WEBVTT captioned by bala, checked by sachac
NOTE Intro to the Talk
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Hello, I'm Andrew Hyatt and I'm going to talk to you
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about large language models and how
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they relate to Emacs.
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And I'm going to talk to you about the technology
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and how we're going to use it in Emacs.
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There'll be demos and there'll be talks about,
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I'll finish up by kind of talking about where
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I think this should go in the future.
NOTE What are LLMs?
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So to start off with, let's just talk like,
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I just want to make sure everyone's on the same page.
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What are large language models?
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Not everyone may be caught up on this.
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Large language models are a way... Basically,
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the current versions of large language models
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are all based on the similar architecture
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called the transformer.
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It's just an efficient way to train and produce output.
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So these things are basically models
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that predict the next word or something like that.
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And they're trained on an enormous corpus of information
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and they get extremely good
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at predicting the next word.
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And from that basic ability, you can train
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through further tuning from human input,
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human ratings and things like that.
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You can train different models based on that
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that will do question answering.
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And this is how basically ChatGPT works.
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There's a base LLM, like GPT.
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And then you have a chat version of that,
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which is just trained to just... You give
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it a prompt, like what do you want it to do?
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And it gives you an output that does what you told it to do,
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or at least attempts to do it.
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Those are the power of large language models is
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they're extremely, extremely impressive.
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Certainly this is, in AI,
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this has been the biggest thing to happen
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probably in my lifetime,
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or at least my lifetime as my working lifetime.
NOTE Power of LLMs (Magit Demo)
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So let me give you a demonstration of
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what kinds of stuff it could do in Emacs.
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So here I have a Emacs file.
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So this is my Emacs init file.
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I have a change.
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Let's commit that change.
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And, you know, I don't like writing commit messages,
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so I can generate it.
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And it did an actually just looking.
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So all it does is it's looking, it's just reading the diff.
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I'm just feeding it the diff with some instructions.
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And it is this a incredible commit message?
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It's not bad, actually.
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You can see that it actually has really extracted
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the meaning of what I'm doing and has written
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a reasonably good commit message.
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Now I have to edit it because this is not quite correct.
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But it's kind of impressive how good it is.
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And my editing, it's kind of easier for me to edit this
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than just to write a new one.
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And quite often it's good enough to just submit as is.
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So this is kind of, you know, you could say
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this is just commit messages.
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You could respond to emails.
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You could, you know, using your own custom instructions
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about what you want your email to say.
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It'll write the email for you.
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It could do like this
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Emacs is a way to interact with buffers.
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This could basically just output text.
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So it's super useful for
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understanding something and outputting text based on that,
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which is just useful for Emacs.
NOTE Drawbacks of LLMs (regex demo)
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So the drawback is, yeah, it's good,
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but it's not that reliable.
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And you'd think it's very easy to get caught up in like,
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oh my gosh, like this is so powerful.
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I bet it could work this, whatever idea could work.
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And these ideas, like they almost can.
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For example, I was thinking, you know what I could do?
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I don't like writing regexes.
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Why can't I have a regex replace that's powered by LLMs?
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And that way I could give just an instruction
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to regex replace.
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And so for example, I could do Emacs LLM regex replace.
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This is not checked in anywhere.
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These are just my own kind of private functions.
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My description lowercase all the org headings.
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Let's see if it works.
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It might work.
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No, it doesn't work.
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So if I, I'm not going to bother to show you
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what it actually came up with, but it's something,
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if you looked at it, it'd be like, wow,
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this is very close to being...
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It looks like it should work, but it doesn't.
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Okay.
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It's not quite good enough to get it right.
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And it's possible that perhaps by giving it
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a few examples of, or explaining more
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what makes Emacs regexes different.
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It could do a better job
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and maybe could solve these problems,
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but it's always a little bit random.
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You're never quite sure what you're going to get.
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So this is the drawback.
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Like there's a lot of things that look like you could do it,
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but when it actually comes down to trying it,
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it's surprisingly hard.
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And, you know, and whatever you're doing,
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it's surprisingly hard to get something
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that is repeatably, that's, that is always good.
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So yeah, that's currently the problem.
NOTE Embeddings
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So I want to talk about embeddings.
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They're another thing that LLMs offer
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and that are extremely useful.
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They are, what they do is they encode from
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a input text that could be a word, a sentence,
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a small document.
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It encodes a vector about what the meaning,
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the semantic meaning of that is.
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That means you could, something that is,
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uses completely different words,
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but is basically talking about the same thing,
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perhaps in a different language, should be pretty close
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as a vector to the other vector.
