Demis Hassabis

And why couldn't that also be applied to finding medicines? And so my hope is 10, 15 years time, we'll look back on the medicine we have today, a bit like how we look back on medieval times and how we used to do medicine then. And that would be, I think, the most incredible benefit we could imagine from AI.

And why couldn't that also be applied to finding medicines? And so my hope is 10, 15 years time, we'll look back on the medicine we have today, a bit like how we look back on medieval times and how we used to do medicine then. And that would be, I think, the most incredible benefit we could imagine from AI.

And why couldn't that also be applied to finding medicines? And so my hope is 10, 15 years time, we'll look back on the medicine we have today, a bit like how we look back on medieval times and how we used to do medicine then. And that would be, I think, the most incredible benefit we could imagine from AI.

So I wouldn't be surprised if we had HEI-like systems within the next decade.

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 look at the large models today and I think they're almost unreasonably effective for what they are.

It's an empirical question whether that will hit an asymptote or a brick wall.

As Gemini are becoming more multimodal and we start ingesting audiovisual data as well as text data, I do think our systems are going to start to understand the physics of the real world better.

The world's about to become very exciting, I think, in the next few years as we start getting used to the idea of what true multimodality means.

Well, it's interesting because intelligence is so broad and, you know, what we use it for is so sort of generally applicable.

I think that suggests that, you know, there must be some sort of high level common things in, you know, common kind of algorithmic themes.

I think, around how the brain processes the world around us.

Of course, then there are specialized parts of the brain that do specific things, but I think there are probably some underlying principles that underpin all of that.

Well, first of all, I think you do actually sometimes get surprising improvement in other domains when you improve in a specific domain.

For example, when these large models improve at coding, that can actually improve their general reasoning.

There is some evidence of some transfer, although I think we would like a lot more evidence of that.

But also, you know, that's how the human brain learns, too, is if we experience and practice a lot of things like chess or, you know, writing, creative writing or whatever that is, we also tend to specialize and get better at that specific thing, even though we're using sort of general learning techniques and general learning systems in order to, you know, to get good at that domain.

Yeah, I think probably, I mean, I'm hoping we're going to see a lot more of this China transfer, but I think things like getting better at coding and math and generally improving your reasoning, that is how it works with us as human learners.

But I think it's interesting seeing that in these artificial systems.

Yeah, well, I don't think our analysis techniques are quite sophisticated enough to be able to hone in on that.