How does human intelligence work, in biological as well as in engineering terms? And how can we use such an understanding of human intelligence to build wiser and more useful machines? On February 1, MIT launched the Intelligence Quest (MIT IQ), - an initiative aimed at addressing these big questions by advancing the science and engineering of both human and machine intelligence. MIT IQ aims “to discover the foundations of human intelligence and drive the development of technological tools that can positively influence virtually every aspect of society.”
MIT has been deeply involved in artificial intelligence since the field’s inception in the 1950s. MIT professors John McCarthy and Marvin Minsky were among the founders and most prominent leaders of the new discipline. AI was one of the most exciting areas in computer sciences in the 1960s and 1970s. Many of the AI leaders in those days were convinced that a machine as intelligent as a human being would be developed within a couple of decades. They were trying to do so by somehow programming the machines to exhibit intelligent behavior, even though to this day we have little idea what intelligence is about, let alone how to translate intelligence into a set of instructions to be executed by a machine. Eventually, all these early AI approaches met with disappointment and were abandoned in the 1980s. After years of unfulfilled promises, a so called AI winter of reduced interest and funding set in that nearly killed the field.
AI was reborn in the 1990s when it adopted a more applied, engineering-oriented paradigm. The new AI paradigm enabled computers to acquire intelligent capabilities by ingesting and analyzing large amounts of data using powerful computers and sophisticated algorithms. Instead of trying to explicitly program intelligence, this new approach was based on feeding lots and lots of data to the machine, and then letting the algorithms discover patterns and extract insights from all that data.
Such a data-driven, machine learning approach produced something akin to intelligence or knowledge. Moreover, unlike the explicit programming-based approaches, the statistical-based ones scaled very nicely. The more data you had, the more powerful the supercomputers, the more sophisticated the algorithms, the better the results. Machine learning and related advances like deep learning, have played a major role in AI’s recent achievements.