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Pilots are not the only ones worrying about the reliability of sophisticated military expert systems. Terry Winograd, an AI pioneer turned critic who is now at Stanford, has formed a Palo Alto-based group called Computer Professionals for Social Responsibility to oppose the use of second-wave systems in military applications. Winograd believes that isolating experts from the unforeseen consequences of their decisions is "perhaps the most ! subtle and dangerous consequence of the patchwork rationality of present expert systems." He is specifically concerned about the use of expert systems in President Reagan's Strategic Defense Initiative, or Star Wars system. In the 1960s, Winograd notes, a computer system announced a Soviet attack when radar signals bounced off the moon, an occurrence that had not been anticipated by the programmer. He contends that the potential for similar errors is greatly magnified with expert systems.
In the near term, the future of the second wave will involve novel applications built with existing software technology such as frames and rules. It has already produced some unanticipated benefits. Companies have discovered, for example, that their engineers use the technology as a reasoning tool. While in the past they would tell a programmer what they wanted in the way of a computer application and hope for the best, now they are creating prototypes for their own systems, then fiddling with them until they are right.
Others, however, are already thinking beyond existing technologies. Johan de Kleer, a respected knowledge-system designer at Xerox, envisions an all- purpose electrical diagnostician that would have specific knowledge, such as the various laws that govern electrical flow and conductivity. But it would also have the common sense to decide whether it was faced with a broken VCR or a broken computer. To build this system, de Kleer has spent ten years codifying what he calls "qualitative" calculus that will provide the language to build "common-sense physics." The problem with common sense is that it requires the computer to skip nimbly among many different perspectives in order to find the approach that best fits a problem. The computer must be able to simultaneously maintain the assumptions underlying these different perspectives, and de Kleer says that this, again, will require massive processing power. He looks to parallel processing for the power to run his systems. "Running my applications on a serial supercomputer would require all the computer time in history," he says.
De Kleer's diagnostic systems are at least five years away. Even further out are general-knowledge systems that would not be limited to a specific function or even a preset agenda but would instead be able to respond appropriately to unexpected tasks and problems. To develop such systems, a rebel generation of AI scientists believes that it is necessary to rebuild their field from the ground up. Their emphasis, says Philosopher Daniel Dennett of Tufts University, is on figuring out how people manage to accomplish the plain, everyday things that account for most human behavior, rather than on creating a mathematical model of the intellect, as an older generation of AI researchers have tried and failed to do.