Wolfram Language

Um, so l- can I ask you about graph theory? Um, just thinking about graphs, nodes, edges, um, sort of atoms and their relationship to the things around them, nodes and their relationships.

Is that the best language that you know of right now for describing like the universe's fundamental structure? Or, um, what does and does not, uh, graphs help you understand about the universe and in your work?

Right.

I mean, in the Ruliad, there are, there are an infinite number of different representations of the Ruliad.

The question for making a model of science, something that sort of bridges between the way the universe actually is and what we can understand in our minds, that's sort of a challenge of making this kind of language for describing what's going on.

I mean, in my sort of day job life has all been about making our computational language, Wolfram Language, that is a way of representing kind of the world, the aspects of the world that we humans care about in a way that can correspond to something precise, can sort of formalize that in a precise way.

And but when it comes to kind of models of the universe and so on, there's-- it is, it is the case that there are an infinite number of possible different representations.

You have to pick one that is a good kind of ergonomic match for human minds.

Of course, that's not how it worked out at all.

Instead, Mathematica, now Wolfram Language, just raised the level of math that can be done, and over the years has led to all sorts of important new math.

If working out symbolic integrals was what one thinks doing math is really about, then yes, Mathematica has essentially replaced it.

But while that kind of problem-solving is what's needed in many applications of math, it's not the core of what math itself in its pure form is about.

Instead of formalizing what I intended, it found a sometimes rather squirrely way to interpret what I asked so that it could successfully prove it.

It's often very hard to tell, though, that this is what happened, not least because the formalized versions of things tend to be, say, as expressed in popular proof assistant systems, the, uh, very low level, very verbose, and very hard for us humans to understand.

So what can one do? Well, actually, I think there's something very good one can do that's centrally based on the tower of ideas and technology that we've been developing for so long in Wolfram Language.

I mean, our goal with Wolfram Language has always been to create a language or in effect a notation that can represent in precise computational terms things we humans think about.

It's the unique kind of language for computers that is intended for humans to read as well as to write.

And so, you know, the kind of emerging workflow has been, well, yeah, you let the AI write some Wolfram Language code, and then you as a human can read that, and you can say, "Is that the thing I wanted?" And you can kind of sculpt it to kind of express your thoughts in this kind of concrete computational way, and then you can build on that.

And that's kind of a, a, uh, uh, a very kind of...

I think it's a very pivotal piece of the whole picture.

I don't think it's been as widely understood yet as it should be.

Um, I think that the sort of the other component of that is just once things are stated precisely and computationally, then you can go and compute, and that's what the Wolfram Engine and Wolfram Language does.

And, uh, that's, uh, that's kind of the, um, the thing that then becomes kind of the, the, uh, the superpower for the AIs is, okay, go, go and compute stuff with Wolfram Language.

But the thing that's important in sort of the humanization of the whole thing is this is the thing that is the link between, this is the formal link between us humans and kind of what AIs can talk about and, and think about in effect.