Demis Hassabis

Yeah, I'm very optimistic about that.

I mean, I think, well, first of all, there's still a lot more data, I think, that can be used, especially for one views like multimodal and video and these kind of things.

And obviously, you know, society is adding more data all the time.

But I think to the Internet and things like that.

But I think that there's a lot of scope for creating synthetic data.

We're looking at it in different ways, partly through simulation, using very realistic games environments, for example, to generate realistic data, but also self-play.

So that's where systems interact with each other or

or converse with each other.

And in the sense of work very well for us with AlphaGo and AlphaZero, where we got the systems to play against each other and actually learn from each other's mistakes and build up a knowledge base that way.

And I think there are some good analogies for that.

It's a little bit more complicated, but to build a general kind of world data.

Yeah.

So there, I think there's a whole science needed.

And I think we're still in the nascent stage of this, of data curation and data analysis.

So actually analyzing the holes that you have in your data distribution and

And this is important for things like fairness and bias and other stuff to remove that from the system is to try and really make sure that your data set is representative of the distribution you're trying to learn.

And there are many tricks there one can use like overweighting or replaying certain parts of the data.

Or you could imagine if you identify some gap in your data set, that's where you put your synthetic generation capabilities to work on.

Well, actually, I think that, you know, there's the history of the sort of last couple of decades has been things coming in and out of fashion, right?

And I do feel like a while ago and, you know, maybe five plus years ago when we were pioneering with AlphaGo and before that DQN where it was the first system, you know, that worked on Atari, our first big system really more than 10 years ago now that scaled up Q learning and reinforcement learning techniques to deal, you know, combine that with deep learning.