571–580 of 1001 posts
I feel very comfortable saying, as a mathematician, that the ability to solve grade school maths problems would not be at all a predictor of ability to solve real mathematical problems at a research level. The reason LLMs fail at solving mathematical problems is because: 1) they are terrible at arithmetic, 2) they are terrible at algebra, but most importantly, 3) they are terrible at complex reasoning (more specifica…
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Are you saying it could , without having read it somewhere?
Maybe I'm unsure what we're arguing here. Did the guys kid drum that up himself or did he learn it from yt? Knowledge can be inferred or extracted. If it comes up with a correct answer and shows it's work, who cares how the knowledge was obtained?
As far as I can tell he inferred that |i+1| needs the Pythagorean theorem and that i and 1 are legs of the right triangle. I don't think anyone ever suggested that "absolute value" is "length". I asked him what |2i+2| would be an his answer of "square root of 8" suggests that he doesn't have it memorized as an answer because if it was he'd have said "2 square root two" or something similar.
I also asked if he'd seen a video about this and he said no. I think he just figured it out himself. Which is mildly spooky.
I feel very comfortable saying, as a mathematician, that the ability to solve grade school maths problems would not be at all a predictor of ability to solve real mathematical problems at a research level. The reason LLMs fail at solving mathematical problems is because: 1) they are terrible at arithmetic, 2) they are terrible at algebra, but most importantly, 3) they are terrible at complex reasoning (more specifica…
Let's say a model runs through a few iterations and finds a small, meaningful piece of information via "self-play" (iterating with itself without further prompting from a human.) If the model then distills that information down to a new feature, and re-examines the original prompt with the new feature embedded in an extra input tensor, then repeats this process ad-infinitum, will the language model's "prime directive…
Unfortunately there are rather a lot of issues which are difficult to describe concisely, so here is probably not the best place.
Primary amongst them is the fact that an LLM would be a horribly inefficient way to do this. There are much, much better ways, which have been tried, with limited success.
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The potential to have a generation of dumb kids. Year 2100: Kids will stop to learn maths and logic, because they understand it has become useless in practice to learn such skills, as they can ask a computer to solve their problem. A stupid generation, but one that can be very easily manipulated and exploited by those who have power.
Agree. Thank god all calculators and engineers who made them were burned down fifty years ago. Can’t imagine what would have happened instead.
If your apple watch could just scan your exam paper and instantly tell you what to write then why would you ever learn anything?
I feel very comfortable saying, as a mathematician, that the ability to solve grade school maths problems would not be at all a predictor of ability to solve real mathematical problems at a research level. The reason LLMs fail at solving mathematical problems is because: 1) they are terrible at arithmetic, 2) they are terrible at algebra, but most importantly, 3) they are terrible at complex reasoning (more specifica…
This comment seems to presume that Q* is related to existing LLM work -- which isn't stated in the article. Others have guessed that the 'Q' in Q* is from Q-learning in RL. In particular backtracking, which you point out LLMs cannot do, would not be an issue in an appropriate RL setup.
Hm? it's pretty trivial to use a sampler for LLMs that has a beam search and will effectively 'backtrack' a 'bad' selection.
It just doesn't normally help-- by construction the LLM sampled normally already approximates the correct overall distribution for the entire output, without any search.
I assume using a beam search does help when your sampler does have some non-trivial constraints (like the output satisfies some grammar or passes an algebraic test, or even just top-n sampling since those adjustments on a token by token basis result in a different approximate distribution than the original distribution filtered by the constraints).
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I am neither a mathematician or LLM creator but I do know how to evaluate interesting tech claims. The absolute best case scenario for a new technology is that it when it seems like a toy for nerds, and doesn't outperform anything we have today, but the scaling path is clear. Its problems just won't matter if it does that one thing with scaling. The web is a pretty good hypermedia platform, but a disastrously bad pla…
How on earth could you evaluate the scaling path with too little information. That's my point. You can't possibly know that a technology can solve a given kind of problem if it can only so far solve a completely different kind of problem which is largely unrelated! Saying that performance on grade-school problems is predictive of performance on complex reasoning tasks (including theorem proving) is like saying that a…
The hyperbole that surrounds them fits the mould nicely.
I feel very comfortable saying, as a mathematician, that the ability to solve grade school maths problems would not be at all a predictor of ability to solve real mathematical problems at a research level. The reason LLMs fail at solving mathematical problems is because: 1) they are terrible at arithmetic, 2) they are terrible at algebra, but most importantly, 3) they are terrible at complex reasoning (more specifica…
That's exactly what Go/Baduk/Weiqi players think some years ago. And superalignment is defintely OpenAI's major research objective:
> https://openai.com/blog/our-approach-to-alignment-research
> our AI systems are proposing very creative solutions (like AlphaGo’s move 37)
When will mathematicians face the move 37 moment?
I feel very comfortable saying, as a mathematician, that the ability to solve grade school maths problems would not be at all a predictor of ability to solve real mathematical problems at a research level. The reason LLMs fail at solving mathematical problems is because: 1) they are terrible at arithmetic, 2) they are terrible at algebra, but most importantly, 3) they are terrible at complex reasoning (more specifica…
> I feel very comfortable saying, as a mathematician, that the ability to solve grade school maths problems would not be at all a predictor of ability to solve real mathematical problems at a research level. At some point in the past, you yourself were only capable of solving grade school maths problems.
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I'd like to have the choice not to. I have extremely limited options for that. And I already live in a country which regularly has all of its electricity generated renewably.
You can choose to join a hippie farming commune. You don't have a choice to force Jimbo to get rid of his F-350 and to not get mad at the government whenever gas or prices rise.
It is very difficult to reply to such a sentiment in a productive way
I want to live in my community, I want my community to exist in peace until it changes, by natural evolution, into something unrecognizable, and to keep doing until the end of time
True, I could abandon my community and go live in a monastery. Or I could gather up the greed heads and gun them down like dogs
I choose neither
I choose, chose, to do the work to change the world one Hacker News comment at a time....
Remember, about a month ago Sam posted a comment along the lines of "AI will be capable of superhuman persuasion well before it is superhuman at general intelligence, which may lead to very strange outcomes". The board was likely spooked by the recent breakthroughs (which were most likely achieved by combining transformers with another approach), and hit the panic button. Anything capable of "superhuman persuasion",…
But they didn’t hit the panic button. They said Sam lied to them about something and fired him.