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You know, as long as they're similarly semantic things,
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like the words
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highway and Camino are two different words.
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They mean the same thing.
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They should have very similar embeddings.
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So it is a way to kind of encode this
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and then you could use this for search.
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For example, I haven't tried to do this yet,
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but you could probably just make an embedding
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for every paragraph in the Emacs manual
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and the Elisp manual.
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And then, and then there's a very standard technique.
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You just... You find that you have a query,
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oh, how do I do whatever, whatever in Emacs again?
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And you could, you just find that 20 things
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that are closest to whatever you're
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trying to... the embedding of your query.
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You send those things to the LLM, as you know,
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with the original query,
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and you're basically telling the--asking the LLM,
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look, the user is trying to do this.
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Here's what I found in the Emacs manual.
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That's on the Elisp manual.
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That's close to what they're trying to do.
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So can you kind of just tell the user what to do?
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And from this, and you could say,
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just use things from this, you know, that I give you.
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Don't just make up your own idea.
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You know, don't use your own ideas,
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because sometimes it likes to do that
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and those things are wrong.
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So you could try to, you know, do this and you get,
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you could get quite good results using this.
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So no one has done this yet,
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but that should not be hard to do.
NOTE Image Generation
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Image generation is something that's, you know,
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it's not quite an LLM in the sense of...
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These are... It's a different technology,
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but these things are kind of packaged together
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in a sense.
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And you'll see that when I talk about Emacs packages,
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a lot of them bundle image generation
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and large language models.
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You know, the APIs are often bundled together by providers.
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And the general idea is it's kind of similar
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because it's very similar to large, you know,
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doing a chat thing where you, you know,
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the chat is like, you give it a text request,
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like write me a sonnet about, you know,
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the battle between Emacs and vi.
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And it could, it could do it.
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It could do a very good job of that.
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But you could also say, you know,
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draw me a picture of Emacs and vi as boxers,
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as a character-character boxing in a ring,
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like a, you know, political cartoon style.
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And it can do that as well.
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And so you could basically think of this
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as just sort of... it's kind of the
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same thing with what you're doing
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with large language models,
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but instead of outputting a text,
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you're outputting a picture.
NOTE Fine-tuning
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There's also, I want to mention the concept of fine-tuning.
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Fine-tuning is a way to take your--
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take a corpus of inputs and outputs and just from
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a large language model, you're like, okay,
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given this base large language model,
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I want to make sure that when I give you input,
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you give me something like output.
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And this is what I'm just going to
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train you further on these,
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these mappings between input and output.
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And for example, you could do this. Like,
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let's say you wanted to fix that regex demo
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I had to make it good.
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I don't think it, I think it'd be
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relatively effective to train,
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to have regex descriptions
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and regex examples, Emacs regex examples
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as inputs and outputs.
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You could get, you know, maybe a hundred,
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a few hundreds of these things.
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You could train it.
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I think that is a reasonable way to,
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let's just say, I don't know how well it would work,
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but these things definitely work some of the time
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and produce pretty good results.
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And you could do this on your own machine.
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Corporations like OpenAI offer APIs with, you know,
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to build your fine tunes on top of OpenAI.
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And I think, I'm not a hundred percent sure,
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but I think then you can share your model
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with other people.
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But if not, then you just, you know,
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you could use your model for your own specialized purposes.
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But in the world of models that you could run,
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for example, based on Llama, which is like...
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Llama is this model you can run on your own machine from Meta.
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There's many fine-tuned models that you could download
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and you could run on your own.
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They can do very different things too.
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Some output Python programs, for example,
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that you could just run.
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So you just say...
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Tell me how old... Let's just say
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you have a random task, like
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tell me how old these five cities are in minutes,
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based on historical evidence.
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It's kind of a weird query, but it probably can figure,
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it could probably run that for you.
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It'll encode its knowledge into whatever
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the Python program, then use the Python program
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to do the correct calculations.
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So pretty, pretty useful stuff.
NOTE Open Source
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So I also want to mention open source
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and basically free software here.
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These LLMs are mostly not free software.
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They're sometimes open source,
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but they're generally not free
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without restrictions to use.
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Most of these things, even Llama,
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which you can use on your own machine,
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have restrictions that you cannot use it
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to train your own model.
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This is something that, you know,
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it costs millions and millions of dollars
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to train and produce these models.
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And that's just computation costs.
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They do not want you
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stealing all that work by training your own models
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based on their output.
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But there are research LLMs that do, I believe,
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conform to free software principles.
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They're just not as good yet.
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And I think that might change in the future.
