Jaeden Schaefer

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 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 tools worked for like a moment and as things changed in the world, they stopped 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.

So instead of telling a computer exactly what to do,

You let it learn from data, and the idea was inspired by human brain neurons, the connections, and then kind of learning from experience.