-based large language models (LLM.) – Aleph Alpha. A company emphasizing that it’s a European AI technology company, which has open sourced its code codebase and doesn’t use customer data to train models. – Cohere. A set of LLMs to generate text, summarize it, classify and retrieve it. – Writer. A generative AI platform that trains on the company’s data.

For learning

2023-05-03 ◦ 10 Ways You Can Use ChatGPT to Learn Better - Scott H Young

How does it work

2023-05-08 ◦ How ChatGPT Works Technically | ChatGPT Architecture - YouTube
2023-05-08 ◦ Ep. 244: Cal Newport’s Thoughts on ChatGPT - YouTube
- Idea #1: Word guessing
  
  [15:30] - [16:01]: sentence the quick brown fox jumped over the we would put that as input into the model it would spit out the next word this approach which is known as Auto regressive text generation is what the models underneath all of these new generation chat Bots like chat gbt actually use they guess one word at a time that word is added to the text and the newly expanded text is put through the model to get the next word so it just generates one word at a time so if you
- Idea #2: Relevant Word Matching
  
  [17:00] - [17:30]: well at its core what’s really happening here and I’m simplifying but at its core what’s really happening here is the model is just looking at the most relevant words from the input it is then going to match those relevant words to actual text that it’s been given we call these Source text in my article so examples of real text it can match the relevant words to where they show up in real text and say what follows these relevant words in real text
- Idea #3: Voting
  
  [19:01] - [19:30]: happens we want to be a little bit more probabilistic so what I I would say a closer way of describing what happens is we can imagine that our large language model is going to look for every instance every instance of the relevant words that we’re looking for and it’s going to see what follows those instances and keep track of it what are all the different words that follow in this example fox jumped over
  
  [19:30] - [20:00]: and every time it finds an example a fox jumped over it says what word follows next let’s give a vote for that word and so if the same word follows in most of the examples it’s going to get most of the votes now I’m using votes here sort of as a metaphor what we’re really doing here is trying to build a normalized probability distribution so in the end what we’re going to get what the what the large language model is going to produce is for every possible next word it is going to produce a probability what is the probability that this should
- Idea #4: Feature Detection
  
  [25:01] - [25:33]: want a chat bot we can’t just have our program generate believable text the text have to actually answer the question being asked by the user so how do we aim this automatic text generation mechanism towards specific types of answers that match what the user is asking well this brings in the notion of feature detection which is the fourth out of the five total ideas I want to go over today so what happens with feature detection is a response you know so we have a a
  
  [29:01] - [29:32]: the source text to figure out it’s relevant and very specific guidelines about how should we then change our votes for words that match Source text that match these properties of these complicated rules if we have enough of these rules then we can start to generate text that’s not only natural sounding but actually seems to reply to or match what is being requested by the user now I think the reason why people have a hard time grasping this step is they imagine how
  
  [29:32] - [30:01]: many rules of them or them and a team of people could come up with and they say I I could come up with a couple dozen maybe if I worked with a team for a couple years we could come up with like a thousand good rules but these rules are complicated even a rule as simple as how do we know they’re asking about VCR instructions and how do we figure out if a given text we’re given is a VCR instruction text I don’t know I’d have to really think about that look at a lot of examples I mean if we worked really hard we could produce a few hundred maybe a thousand of these rules and that’s not going to be nearly enough that’s not going to
  
  [30:01] - [30:30]: cover nearly enough scenarios for the all of the topics that the more than one million users who’ve signed up for chat CPT for example all the topics they could ask about it turns out that the number of rules you really need to be as Adept as chat PBT just blows out of proportion any scale any human scale we can think of you know I did a little bit of back of envelope math for my New Yorker article if you took all of the parameters that
  
  [30:30] - [31:01]: Define gpt3 which is the the large language model that chat CPT then refined and is based on so the parameters we can think of as the things they actually changed actually train so this is really like the description of all of its rules if we just wrote out all of the numbers that Define the gpt3 we would fill over 1.5 million average length books so the number of rules you would have to have if we were writing them out would fill a large University Library full of
  
  [31:01] - [31:31]: rules that scale is so big we have a really hard time imagining it and that’s why when we start to see um oh my goodness this thing can answer almost anything I send to it can answer almost any question I I Ask of it we think there must be some uh adaptable intelligence in there that it’s just learning about things trying to understand and interact with us because we couldn’t imagine just having enough wrote rules to handle every topic that we could ask but there is a lot of rules there’s 1.5
  
  [31:31] - [32:00]: million books full of rules inside the stat GPT and so you have to wrap your mind around that scale and then you have to imagine that not only is that many rules but we can apply them in all sorts of combinations VCR instructions but also about a peanut butter sandwich also in the style of King James Bible stack those three rules and we get that first example that we saw earlier on all right so then the final idea is how in the world are we going to come up with all those rules 1.5 million books full of rules how are we going to do
- Idea #5: Self-Training
  
  [35:31] - [36:01]: there’s one estimate I found online* that said training chat GPT on a a single processor would take over 350 years of compute time and the only way that they could actually train on so much data so long was to have many many processors working in parallel spending well over a million dollars* I’m sure with a compute time just to get this training done and it still probably took weeks if not months to actually complete that process but here’s the leap of faith I want you to make after this final idea if you do this training the simple training
  
  [36:01] - [36:30]: process on enough passages drawn from enough Source text covering enough different types of topics from VCR instructions to Seinfeld Scripts these rules through all of these nudging these 1.5 million books worth of rules will eventually become really really smart and it’ll eventually be way more comprehensive and nuanced than any one team of humans could ever produce and they’re going to recognize that this is a Bible verse you want VCR instructions here and bubble sort is an algorithm and

ChatGPT

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