Brian Gerkey

Maybe it's a piece of metal that is going to go into a CNC machine.

It's going to get cut and produce a different product.

But that kind of, that's a core capability to be able to interact with these variability over time.

That's what's been missing from automation.

So...

You know, I think AI is one of those terms that is a, it means different things at different points in time.

You know, when I was in, when I was in undergrad and I, when I first encountered robots, I took a class called artificial intelligence and we had a, we, I know we had a textbook from the, on the older side, this is the mid nineties.

And there was a, you know, a book and we learned all about AI.

Now there was a, there was a, I don't know, a chapter or two in there on this concept of neural networks.

which is like, yeah, this is a thing.

It's been around for a while.

You can go back to the Perceptron from like the 60s, I think.

So the concept of can we, you know, in a way that's loosely inspired by how we understand the human brain to work, can we build these networks and then train them to answer questions for us?

And it was like, yeah, that's kind of cool.

But there were lots of other things along the way that we also consider AI.

And I still consider them AI because I learned them that way.

I think that

One of the challenges of AI is once things start working, we stop thinking about them as AI.

We tend to think, we tend to attach the label AI to whatever the frontier is of the field.

So there are lots of things that we now take for granted that, you know, like Google search, Netflix recommendations.