Demis Hassabis

to create deep reinforcement learning, and then use that to scale up to complete some, master some pretty complex tasks like playing Atari games just from the pixels.

And I do actually think a lot of those ideas need to come back in again.

And as we talked about earlier, combine it with the new advances in large models and large multimodal models, which is obviously very exciting as well.

So I do think there's a lot of potential for combining some of those older ideas together with the new ones.

I think I certainly, you know, theoretically, I think there's no reason why you couldn't go full alpha zero like on it.

And there are some people here at Google DeepMind and in the RL community who work on that, right?

Fully assuming no priors, no data, and just build all knowledge from scratch.

And I think that's valuable because, of course, you know, those ideas and those algorithms should also work when you have some knowledge too.

Yeah.

But having said that, I think by far, probably my betting would be the quickest way to get to AGI and the most likely plausible way is to use all the knowledge that's existing in the world right now on things like the web and that we've collected.

And we have these scalable algorithms like Transformers that are capable of ingesting all of that information.

And I don't see why you wouldn't start with a model as a kind of prior or to build on and to make predictions that helps bootstrap your learning.

I just think it doesn't make sense not to make use of that.

So my betting would be is that the final AGI system will have these large multimodal models as part of the overall solution, but probably won't be enough on their own.

You will need this additional planning search on top.

Yeah, look, my view is this is kind of an empirical question right now.

So I think it was pretty surprising to almost everyone, including the people who first worked on the scaling hypotheses, that how far it's gone.

In a way, I mean, I sort of look at the large models today and I think they're almost unreasonably effective for what they are.

You know, I think it's pretty surprising some of the properties that emerge, things like

it's clearly, in my opinion, got some form of concepts and abstractions and some things like that.