Stephen Wolfram

Right, and so it has this thing where you can, but, you know, thinking about sort of chat GPT and its use and so on,

One of the big things about it, I think, is it's a linguistic user interface.

That is, so a typical use case might be, and take the journalist case, for example.

It's like, let's say I have five facts that I'm trying to turn into an article, or I'm trying to write a report where I have basically five facts that I'm trying to include in this report.

But then I feed those five facts to ChatGPT, it puffs them out into this big report.

And then that's a good interface for it.

If I just gave...

if I just had in my terms, those five bullet points and I gave them to some other person, the person would say, I don't know what you're talking about because these are, you know, this is your version of this sort of quick notes about these five bullet points.

But if you puff it out into this thing, which is kind of connects to the collective understanding of language, then somebody else can look at it and say, okay, I understand what you're talking about.

Now you can also have a situation where that thing that was puffed out is fed to another large language model.

You know, it's kind of like,

You know, you're applying for the permit to, you know, I don't know, grow fish in some place or something like this.

And you have these facts that you're putting in, you know, I'm going to have a, you know, I'm going to...

have this kind of water and I don't know what it is.

You've just got a few bullet points.

It puffs it out into this big application.

You fill it out.

Then at the other end, the fisheries bureau has another large language model that just crushes it down because the fisheries bureau cares about these three points.

And it knows what it cares about.

So it's really the natural language produced by the large language model is sort of a transport layer