Demis Hassabis

what you want to do is when you figure out something where there's one of these hybrid systems, what you ultimately want to do is upstream it into the learning component.

So it's always better if you can do end-to-end learning and directly predict the thing that you're after from the data that you're given.

So once you've figured out something using one of these hybrid systems, you then try and go back and reverse engineer what you've done and see if you can incorporate that learning, that information into the learning system.

And this is sort of what we did with AlphaZero, the more general form of AlphaGo.

So AlphaGo had some Go-specific knowledge in it.

But then with AlphaZero, we got rid of that, including the human data, human games that we learned from, and actually just did self-learning from scratch.

And of course,

then it was able to learn any game, not just Go.

Look, interestingly, again, I think both cases are true in the sense that, especially us at Google and at DeepMind, we focus a lot on very efficient models.

that are powerful.

Because we have our own internal use cases, of course, where we need to serve, say, AI overviews to billions of users every day.

And it has to be extremely efficient, extremely low latency, and very cheap to serve.

And so we've kind of pioneered many techniques that allow us to do that, like distillation, where you sort of have a bigger model internally that trains the smaller model.

So you train the smaller model to mimic the bigger model.

And over time, if you look at the progress of the last two years, the model efficiencies are like 10x, even 100x better for the same performance.

Now, the reason that that isn't reducing demand is because we're still not got to AGI yet.

So also the frontier models, you keep wanting to train and experiment with new ideas at larger and larger scale, whilst at the same time, at the serving side, things are getting more and more efficient.

So both things are true.

And in the end, I think that from the energy perspective,

I think AI systems will give back a lot more to energy and climate change and these kind of things than they take in terms of efficiency of grid systems and electrical systems, material design, new types of properties, new energy sources.