Yoshua Bengio

Even though there's AlphaGo, Google is not making money on this right now.

But I think over the long term, this is really, really important for many reasons.

So in other words, I would say reinforcement learning may be more generally agent learning because it doesn't have to be with rewards.

It could be in all kinds of ways that an agent is learning about its environment.

Well, GANs or other generative models, I believe, will be crucial ingredients in building agents that can understand the world.

A lot of the successes in reinforcement learning in the past has been with policy gradient where you just learn a policy.

You don't actually learn a model of the world.

But there are lots of issues with that.

And we don't know how to do model-based RL right now.

But I think this is where we have to go.

in order to build models that can generalize faster and better, like to new distributions that capture to some extent, at least the underlying causal mechanisms in the world.

Last question.

You know, when I was an adolescent, I was reading a lot and then I started reading science fiction.

There you go.

That's it.

That's where I got hooked.

And then, you know, I had one of the first personal computers and I got hooked in programming.

And so it just, you know.

Start with fiction and then make it a reality.