Welcome to the Artificial Intelligence Podcast. My name is Lex Friedman. I'm a research scientist at MIT.

If you enjoy this podcast, please rate it on iTunes or your podcast provider of choice.

I simply connect with me on Twitter and other social networks at Lex Friedman spelled F-R-I-D.

Today is a conversation with Yoshio Benjero. Along with Jeff Hinton and Yon Lakun,

he's considered one of the three people most responsible for the advancement of deep learning

during the 1990s and the 2000s and now. Sighted 139,000 times, he has been integral to some of

the biggest breakthroughs in AI over the past three decades.

What difference between biological and neural networks and artificial neural networks

is most mysterious, captivating and profound for you? First of all, there's so much we don't know

about biological neural networks and that's very mysterious and captivating because maybe it holds

the key to improving artificial neural networks. One of the things I studied recently

is something that we don't know how biological neural networks do but would be really useful

for artificial ones is the ability to do credit assignment through very long time spans.

There are things that we can, in principle, do with artificial neural nets but it's not very

convenient and it's not biologically plausible and this mismatch, I think, this kind of mismatch,

maybe an interesting thing to study to understand better how brains might do these things because we

don't have good corresponding theories with artificial neural nets and be maybe provide new ideas

that we could explore about things that brain do differently and that we could incorporate in

artificial neural nets. So let's break credit assignment up a little bit. So what?

It's a beautifully technical term but it could incorporate so many things. So is it more on the

RNN memory side? Is that thinking like that or is it something about knowledge, building up common

sense knowledge over time or is it more in the reinforcement learning sense that you're picking

...