I feel like a lot of manifold is virtue signaling .

Just curious. How do you square the rise in AI stocks taking so long? Many people here thought it was obvious since 2022 and made a ton of money. 

 

Keep in mind the current administration is replacing incompetent bureaucracies with self assembling corporations. The organization is still there, just more competent and under a different name. A government project could just look like telling the labs to create 1 data center, throwing money at them, and cutting red tape for building gas plants. 

Seems increasingly likely to me that there will be some kind of national AI project before AGI is achieved as the government is waking up to its potential pretty fast. Unsure what the odds are, but last year, I would have said <10%. Now I think it's between 30% and 60%.

Has anyone done a write up on what the government-led AI project(s) scenario would look like?   

It might just be a perception problem. LLMs don't really seem to have a good understanding of a letter being next to another one yet or what a diagonal is. If you look at arc-agi with o3, you see it doing worse as the grid gets larger with humans not having the same drawback. 

EDIT: Tried on o1 pro right now. Doesn't seem like a perception problem, but it still could be. I wonder if it's related to being a succcesful agent. It might not model a sequence of actions on the state of a world properly yet. It's strange that this isn't unlocked with reasoning. 

Ah so there could actually be a large compute overhang as it stands?

Does deepseek v3 imply current models are not trained as efficiently as they could be? Seems like they used a very small fraction of previous models resources and is only slightly worse than the best LLM.

They did this on the far easier training set though?

An alternative story is they trained until a model was found that could beat the training set but many other benchmarks too, implying that there may be some general intelligence factor there. Maybe this is still goodharting on benchmarks but there’s probably truly something there.

No, I believe there is a human in the loop for the above if that’s not clear.

You’ve said it in another comment. But this is probably an “architecture search”.

I guess the training loop for o3 is similar but it would be on the easier training set instead of the far harder test set.

I think there is a third explanation here. The Kaggle model (probably) does well because you can brute force it with a bag of heuristics and gradually iterate by discarding ones that don't work and keeping the ones that do.