“It depends on what you mean by artificial intelligence.” Douglas Hofstadter is in a grocery store in Bloomington, Indiana, picking out salad ingredients. “If somebody meant by artificial intelligence the attempt to understand the mind, or to create something human-like, they might say—maybe they wouldn’t go this far—but they might say this is some of the only good work that’s ever been done.”
Hofstadter says this with an easy deliberateness, and he says it that way because for him, it is an uncontroversial conviction that the most-exciting projects in modern artificial intelligence, the stuff the public maybe sees as stepping stones on the way to science fiction—like Watson, IBM’s Jeopardy-playing supercomputer, or Siri, Apple’s iPhone assistant—in fact have very little to do with intelligence. For the past 30 years, most of them spent in an old house just northwest of the Indiana University campus, he and his graduate students have been picking up the slack: trying to figure out how our thinking works, by writing computer programs that think.
Their operating premise is simple: the mind is a very unusual piece of software, and the best way to understand how a piece of software works is to write it yourself. Computers are flexible enough to model the strange evolved convolutions of our thought, and yet responsive only to precise instructions. So if the endeavor succeeds, it will be a double victory: we will finally come to know the exact mechanics of our selves—and we’ll have made intelligent machines.
The idea that changed Hofstadter’s existence, as he has explained over the years, came to him on the road, on a break from graduate school in particle physics. Discouraged by the way his doctoral thesis was going at the University of Oregon, feeling “profoundly lost,” he decided in the summer of 1972 to pack his things into a car he called Quicksilver and drive eastward across the continent. Each night he pitched his tent somewhere new (“sometimes in a forest, sometimes by a lake”) and read by flashlight. He was free to think about whatever he wanted; he chose to think about thinking itself. Ever since he was about 14, when he found out that his youngest sister, Molly, couldn’t understand language, because she “had something deeply wrong with her brain” (her neurological condition probably dated from birth, and was never diagnosed), he had been quietly obsessed by the relation of mind to matter. The father of psychology, William James, described this in 1890 as “the most mysterious thing in the world”: How could consciousness be physical? How could a few pounds of gray gelatin give rise to our very thoughts and selves?
Roaming in his 1956 Mercury, Hofstadter thought he had found the answer—that it lived, of all places, in the kernel of a mathematical proof. In 1931, the Austrian-born logician Kurt Gödel had famously shown how a mathematical system could make statements not just about numbers but about the system itself. Consciousness, Hofstadter wanted to say, emerged via just the same kind of “level-crossing feedback loop.” He sat down one afternoon to sketch his thinking in a letter to a friend. But after 30 handwritten pages, he decided not to send it; instead he’d let the ideas germinate a while. Seven years later, they had not so much germinated as metastasized into a 2.9‑pound, 777-page book called Gödel, Escher, Bach: An Eternal Golden Braid, which would earn for Hofstadter—only 35 years old, and a first-time author—the 1980 Pulitzer Prize for general nonfiction. (...)
In GEB, Hofstadter was calling for an approach to AI concerned less with solving human problems intelligently than with understanding human intelligence—at precisely the moment that such an approach, having borne so little fruit, was being abandoned. His star faded quickly. He would increasingly find himself out of a mainstream that had embraced a new imperative: to make machines perform in any way possible, with little regard for psychological plausibility.
Take Deep Blue, the IBM supercomputer that bested the chess grandmaster Garry Kasparov. Deep Blue won by brute force. For each legal move it could make at a given point in the game, it would consider its opponent’s responses, its own responses to those responses, and so on for six or more steps down the line. With a fast evaluation function, it would calculate a score for each possible position, and then make the move that led to the best score. What allowed Deep Blue to beat the world’s best humans was raw computational power. It could evaluate up to 330 million positions a second, while Kasparov could evaluate only a few dozen before having to make a decision.
Hofstadter wanted to ask: Why conquer a task if there’s no insight to be had from the victory? “Okay,” he says, “Deep Blue plays very good chess—so what? Does that tell you something about how we play chess? No. Does it tell you about how Kasparov envisions, understands a chessboard?” A brand of AI that didn’t try to answer such questions—however impressive it might have been—was, in Hofstadter’s mind, a diversion. He distanced himself from the field almost as soon as he became a part of it. “To me, as a fledgling AI person,” he says, “it was self-evident that I did not want to get involved in that trickery. It was obvious: I don’t want to be involved in passing off some fancy program’s behavior for intelligence when I know that it has nothing to do with intelligence. And I don’t know why more people aren’t that way.”
One answer is that the AI enterprise went from being worth a few million dollars in the early 1980s to billions by the end of the decade. (After Deep Blue won in 1997, the value of IBM’s stock increased by $18 billion.) The more staid an engineering discipline AI became, the more it accomplished. Today, on the strength of techniques bearing little relation to the stuff of thought, it seems to be in a kind of golden age. AI pervades heavy industry, transportation, and finance. It powers many of Google’s core functions, Netflix’s movie recommendations, Watson, Siri, autonomous drones, the self-driving car.
“The quest for ‘artificial flight’ succeeded when the Wright brothers and others stopped imitating birds and started … learning about aerodynamics,” Stuart Russell and Peter Norvig write in their leading textbook, Artificial Intelligence: A Modern Approach. AI started working when it ditched humans as a model,because it ditched them. That’s the thrust of the analogy: Airplanes don’t flap their wings; why should computers think?
by James Somers, The Atlantic | Read more:
Image: MIT
