Geoffrey Hinton

Or it might be a crow.

So they might be black, they might be white, they might be tiny, they might be flying, they might be close, you might just see a little bit of them.

There might be lots of other cluttered things around, like it might be a bird in the middle of a forest.

So it turns out it's not trivial to say whether there's a bird in the image or not.

And so what I'm going to do now is explain to you, if I was building a neural network by hand, how I would go about doing that.

And once I've explained how I would build the neural network by hand, I can then explain how I might learn all the connection strengths instead of putting them in by hand.

It's just got a bunch of numbers.

So what do you do?

Well, the question is, how do you just know that?

There's something going on in your brain, right?

What might be going on in your brain so that you just know that's a bird is a whole bunch of activation levels of different neurons, which you could think of as mathematical values.

And what would be the... Oh, okay.

All right, here comes your answer.

There's something called generalization.

So if you see a lot of data, obviously you can make a system that just remembered all that data.

But in a neural net, it'll do more than just remember the data.

In fact, it won't literally remember the data at all.

What it'll do is, as it's learning on the data, it'll find all sorts of regularities.

And it'll generalize those regularities to new data.

So it will be able to, for example, recognize a unicorn.