Geoffrey Hinton

You know, it gets the information it needs to know whether it needs to increase the connection strength to be better at a task, or to decrease that connection strength.

But what we do know is we know how to do it in digital computers now.

At doing this particular function.

At one thing.

And that's what got me really nervous in the beginning of 2023.

The idea that digital intelligence might just be better than the analog intelligence we've got.

I do have an 18-hour course on this, but I will try and cut it down to less than 18 hours.

Please do.

So, I imagine a lot of your audience knows some physics.

And one way into it is to think about something like the gas laws.

You know, you compress the gas and it gets hotter.

Why does it do that?

Well, underneath, there's a kind of seething mass of atoms that are buzzing around.

And so the real explanation for the gas laws is in terms of these microscopic things that you can't even see buzzing around.

And so you explain some macroscopic behavior

by lots and lots and lots of little things of a completely different type from macroscopic behavior interacting.

And that was sort of the inspiration for the neural net view, that there's things going on in big networks of brain cells

that are a long way away from the kind of conscious, deliberate symbol processing we do when we're reasoning, but that underpin it, and that are maybe better at other things than reasoning, like perception or reasoning by analogy.

So the symbolic people could never deal with how do we reason by analogy, not very satisfactorily, whereas the neural nets could.

So before I get into the sort of fine details of how it works,