Next Up Previous Contents Index
Next: John's Answer Up: Why Cellular Automata? Previous: Why Cellular Automata?

Rudy's Answer

  The remarkable thing about CAs is their ability to produce interesting and logically deep patterns on the basis of very simply stated preconditions. Just as the Mandelbrot set arises   from the repeated iteration of the simple equation Z = Zē + C, iterating the steps of a CA computation can produce fabulously rich output. A good CA is like an acorn which grows an oaktree, or more accurately, a good CA is like the DNA inside the acorn, busily orchestrating the protein nanotechnology that builds the tree.

    I feel that science's greatest task in the late twentieth century is to build living machines: intelligent artificial life, known as a-life for short. In Cambridge, Los Alamos, Silicon Valley and beyond, this is the computer scientist's Great Work as surely as the building of the Nôtre Dame cathedral on the Ile de France was the Great Work of the medieval artisan.

There are two approaches to the problem of creating a-life: the top/down approach, and the bottom/up approach.

The top/down approach is associated with AI (artificial intelligence), the bottom/up with CA (the study of cellular automata). Both approaches are needed for intelligent artificial life, and I predict that someday soon chaos theory, neural nets and fractal mathematics will provide a bridge between the two. What a day that will be when our machines begin to live and speak and breed--a day like May 10, 1869, when the final golden spike completed the U.S. transcontinental railroad! The study of CAs brings us ever closer to the forging of that last golden link in the great chain between bottom and beyond. If all goes well, many of us will see live robot boppers on the Moon.

A heckler might say, ``Sure that's fine, but why are CAs needed? Why have a bottom/up approach at all? What do mindless colored dots have to do with intelligent artificial life?''

For all humanity's spiritual pretensions, we need matter to live on. And CAs can act as the ``matter'' on which intelligent life can evolve. CAs provide a lively, chaotic substrate capable of supporting the most diverse emergent behaviors. Indeed, it is at least possible that human life itself is quite literally based on CAs.

How so? View a person as wetware: as a protein factory. The proteins flip about like John Holland's genetic programs or like A. K. Dewdney's flibs; generating hormones, storing memories. Looking deeper, observe that the proteins' nanotech churning is a pattern made up of flows and undulations in the potential surfaces of quantum chemistry. These surfaces ``smell out'' minimal energy configurations by using the fine fuzz of physical vacuum noise--far from being like smooth rubber sheets, they are like pocked ocean swells in a rainstorm. The quantum noise obeys local rules that are quite mathematical; and these rules are in fact very well simulated by CAs.

Why is it that CAs are so good at simulating physics? Because, just as in physics, cellular automaton computations are i) parallel, ii) local, and iii) homogeneous. In both physics and in CAs, i) the world is happening in many different places at once, ii) there is no action at a distance ,[Footnote] and iii) the laws of nature are the same everywhere.

Whether or not the physical world really is a cellular automaton, the point is that CAs are rich enough that a ``biological'' world could live on them. We human hackers live on language games on biology on chemistry on physics on mathematics on--something very like the iterated parallel computations of a CA. Life needs something to live on, intelligence needs something to think on, and it is this seething information matrix which CAs can provide. If AI is the surfer, CA is the sea.

That's why I think cellular automata are interesting: A-life! CAs will lead to intelligent artificial life!

Rudimentary CA a--life already exists in the form of Brain's haulers, Vote's oscillators, and such classic Life patterns as Gosper's glider gun.

  In the 1970s, Berlenkamp, Conway, and Guy proved that putting a lot of these objects together can make a universal serial computer, such as a PC. Any serial computation can be done by a CA, and any CA computation can in turn be done by a serial computer--in support of this last point, note that all the programs on this disk are serial programs written in C, Pascal, BASIC, and/or 8086 assembly language.

Many computations can be done much more rapidly and efficiently by a succession of massively parallel CA steps. And one does best to use the CA intrinsically, rather than simply using it as a simulation of the old serial mode--emulating an Intel chip by using a galaxy--sized array of blocks and glider guns is not the way to go. No, when we use CAs best, we do not use them as limpware animations of circuit diagrams. While behaviors can be found in top/down expert-system style by harnessing particular patterns to particular purposes, I think by far the more fruitful course is to use the bottom/up freestyle surfing CA style summed up in the slogan:

Seek Ye The Gnarl!

New dimensional CA hacks are possible, new and marketable techniques of parallel programming are lying around waiting to be found, both in the form of individual CA structures and in the form of wholly different rules.

CA structures are labile and breedable in three senses: one can collide and interface different local patterns within the framework of a fixed CA rule, one can combine globally different CA rules (or ideas about them) to produce wholly new ecologies, or one can ``gene-splice'' the logic of successful rules. Then, like Alexander von Humboldt in   the Americas, one botanizes and zoologizes and mineralizes, looking for whatever artificially alive information structures can be found in the new worlds. As always both top/down and bottom/up approaches are viable. We use bottom/up to find new ecologies and their flora and fauna. We use top/down to seed a given instance of a particular ecology with the sort of gene-tailored computer agents we want to breed.

In my own bottom/up searches I begin simply by hoping that my programs will display interesting output for a long time. Then I begin to hope that my programs will be robust under varying initial conditions, and that they will be reactive in anthropomorphizable ways. Once the program is, at this very rudimentary level, artificially alive, I may cast about for applications in some practical domain.

I think the most productive near-term applications of CAs are to image generation and image processing. A cycle or two of Vote, for instance, can be used for easy image cleanup, munching down all stray ``turd bits''. This technique, known as ``convolution'' in the literature, is used every day by NASA's massively parallel computer in Beltsville, Maryland, to process terabyte arrays of satellite photo data. Present-day designers of the newest commercial paint and graphics packages for the VGA will be putting CA rules into their image processor toolboxes. (Look, for   instance, at what Border does to Dr. Tim's face.)

In the area of original image generation, I predict that one of the next big commercial computer graphics fads will be CAs. How about a logo that instead of being chrome is matte and luminous, with a smooth curved surface made of tiny moving mosaics of light, lightbits that form the crawling dirty haulers of Brain or the psychedelic shudder of Rug? These are what the expressive ``flickercladding'' skins of the robots look like in my two a-life science fiction novels, Software and Wetware.

Many simulation applications exist as well. The idea is to find a CA rule that looks like something you want to model. If you are lucky there will be some common underlying mathematics between the two. The Rug rules, for instance, are difference method solutions of the same differential equation, the Laplacian heat equation:

This means, e.g., that a fine-grained Rug rule inside a fixed circular boundary set may serve as a viable model of a vibrating drumhead!

A last current application of CAs is to encryption. Either a CA can serve as a cheap source of ``essentially random'' encryption bits, or the whole message can be fed to a reversible CA.   Stephen Wolfram claims actually to have patented the one-dimensional rule with Wolfram code #30 as part of an encryption scheme.[Footnote]

But to recapitulate, the real reason for studying CAs is to promote artificial life. The most important use for cellular automata will be as ``universes'' or ``arenas'' in which to evolve better fractals, flibs, core-warriors, neural nets and expert agents, using gene-splicing, mutation, and our own ``divine interventions'' to achieve a rapid and dramatic evolution in these parallel processes. CA workers need your help in accomplishing the manifest destiny of mankind: to pass the torch of life and intelligence on to the computer. There are no more than a few hundred active workers in the CA field today;[Footnote] twenty-first century technology will need thousands more!

© 1989 Rudy Rucker -- Reprinted by permission.

Next Up Previous Contents Index
Next: John's Answer Up: Why Cellular Automata? Previous: Why Cellular Automata?

Editor: John Walker