NOTE The Future
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So speaking of the future,
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one of the things I'd like to point out
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is that like the demos I showed you are based on,
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I'm using OpenAI 3.5 model.
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That's more than, well, no,
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it's like a year old basically at this point.
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And things are moving fast.
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They came out with 4.0.
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4.0 is significantly better.
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I don't have access to it.
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Even though I'm using the API and I'm paying money for it,
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you only can get access to 4.0
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if you can spend a dollar.
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And I've never been able to spend,
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use so much API use that I've spent a dollar.
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So I have, I don't have 4.0, but I've tried it
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because I do pay for this
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so I could get access to 4.0
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and it is substantially better.
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By all reports, it's,
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the difference is extremely significant.
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I would not be surprised
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if some of the limitations and drawbacks I described
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mostly went away with 4.0.
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We're probably at a stage
00:13:06.680 --> 00:13:09.239
where regexes will work maybe 5% of the time
00:13:09.240 --> 00:13:10.119
if you try them.
00:13:10.120 --> 00:13:13.639
But with 4.0, it could work like 80% of the time.
00:13:13.640 --> 00:13:14.559
Now, is that good enough?
00:13:14.560 --> 00:13:17.279
Probably not, but it's a,
00:13:17.280 --> 00:13:20.319
I wouldn't be surprised if you got results like that.
00:13:20.320 --> 00:13:22.919
And in a year's time, in two years time,
00:13:22.920 --> 00:13:26.679
no one knows how much this is going to play out
00:13:26.680 --> 00:13:27.519
before progress stalls,
00:13:27.520 --> 00:13:32.319
but there are a lot of interesting research.
00:13:32.320 --> 00:13:34.279
I don't think, research wise,
00:13:34.280 --> 00:13:35.759
I don't think things have slowed down.
00:13:35.760 --> 00:13:38.719
You're still seeing a lot of advances.
00:13:38.720 --> 00:13:40.999
You're still seeing a lot of models coming out
00:13:41.000 --> 00:13:41.839
and that will come out.
00:13:41.840 --> 00:13:46.279
That will be each one, one upping the other one
00:13:46.280 --> 00:13:49.959
in terms of quality.
00:13:49.960 --> 00:13:52.759
It'll be really interesting to see how this all plays out.
00:13:52.760 --> 00:13:55.919
I think that message here is that
00:13:55.920 --> 00:13:57.999
we're at the beginning here.
00:13:58.000 --> 00:14:01.239
This is why I think this talk is important.
00:14:01.240 --> 00:14:02.279
I think this is why we should be
00:14:02.280 --> 00:14:04.159
paying attention to this stuff.
NOTE LLMs in Emacs - existing packages
00:14:08.200 --> 00:14:11.039
Let's talk about the existing packages.
00:14:11.040 --> 00:14:13.199
Because there's a lot out there, people have,
00:14:13.200 --> 00:14:17.039
I think people have been integrating with
00:14:17.040 --> 00:14:21.239
these LLMs that often have a relatively easy to use API.
00:14:21.240 --> 00:14:24.039
So it's kind of natural that people
00:14:24.040 --> 00:14:25.679
have already put out a lot of packages.
00:14:25.680 --> 00:14:28.319
Coming off this problem from a lot of different angles,
00:14:28.320 --> 00:14:30.639
I don't have time to go through
00:14:30.640 --> 00:14:31.959
all of these packages.
00:14:31.960 --> 00:14:33.559
These are great packages though.
00:14:33.560 --> 00:14:35.279
If you're not familiar with them,
00:14:35.280 --> 00:14:37.679
please check them out.
00:14:37.680 --> 00:14:40.999
And they all are doing slightly different things.
00:14:41.000 --> 00:14:43.959
Some of these are relatively straightforward.
00:14:43.960 --> 00:14:47.919
Interactions, just a way to
00:14:47.920 --> 00:14:52.679
almost in a comment sort of way to kind of
00:14:52.680 --> 00:14:54.199
have just an interaction,
00:14:54.200 --> 00:14:55.479
long running interaction with an LLM
00:14:55.480 --> 00:14:59.039
where you kind of build off previous responses,
00:14:59.040 --> 00:15:01.799
kind of like the OpenAI's UI.
00:15:01.800 --> 00:15:08.559
Two very more Emacsy things where you can sort of
00:15:08.560 --> 00:15:13.679
embed these LLM responses within a org-mode block
00:15:13.680 --> 00:15:15.239
using the org-mode's context.
00:15:15.240 --> 00:15:20.959
Or GitHub Copilot integration where you can use it
00:15:20.960 --> 00:15:23.319
for auto completion in a very powerful,
00:15:23.320 --> 00:15:27.319
you know, this stuff is very useful if it could figure out
00:15:27.320 --> 00:15:29.199
what you're trying to do based on the context.
