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I don't know how you get here from “predict the next word”

grumpy-economist.com

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Re: I don't know how you get here from “predict the next word”

#101

Post by wavemode »

selridge wrote: post #56
wavemode wrote: post #41

Earlier quoted context omitted.

Statistical models generalize. If you train a model that f(x) = 5 and f(x+1) = 6, the number 7 doesn't have to exist in the training data for the model to give you a correct answer for f(x+2) Similarly, if there are millions of academic papers and thousands of peer reviews in the training data, a review of this exact paper doesn't need to be in there for the LLM to write something convincing. (I say "convincing" rath…

Ok cool cool. Instead of pretending you need to teach me, you could engage with what I'm saying or even the OP! "I don't know how you get here from "predict the next word"" is not really so much a statement of ignorance where someone needs you to step in but a reflection that perhaps the tech is not so easily explained as that. No magic needs to be present for that to be the case.

If you disagree with someone on the internet, you can just say "I disagree, and here's why". You don't have to aggressively accuse them of "not engaging" with the text.

I engaged. You just don't like what I wrote. That's okay.

Re: I don't know how you get here from “predict the next word”

#102

Post by measurablefunc »

brookst wrote: post #90
measurablefunc wrote: post #55

Earlier quoted context omitted.

I don't know, that's why I asked b/c I always see a lot of empty platitudes when it comes to LLM praise so I'm curious to see if people can actually back up their claims. I haven't done any 3D modeling so I'll take your word for it but I can tell you that I am working on a very simple interpreter & bytecode compiler for a subset of Erlang & I have yet to see anything novel or even useful from any of the coding assist…

It’s taken me a while to get good at using them. My advice: ask for more than what you think it can do. #1 mistake is failing to give enough context about goals, constraints, priorities. Don’t ask “complete this one small task”, ask “hey I’m working on this big project, docs are here, source is there, I’m not sure how to do that, come up with a plan”

The specification is linked in another comment in this thread & you can decide whether it is ambitious enough or not but what I can tell you is that none of the existing coding agents can complete the task even w/ all the details. If you do try it you will eventually get something that will mostly work on simple tests but fail miserably on slightly more complicated test cases.

Re: I don't know how you get here from “predict the next word”

#103

Post by steve1977 »

grey-area wrote: post #70
ChaitanyaSai wrote: post #43

The whole next word thing is interesting isn't it. I like to see it with Dennett's "Competence and comprehension" lens. You can predict the next word competently with shallow understanding. But you could also do it well with understanding or comprehension of the full picture. A mental model that allows you to predict better. Are the AIs stumbling into these mental models? Seems like it. However, because these are suc…

Given their failure on novel logic problems, generation of meaningless text, tendency to do things like delete tests and incompetence at simple mathematics, it seems very unlikely they have built any sort of world model. It’s remarkable how competent they are given the way they work. Predict the next word is a terrible summary of what these machines do though, they certainly do more than that, but there are significa…

> Predict the next word is a terrible summary of what these machines do though, they certainly do more than that

What would that be?

Re: I don't know how you get here from “predict the next word”

#104

Post by tsunamifury »

I think it’s funny that at Google I invented and productized next word (and next action) predictor in Gmail and hangouts chat and I’ve never had a single person come to me and ask how this all works.

To me LLMs are incredibly simple. Next word next sentence next paragraph and next answer are stacked attention layers which identify manifolds and run in reverse to then keep the attention head on track for next token. It’s pretty straight forward math and you can sit down and make a tiny LLM pretty easily on your home computer with a good sized bag of words and context

To me it’s baffling everyone goes around saying constantly that not even Nobel prize winners know how this works it’s a huge mystery.

Has anyone thought to ask the actual people like me and others who invented this?

Re: I don't know how you get here from “predict the next word”

#105

Post by Kim_Bruning »

wavemode wrote: post #41
selridge wrote: post #34

Earlier quoted context omitted.

>You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. This is just as stuck in a moment in time as "they only do next word prediction" What does this even mean anymore? Are we supposed to believe that a review of this paper that wasn't written when that model (It's putatively not an "LLM", but IDK enough about it to be pushy there) was trained? Does that even make sense?…

Statistical models generalize. If you train a model that f(x) = 5 and f(x+1) = 6, the number 7 doesn't have to exist in the training data for the model to give you a correct answer for f(x+2) Similarly, if there are millions of academic papers and thousands of peer reviews in the training data, a review of this exact paper doesn't need to be in there for the LLM to write something convincing. (I say "convincing" rath…

If you run an LLM in an autoregressive loop you can get it to emulate a turing machine though. That sort of changes the complexity class of the system just a touch. 'Just predicts the next word' hits different when the loop is doing general computation.

