Kurzweil’s take on “A New Kind of Science”

A few days ago, I wrote about Tom Ray’s neat dispatch of Ray Kurzweil’s contention that computers will soon be smarter than we are. To give Mr. Kurzweil his due, here’s a link to a lovely essay critiquing Stephen Wolfram’s A New Kind of Science.
Wolfram’s book became a controversial best-seller based on the author’s claim that computational methods enable a revolutionary approach to science. Many people have criticized the book because Wolfram is an egomaniac who claims to be smarter than everyone else on the planet; because he doesn’t go through the traditional scientific peer review process; and because the sprawling, self-published 1192-page tome really could have used an editor.
Kurzweil ignores the gossip and the copy-editing, and deals with the ideas. Kurzweil’s essay analyzes two of Wolfram’s revolutionary claims: that computational approaches based on cellular automata can explain life and intelligence, and that they define physics.
A quick definition: cellular automata are a type of logical system composed of simple objects whose state is determined by following simple rules about the state of fellow objects; like junior high school girls who will wear tomorrow what the popular girls wore today. The results of many cellular automata are quite boring. Either they fall into a steady state, where nothing changes (class 1), or a simple pattern repeats tediously (class 2), or they twitch forever without any detectable pattern (class 3) But some cellular automata (class 4) are much more interesting. A class 4 automaton generates a complicated pattern that, in Kurzweil’s words, “is neither regular nor completely random. It appears to have some order, but is never predictable.” A class 4 automaton can be used to convey information, and hence can be used as a “universal computer.”
Do cellular automata explain life?
Wolfram argues that because cellular automata can generate behavior of arbitrary complexity, they therefore explain living systems and intelligence. Kurzweil neatly explains that just because cellular automata can generate complex patterns, doesn’t mean that life and intelligence will automatically follow.
In Kurzweil’s words, “One could run these automata for trillions or even trillions of trillions of iterations, and the image would remain at the same limited level of complexity. They do not evolve into, say, insects, or humans, or Chopin preludes, or anything else that we might consider of a higher order of complexity than the streaks and intermingling triangles that we see in these images.”
As discussed in this essay on artificial life, the software for life is based on a layered architecture with many components and layers: evolution, growth, metabolism, ecosystems. Just cause we can program computers — using CAs or any other method — doesn’t mean that we know how to build every kind of software in the universe.
Do cellular automata explain physics?
Wolfram claims that cellular automata provide a better model for physics than traditional equations, and more than that, the universe itself is one big cellular automaton.
Kurzweil puts Wolfram’s claims about physics into context, as part of a school of thought whose advocates, including Norbert Weiner and Ed Fredkin, argue that the universe is fundamentally composed of information. Particles and waves, matter and energy, are manifestations of patterns of information.
The way to go about demonstrating this hypothesis is to use cellular automata to emulate the laws of physics, to see if this generates equivalent or better results than the existing sets of equations. The mapping is apparently easy for Newtonian physics; workable but not particularly elegant for Einstein’s special relativity, and potentially an elegant and even superior way to represent quantum physics, because CAs generate patterns that are recognizably regular, whose details are impossible to predict.
In summary, Kurtzweil thinks that Wolfram’s thesis regarding physics is plausible, but it has yet to be proven, and Wolfram hasn’t proved it.
One thing I don’t understand about this hypothesis is why it proves that the universe IS a computer. If you prove that computation is a better model for physical phenomena, how have you proven that the model is reality itself? A equation can predict where a ball will land, based on the speed and direction of its flight, but the ball itself isn’t an equation. Some day, I’ll take a look at Kurzweil’s book The Age of Intelligent Machines, which covers this topic, and see what I think.
With Kurzweil’s synopsis as a guide, I’ll take a stab at reading Wolfram’s tome. Not because I think it will contain the answer to every question, but because I expect an interesting exploration of cellular automata, and an interesting take on the information hypothesis to physics.

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