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Charting a Course for “Complexity”: Metamodeling, Ruliology and More

Stephen Wolfram

Could it really be that this was the secret that nature had been using all along to make complexity? It wasn’t long before I realized something fundamental: that this was at its core a computational phenomenon. But it really wasn’t physics, or computer science, or math, or biology, or economics, or any known field.

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The Physicalization of Metamathematics and Its Implications for the Foundations of Mathematics

Stephen Wolfram

And if we’re going to make a “general theory of mathematics” a first step is to do something like we’d typically do in natural science, and try to “drill down” to find a uniform underlying model—or at least representation—for all of them. From a computer science perspective, we can think of it as being like a type hierarchy.

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The Concept of the Ruliad

Stephen Wolfram

The global structures of metamathematics , economics , linguistics and evolutionary biology seem likely to provide examples—and in each case we can expect that at the core is the ruliad, with its unique structure. But what about other models of computation—like cellular automata or register machines or lambda calculus?

Physics 122
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What Is ChatGPT Doing … and Why Does It Work?

Stephen Wolfram

It’s not obvious that it would be feasible to find the path of the steepest descent on the “weight landscape” But calculus comes to the rescue. As we mentioned above, one can always think of a neural net as computing a mathematical function—that depends on its inputs, and its weights.

Computer 145