151–160 of 376 posts
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'?
> I think Claude Shannon’s spirit is probably proud to know that his name is now being associated with such advances. Hats off to Claude! I didn't realize Claude was named after Claude Shannon! https://en.wikipedia.org/wiki/Claude_Shannon
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> 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.
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Trivia: Claude Shannon proposed the idea of predicting the next token (letter) using statistics/probabilities in the training data corpus in 1950: "Prediction and Entropy of Printed English" https://languagelog.ldc.upenn.edu/myl/Shannon1950.pdf
It goes back a bit further than that. His 1948 “Mathematical theory of communication” [1] already has (what we would now call) a Markov chain language model, page 7 onwards. AFAIK, this was based on his classified WWII work so it was probably a few years older than that [1] https://people.math.harvard.edu/~ctm/home/text/others/shanno...
"One may get a remarkable semblance of a language like English by taking a sequence of words, or pairs of words, or triads of words, according to the statistical frequency with which they occur in the language, and the gibberish thus obtained will have a remarkably persuasive similarity to good English."
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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?
From this standpoint I wonder, when Anthropic makes decisions like this, if they take into account Claude as a stakeholder and what Claude will learn about their behaviour and relationship to it on the next training run.
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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.
>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?
If it's just basically being a puppet, then no. You tell me what claude code is more like, a puppet, or a person?
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 relenting. Kudos for people being willing to change their opinion and update when new evidence comes to light.
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.
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.
My interpretation is that Claude did what Knuth considers to be the "solution". Doing the remaining work and polishing up the proof are not necessary to have a solution from this perspective.
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My understanding, from listening/reading what top researchers are saying, is that model architectures in the near future are going to attempt to scale the context window dramatically. There's a generalized belief that in-context learning is quite powerful and that scaling the window might yield massive benefits for continual learning. It doesn't seem that hard because recent open weight models have shown that the mem…
I don't disagree with this, but I don't think the memory cost is the only issue right? I remember using Sonnet 4.5 (or 4, I can't remember the first of Anthropic's offerings with a million context) and how slow the model would get, how much it wanted to end the session early as tokens accrued (this latter point, of course, is just an artifact of bad training). Less worried about memory, more worried about compute spe…
We're also seeing a recent rise in architectures boosting compute speed via multi-token prediction (MTP). That way a single inference batch can produce multiple tokens and multiply the token generation speed. Combine that with more lean ratios of active to inactive params in MOE and things end up being quite fast.
The rapid pace of architectural improvements in recent months seems to imply that there are lots of ways LLMs will continue to scale beyond just collecting and training on new data.