Yoshua Bengio

Well, yeah.

So there's the sensory space like pixels where everything is entangled.

the the the information like the variables are completely interdependent in very complicated ways and also computation like the the it's not just variables it's also how they are related to each other is is all intertwined but but i i'm hypothesizing that in the right high level representation space both

the variables and how they relate to each other can be disentangled, and that will provide a lot of generalization power.

Generalization power.

Yes.

Right.

This is where current machine learning is too weak.

It doesn't tell us anything.

It's not able to tell us anything about how our neural nets, say, are going to generalize to a new distribution.

And people may think, well, but there's nothing we can say if we don't know what the new distribution will be.

The truth is, humans are able to generalize to new distributions.

How are we able to do that?

Because there is something, these new distributions, even though they could look very different from the twin distributions,

They have things in common.

So let me give you a concrete example.

You read a science fiction novel.

The science fiction novel maybe brings you in some other planet where things look very different on the surface, but it's still the same laws of physics.

And so you can read the book and you understand what's going on.

So the distribution is very different, but because you can transport a lot of the knowledge you had from Earth about the underlying cause and effect relationships and physical mechanisms and all that, and maybe even social interactions, you can now make sense of what is going on on this planet where visually, for example, things are totally different.