211–220 of 224 posts
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They did not use modern techniques. Therefore it is meaningless. That’s not to mention that modern frontier LLMs can also be demonstrated to do this task, which is an existence proof in and of itself.
I am not interested in this discussion anymore. Bye.
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Why are we sending the trillion?
It must be deposited into OpenAI's bank account so that they can then deposit it into NVIDIA's account who can then in turn make a deal w/ OpenAI to deposit it back into OpenAI's account for some stock options. I think you can see how it works from here but if not then maybe one of the scaled up "reasoning" AIs will figure it out for you.
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> For example, how many humans can write hundred of lines of code (in seconds mind you) and regularly not have any syntax errors or bugs? Ez, just use codegen. Also the second part (not having bugs) is unlikely to be true for the LLM generated code, whereas traditional codegen will actually generate code with pretty much no bugs.
What's your point? Traditional codegen tools are inflexible in the extreme compared to what LLMs can do. The realistic comparison is between humans and LLMs, not LLMs and codegen tools.
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Being exact doesn't mean it is not an approximation, which was the initial topic. Being exact in science means that 2+2=4 and that can be demonstrated following a logical chain. But that doesn't make our knowledge of the universe exact. It is still an approximation. What it can be "exact" is how we obtain and reproduce the current knowledge we have of it.
The speed of light, or plank's constant - are these approximations?
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Ah yes then, let me then unchain my LLM on those nasty unsolved math and logic problems I've absolutely not be struggling with in the course of my career.
I've "unchained" my LLM on a lot of problems that I probably could solve, but that would take me time I don't have, and that it has solved in many case faster than I could. It may not be good enough to solve problems that are beyond us for most of us, but it certainly can solve a lot of problems for a lot of us that have gone unsolved for lack of resources.
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A lot of maths students would also struggle to contribute to frontier math problems, but we would still say they are reasoning. Their skill at reasoning might not be as good as professional mathematicians, but that does not stop us from recognising that they can solve logic problems without memorisation, which is a form of reasoning. I am just saying that LLMs have demonstrated they can reason, at least a little bit.…
Thank you for picking at this. A lot of people appear to be - often not consciously or intentionally - setting the bar for "reasoning" at a level many or most people would not meet. Sometimes that is just a reaction to wanting an LLM that is producing result that is good for their own level. Sometimes it reveals a view of fellow humans that would be quite elitist if stated outright. Sometimes it's a kneejerk attempt…
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> They are good at repeating their training data, not thinking about it. Which shouldn't come as a surprise, considering that this is, at the core of things, what language models do: Generate sequences that are statistically likely according to their training data.
This is too large of an oversimplification of how an LLM works. I hope the meme that they are just next token predictors dies out soon, before it becomes a permanent fixture of incorrect but often stated “common sense”. They’re not Markov chains.
It makes the statement of a fact a type of rhetorical device.
It is the difference between saying "I am a biological entity" and "I am just a biological entity". There are all kinds of connotations that come along for the ride with the latter statement.
Then there is the counter with the romantic statement that "I am not just a biological entity".
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I've "unchained" my LLM on a lot of problems that I probably could solve, but that would take me time I don't have, and that it has solved in many case faster than I could. It may not be good enough to solve problems that are beyond us for most of us, but it certainly can solve a lot of problems for a lot of us that have gone unsolved for lack of resources.
Can solve problems you already know how to solve, if you micro-manage it and it'll BS a lot on the way. If this is the maximum AGI-PhD-LRM can do, that'll be disappointing compared to investments. Curious to see what all this will become in few years.
I wonder if we can get models to reason in a structured and verifiable way, like we have formal logic in math.
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Most amazing is asking any of the models to draw an 11-sided polygon and number the edges.
I asked gpt5, and it worked really well with a correct result. Did you expect it to fail?