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Here are my notes from Wolfram’s talk. He pulled up a visualization of the first 400 rows of Rule 30 (see comments below) in his exploration of the simple iterative math of cellular automata:
“Rule 30 is my favorite discovery in all of science. I first saw in 1982. I didn’t appreciate it at first. It is like turning a telescope into the mathematical universe and seeing the moons of Jupiter for the first time.”

“Rule 30 was the random number generator for Wolfram Alpha for many years. We screened trillions of alternatives before finding more efficient one.”

Wolfram mentioned Estonia ten times in his query examples. Such a warm start.

“I started to study cellular automata in 1980. I wanted to study neural networks but they were too much of a mess. So I went to cellular automata.”

“I have become interested in smart contracts. Proof of work in Ethereum is an instance of computational irreducibility.”

In response to my question at the end: “We are stuck in a universe that is stuck with the computational constraints of a Turing machine. That is why we have computational irreducibility. But the frontier of interesting theorems expands forever. We will never invent all of the interesting things.”

“Evolution is a lame way of explaining what’s possible. It only explores small moves.”

In 2004, I asked Wolfram about quantum computational equivalence.

I still wonder if quantum computers will overturn the constraints of the Turing thesis. See Aaronson’s comments here

2 responses to “Wolfram on the Age of AI”

  1. Wolfram and his baby, Rule 30 Speaking at the Age of AI today
    With Wolfram and Urban and a bunch of bright folks. Agenda
    And Rule 110, starting with random seeds… It turns out to be computationally complete

  2. Wolfram just gave an update to his skepticism about quantum computers in his blog:

    "We’ve often been asked: “What does all this mean for quantum computing?” The basic idea of quantum computing—captured in a minimal form by something like a multiway Turing machine—is to do different computations in parallel along different possible threads of history. But the key issue (that I’ve actually wondered about since the early 1980s) is then how to corral those threads of history together to figure out a definite answer for the computation. And our models give us ways to look “inside” that process, and see what’s involved, and how much time it should take. We’re still not sure about the answer, but the preliminary indication is that at least at a formal level, quantum computers aren’t going to come out ahead. (In practice, of course, investigating physical processes other than traditional semiconductor electronics will surely lead to even perhaps dramatically faster computers, even if they’re not “officially quantum”.)"

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