Jaden Schaefer

There's not just this math that you can kind of have a computer solve.

And so no matter how many rules you write, you never can actually capture everything that happens in reality.

And so

This led to one of the first big AI disappointments, you could say.

And because of this, a lot of funding to the program dried up a lot of expectations, people just, you know, like kind of basically collapsed.

And this kind of was known as by a lot of researchers as AI winter.

So governments, universities, basically all just like, yeah, well, this isn't really AI, it's not really working, we'll continue developing computers, but we're not really focusing on that specific direction.

So

That kind of froze for a while.

But then in the 1980s, AI came back in a bit of a new form.

And that was these expert systems.

So they were essentially programs designed to replicate decision making of human experts.

So doctors, engineers, chemists, people with very specialized knowledge.

And companies poured a ton of money into those systems.

Because again, you know, in really narrow domains, they actually worked quite well.

So you could encode expert knowledge, you could get really interesting outputs.

The problem was that they were very brittle systems.

They're also incredibly expensive to build.

And I don't think enough people talk about that.

They're really expensive to maintain.