Jaden Schaefer

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.

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.

So

Early kind of versions of neural networks existed like all the way as far back as the 1950s, but they were super, super limited.

Computers were very slow.

Data, of course, there's not a lot of data on this and the math was very hard.