Stephen Wolfram

So it's kind of a, you know, that's a thing where you can kind of extrapolating from all those examples makes it easier to do that task.

Oh, no, no, no.

I mean, so there are many steps here.

Okay, so first, the first thing is you type natural language.

It generates well from language.

It's like take my heart rate data and, you know, figure out whether I, you know, make a moving average every seven days or something and work out what the – and make a plot of the results.

Okay?

So that's a thing which is, you know –

about two-thirds of a line of orphan language code.

I mean, it's, you know, list plot of moving average of some data bin or something of the data, and then you'll get the result.

And, you know, the vague thing that I was just saying in natural language could...

would almost certainly correctly turn into that very simple piece of orphan language code.

So you start mumbling about heart rate.

Right.

You say average over seven days.

Maybe it'll figure out that that can be encapsulated as this moving average idea.

I'm not sure.

But then the typical workflow that...

I'm seeing is you generate this piece of orphan language code.

It's pretty small, usually.