Some scientists believe it’s 30 years away others talk about centuries. But the ultimate goal is artificial general intelligence, a self-teaching system that can outperform humans across a wide range of disciplines. These so-called “narrow” AIs are everywhere, embedded in your GPS systems and Amazon recommendations. One Alibaba scientist declared the victory a “milestone.” The algorithms “read” a series of Wikipedia entries on things like the rise of Genghis Khan and the Apollo space program and then answered a series of questions about them more accurately than people did. Earlier this year, two AIs-one created by the Chinese company Alibaba and the other by Microsoft-beat a team of two-legged competitors in a Stanford reading-comprehension test. Algorithms, freed from human programmers, are training themselves on massive data sets and producing results that have shocked even the optimists in the field. In just the last few years, “machine learning” has come to seem like the new path forward. Researchers no longer speak of just one AI, but of hundreds, each specializing in a complex task-and many of the applications are already lapping the humans that made them. Hedge funds are using AI to beat the stock market, Google is utilizing it to diagnose heart disease more quickly and accurately, and American Express is deploying AI bots to serve its customers online. What was once a plot device in sci-fi flicks is in the process of being born. But today nations and corporations are pouring billions into AI, whose recent advancements have startled even scientists working in the field. In the following years, the pursuit faltered, enduring several “winters” where it seemed doomed to dead ends and baffling disappointments. The “Dartmouth workshop” kicked off the decades-long quest for artificial intelligence. What the scientists were talking about in their sylvan hideaway was how to build a machine that could think.
The talks-on everything from cybernetics to logic theory-went on for weeks, in an atmosphere of growing excitement. “People didn’t agree on what it was, how to do it or even what to call it,” Grace Solomonoff, the widow of one of the scientists, recalled later.
Most of them settled into the red-bricked Hanover Inn, then strolled through the famously beautiful campus to the top floor of the math department, where groups of white-shirted men were already engaged in discussions of a “strange new discipline”-so new, in fact, that it didn’t even have a name. In June of 1956, A few dozen scientists and mathematicians from all around the country gathered for a meeting on the campus of Dartmouth College.
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