Machines of Loving Grace Read Free Page A

Breaking Away, he joined a local cycling club and rode his bike in the hills behind Stanford. In the 1970s the movie would transform the American perception of bike racing, but in the 1960s cycling was still a bohemian sport, attracting a ragtag assortment of individualists, loners, and outsiders. That image fit Duvall’s worldview well. Before high school he attended the Peninsula School, an alternative elementary and middle school that adhered to the philosophy that children should learn by doing and at their own pace. One of his teachers had been Ira Sandperl, a Gandhi scholar who was a permanent fixture behind the cash register at Kepler’s, a bookstore near the Stanford Campus. Sandperl had also been Joan Baez’s mentor and had imbued Duvall with an independent take on knowledge, learning, and the world.
    Duvall was one of the first generation of computer hackers, a small subculture that had originally emerged at MIT, where computing was an end in itself and where the knowledge and code needed to animate the machines were both freely shared. The culture had quickly spread to the West Coast, where it had taken root at computing design centers like Stanford and the University of California at Berkeley.
    It was an era in which computers were impossibly rare—a few giant machines were hidden away in banks, universities, and government-funded research centers. At SRI, Duvall had unfettered access to a room-sized machine first acquired for an elite military-funded project and then used to run the software controlling Shakey. At both SRI and at the nearby Stanford Artificial Intelligence Laboratory (SAIL), tucked away in the hills behind Stanford University, there was a tightly knit group of researchers who already believed in the possibility of building a machine that mimicked human capabilities. To this group, Shakey was a striking portent of the future, and they believed that the scientific breakthrough to enable machines to act like humans would come in just a few short years.
    Indeed, during the mid-sixties there was virtually boundless optimism among the small community of artificial intelligence researchers on both coasts. In 1966, when SRI and SAIL were beginning to build robots and AI programs in California, another artificial intelligence pioneer, Marvin Minsky, assigned an undergraduate to work on the problem of computer vision on the other side of the country, at MIT. He envisioned it as a summer project. The reality was disappointing. Although AI might be destined to transform the world, Duvall, who worked on several SRI projects before transferring to the Shakey project to work in the trenches as a young programmer, immediately saw that the robot was barely taking baby steps.
    Shakey lived in a large open room with linoleum floors and a couple of racks of electronics. Boxlike objects were scattered around for the robot to “play” with. The mainframe computer providing the intelligence was nearby. Shakey’s sensors would capture the world around it and then “think”—standing motionless for minutes on end—before resuming its journey, even in its closed and controlled world. It was like watching grass grow. Moreover, it frequently broke down or would drain its batteries after just minutes of operation.
    For a few months Duvall made the most of his situation. He could see that the project was light-years away from the stated goal of an automated military sentry or reconnaissance agent. He tried to amuse himself by programming the rangefinder, a clunky device based on a rotating mirror. Unfortunately it was prone to mechanical failure, making software development a highly unsatisfying exercise in error prediction and recovery. One of the managers told him that the project was in need of a “probabilistic decision tree” to refine the robot’s vision system. So rather than working on that special-purpose mechanism, he spent his time writing a programming tool that could generate such trees