From the engineering perspective, Artificial Intelligence is a grand success. In education, Computer Science majors expect to take a subject or two in Artificial Intelligence and prospective employers expect it. In practice, big systems all seem to contain elements that have roots in the past half century of research in Artificial Intelligence.
From the scientific perspective, not so much has been accomplished, and the goal of understanding intelligence, from a computational point of view, remains elusive. Reasoning programs still exhibit little common sense. Language programs still have trouble with idioms, metaphors, convoluted syntax, and ungrammatical expressions. Vision programs still stumble when asked to describe an office environment.
Why not more progress? Since the field of Artificial Intelligence was born in the 1960s, most of its practitioners have believed—or at least acted as if they have believed—that language, vision, and motor faculties are the I/O channels of human intelligence. Over the years I have heard distinguished leaders in the field suggest that people interested in language, vision, and motor issues should attend their own conferences, lest the value of Artificial Intelligence conferences be diminished by irrelevant distractions.
To me, ignoring the I/O is wrong headed, because I believe that most of our intelligence is in our I/O, not behind it, and if we are to understand intelligence, we must understand the contributions of language, vision, and motor faculties. Further, we must understand how these faculties, which must have evolved to support survival in the physical world, enable abstract thought and the reuse of both concrete and abstract experience. We must also understand how imagination arises from the concert of communication among our putative I/O faculties, and we must learn how language’s symbols ground out in visual and other perceptions.
Surely, with such intriguing problems to work on, and with allied fields on the march, this should be a time for universal optimism and expectation, yet many of today’s young, emerging practitioners seem to have abandoned the grand original goals of the field, working instead on applied, incremental, and fundamentally boring problems. Too bad. Pessimists will miss the thrill of discovery on the Watson-and-Crick level.