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machine, some looked to the architecture of the human brain. Indeed, while Ferrucci was grappling with expert systems, other researchers were piecing together an altogether different species of program, called “neural networks.” The idea had been bouncing around at least since 1948, when Alan Turing outlined it in a paper called “Intelligent Machinery.” Like much of his thinking, Turing’s paper was largely theoretical. Computers in his day, with vacuum tubes switching the current on and off, were too primitive to handle such work. (He died in 1954, the year that Texas Instruments produced the first silicon transistor.) However, by the ’80s, computers were up to the job. Based on rudimentary models of neurons, these networks analyzed the behavior of complex systems, such as financial markets and global weather, and used statistical analysis to predict how they would behave over time.

A neural network functioned a bit like a chorus. Picture a sing-along concert of Handel’s Messiah in Carnegie Hall. Some five thousand people show up, each one wearing a microphone. You play the music over loudspeakers and distribute musical scores. That’s the data input. Most of the people start singing while others merely hum or chat with their neighbors. In a neural net, the learning algorithm picks out the neurons that appear to be replicating the pattern, and it gives them more sway. This would be like turning up the microphones of the people who are singing well, turning down the mikes of those who sing a tad off key—and shutting out the chatterers altogether. The net focuses not only on the individuals but on the connections among them. In this analogy, perhaps the singers start to pay attention to one another and organize, the tenors in one section, sopranos in another. By the end of a long training process, the Carnegie Hall network both interprets the data and develops an expertise in Handel’s motifs and musical structure. The next week, when the music switches to Gershwin, new patterns emerge. Some of the chatterers, whose mikes were turned off, become stars. With time, this assemblage can identify new pieces of music, recognizing similar themes and variations. And the group might even set off an alarm if the director gets confused and starts playing Vivaldi instead of Handel.

Neural networks learned, and even evolved. In that sense, they crudely mimicked the human brain. People driving cars, for example, grow to respond to different patterns—the movement of traffic, the interplay between the wheel and the accelerator—often without thinking. These flows are reflected by neural connections in the brain, lots of them working in parallel. They’re reinforced every time an experience proves their usefulness. But a change, perhaps a glimpse of a cyclist riding against traffic, snaps them from their reverie. In much the same way, neural networks became very good at spotting anomalies. Credit card companies began to use them to note unexpected behavior—an apparent teetotaler buying $500 of Finnish vodka or a frugal Nebraskan renting luxury suites in Singapore. Various industries, meanwhile, used neural networks to look ahead. As long as the future stayed true to the past—not always a safe assumption, as any mortgage banker can attest—they could make solid predictions.

Unlike the brittle expert systems, neural networks were supple. They specialized in pattern detection, not a series of if/then commands. They never choked on changes in the data but simply adjusted. While expert systems processed data sequentially, as if following a recipe, the electronic neurons crunched in unison—in parallel. Their weakness? Since these collections of artificial neurons learned by themselves, it was nearly impossible to figure out how they reached their conclusions or to understand what they were picking up about the world. A neural net was a black box.

By the time Ferrucci returned to IBM Research, in 1995, he was looking beyond expert systems and neural nets. In his spare time, he and a colleague from RPI, Selmer Bringsjord, were building a machine called