00:15:29.200 --> 00:15:31.839
It's quite effective.
00:15:31.840 --> 00:15:36.359
But I want to kind of call out one thing
00:15:36.360 --> 00:15:38.239
that I'd like to see change.
00:15:38.240 --> 00:15:42.599
Which is that users right now,
00:15:42.600 --> 00:15:45.199
not all of these have a choice of,
00:15:45.200 --> 00:15:47.959
first of all, there's a lot of them.
00:15:47.960 --> 00:15:49.639
Each one of them is doing their own calls.
00:15:49.640 --> 00:15:53.999
And each one of them is, so each one of them
00:15:54.000 --> 00:15:55.239
has their own interfaces.
00:15:55.240 --> 00:15:57.719
They're rewriting the interface to OpenAI or wherever.
00:15:57.720 --> 00:16:00.119
And they're not, they don't, most of these
00:16:00.120 --> 00:16:05.119
do not make it that configurable or at all configurable
00:16:05.120 --> 00:16:06.599
what LLM use.
00:16:06.600 --> 00:16:07.239
This is not good.
00:16:07.240 --> 00:16:09.679
It is important that we use,
00:16:09.680 --> 00:16:15.679
we give the user a way to change the LLM they use.
00:16:15.680 --> 00:16:21.079
And that is because you might not be comfortable
00:16:21.080 --> 00:16:24.439
sending your requests over to a private corporation
00:16:24.440 --> 00:16:27.799
where you don't get to see how they use their data.
00:16:27.800 --> 00:16:29.799
Your data, really.
00:16:29.800 --> 00:16:33.319
That's especially true with things like embeddings
00:16:33.320 --> 00:16:35.039
where you might be sending over your documents.
00:16:35.040 --> 00:16:37.519
You're just giving them your documents, basically.
00:16:37.520 --> 00:16:40.759
And, you know, that does happen.
00:16:40.760 --> 00:16:43.599
I don't think really that there's a reason
00:16:43.600 --> 00:16:44.639
to be uncomfortable with this,
00:16:44.640 --> 00:16:51.439
but that, you know, people are uncomfortable and that's okay.
00:16:51.440 --> 00:16:53.239
People might want to use a local machine,
00:16:53.240 --> 00:16:58.359
a local LLM for maximum privacy.
00:16:58.360 --> 00:17:00.639
That's something we should allow.
00:17:00.640 --> 00:17:04.519
People might want to especially use free software.
00:17:04.520 --> 00:17:05.839
That's something we should definitely allow.
00:17:05.840 --> 00:17:07.279
This is Emacs.
00:17:07.280 --> 00:17:08.239
We need to encourage that.
00:17:08.240 --> 00:17:12.159
But right now, as most of these things are written,
00:17:12.160 --> 00:17:13.959
you can't do it.
00:17:13.960 --> 00:17:17.839
And they're spending precious time
00:17:17.840 --> 00:17:18.879
just doing things themselves.
00:17:18.880 --> 00:17:20.839
This is why I wrote LLM, which is...
00:17:20.840 --> 00:17:23.039
it will just make that connection to the LLM for you
00:17:23.040 --> 00:17:26.719
and it will connect to, you know, it has plugins.
00:17:26.720 --> 00:17:30.279
So if you can, the user can configure what plugin
00:17:30.280 --> 00:17:31.359
it actually goes to.
00:17:31.360 --> 00:17:32.399
Does it go to OpenAI?
00:17:32.400 --> 00:17:35.239
Does it go to Google Cloud Vertex?
00:17:35.240 --> 00:17:36.999
Does it go to Llama on your machine?
00:17:37.000 --> 00:17:38.399
We're using Ollama,
00:17:38.400 --> 00:17:40.999
which is just a way to run Llama locally.
00:17:41.000 --> 00:17:47.959
And more things in the future, I hope.
00:17:47.960 --> 00:17:52.079
So this is, I'm hoping that we use this.
00:17:52.080 --> 00:17:54.839
It's designed to be sort of maximally usable.
00:17:54.840 --> 00:17:56.279
You don't need to install anything.
00:17:56.280 --> 00:17:58.359
It's on GNU ELPA.
00:17:58.360 --> 00:17:59.879
So even if you write something
00:17:59.880 --> 00:18:01.079
that you want to contribute to GNU ELPA,
00:18:01.080 --> 00:18:02.879
you can use it because it's on GNU ELPA.
