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Claude's Cycles [pdf]

www-cs-faculty.stanford.edu

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Re: Claude's Cycles [pdf]

#41

Post by charcircuit »

mlyle wrote: post #34
bitexploder wrote: post #21

Earlier quoted context omitted.

This is the most fundamental argument that they are not, directly, an intelligence. They are not ever storing new information on a meaningful timescale. However, if you viewed them on some really large macro time scale where now LLMs are injecting information into the universe and the re-ingesting that maybe in some very philosophical way they are a /very/ slow oscillating intelligence right now. And as we narrow tha…

There's nothing to say that you can't build something intelligent out of them by bolting a memory on it, though. Sure, it's not how we work, but I can imagine a system where the LLM does a lot of heavy lifting and allows more expensive, smaller networks that train during inference and RAG systems to learn how to do new things and keep persistent state and plan.

Memory is not just bolted on top of the latest models. They under go training on how and when to effectively use memory and how to use compaction to avoid running out of context when working on problems.

Re: Claude's Cycles [pdf]

#42

Post by throw310822 »

ainiriand wrote: post #4

Are not LLMs supposed to just find the most probable word that follows next like many people here have touted? How this can be explained under that pretense? Is this way of problem solving 'thinking'?

> just find the most probable word that follows next

Well, if in all situations you can predict which word Einstein would probably say next, then I think you're in a good spot.

This "most probable" stuff is just absurd handwaving. Every prompt of even a few words is unique, there simply is no trivially "most probable" continuation. Probable given what? What these machines learn to do is predicting what intelligence would do, which is the same as being intelligent.

Re: Claude's Cycles [pdf]

#43

Post by Jweb_Guru »

anematode wrote: post #23
bitexploder wrote: post #21

Earlier quoted context omitted.

This is the most fundamental argument that they are not, directly, an intelligence. They are not ever storing new information on a meaningful timescale. However, if you viewed them on some really large macro time scale where now LLMs are injecting information into the universe and the re-ingesting that maybe in some very philosophical way they are a /very/ slow oscillating intelligence right now. And as we narrow tha…

But they're not "slow"! Unlike biological thinking, which has a speed limit, you can accelerate these chains of thought by orders of magnitude.

I assure you that LLM thinking also has a speed limit.

Re: Claude's Cycles [pdf]

#44

Post by rustyhancock »

bob1029 wrote: post #40
rustyhancock wrote: post #26

Earlier quoted context omitted.

I think a very long time because part of our limit is experiment. We need enough experimental results to explain to solve these theoretical mismatches and we don't and at present can't explore that frontier. Once we have more results at that frontier we'd build a theory out from there that has two nearly independent limits for QFT and GR. What we'd be asking if the AI is something that we can't expect a human to solv…

What prevents us from giving this system access to other real systems that live in physical labs? I don't see much difference between parameterizing and executing a particle accelerator run and invoking some SQL against a provider. It's just JSON on the wire at some level.

Nothing, we can give it all the data we have and have it lead experiments.

But we can not yet experiment at the GR/QFT frontier.

To do so with a particle accelerator it would need to be the size of the milky way.

Re: Claude's Cycles [pdf]

#45

Post by zarzavat »

worldsavior wrote: post #24
ecshafer wrote: post #16

I wonder how long we have until we start solving some truly hard problems with AI. How long until we throw AI at "connect general relativity and quantum physics", give the AI 6 months and a few data centers, and have it pop out a solution?

If AGI will ever come, then. Currently, AI is only a statistical machines, and solutions like this are purely based on distribution and no logic/actual intelligence.

I swear that AI could independently develop a cure for cancer and people would still say that it's not actually intelligent, just matrix multiplications giving a statistically probable answer!

LLMs are at least designed to be intelligent. Our monkey brains have much less reason to be intelligent, since we only evolved to survive nature, not to understand it.

We are at this moment extremely deep into what most people would have been considered to be actual artificial intelligence a mere 15 years ago. We're not quite at human levels of intelligence, but it's close.

Re: Claude's Cycles [pdf]

#46

Post by iandanforth »

TLDR (story, not math) - Knuth poses a problem, his friend uses Claude to conduct 30 some explorations, with careful human guidance, and Claude eventually writes a Python program that can find a solution for all odd values. Knuth then writes a proof of the approach and is very pleased by Claude's contribution. Even values remain an open question (Claude couldn't make much progress on them)

Re: Claude's Cycles [pdf]

#47

Post by fragmede »

rustyhancock wrote: post #26
ecshafer wrote: post #16

I wonder how long we have until we start solving some truly hard problems with AI. How long until we throw AI at "connect general relativity and quantum physics", give the AI 6 months and a few data centers, and have it pop out a solution?

I think a very long time because part of our limit is experiment. We need enough experimental results to explain to solve these theoretical mismatches and we don't and at present can't explore that frontier. Once we have more results at that frontier we'd build a theory out from there that has two nearly independent limits for QFT and GR. What we'd be asking if the AI is something that we can't expect a human to solv…

The question is, if you trained an LLM on everything up until 1904, could it come up with E=MC² or not?

Re: Claude's Cycles [pdf]

#48

Post by romaniv »

jcims wrote: post #17
miroljub wrote: post #7

Solves? It's a part of the training set. Nothing more, nothing less.

Prove it.

I would like to note that it would be trivial to definitively prove or disprove such things if we had a searchable public archive of the training data. Interestingly, the same people (and corporate entities) who loudly claim that LLMs are creating original work seem to be utterly disinterested in having actual, definitive proof of their claims.

Re: Claude's Cycles [pdf]

#49

Post by sega_sai »

ainiriand wrote: post #4

Are not LLMs supposed to just find the most probable word that follows next like many people here have touted? How this can be explained under that pretense? Is this way of problem solving 'thinking'?

In some sense that is still correct, i.e. the words are taken from some probability distribution conditional on previous words, but the key point is that probability distribution is not just some sort of average across the internet set of word probabilities. In the end this probability distribution is really the whole point of intelligence. And I think the LLMs are learning those.

Re: Claude's Cycles [pdf]

#50

Post by gpm »

vidarh wrote: post #38
gpm wrote: post #32

Earlier quoted context omitted.

I'd disagree, the other training on top doesn't alter the fundamental nature of the model that it's predicting the probabilities of the next token (and then there's a sampling step which can roughly be described as picking the most probable one). It just changes the probability distribution that it is approximating. To the extent that thinking is making a series of deductions from prior facts, it seems to me that thi…

Put a loop around an LLM and, it can be trivially made Turing complete, so it boils down to whether thinking requires exceeding the Turing computable, and we have no evidence to suggest that is even possible.

What are you doing in your loop?

As typically deployed [1] LLMs are not turing complete. They're closer to linear bounded automaton, but because transformers have a strict maximum input size they're actually a subset of the weaker class of deterministic finite automaton. These aren't like python programs or something that can work on as much memory as you supply them, their architecture works on a fixed maximum amount of memory.

I'm not particularly convinced turing complete is the relevant property though. I'm rather convinced that I'm not turing complete either... my head is only so big after all.

[1] i.e. in a loop that appends output tokens to the input and has some form of sliding context window (perhaps with some inserted instructions to "compact" and then sliding the context window right to after those instructions once the LLM emits some special "done compacting" tokens).

[2] Common sampling procedures make them mildly non-deterministic, but I don't believe they do so in a way that changes the theoretical class of these machines from DFAs.

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