Demis Hassabis

So I think it was in the same ballpark.

I think we're very efficient with our compute and we use our compute for many things.

One is not just the scaling, but going back to earlier to these more innovation and ideas, it's only useful, a new innovation, a new invention, if it also can scale.

So in a way, you also need quite a lot of compute to do new invention because you've got to test many things at least some reasonable scale and make sure that they work at that scale.

And also some new ideas may not work at a toy scale, but do work at a larger scale.

And in fact, those are the more valuable ones.

So you actually, if you think about that exploration process, you need quite a lot of compute to be able to do that.

I mean, the good news is, is I think, you know, we're pretty lucky at Google that we, I think this year, certainly we're going to have the most compute by far of any sort of research lab.

And, you know, we hope to make very efficient and good use of that in terms of both scaling and the capability of our systems and also new inventions.

We thought that, and actually, I know you've interviewed my colleague Shane, and he always thought that in terms of compute curves and then maybe comparing roughly to the brain and how many neurons and synapses there are very loosely.

But we're actually, interestingly, in that kind of regime now, roughly in the right order of magnitude of number of synapses in the brain and the sort of compute that we have.

But I think more fundamentally, we always thought that we bet on generality and learning.

So those were always at the core of any technique we would use.

That's why we triangulated on reinforcement learning and search and deep learning as three types of algorithms that would scale and would be very general and not require a lot of handcrafted human priors, which we thought was the sort of failure mode really of the efforts to build AI in the 90s, places like MIT where there were very logic-based systems, expert systems.

you know, masses of hand coded, handcrafted human information going into that turned out to be wrong or too rigid.

So we wanted to move away from that.

And I think we spotted that trend early and became, you know, and obviously we use games as our proving ground and we did very well with that.

And I think all of that was very successful.

And I think it's maybe inspired others to, you know, things like AlphaGo, I think was a big moment for inspiring many others to

think, oh, actually, these systems are ready to scale.