It’s better at questions but subjectively there doesn’t feel like there’s much transfer. It still gets some basic questions wrong.

O1’s release has made me think Yann Lecun’s AGI timelines are probably more correct than shorter ones

Why is the built-in assumption for almost every single post on this site that alignment is impossible and we need a 100 year international ban to survive? This does not seem particularly intellectually honest to me. It is very possible no international agreement is needed. Alignment may turn out to be quite tractable.

I guess in the real world the rules aren’t harder per se but just less clear and not written down. I think both the rules and tools needed to solve contest math questions at least feel harder than the vast majority of rules and tools human minds deal with. Someone like Terrence Tao, who is a master of these, excelled in every subject when he was a kid (iirc).

I think LLMs have a pretty good model of human behavior, so for anything related to human judgement, in theory this isn’t why it’s not doing well.

And where rules are unwritten/unknown (say biology), are the rules not at least captured by current methods? The next steps are probably like baking the intuitions of something like alphafold into something like o1. Whatever that means. R&D is what’s important and there is generally vast sums of data there.

O1 probably scales to superhuman reasoning:

O1 given maximal compute solves most AIME questions. (One of the hardest benchmarks in existence). If this isn’t gamed by having the solution somewhere in the corpus then:

-you can make the base model more efficient at thinking

-you can implement the base model more efficiently on hardware

-you can simply wait for hardware to get better

-you can create custom inference chips

Anything wrong with this view? I think agents are unlocked shortly along with or after this too.

https://x.com/arcprize/status/1849225898391933148?s=46&t=lZJAHzXMXI1MgQuyBgEhgA

My read of the events. Anthropic is trying to raise money and rushed out a half baked model.

3.5 opus has not yet had the desired results. 3.5 sonnet, being easier to iterate on, was tuned to beat OpenAI’s model on some arbitrary benchmarks in an effort to wow investors.

With the failed run of Opus, they presumably tried to get o1 like reasoning results or some agentic breakthrough. The previous 3.5s was also particularly good because of a fluke of the training run rng (same as gpt4-0314), which makes it harder for iterations to beat it.

They are probably now rushing to scale inference time compute. I wonder if they tried doing something with steering vectors initially for 3.5 opus.

A while ago I predicted that I think there's a more likely than not chance Anthropic would run out of money trying to compete with OpenAI, Meta, and Deepmind (60%).  At the time and now, it seems they still have no image video or voice generation unlike the others, and do not process image as well in inputs either. 
 

 OpenAI's costs are reportedly at 8.5 billion. Despite being flush in cash from a recent funding round, they were allegedly at the brink of bankruptcy and required a new, even larger, funding round.  Anthropic does not have the same deep pockets as the other players. Big tech like apple who are not deeply invested in AI seem to be wary of investing in OpenAI. It stands to reason, Amazon may be as well. It is looking more likely that Anthropic will be left in the dust (80%), 
 

The only winning path I see is a new more compute efficient architecture emerges, they are first, and they manage to kick of RSI before more funded competitors rush in to copy them. Since this seems unlikely I think they are not going to fare well. 

Really? He seems pretty bullish. He thinks it will co author math papers pretty soon. I think he just doesn’t think or at least state his thoughts on implications outside of math.

Except billionaires give out plenty of money for philanthropy. If the AI has a slight preference to keeping humans alive, things probably work out well. Billionaires have a slight preference to things they care about instead of random charities. I don’t see how preferences don’t apply here.

This is a vibes based argument using math incorrectly. A randomly chosen preference from a distribution of preferences is unlikely to involve humans, but that’s not necessarily what we’re looking at here is it.