Jaden Schaefer

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.

And then of course, they don't scale, right?

Every time the world changed, you had to update the rules manually and then

Once, you know, that happens, and if it breaks, then of course, the hype is kind of ahead of the reality.

And so everyone gets disappointed.

And then you get another AI winter, right?

Because these these tools worked for like a moment.

And as things change in the world, they stop working.

So this is where I think it kind of gets a little bit interesting for AI, the whole field took an interesting turn.

So symbolic AI was definitely struggling.

It was definitely a totally different approach than what we needed to do, because what we needed to do was machine learning.