Computerworld - Techno-entrepreneur Ray Kurzweil recently bet Lotus founder Mitch Kapor $10,000 that a computer will pass the Turing test before 2029. British computer science pioneer Alan Turing in 1950 said that if a human interrogator, communicating blindly via text messages, couldn't distinguish responses from a human from those of a computer, then the computer could be deemed to have human intelligence.
Kurzweil maintains that by 2029, we will use nano-scale brain-scanning technology to completely map and understand how the brain works and then reverse-engineer it in a computer.
But Kapor says Kurzweil is making a dubious assumption - that the brain in fact works like a computer, albeit a very complex one. Kapor argues that we shouldn't engage in "distant extrapolation" of the brain-as-computer metaphor. An overreliance on biological metaphors has been the undoing of much of artificial intelligence, he says.
Indeed, we have used concepts from biology as computational metaphors ever since Aetna Insurance installed its first "electronic brain" (an IBM 650) in 1954. Such metaphors can give the layman a shallow inkling of what's going on. But computer scientists and application developers would never rely on them to guide their work, would they?
It turns out researchers are increasingly doing just that. Stephanie Forrest, a computer scientist at the University of New Mexico, is building systems that can detect hacker intrusions by imitating the human immune system. A key challenge in computer security is determining what is normal behavior and what is potentially harmful behavior in a computer or network, especially when threats are changing regularly.
Forrest's systems automatically "discover" what is normal and what is not, just as our immune systems have learned to do. Her software is largely self-maintaining and doesn't require updating by experts. A computer scientist at Los Alamos National Laboratory, appointed to an antiterrorism research task force after Sept. 11, told me the technique holds great promise for homeland security.
Now consider the ant. Rather than relying on complex, centralized logic, systems that mimic ant behavior use many small, autonomous software agents. With each acting on the simplest of rules, just as ants do, these agents together can solve problems that, viewed as a whole, are enormously complex. Today, software based on ant behavior is used for optimization applications such as factory scheduling, vehicle routing and telecommunications switching.
Meanwhile, other researchers are developing systems based on "evolutionary computing" to solve factory scheduling and optimization problems. The systems iterate through many trial solutions, breeding better and better ones from the most promising parents in each generation of trials. Solutions literally evolve in
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