Aristotle was one of the world's greatest thinkers ever. Digital Aristotle, on the other hand, knows as much about chemistry as a reasonably bright high school student, and nothing else. Yet a Seattle boutique investment firm, Vulcan Inc., is spending millions of dollars over the next few years trying to turn Digital Aristotle into, well, a digital Aristotle.
And why not? That's just a fraction of a percent of Vulcan founder Paul G. Allen's net worth. Even a noble failure would surely be at least as worthwhile as the Portland Trail Blazers professional basketball team, another of Allen's many interests. Allen, whose fortune derives from his standing as Bill Gates's original partner in Microsoft Corp., created Vulcan in 1986 to manage his investments.
Digital Aristotle began in 2003 as a contest, dubbed Project Halo. Three sets of high-powered researchers competed to create software that could do well on a high school advanced-placement exam in chemistry. They all succeeded. The winning program, written by a collaborative team from SRI International, in Menlo Park, Calif.; the University of Texas at Austin; and Boeing Phantom Works, in Seal Beach, Calif., scored a 3.00 on the exam out of a possible 5.00. That's better than the human student median grade of 2.82.
It's worth noting, though, that the program "learned" the knowledge contained in 71 printed pages of chemistry information at a cost of about US $10 000 per page. In fact, each of the three competing teams spent that, making the total outlay for knowledge acquisition a whopping $2.1 million. Much of the cost was for the salaries of artificial intelligence researchers, but one team also employed expert chemists.
Now, for a second stage of the contest, the same three teams are designing software tools that would allow Ph.D. graduate students to create collections of facts and inferences, so-called knowledge bases, much more cheaply. These tools would turn ordinary sentences of scientific knowledge—a definition of electrical resistance, for example, or the fact that all mammals are vertebrates—into what are called "knowledge constructs": well-defined concepts and quasi-mathematical relationships among them. Once the constructs have been collected and stored, tried-and-true problem-solving methods and other AI technologies could be brought to bear on the task of answering questions on the chemistry test, for example.
Of course, it will take millions of dollars more to build those tools. But for Allen, the third richest man in the world, according to Forbes magazine's most recent list, that's just the change that drops down behind the sofa cushions. And if Allen is the wealthiest knowledge suitor to be smitten by the charms of AI, he's hardly the first. That honor might go to Aristotle himself, the human one. Some 2200 years ago, he dreamed of an "instrument" that "could accomplish its own work, obeying or anticipating the will of others." At the dawn of the computer age, Alan Turing dreamed of a machine so humanlike that a panel of judges wouldn't be able to distinguish it from a real person. Since then, AI has had more flashes in the pan than a French restaurant.
To be sure, AI has its successes. Factory robots use machine vision to track parts. Automotive suspension systems and camcorders use fuzzy logic to smooth out jarring motions. Hospitals use large knowledge bases of drug effects and interactions to ensure that prescribed drugs don't conflict with one another. Computer programs now repeatedly beat the world's chess champions. Part of AI's image problem stems from the fact that whenever a development moves from lab to market, it's no longer artificial intelligence; it's just software.
Still, these successes have something in common: each mimics a human capability in an artificially and greatly narrowed domain. Robots "see" stages of a predefined assembly process; they couldn't tell a rabbit from a fox running through the woods. The camcorder system isn't a general way of holding steady a variety of electronic devices, let alone a martini glass at a crowded cocktail party. And chess-playing computers still can't hold an intelligent conversation about the game, or anything else for that matter.
Similarly, in Project Halo's first contest, Noah Friedland, the program manager responsible for the project at Vulcan, stuck to a very well-defined problem, the chemistry advanced-placement exam. Even Halo's second phase is limited to the hard sciences—physics, chemistry, and biology. Indeed, the chosen task is almost exactly the same, answering advanced-placement-type questions in a narrow domain. The goal now is to slash the per-page cost.