FutureWatch: Using computers to outthink terrorists

Can software algorithms predict a terrorist's next move before he makes it?

By Dan Verton

September 1, 2003 12:00 PM ET

Computerworld - Some of the technology shown in last year's blockbuster movie Minority Report may soon be a reality and a centerpiece of the intelligence community's war on terrorism. In the futuristic thriller, Tom Cruise played the head of a police unit that uses psychic technology to arrest and convict murderers before they commit their crimes.
Research into new intelligence technology is taking place as part of a $54 million program known as Genoa II, a follow-on to the Genoa I program, which focused on intelligence analysis.
In Genoa II, the Defense Advanced Research Projects Agency (DARPA) is studying potential IT that may not only enable new levels of collaboration among teams of intelligence analysts, policy-makers and covert operators, but could also make it possible for humans and computers to "think together" in real time to "anticipate and preempt terrorist threats," according to official program documents.
"While Genoa I focused on tools for people to use as they collaborate with other people, in Genoa II, we also are interested in collaboration between people and machines," said Tom Armour, Genoa II program manager at DARPA, speaking at last year's DARPATech 2002 conference in Anaheim, Calif. "We imagine software agents working with humans ... and having different sorts of software agents also collaborating among themselves."
Genoa II may be shelved because of its central role in the controversial Terrorism Information Awareness program, but private-sector researchers say many significant advances are still possible and are, in fact, already happening.
For example, private-sector researchers are studying cognitive amplifiers that can enable software to model current situations and predict "plausible futures." Researchers are also on the verge of creating practical applications to support cognitive machine intelligence, associative memory, biologically inspired algorithms and Bayesian inference networks, which are based on a branch of mathematical probability theory that says uncertainty about the world and outcomes of interest can be modeled by combining common sense with evidence observed in the real world.
Anticomplexity


Credit: Larry Goode

The goal of all of this research is to find a way to make computers do the one thing they aren't very good at: mimicking the human brain's ability to reduce complexity. Computers are good at doing things like playing chess but are incapable of "seeing" and deciphering a word within an image. Biologically inspired algorithms -- the mathematical underpinnings of cognitive machine intelligence -- could change that.
"One way to make computers more intelligent and lifelike is to look at living systems and imitate them," says Melanie Mitchell, an associate professor at Oregon Health

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