Michael Levin

Okay, that makes sense.

But here's something interesting.

Suppose we plot at any given moment, we plot the degree of sortedness of the string as a function of time.

If you run the normal algorithm, it's guaranteed to get where it's going.

It's got a sort and it will always reach the end.

But when it encounters one of the broken digits,

what happens is the actual sortedness goes down in order to then recoup and get better order later.

What it's able to do is to go against the thing that it's trying to do, to go around in order to meet its goal later on.

Now, if I showed this to a behavior scientist and I didn't tell them what system was doing, they will say, well, we know what this is.

This is delayed gratification.

This is the ability of a system to go against its gradient and get what it needs to do.

Now, imagine two magnets.

Imagine you take two magnets and you put a piece of wood between them and they're like this.

What the magnet is not going to do is to go around the barrier and get to its goal.

They're not smart enough to go against their gradient.

They're just going to keep doing this.

Some animals are smart enough, right?

They'll go around and...

The sorting algorithm is smart enough to do that, but the trick is there are no steps in the algorithm for doing that.

You could stare at the algorithm all day long.