Dr. Jeff Beck

And then, of course, what does someone do?

They figure out the new training protocol, slightly different architecture, or they just train it to pull rabbits out of hats and then suddenly it can't.

And then someone proposes a new challenge and a new challenge and a new challenge.

And it's always this game of like one-upsmanship.

So the question becomes, well, what's the point at which there are no more new challenges?

And I'm not entirely certain we're ever going to get there, right?

It may very well be the case that we get these sort of algorithms that are capable of replicating the complete suite of human behaviors.

And then someone will come up with some criticism like, yeah, but it's not really doing X. It's just faking it, right?

This is just the direction things go because people really do think they're important.

Yes.

So I think that one of the most critical missing elements right now is some form of continual learning.

At the end of the day, you really want an algorithm that doesn't just learn on the training set and then just gets deployed.

You want something that runs around in the world and comes across things that it doesn't understand.

And then is able to build, append its model in some sense.

So this is like, and there are some approaches that it's all based on like Bayesian non-parametrics and Dirichlet process priors and stuff like that, where you sort of see something that's surprising or unique or different, something you didn't expect.

And it causes you to say, I need to turn learning on because I got to figure this out.

That is an absolutely critical element that we need to be developing.

We are developing that.

And it turns out that that's one of the nice things about this sort of object-centered physics discovery thing is because it's object-centered, if it comes across a new situation that it does not understand, it is capable of instantiating a completely brand new object just to explain this new situation.

That's a very good question.