00:18:02.880 --> 00:18:06.439
It's part of the Emacs package, Emacs core packages.
00:18:06.440 --> 00:18:09.879
So, but it has no functionality.
00:18:09.880 --> 00:18:11.719
It's really just there as a library
00:18:11.720 --> 00:18:14.439
to use by other things offering functionality. Okay.
NOTE Abstracting LLM challenges
00:18:15.960 --> 00:18:19.839
And it's a little bit difficult to abstract.
00:18:19.840 --> 00:18:21.159
I want to point this out
00:18:21.160 --> 00:18:23.599
because I think it's an important point
00:18:23.600 --> 00:18:29.519
is that the, it's, some of these LLMs, for example,
00:18:29.520 --> 00:18:30.439
have image generation.
00:18:30.440 --> 00:18:31.279
Some do not.
00:18:31.280 --> 00:18:35.319
Some of them have very large context windows, even for chat.
00:18:35.320 --> 00:18:36.999
You say, okay, all these things can do chat.
00:18:37.000 --> 00:18:37.319
Okay.
00:18:37.320 --> 00:18:38.079
Yeah, kind of.
00:18:38.080 --> 00:18:39.999
Some of these things you could pass a book to,
00:18:40.000 --> 00:18:41.239
like Anthropic's API.
00:18:41.240 --> 00:18:43.039
Most, you cannot.
00:18:43.040 --> 00:18:45.559
So there really are big differences
00:18:45.560 --> 00:18:46.399
in how these things work.
00:18:46.400 --> 00:18:51.539
I hope those differences diminish in the future.
00:18:51.540 --> 00:18:53.800
But it's just one of the challenges
00:18:53.801 --> 00:18:57.520
that I hope we can work through in the LLM library.
00:18:57.521 --> 00:19:02.160
So it's compatible, but there's definitely
00:19:02.161 --> 00:19:04.079
limits to that compatibility.
NOTE Emacs is the ideal interface for LLMs
00:19:04.080 --> 00:19:06.160
I want to point out just to finish off,
00:19:06.161 --> 00:19:12.879
Emacs is the, Emacs has real power here
00:19:12.880 --> 00:19:15.679
that nothing else I think in the industry is offering.
00:19:15.680 --> 00:19:19.279
First of all, people that use Emacs
00:19:19.280 --> 00:19:20.439
tend to do a lot of things in Emacs.
00:19:20.440 --> 00:19:22.159
We have our to-dos in Emacs with the org mode.
00:19:22.160 --> 00:19:22.999
We have mail.
00:19:23.000 --> 00:19:25.719
We, you know, we might read email and we might,
00:19:25.720 --> 00:19:27.679
and respond to email in Emacs.
00:19:27.680 --> 00:19:29.199
We might have notes in Emacs.
00:19:29.200 --> 00:19:31.359
This is very powerful.
00:19:31.360 --> 00:19:34.159
Using... there's not other stuff like that.
00:19:34.160 --> 00:19:35.759
And you could feed this stuff to an LLM.
00:19:35.760 --> 00:19:37.039
You could do interesting things
00:19:37.040 --> 00:19:38.559
using a combination of all this data.
00:19:38.560 --> 00:19:40.399
No one else could do this.
00:19:40.400 --> 00:19:41.759
We need to start thinking about it.
00:19:41.760 --> 00:19:45.039
Secondly, Emacs can execute commands.
00:19:45.040 --> 00:19:46.239
This might be a bad idea.
00:19:46.240 --> 00:19:48.399
This might be how the robots take over,
00:19:48.400 --> 00:19:51.799
but you could have the LLMs respond with Emacs
00:19:51.800 --> 00:19:54.199
commands and run those Emacs commands
00:19:54.200 --> 00:19:57.079
and tell the LLM the response and have it do things
00:19:57.080 --> 00:19:58.679
as your agent in the editor.
00:19:58.680 --> 00:20:01.599
I think we need to explore ideas like this.
NOTE Outro
00:20:01.960 --> 00:20:04.279
And I think we need to share these ideas
00:20:04.280 --> 00:20:07.039
and we need to make sure that we're pushing the
00:20:07.040 --> 00:20:10.519
envelope for Emacs and actually, you know, doing things,
00:20:10.520 --> 00:20:12.959
sharing ideas, sharing progress,
00:20:12.960 --> 00:20:15.199
and kind of seeing how far we can push this stuff.
00:20:15.200 --> 00:20:20.639
Let's really help Emacs out, be sort of,
00:20:20.640 --> 00:20:24.519
take advantage of this super powerful technique.
00:20:24.520 --> 00:20:26.160
Thank you for listening.