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Claude's Cycles [pdf]

www-cs-faculty.stanford.edu

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Re: Claude's Cycles [pdf]

#181

Post by mccoyb »

whimsicalism wrote: post #171
mccoyb wrote: post #2

It's fascinating to think about the space of problems which are amenable to RL scaling of these probability distributions. Before, we didn't have a fast (we had to rely on human cognition) way to try problems - even if the techniques and workflows were known by someone. Now, we've baked these patterns into probability distributions - anyone can access them with the correct "summoning spell". Experts will naturally us…

> how these models are going to keep up with the expanding boundary of science The same way humans do? The phraseology in this comment: 'probability distributions', 'baked these patterns' IMO has all the trappings of the stochastic parrot-style HN-discourse that has been consistently wrong for almost a decade now. The reference to how AI will keep up with AI-assisted human progress in science in 2030 is meant to reas…

Sorry, are you familiar with what a next token distribution is, mathematically speaking?

If you are not, let me introduce you to the term: a probability distribution.

Just because it has profound properties ... doesn't make it different.

> has all the trappings of the stochastic parrot-style HN-discourse that has been consistently wrong for almost a decade now

Perhaps respond to my actual comment compared to whatever meta-level grouping you wish to interpret it as part of?

> It contains a number of premises that we have no business being confident in. We are potentially witnessing the obviation of human cognitive labor.

What premises? Be clear.

Re: Claude's Cycles [pdf]

#182

Post by buffalobuffalo »

konne88 wrote: post #120

I didn't expect such a misleading intro from Knuth. It reads like Claude solved Knuth's math problem. In reality, Claude generated various example solution, and Knuth then manually generalized that to a formal proof. What Claude did is certainly useful, but it would have been nice to be clear about the scope of the contribution in the intro.

While not on the same level as these guys, I've done some similar stuff using Claude. This is a classic synergy example, where the output of human + LLM is far greater than just the human or just the LLM working on a problem. My experience has been that the LLM lacks fine grained judgement when it comes to allocating resources, or choosing a direction to work in. But once a direction is pointed out, it can do a deep exploration of that possibility space. Left alone, it would probably just go off on a tangent. But with someone holding the leash and pointing out areas to explore, it is a very useful partner.

Re: Claude's Cycles [pdf]

#183

Post by semessier »

iandanforth wrote: post #46

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)

looks like he is trying to make a point that the actual (formal) proof for 2Z + 1 (odd numbers) is still human - by himself that is. Not sure who came up with the core modular arithmetic idea of with s = 0 k increasing by 2 mod m.

Re: Claude's Cycles [pdf]

#184

Post by Yiin »

Wowfunhappy wrote: post #170
Filligree wrote: post #138

Earlier quoted context omitted.

It’s an extremely difficult problem, and if you know how to do that you could be a billionaire. It’s not impossible, obviously—humans do it—but it’s not yet certain that it’s possible with an LLM-sized architecture.

> It’s not impossible, obviously—humans do it It's still not at all obvious to me that LLMs work in the same way as the human brain, beyond a surface level. Obviously the "neurons" in neural nets resemble our brains in a sense, but is the resemblance metaphorical or literal?

https://www.youtube.com/watch?v=l-OLgbdZ3kk

Re: Claude's Cycles [pdf]

#185

Post by slopinthebag »

empath75 wrote: post #131
qsera wrote: post #60

Earlier quoted context omitted.

>Probable given what? The training data.. >predicting what intelligence would do No, it just predict what the next word would be if an intelligent entity translated its thoughts to words. Because it is trained on the text that are written by intelligent entities. If it was trained on text written by someone who loves to rhyme, you would be getting all rhyming responses. It imitates the behavior -- in text -- of what…

It is impossible to accurately imitate the action of intelligent beings without being intelligent. To believe otherwise is to believe that intelligence is a vacuous property.

An unintelligent device can accurately imitate the action of intelligent beings within a given scope, in the same way an actor can accurately imitate the action of a fictional character in a given scope (the stage or camera) without actually being that character.

If the idea is that something cannot accurately replicate the entirety of intelligence without being intelligent itself, then perhaps. But that isn't really what people talk about with LLMs given their obvious limitations.

Re: Claude's Cycles [pdf]

#186

Post by Robdel12 »

mccoyb wrote: post #2

It's fascinating to think about the space of problems which are amenable to RL scaling of these probability distributions. Before, we didn't have a fast (we had to rely on human cognition) way to try problems - even if the techniques and workflows were known by someone. Now, we've baked these patterns into probability distributions - anyone can access them with the correct "summoning spell". Experts will naturally us…

That’s AGI, right? For the model to learn novel things itself and retain it?

I have no idea but I’m along for the ride!

Re: Claude's Cycles [pdf]

#187

Post by slopinthebag »

UltraSane wrote: post #180
qsera wrote: post #141

Earlier quoted context omitted.

>It is impossible to accurately imitate the action of intelligent beings without being intelligent. Wait what? So a robot who is accurately copying the actions of an intelligent human, is intelligent?

How can you distinguish intelligence form a sufficiently accurate imitation of intelligence?

By "sufficiently accurate" do you mean identical? Because if so, it's not an imitation of intelligence at all, and the question is thus nonsensical.

Re: Claude's Cycles [pdf]

#188

Post by red75prime »

hmmmmmmmmmmmmmm wrote: post #118
throw310822 wrote: post #69

Earlier quoted context omitted.

> The training data If the prompt is unique, it is not in the training data. True for basically every prompt. So how is this probability calculated?

Hamiltonian paths and previous work by Donald Knuth is more than likely in the training data.

The specific sequence of tokens that comprise the Knuth's problem with an answer to it is not in the training data. A naive probability distribution based on counting token sequences that are present in the training data would assign 0 probability to it. The trained network represents extremely non-naive approach to estimating the ground-truth distribution (the distribution that corresponds to what a human brain might have produced).

Re: Claude's Cycles [pdf]

#189

Post by 3abiton »

zoogeny wrote: post #157

I recall an earlier exchange, posted to HN, between Wolfram and Knuth on the GPT-4 model [1]. Knuth was dismissive in that exchange, concluding "I myself shall certainly continue to leave such research to others, and to devote my time to developing concepts that are authentic and trustworthy. And I hope you do the same." I've noticed with the latest models, especially Opus 4.6, some of the resistance to these LLMs is…

> Kudos for people being willing to change their opinion and update when new evidence comes to light. > 1. https://cs.stanford.edu/~knuth/chatGPT20.txt

I think that's what make the bayesian faction of statistics so appealing. Updating their prior belief based on new evidence is at the core of the scinetific method. Take that frequentists.

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