Demis Hassabis

I think that's actually one of the areas that

A lot more research needs to be done on mechanistic analysis of the representations that these systems build up.

I sometimes like to call it virtual brain analytics.

In a way, it's a bit like doing fMRI or single cell recording from a real brain.

What's the analogous analysis techniques for these artificial minds?

There's a lot of great work going on on this sort of stuff.

People like Chris Ola.

I really like his work and a lot of computational neuroscience techniques, I think, could be brought to bear on analyzing these current systems we're building.

In fact, I try to encourage a lot of my computational neuroscience friends to start thinking in that direction and applying their know-how to the large models.

I think neuroscience has added a lot.

If you look at the last sort of 10, 20 years that we've been at it at least, and I've been thinking about this for 30 plus years.

I think in the earlier days of this sort of new wave of AI, I think neuroscience was providing a lot of interesting directional clues.

So things like reinforcement learning, combining that with deep learning, some of our pioneering work we did there, things like experience replay, even the notion of attention, which has become super important.

A lot of those original sort of inspirations come from some understanding about how the brain works, not the exact specifics.

Of course, one's an engineered system, the other one's a natural system.

So it's not so much about a one-to-one mapping of a specific algorithm.

It's more kind of inspirational direction, maybe some ideas for architecture or algorithmic ideas or representational ideas.

And because you know the brain's an existence proof that general intelligence is possible at all, I think the history of human endeavors has been that once you know something's possible, it's easier to push hard in that direction because you know it's a question of effort then and sort of a question of when, not if.

And that allows you to, I think, make progress a lot more quickly.

So I think neurosciences has inspired a lot of the thinking, at least in a soft way, behind where we are today.