The following is a conversation with Jay McClelland, a cognitive scientist that Stanford

and one of the seminal figures in the history of artificial intelligence and specifically

neural networks.

Having written the parallel distributed processing book with David Rommelhart, who co-authored

the Backpropagation paper with Jeff Hinton.

In their collaborations, they've paved the way for many of the ideas at the center of

the neural network-based machine learning revolution of the past 15 years.

To support this podcast, please check out our sponsors in the description.

As usual, I'll do a few minutes of ads now, no ads in the middle.

I try to make these interesting, so hopefully you don't skip, but if you do, please

still check out the sponsor links in the description, it's the best way to support this podcast.

I use their stuff, I enjoy it, maybe you will too.

This show is brought to you by PaperSpace Gradient, which is a platform that lets you build, train,

and deploy machine learning models of any size and complexity.

I love how powerful any intuitive it is.

I'm likely going to use PaperSpace for a couple of machine learning experiments I'm doing

as part of an upcoming video.

Fast AI, maybe you've heard of them, Jeremy Howard runs it, it's a course I highly recommend,

and Fast AI uses PaperSpace Gradient.

You can host notebooks on there, you can swap out computer instances at any time, start

on a small scale GPU instance or even CPU instance, and then swap out once your compute

needs increase.

...