Took me a bit of messing around, but try to write out each state sequentially, with a check step between each.

Re: I don't know how you get here from “predict the next word”

#106

Post by steve1977 »

mzhaase wrote: post #100
ChaitanyaSai wrote: post #43

The whole next word thing is interesting isn't it. I like to see it with Dennett's "Competence and comprehension" lens. You can predict the next word competently with shallow understanding. But you could also do it well with understanding or comprehension of the full picture. A mental model that allows you to predict better. Are the AIs stumbling into these mental models? Seems like it. However, because these are suc…

It always occurred to me that LLMs may be like the language center of the brain. And there should be a "whole damn rest of the brain" behind it to steer it. LLMs miss very important concepts, like the concept of a fact. There is no "true", just consensus text on the internet given a certain context. Like that study recently where LLMs gave wrong info if there was the biography of a poor person in the context.

I think much along the same lines. LLMs are probably even just a part of the language center.

And of course they also miss things like embodiment, mirror neurons etc.

If an LLM makes a mistake, it will tell you it is sorry. But does it really feel sorry?

Re: I don't know how you get here from “predict the next word”

#107

Post by selridge »

wavemode wrote: post #101
selridge wrote: post #56

Earlier quoted context omitted.

Ok cool cool. Instead of pretending you need to teach me, you could engage with what I'm saying or even the OP! "I don't know how you get here from "predict the next word"" is not really so much a statement of ignorance where someone needs you to step in but a reflection that perhaps the tech is not so easily explained as that. No magic needs to be present for that to be the case.

If you disagree with someone on the internet, you can just say "I disagree, and here's why". You don't have to aggressively accuse them of "not engaging" with the text. I engaged. You just don't like what I wrote. That's okay.

Thanks but no thanks.

Re: I don't know how you get here from “predict the next word”

#108

Post by steve1977 »

basch wrote: post #95
ChaitanyaSai wrote: post #43

The whole next word thing is interesting isn't it. I like to see it with Dennett's "Competence and comprehension" lens. You can predict the next word competently with shallow understanding. But you could also do it well with understanding or comprehension of the full picture. A mental model that allows you to predict better. Are the AIs stumbling into these mental models? Seems like it. However, because these are suc…

It's honestly disheartening and a bit shocking how everyone has started repeating the predict the next syllable criticism. The language model predicts the next syllable by FIRST arriving in a point in space that represents UNDERSTANDING of the input language. This was true all the way back in 2017 at the time of Attention Is All You Need. Google had a beautiful explainer page of how transformers worked, which I am st…

From what I understand, it's more like "input is 1, 3, 5, 7" so "output is likely to be 9".

Understanding would be a bit generous of a term for that I guess, but that also depends on the definition of understanding.

Re: I don't know how you get here from “predict the next word”

#109

Post by jcul »

ejolto wrote: post #51
callmeal wrote: post #29

The "predict the next word" to a current llm is at the same level as a "transistor" (or gate) is to a modern cpu. I don't understand llms enough to expand on that comparison, but I can see how having layers above that feed the layers below to "predict the next word" and use the output to modify the input leading to what we see today. It is turtles all the way down.

There is a big difference, because I understand how those transistors produce a picture on a screen, I don’t understand how LLMs do what they do. The difference is so big that the comparison is useless.

I understand how transistors work too, and how they can result in a picture on a screen. But I think most people outside the software / electronics areas don't and to them it's just magic.

Re: I don't know how you get here from “predict the next word”

#110

Post by echelon »

qsera wrote: post #63
echelon wrote: post #49

Earlier quoted context omitted.

Humans are future predictors. Our vision systems, our mental models of our careers. People that predict the future tend to do well financially. Now the machines are getting better than we are. It's exciting and a little bit terrifying. We were polymers that evolved intelligence. Now the sand is becoming smart.

>Now the machines are getting better than we are Then AI companies should stop looking for investors and instead play stock markets with all that predictive powers!

The real money is in using the models to build utility and money-making companies. You're removed from orders of magnitude in upside potential if you have to wait for the public markets.
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