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Recent AI model progress feels mostly like bullshit

(www.lesswrong.com)
579 points paulpauper | 3 comments | 06 Apr 25 18:01 UTC | HN request time: 0.88s | source
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InkCanon ◴[06 Apr 25 20:03 UTC] No.43604503[source]▶
The biggest story in AI was released a few weeks ago but was given little attention: on the recent USAMO, SOTA models scored on average 5% (IIRC, it was some abysmal number). This is despite them supposedly having gotten 50%, 60% etc performance on IMO questions. This massively suggests AI models simply remember the past results, instead of actually solving these questions. I'm incredibly surprised no one mentions this, but it's ridiculous that these companies never tell us what (if any) efforts have been made to remove test data (IMO, ICPC, etc) from train data.
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bglazer ◴[06 Apr 25 22:20 UTC] No.43605451[source]▶
Yeah I’m a computational biology researcher. I’m working on a novel machine learning approach to inferring cellular behavior. I’m currently stumped why my algorithm won’t converge.

So, I describe the mathematics to ChatGPT-o3-mini-high to try to help reason about what’s going on. It was almost completely useless. Like blog-slop “intro to ML” solutions and ideas. It ignores all the mathematical context, and zeros in on “doesn’t converge” and suggests that I lower the learning rate. Like, no shit I tried that three weeks ago. No amount of cajoling can get it to meaningfully “reason” about the problem, because it hasn’t seen the problem before. The closest point in latent space is apparently a thousand identical Medium articles about Adam, so I get the statistical average of those.

I can’t stress how frustrating this is, especially with people like Terence Tao saying that these models are like a mediocre grad student. I would really love to have a mediocre (in Terry’s eyes) grad student looking at this, but I can’t seem to elicit that. Instead I get low tier ML blogspam author.

**PS** if anyone read this far (doubtful) and knows about density estimation and wants to help my email is bglazer1@gmail.com

I promise its a fun mathematical puzzle and the biology is pretty wild too

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1. MoonGhost ◴[07 Apr 25 08:39 UTC] No.43609218[source]▶
I was working some time ago on image processing model using GAN architecture. One model produces output and tries to fool the second. Both are trained together. Simple, but requires a lot extra efforts to make it work. Unstable and falls apart (blows up to unrecoverable state). I found some ways to make it work by adding new loss functions, changing params, changing models' architectures and sizes. Adjusting some coefficients through the training to gradually rebalance loss functions' influence.

The same may work with you problem. If it's unstable try introduce extra 'brakes' which theoretically are not required. May be even incorrect. Whatever it is in your domain. Another thing to check is optimizer, try several. Check default parameters. I've heard Adams defaults lead to instability later in training.

PS: it would be heaven if models could work at human expert level. Not sure why some really expect this. We are just at the beginning.

PPS: the fact that they can do known tasks with minor variations is already a huge time saver.

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2. bglazer ◴[07 Apr 25 15:38 UTC] No.43612717[source]▶
Yes, I suspect that engineering the loss and hyperparams could eventually get this to work. However, I was hoping the model would help me get to a more fundamental insight into why the training falls into bad minima. Like the Wasserstein GAN is a principled change to the GAN that improves stability, not just fiddling around with Adam’s beta parameter.

The reason I expected better mathematical reasoning is because the companies making them are very loudly proclaiming that these models are capable of high level mathematical reasoning.

And yes the fact I don’t have to look at matplotlib documentation anymore makes these models extremely useful already, but thats qualitatively different from having Putnam prize winning reasoning ability

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3. MoonGhost ◴[08 Apr 25 01:19 UTC] No.43617488[source]▶
One thing I forgot. Your solution may never converge. Like in my case with GAN after training models start wobbling around some point trying to outsmart each other. Then they _always_ explode. So, I was saving them periodically and took the best intermediate weights.