Expert system

In 1996, the dominant paradigm for artificial intelligence was completely different from what we're looking at now.

Gates specifically mentions these early approaches like Prolog and expert systems, which had, you know, mostly led nowhere.

Yeah.

And to understand his skepticism, we have to look at why those systems failed.

It relied on what we call symbolic AI.

Right, symbolic AI.

Which is a crucial distinction because symbolic AI or expert systems, they operated on this assumption that you could create intelligence by manually hard coding every single rule of the universe into a computer.

Like if A happens, then do B.

And most people did not know about AI back then.

Well, back then, there was a particular way of understanding how to build intelligent systems that was called expert systems, by the way.

That was back then.

And the idea was to kind of capture the knowledge of some expert and put that knowledge in some kind of a...

Okay.

So that was actually the first generation of AI.

It's called expert systems.

And over time, what we realized was that These systems are very brittle, meaning that if you go a little bit outside of that knowledge base that's been captured and represented, the system either doesn't know what to do at all or it gives some completely wrong and well, you know, falsifiable kinds of information, which nobody liked.

adopt it? So it's actually been about 11 years since we started playing around with AI.

in our immigration practice, and we were building what is, a lot of people don't even think of as AI at this point, these expert systems that were, you know, basically, I'm not, I am, by the way, I come from, I have a political science degree.

I'm not a, you know, I didn't come from a tech background.

There are a lot of lawyers who have those, and some of them are pretty, are pretty amazing, but I have always been pretty good at figuring out how to make technology work for me, but not being somebody who could just like go out and code.