Jaeden Schaefer

You see this pattern, and if the computer sees a pattern, it's going to respond in a specific way.

And in that really narrow domain, this actually worked.

You could build programs that played chess or that solved logic puzzles or things that did basic math proofs.

But the second that you took them outside of these kind of, you know, really small controlled environments, everything broke, right?

This is not actual intelligence.

I mean, we know what these are.

It's just kind of computer systems.

But at the time, they believed they truly had achieved, you know, artificial intelligence.

So...

Of course, we know that the real world is very messy.

Language is ambiguous.

Vision is very noisy.

As humans, we are relying on intuition in our experience and also on context.

There's a lot of things that aren't just rules.

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

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

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 they collapsed and this kind of was uh 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.