Thanks David. Most people don't realize that as Kurzweil (1999) tells us,
evolutionary robotics or "genetic agorithms" already constitutes a
multi-billion dollar industry in practical use. Investment firms in
particular use this technology. In essence a software "robotic financial
expert" is created. Every now and then the software robot tries a little
random "mutating" to see if it can evolve or self-improve. In theory there
is no limit to the hardware or software self-improvements which could be
undertaken. At some time before 2099 there is no doubt that robots with
the ability to learn in all ways, better than humans will exist. Part of
that learning is "self-improvement learning" or evolution. Now try to
imagine robots which can learn better than humans and can work on
self-improvement tirelessly, 24 hours a day and can replicate themselves
(reproduce) quickly so they can have teams of super-human humanoids
engaged in self-improvement. Implications?
FWP.
http://users.uniserve.com/~culturex/Machine-Psychology.htm
On Mon, 24 Apr 2000, David Sutherland wrote:
> 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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