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the subject of the question, the “entity,” and whether that might be a person, a state, or perhaps an animal or a machine. Each one had different characteristics. “Francis” and “Scott” looked like names. But “Key”? That could be a metal tool to open doors or a mental breakthrough to solve problems. In its hunt, the computer might even spend a millisecond or two puzzling over Key lime pies. Clearing up these doubts might require a visit to the system’s “disambiguation” unit, where the answering program consulted a dictionary or looked for contextual clues in the surrounding words. Could “Key” be something the ingenious Francis Scott invented, collected, planted, or stole? Could he have baked it? Probably not. The structure of the question, with no direct object, made it look like the third name of a person. The capital K on Key strengthened that case.

A person confronting that question either knew or did not know that Francis Scott Key wrote the U.S. national anthem, “The Star-Spangled Banner.” But he or she wasted no time searching for the subject and object in the sentence or wondering if it was a last name, a metal tool, or a tangy South Florida dessert.

For the machine, things only got worse. The question lacked a verb, which could disorient the computer. If the question were, “What did Francis Scott Key write?” the machine could likely find a passage of text with Key writing something, and that something would point to the answer. The only pointer here—“is known for”—was maddeningly vague. Assuming the computer had access to the Internet (a luxury it wouldn’t have on the show), it headed off with nothing but the name. In Wikipedia, it might learn that Key was “an American lawyer, author and amateur poet, from Georgetown, who wrote the words to the United States national anthem, ‘The Star-Spangled Banner.’” For humans, the answer was right there. But the computer, with no verb to guide it, might answer that Key was known as an amateur poet or a lawyer from Georgetown. In the TRec competitions, IBM’s Piquant botched two out of every three questions.

All too often, the system failed to understand the question or to put it in the right context. For this, a growing school of Artificial Intelligence argued, systems needed to spend more time in the computer equivalent of infancy, mastering the concepts that humans take for granted: time, space, and the basic laws of cause and effect.

Toddlerhood is a tribulation for computers, because it represents knowledge that is tied to the human experience: the body and the senses. While crawling, we learn about space and physical objects, and we get a sense of time. The toddler reaches for the jar on the table. Moments later pieces of it lie scattered on the floor. What happened between those two states? It fell. Such lessons establish notions of before and after, cause and effect, and the nature of gravity. These experiences, most of them accompanied by a steady stream of human language, set the foundation for practically everything we learn. “You crawl around and bump into things,” said David Gunning, a senior manager at Vulcan Inc., an AI incubator in Seattle. “That’s basic research.” It isn’t just jars that fall, the toddler notices. Practically everything does. (Certain balloons are exceptions, which seem magical.) The child turns these observations into theory. Unlike computers, humans generalize.

Even the metaphors in our language lead back to the tumbles and accidents seared into our consciousness in our early years. We “fall” for a sales pitch or “fall” in love, and we cringe at hearing “sharp” words or “stinging” rebukes. We process such expressions on such a basic level that they seem closer to feeling than thought (though for humans, unlike computers, the two are intertwined). Over the course of centuries, these metaphors infused language and, consequently, were fundamental to understanding Jeopardy clues. Yet to a machine with no body or experience in the physical world, each one was a puzzle.

In some Artificial Intelligence labs, scientists were attempting to transmit