Are we playing the question game because the thread was started by Rosencranz? Is China doing well in the EV space a bad thing?

Is it the case that the tech would exist without him? I think that's pretty unclear, especially for SpaceX, where despite other startups in the space, nobody else managed to radically reduce the cost per launch in a way that transformed the industry.

Even for Tesla, which seems more pedestrian (heh) now, there were a number of years where they had the only viable car in the market. It was only once they proved it was feasible that everyone else piled in.

Progress in ML looks a lot like, we had a different setup with different data and a tweaked algorithm and did better on this task. If you want to put an asterisk on o3 because it trained in some specific way that's different from previous contenders, then basically every ML advance is going to have a similar asterisk. Seems like a lot of asterisking.

Hm I think the main thrust of this post misses something, which is that different conditions, even contradictory conditions, can easily happen locally. Obviously, it can be raining in San Francisco and sunny in LA, and you can have one person wearing a raincoat in SF and the other one the beach in LA with no problem, even if they are part of the same team.

I think this is true of wealth inequality.

Carnegie or Larry Page or Warren Buffett got their money in a non exploitative way, by being better than others at something that was extremely socially valuable. Part of what enables that is living in a society where capital is allocated by markets and there are clear price signals.

But many places in the world are not like this. Assad and Putin amassed their wealth via exploitative and extractive means. Wealth at the top in their societies is a tool of oppression.

I think this geographic heterogeneity implies that you should have one kind of program in the US (e.g. with about market failures with goods with potentially very high negative externalities like advanced AI) and another in e.g. Uganda where direct cash transfers (if you are careful to ensure they don't get expropriated by whenever the local oppressors are) could be very high impact.

It seems very strange to me to say that they cheated, when the public training set is intended to be used exactly for training. They did what the test specified! And they didn't even use all of it.

The whole point of the test is that some training examples aren't going to unlock the rest of it. What training definitely does it teach the model how to output the JSON in the right format, and likely how to think about what to even do with these visual puzzles.

Do we say that humans aren't a general intelligence even though for ~all valuable tasks, you have to take some time to practice, or someone has to show you, before you can do it well?

Why does RL necessarily mean that AIs are trained to plan ahead?

"Reliable fact recall is valuable, but why would o1 pro be especially good at it? It seems like that would be the opposite of reasoning, or of thinking for a long time?"

Current models were already good at identifying and fixing factual errors when run over a response and asked to critique and fix it. Works maybe 80% of the time to identify whether there's a mistake, and can fix it at a somewhat lower rate.

So not surprising at all that a reasoning loop can do the same thing. Possibly there's some other secret sauce in there, but just critiquing and fixing mistakes is probably enough to see the reported gains in o1.

Aha, thanks, that makes sense.