541–550 of 560 posts
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"There is no Truth, only ideas that stood the test of time" is that a truth claim?
It's an idea that's stood the test of time, IMO. Perhaps there is truth, and it only looks like we can't find it because only some of us are magic?
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There are limits to such algorithms, as proven by Kurt Godel. https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_...
True, and in the case of Solomonoff Induction, incompleteness manifests in the calculation of Kolmogorov complexity used to order programs. But what incompleteness actually proves is that there is no single algorithm for truth, but a collection of algorithms can make up for each other's weaknesses in many ways, eg. while no single algorithm can solve the halting problem, different algorithms can cover cases for which…
Given the pushes for political truths in all of the LLMs I am uncertain if they would be implemented even if they existed.
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This is a childrearing technique, too: say “please do X”, where X precludes Y, rather than saying “please don’t do Y!”, which just increases the salience, and therefore likelihood, of Y.
Don't put marbles in your nose https://www.youtube.com/watch?v=xpz67hBIJwg
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You’re absolutely wrong! This is not how reasoning models work. Chain-of-thought did not produce reasoning models.
How do they work then? Because I thought chain of thought made for reasoning. And the first google result for 'chain of thought versus reasoning models' says it does: https://medium.com/@mayadakhatib/the-era-of-reasoning-models... Give me a better source.
CoT produces the linguistic scaffolding for reasoning, but doesn't actually provide much accuracy in doing so.
e.g. https://developer.nvidia.com/blog/maximize-robotics-performa...
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You’re absolutely wrong! This is not how reasoning models work. Chain-of-thought did not produce reasoning models.
Then I can't explain why it's producing the results that it does. If you have more information to share, I'm happy to update my knowledge... Doing a web search on the topic just comes up with marketing materials. Even Wikipedia's "Reasoning language model" article is mostly a list of release dates and model names, with as only relevant-sounding remark as to how these models are different: "[LLMs] can be fine-tuned on…
Actual reasoning requires training on diverse data sources, as you noted, but also coached experimentation (supervised fine-tuning) not just adding "think step by step" instruction to a model trained on typical textual datasets. "Think step by step" came first and produced increased performance on a variety of tasks, but was overhyped in its approximation of reasoning.
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It's an idea that's stood the test of time, IMO. Perhaps there is truth, and it only looks like we can't find it because only some of us are magic?
so something being believed for a long period of time makes it true?
… and also for your own personal observations: https://en.wikipedia.org/wiki/Problem_of_induction
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It's going to take legislation to fix it. Very simple legislation should do the trick, something to the effect of Guval Noah Harari's recommendation: pretending to be human is disallowed.
Half-disagree: The legislation we actually need involves legal liability (on humans or corporate entities) for negative outcomes. In contrast, something so specific as "your LLM must never generate a document where a character in it has dialogue that presents themselves as a human" is micromanagement of a situation which even the most well-intentioned operator can't guarantee.
Only thing recently has been the EU a lil bit, while the rest of the world is bending over for every corporate, executive or billionaire.
I've spent a lot of time trying to get LLM to generate things in a specific way, the biggest take away I have is, if you tell it "don't do xyz" it will always have in the back of its mind "do xyz" and any chance it gets it will take to "do xyz" When working on art projects, my trick is to specifically give all feedback constructively, carefully avoiding framing things in terms of the inverse or parts to remove.