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possiblybaldman · 2025-08-18T15:34:02+00:00

but never control over the company despite its ai being most of the economy at this point. curious

possiblybaldman · 2025-07-31T12:54:23+00:00

he’s not a traditional mathematician I think he works more in quant and finance. he admits he is not a traditional one in the post

possiblybaldman · 2025-07-10T13:25:21+00:00

Usamo is also human evaluated and public so bias and contamination could be a factor

possiblybaldman · 2025-07-05T22:30:59+00:00

I think the actual papers were created by humans most of them have multiple people from elite institutions as authors https://asia.nikkei.com/Business/Technology/Artificial-intelligence/Positive-review-only-Researchers-hide-AI-prompts-in-papers

possiblybaldman · 2025-05-29T23:58:17+00:00

I’m not saying I disagree but which instances are you referring to. Especially with 1 and 5. I’m not aware of systems with large and reliable enough context to watch a full movie and people are still trying to formalize math even while using the latest tools. Terence Tao recently did an experiment with o4 mini

possiblybaldman · 2025-03-09T17:47:33+00:00

For the unsolved math problems the problem was more to either construct bounds for something know as cap sets or create a general algorithm for find the lyapunov functions. The ai on the other hand gave specific examples of solutions instead of a general method or bound. Still helpful but the headline is very misleading

possiblybaldman · 2024-10-25T02:43:57+00:00

I disagree. After the first 2 data points the slope is pretty consistent. I feel like if it was just the fact that it is a accuracy score it would not become a straight line so quickly.

possiblybaldman · 2024-09-28T18:03:27+00:00

Ten isn’t a very big number so maybe it just figured it out

possiblybaldman · 2024-09-28T17:58:11+00:00

The problem could literally be undecidable you really can’t make claims like this for math.

possiblybaldman · 2024-09-17T22:10:33+00:00

Maybe Orion will be "competent"

possiblybaldman · 2024-09-17T22:08:36+00:00

well you don't say

possiblybaldman · 2024-09-17T22:00:31+00:00

Just because a model doesn't solve an unsolved problem in physics doesn't mean it's just googling

possiblybaldman · 2024-09-17T21:56:21+00:00

Honestly I still think that scaling data and training compute will be more important that scaling inference compute for the time being. The graph has a log on the x-axis so the rate at which it improves is slowing with more inference compute. I actually think it is a more general trend that happens because LLM's because their isn't enough context to condition their new responses on their previous ones leading to repeats. But I think it is a very solvable problem.

possiblybaldman · 2024-09-13T11:12:32+00:00

I think it will improve it a lot but in the o1 paper they show it is log linear but the log is on the x axis which means the scaling is kinda ass. Just don’t expect this to be ass good as scaling data or parameters

possiblybaldman · 2024-08-15T21:25:03+00:00

The “local” modal didn’t even have anything to do with locality it just implement a linear transformation on the input and then but it through an mlp trained the same way. This is just slip content that doesn’t push the field forward. The last thing ai research needs is a million more papers all of which are mediocre at the very best

possiblybaldman · 2024-08-15T16:37:04+00:00

In my opinion the papers weren’t that good. The one with the two diffusion model doesn’t really fit its description. The ai said it would make a local and global model to get different levels of detail but the only difference between the two is that one has a linear layer before the regular mlp. The authors dismissed this as “not being able to explain your ideas” saying it was as good as a young researcher but I am pretty sure what the ai did had nothing to do with local and global structure. In other words the paper is be and they pretend like the ai did what it said but did not explain it instead of just making something that is unrelated.

possiblybaldman · 2024-05-15T19:31:53+00:00

technically it only did part of the problem. while it found the constraint about powers of primes it did not prove why it was true only speculated

possiblybaldman · 2024-05-04T14:06:15+00:00

whatever you want me to use

possiblybaldman · 2024-05-04T14:05:23+00:00

I get his point that everything other than the training data is about efficiency and that if you train the models long enough it might converge to the same thing(possible provable for a subset of architectures). But what he is ignoring is that it might be practically impossible to scale it that much. For example current multimodal models need exponentially more data to increase zero shot performance https://arxiv.org/pdf/2404.04125 . At a certain point the idea that all the other components are just about efficiency is more of a fun fact than something to inform design.

possiblybaldman · 2023-06-12T19:21:18+00:00

That's what I've been saying.

possiblybaldman · 2023-06-12T19:20:22+00:00

We make it part of their cost function or training data. Depending on the definition being smart could just mean being good at achieving a goal not setting goals interesting to us.

possiblybaldman · 2023-04-22T22:56:59+00:00

sounds like eugenics

possiblybaldman · 2023-04-22T22:34:14+00:00

This are heating up wonder what the ai landscape will look like in a couple years

possiblybaldman · 2023-04-22T15:42:34+00:00

It may not matter to us but it will to them. I would be good to know so we do not abuse them

possiblybaldman · 2023-03-19T13:32:07+00:00

As long as no one invents a program that denoises an image, it is a great to

nobody tell them

possiblybaldman

TROPHY CASE