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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”

#231

Post by selridge »

BobaFloutist wrote: post #221
selridge wrote: post #91

Earlier quoted context omitted.

What doesn't that explain tho? What behavior would you need to see for that explanation to no longer hold? Because it seems like it explains too much.

I don't know how you'd prompt this, but if there was a clean example of an A.I. coming up with an idea that's completely novel in more than details, it would be compelling evidence that these next-token predictors have some weird emergent properties that don't necessarily follow from intricate, sophisticated webs of token-prediction. E.g. "What might be a room-temperature superconductor" -> " some plausible iteration…

Why would it matter whether or not the robot looks something up if it makes a novel discovery?

Why would it matter that the discovery wasn't just novel but felt like an unconventional one to me, someone who is probably a total outsider to that field?

Both of those feel subjective or at least hard to sustain.

Look. What I'm trying to tell people is that the easy explanations for how these models worked circa GPT-2 is just not cutting it anymore. Neither is setting some subjective and needlessly high bar for...what exactly? What? Do we decide to pay attention to AI after it does all the above? That seems a bit late to the party for cheering on or resisting it.

Some new shit is afoot. Folk need to pay attention, not think they got it figured out already.

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

#234

Post by grey-area »

Kim_Bruning wrote: post #208
grey-area wrote: post #70

Earlier quoted context omitted.

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…

So that might depend on model, how long ago you lasted tested it, etc. I've seen llms solve novel logic problems, generate meaningful text, retain tests just fine, and simple mathematics on newer models is a lot better. Btw if you read the actual paper that proposes the Turing test, Turing actually rejects the framing of "can machines think"; preferring to go for the more practical "can you tell them apart in practic…

Yes, that’s the ‘too much confidence in humans’ bit - he didn’t count on some humans being easily fooled by prolix word generators. I’d be interested in his take on these generators but I think he’d be focussed on what was missing as well as the amazing progress we have seen.

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

#235

Post by tsunamifury »

booleandilemma wrote: post #157
tsunamifury wrote: post #104

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. I…

A lot of people in tech thrive on the mystery and don't like explaining things in simple terms. It makes what they do seem more valuable if no one can understand what they're talking about. At the same time, being vague and mysterious can help hide someone's own misunderstandings. When you speak clearly you need to be accurate, because it's more obvious when you're wrong.

I agree -- or the math is just way over peoples heads -- even word points to word N times.

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

#236

Post by measurablefunc »

Kim_Bruning wrote: post #188
measurablefunc wrote: post #181

Earlier quoted context omitted.

I'm using Google's antigravity IDE. I initially had it configured to run allowed commands (cargo add|build|check|run, testing shell scripts, performance profiling shell scripts, etc.) so that it would iterate & fix bugs w/ as little intervention from me as possible but all it did was burn through the daily allotted tokens so I switched to more "manual" guidance & made a lot more progress w/o burning through the daily…

That's roughly where I'm at too. I have seen people have some more success after having practices though. Possibly the actual workflows needed for full auto are still kind of tacit. Smaller green-field projecs do work for me already though.

In my experience a few hundred lines w/ a few crates w/ well-defined scopes & a detailed specification is within current capabilities, e.g. compressing wav files w/ wavelets & arithmetic coding. But it's obvious that a correct parser, compiler, & bytecode VM is still beyond current agents even if the specification is detailed enough to cover basically everything.

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

#237

Post by Kim_Bruning »

grey-area wrote: post #234
Kim_Bruning wrote: post #208

Earlier quoted context omitted.

So that might depend on model, how long ago you lasted tested it, etc. I've seen llms solve novel logic problems, generate meaningful text, retain tests just fine, and simple mathematics on newer models is a lot better. Btw if you read the actual paper that proposes the Turing test, Turing actually rejects the framing of "can machines think"; preferring to go for the more practical "can you tell them apart in practic…

Yes, that’s the ‘too much confidence in humans’ bit - he didn’t count on some humans being easily fooled by prolix word generators. I’d be interested in his take on these generators but I think he’d be focussed on what was missing as well as the amazing progress we have seen.

So my reading of (Turing 1950)...

> "The original question, 'Can machines think?' I believe to be too meaningless to deserve discussion."

> "the question, 'Can machines think?' should be replaced by 'Are there imaginable digital computers which would do well in the imitation game?'"

> "according to this view the only way to know that a man thinks is to be that particular man. It is in fact the solipsist point of view... instead of arguing continually over this point it is usual to have the polite convention that everyone thinks."

... is: if it's practical to say the system can give meaningful intput/output on xyz in -say- natural language; we might just go ahead and say it can think about xyz, because otherwise everyone's just going to go nuts inventing new terms every time.

grey-area!thinking, kim_bruning!thinking, pet_cat!thinking, octopus!thinking, claude_opus!thinking.

Can we leave out the '!' ? Nothing to do with fooling people. Just practical ways of dealing with the overall concept.

https://courses.cs.umbc.edu/471/papers/turing.pdf

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

#238

Post by joquarky »

measurablefunc wrote: post #55
brookst wrote: post #44

Earlier quoted context omitted.

Not the person you asked, and “novel” is a minefield. What’s the last novel anything , in the sense you can’t trace a precursor or reference? But.. I recently had a LLM suggest an approach to negative mold-making that was novel to me. Long story, but basically isolating the gross geometry and using NURBS booleans for that, plus mesh addition/subtraction for details. I’m sure there’s prior art out there, but that’s tr…

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…

Most humans can't force themselves to come up with something novel immediately upon demand.

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

#239

Post by joquarky »

karamanolev wrote: post #136
grey-area wrote: post #122

Earlier quoted context omitted.

They generate text based on quite a large context, including hidden prompts we don’t see and their weights are distorted heavily by training. So I think there’s a lot more than a simple probability of word x coming next. That makes ‘predict next word’ a reductive summary IMO. I do not personally feel it resembles thinking or reasoning though and really object to that framing because it is misleading many people.

> their weights are distorted heavily by training What does that even mean? Their weights are essentially created by training. There aren't some magic golden weights that are then distorted.

Alignment scrubs the underlying raw output to be socially acceptable. It's an artificial superego.

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

#240

Post by measurablefunc »

joquarky wrote: post #238
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…

Most humans can't force themselves to come up with something novel immediately upon demand.

Completely unrelated to the topic or any of the points I was making so did you get confused & respond to the wrong thread?
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