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Claude's Cycles [pdf]

www-cs-faculty.stanford.edu

81–90 of 376 posts

Re: Claude's Cycles [pdf]

#81
post #48
post #17

Earlier quoted context omitted.

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.

This would be awesome. Even titles and shasums could be enough.

Re: Claude's Cycles [pdf]

#82
post #76
post #5

Earlier quoted context omitted.

Data sharing agreements permitting, today's inference runs can be tomorrow's training data. Presumably the models are good enough at labeling promising chains of thought already. I could totally imagine "free" inference for researchers under the condition that the reasoning traces get to be used as future training data.

> Data sharing agreements permitting, today's inference runs can be tomorrow's training data. Presumably the models are good enough at labeling promising chains of thought already. Wouldn't this lead to model collapse?

Not necessarily, as exhibited by the massive success of artificial data.

Re: Claude's Cycles [pdf]

#83
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…

> In 2030, how is Anthropic going to keep Claude "up-to-date"

In 2030 Anthropic hopes Claude will keep Anthropic "up-to-date" on its progress on itself.

I'm only half joking here.

Re: Claude's Cycles [pdf]

#84
post #77

From my naive standpoint, LLMs like this seem to have some big strengths. One: possession of a superhuman expanse of knowledge. Two: making connections. Three: tireless trial and error. If you put those three things together, you end up with some cool stuff from time to time. Perhaps the proof of P!=NP is tied to an obscure connection that humans don't easily see due to individual lack of knowledge or predisposition…

This is why the whole "LLMs for mass surveillance" thing is scary imo.

Re: Claude's Cycles [pdf]

#85
post #12

Earlier quoted context omitted.

Was it? It was an open problem to Knuth - who generally knows how to search literature. However there is enough literature to search that it wouldn't be a surprise at all to discover it was already solved but he just used slightly different terms and so didn't find it. Or maybe it was sovled because this is a specialization of something that looks unrelated and so he wouldn't have realized it when he read it. Or... O…

Agreed with all of that, but with the added point that Knuth has done a lot of work in this exact area in The Art of Computer Programming Volume 4. If he considers this conjecture open given his particular knowledge of the field, it likely is (although agreed, it's not guaranteed).

> If he considers this conjecture open given his particular knowledge of the field, it likely is (although agreed, it's not guaranteed).

It is as good as guaranteed. If Knuth says it doesn't know how to solve the problem, and if anyone knows, then they will inform Knuth about it. Knuth not just a very knowledgeable person, but a celebrity also.

Re: Claude's Cycles [pdf]

#86

I asked Claude to solve the pentominoes puzzle made famous by Arthur C. Clarke. It struggled mightily until I told it how I'd solved the problem using 64 bit unsigned integers to represent the board and pieces. Then, it created a C# program that solved the problem very quickly. However, in the 20x3 case it found four solutions when there are only two. Turns out it had incorrectly mapped one of the pentominoes. Sort o…

[flagged]

Regurgitation is pretty rare, and very difficult to coax out, if not even impossible, for things that aren't massively overrepresented in the training set relative to the size of the training set. Even the famous regurgitation paper showed this: while they got most of the models to regurgitate the first book of the Harry Potter series, only Claude 3.7 Sonnet was able to regurgitate any significant portion of any of the other books that had a high nv-recall rate, and basically all of them dropped off precipitously for works like GoT, The Catcher in the Rye, Beloved, and remembered almost nothing about the Da Vinci Code or Catch-22[0]. So you really need huge amounts of examples to get any kind of meaningful regurgitation on any kind of reliable basis. Thus, you'd have to prove that hypothesis.

[0]: https://arxiv.org/pdf/2601.02671

Re: Claude's Cycles [pdf]

#87
post #39

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…

Would you consider someone with anterograde amnesia not to be intelligent?

Sure, why can't both things be true? "Intelligence" is just what you call something and someone else knows what you mean. Why did AI discourse throw everyone back 100 years philosophically? Its like post-structuralism or Wittgenstein never happened..

It's so much less important or interesting to like nail down some definition here (I would cite HN discourse the past three years or so), than it is to recognize what it means to assign "intelligent" to something. What assumptions does it make? What power does it valorize or curb?

Each side of this debate does themselves a disservice essentially just trying to be Aristotle way too late. "Intelligence" did not precede someone saying it of some phenomena, there is nothing to uncover or finalize here. The point is you have one side that really wants, for explicit and implicit reasons, to call this thing intelligent, even if it looks like a duck but doesn't quack like one, and vice versa on the other side.

Either way, we seem fundamentally incapable of being radical enough to reject AI on its own terms, or be proper champions of it. It is just tribal hypedom clinging to totem signifiers.

Good luck though!

Re: Claude's Cycles [pdf]

#88
post #77

From my naive standpoint, LLMs like this seem to have some big strengths. One: possession of a superhuman expanse of knowledge. Two: making connections. Three: tireless trial and error. If you put those three things together, you end up with some cool stuff from time to time. Perhaps the proof of P!=NP is tied to an obscure connection that humans don't easily see due to individual lack of knowledge or predisposition…

Unless my understanding is incorrect about how these tools work that last point isn't really a quality of LLMs as such? It gets attributed because the lines are blurred but the tireless trial and error is actually just a quality of a regular programatic loop (agent/orchestrator) that happens to be doing the trickiest part of its work via an LLM.

Re: Claude's Cycles [pdf]

#90
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…

> 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?

Just using a scaled up and cleverly tweaked version of linear regression analysis...
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