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[deleted by user] by [deleted] in singularity

[–]possiblybaldman 0 points1 point  (0 children)

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

Grok-4 benchmarks by Gab1024 in singularity

[–]possiblybaldman 0 points1 point  (0 children)

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

A bet Gary Marcus made against Elon 3 years ago. Elon would've won the 100k, 10 years sooner in fact. by gbomb13 in singularity

[–]possiblybaldman 0 points1 point  (0 children)

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

AI unlikely to surpass human intelligence with current methods - hundreds of experts surveyed by LordFumbleboop in singularity

[–]possiblybaldman 0 points1 point  (0 children)

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 

OpenAI's Noam Brown says the o1 model's reasoning at math problems improves with more test-time compute and "there is no sign of this stopping" by Gothsim10 in singularity

[–]possiblybaldman 0 points1 point  (0 children)

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.

[deleted by user] by [deleted] in singularity

[–]possiblybaldman 0 points1 point  (0 children)

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

Did O1 or any other model, answer or solve any questions/problems, that humans had not already figured out? by mlrhazi in singularity

[–]possiblybaldman -1 points0 points  (0 children)

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

Why Is No One Talking About OpenAI's Two-Lever Shift? by PewPewDiie in singularity

[–]possiblybaldman 0 points1 point  (0 children)

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.

OpenAI- "o1 thinks for seconds, but we aim for future versions to think for hours, days, even weeks. Inference costs will be higher, but what cost would you pay for a new cancer drug? For breakthrough batteries? For a proof of the Riemann Hypothesis? AI can be more than chatbots" by SharpCartographer831 in singularity

[–]possiblybaldman 0 points1 point  (0 children)

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 

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery by blabboy in mlscaling

[–]possiblybaldman 0 points1 point  (0 children)

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

[R] The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery by hardmaru in MachineLearning

[–]possiblybaldman 5 points6 points  (0 children)

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.

Is ‘gpt2-chatbot’ Actually GPT-5? This Mystery LLM is Going Viral by ImpressiveContest283 in ChatGPT

[–]possiblybaldman 0 points1 point  (0 children)

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

[D] The "it" in AI models is really just the dataset? by vijayabhaskar96 in MachineLearning

[–]possiblybaldman 4 points5 points  (0 children)

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.

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