Playing around with genetics
A new breed of video game lets you experiment with virtual evolution - and
sociology as well By Steven Johnson FEED MAGAZINE
April 14 - Breeding software used to be the stuff of science fiction and
esoteric artificial intelligence research. Now it's coming to a video game
store near you. What happens when Darwin meets Mario?
WILL ANDROIDS DREAM of sorting numbers? The annals of artificial
intelligence are littered with references to chess-playing automatons and
Turing Tests, but the seemingly elementary task of arranging digits in
numerical order has played a seminal role in AI's history as well.
Consider the program for number-sorting devised several years ago by
supercomputing legend Danny Hillis, a program that undermines all of our
conventional assumptions about how software should be produced. Hillis's
creation was a recipe for learning, a program for creating another
program. In other words, Hillis didn't teach the computer how to sort
numbers. He taught the computer to figure out how to sort numbers on its
own.
Hillis pulled off this sleight of hand by connecting the formidable powers
of natural selection to a massively parallel supercomputer. Instead of
designing a number-sorting program himself - writing out lines of code and
debugging - Hillis instructed the computer to generate thousands of
mini-programs, each composed of random combinations of instructions,
creating a kind of digital gene pool. Each program was confronted with a
disorderly sequence of numbers and each tried its hand at putting them in
the correct order. The first batch of programs were, as you might imagine,
utterly inept at number- sorting. (In fact, the overwhelming majority of
the programs were good for nothing at all.) But some programs were better
than others, and because Hillis had established a quantifiable goal for
the experiment - numbers arranged in the correct order - the computer
could select the few programs that were in the ballpark. Those programs
became the basis for the next iteration: only Hillis would also mutate
their code slightly, and cross-breed it with the other promising programs.
And then the whole process would repeat itself: The most successful
programs of the new generation would be chosen, then subjected to the same
transformations. Mix, mutate, evaluate, repeat.
At the end of thousands of cycles, the computer had evolved a batch of
programs that could sort any assemblage of random numbers - and indeed,
could sort numbers faster than any programs that Hillis himself had
written using traditional programming techniques. Hillis's system
functioned, in biological terms, more like an environment than a organism:
It created a space where intelligent programs could grow, and in some
cases exceed the capacities of flesh-and-blood programmers.
"One of the interesting things about the sorting programs that evolved in
my experiment is that I do not understand how they work," Hillis writes in
his recent book, "The Pattern in the Stone." "I have carefully examined
their instruction sequences, but I do not understand them: I have no
simpler explanation of how the programs work than the instruction
sequences themselves. It may be that the programs are not understandable."
Proponents of evolutionary software have long made ambitious claims for
their field. The most grandiose of those involve scenarios where digital
Darwinism leads to a simulated intelligence, capable of open-ended
learning and complex interaction with the outside world. (Most advocates
don't think that such a intelligence will necessarily resemble human
smarts, but that's another matter.) In the short term, though,
evolutionary software promises to transform the way that we think about
creating code: in the next decade, we may well see a shift from top-down,
designed programs, to bottom-up evolved versions, like Hillis'
number-sorting applet - "less like engineering a machine," Hillis says,
"than baking a cake, or growing a garden."
That transformation may be revolutionary for the programmers, but if it
does its job, it won't necessarily make much of a difference for the end
users. We might notice our spreadsheets recalculating a little faster and
our grammar checker finally working, but we'll be dealing with the end
results of evolutionary software then, not the process itself (the
organisms, in other words, and not the environment that nurtured them).
But there is one domain where we'll be able to experiment directly with
evolutionary software, growing our digital gardens of code. In fact, we
can get our hands dirty already. And we can do it just by playing a game.
Full Story:
http://www.msnbc.com/news/394544.asp
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