Rob Wiblin

I think most people would say that they were impressed by how good the reasoning was and how useful it seemed like it was going to be.

But it sounds like,

you weren't impressed by.

It wasn't surprisingly good to you.

Or maybe that's not it.

It's that it wasn't as generalizable, that they were good at the reasoning tasks that they had been RL'd on, but I didn't expect that that was going to generalize to other tasks and be very economically transformative, and indeed it has not.

Yeah, that's basically right.

Okay, so that's the thing that would cause you to have a timelines freakout would be if you trained models on one kind of practical task and then you found that they were actually like good and like useful at doing quite different kinds of tasks.

Let's talk about Google DeepMind, which for a very important organization, I feel like isn't super well understood by people outside, including listeners and I guess I'd say outsiders just in general.

A lot of people outside of AI companies produce research, I guess like both governance research and technical research, hoping that it will be read and absorbed and adopted and used by those companies, including GDM.

What can people do to make it more likely that anything that they do actually does have any, like it's read at all or is used at all by people inside an AI company?

If people consistently did that, do you think it'd be like a lot of your time would then be eaten up replying to these emails?

What should people do other than email to ask whether what they're doing is going to be useful?

Okay, but you're trying to stop it now.

We are trying to stop it now, yes.

I guess I should say all this advice is predicated on the idea that you're doing research hoping that an AI company is going to read it and absorb it and use it.

There's people who have their own different ideas and they'll be developing stuff hoping that people will become persuaded later on that it's useful or it'll be valuable in a different way.

Yeah.

Are there any other examples of things that are easy to implement other than that one?

I guess you're saying cutting edge stuff might be useful in some other way that you advance the science somehow and could be valuable down the line.