Hello and welcome to 10 blocks. I'm Jordan McGillis, economics editor of City Journal. On the show today is Nick Whitaker.

Nick is a Manhattan Institute fellow, and he's just published a report on artificial intelligence, titled A Playbook for AI Policy. Nick, thanks for joining me.

Thanks for having me. First things first, explain AI to me like I'm five.

Yeah, so look, AI as a term has been around for a long time, at least since the 1950s.

And I think you and I were growing up, you know, we heard about AIs in video games.

And we heard about, you know, AI and software applications.

But these weren't necessarily AIs in any kind of significant way.

It was in sort of the strictest sense, automating cognitive labor, you know, the video game

would, you know, have an NPC that would play against you.

But it wasn't really learning.

It wasn't really able to operate on its own.

It wasn't really able to kind of consider a new idea or to sort

to sort of do anything that hadn't been directly programmed to do. That really started to change

in the 2010s when what's called the Deep Learning Revolution kicked off, where models were made

that could, you know, sort of be trained over large quantities of information.

And then those models actually could sort of engage with you in a more sort of open and general way than any kind of previous thing that was called AI had been able to.

So you first saw these in sort of computer vision and image networks. There

was a program called AlexNet, which was able to recognize objects and images, like a cat or a dog,

with far more accuracy than any previous program had to be able to. You then saw this in kind of the

most pronounced examples in AlphaGo, where for the first time,

a computer was able to not only beat top players in chess and go, but actually able to do this

by simply watching games of chess occur rather than being sort of given

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