The Rockaway Irregular
A few weeks ago technology took another great leap forward when IBM’s computational platform, “Watson,” using 90 linked high powered computer processors in a massively parallel array, sophisticated natural language programming and a humongously encyclopedic database beat two human Jeopardy! champions on national TV. I don’t watch the show as a rule but my son, who has an interest in cognitive science from his college days, called to say you gotta see this, Dad!
He knew I shared his interest in the possibility of artificial intelligence and, even more, artificial consciousness, and that one of the things that’s always fascinated me is what it means to have a mind. Silly question right? Except it isn’t because being the thinking, knowing, aware creatures that we are is not as easily explainable as the rest of the stuff we know about the universe. We can explain the way physical things work, we know how to put humans into space, study the stars through massively sophisticated telescopes, deconstruct the mechanics of biological organisms, and cure illnesses that tormented mankind for centuries. We can build vast cities with skyscrapers and superhighways. And we’ve got jet planes and submarines, rockets and nuclear technology. But we still don’t understand ourselves.
Yes, we know we’re biological organisms and that genes provide the blueprints for what we are, that certain organic molecules combine to produce each of us according to preset genetic plans encoded in our DNA in a series of biological processes. But what enables us to know it? What is there about the particular biological device in our heads we call a brain that makes us more than just a mobile piece of meat, an organic robot? Why and how do we know, feel and think about anything at all?
Roughly a decade ago IBM’s Big Blue supercomputer beat the reigning human chess champion, Gary Kasparov, in a series of games that set pundits abuzz. But Big Blue didn’t win by out-thinking Kasparov but by out processing him. The computer program simply crunched more possibilities faster and with more efficiency than his human brain could. Our brains are slower than computers, in any case, because they pass electrical charges from neuron to neuron chemically rather than electronically as computers do. Yet even slower, we still have something computers lack — Big Blue included.
So IBM’s newest supercomputer based program (named for one of IBM’s founding fathers, not Sherlock Holmes’ famous sidekick) awed us when it moved beyond chess to actually compete successfully with humans in answering unpredictable, often complex, real language questions. It did it not by having pre-programmed answers (the way computers usually do it) or even by relying on decision trees the way expert systems do. It relied, instead, on a natural language program that’s adept at determining meanings in words, using a complex associative process to select and develop appropriate responses from data stored in its memory banks. Jeopardy! questions are famously nuanced and ambiguous, depending on implication and allusion. Their scope and unpredictability make pre-programming the right answers all but impossible. Still, “Watson,” like Big Blue before it, won.
Shades of The Matrix in which super intelligent machines take over the world, turning humans into batteries to be their power source! Is that our future then?
Not to worry says renowned philosopher John Searle who teaches philosophy of mind at the University of California in Berkeley. Invoking his longstanding argument that computational processes amount to no more than what you get if you lock a man in a room with a set of rules for matching inputted symbols, whose meanings he cannot fathom, to other equally opaque symbols, Searle assures us that “Watson” not only doesn’t understand anything but cannot reach a point where it does.
Writing in the February 23rd edition of the Wall Street Journal, Searle notes that symbol matching, based on rules relying on nothing more than a symbol’s shape (or other non-meaning related criteria), is merely syntactic while grasping meaning involves more. No computer, he stresses, can ever achieve that extra something because computers operate entirely by syntax.
But IBM’s “Watson,” run on a massively parallel system and built to respond to a broad range of natural language questions via implication, allusion and so forth, does seem to be a bit more than a mere symbol matching device. Searle is surely right that “Watson” doesn’t know things the way we do. It doesn’t even know it won its game, as he put it in the recent article. But then it wasn’t built to. The real issue is what would be needed for it to know things, what would have had to be engineered into it by its makers? And here Searle has little to offer.
What’s missing, he tells us, is something we all find in ourselves but he never attempts to break that down and ascertain what it is. As with pornography in the famous Supreme Court decision, he seems to believe we know it when we see it. Sometimes we call it “awareness” (though others may think of it as “feeling” or “intentionality,” etc.). When we think about anything, we “see” it (or something about it) in our minds. We have mental pictures which kick up other mental pictures in a stream-of-consciousness process of ongoing associative events.
Searle argues that computers can never have that because their underlying processes are just rote symbol matching, nothing more. But the fact that a computer’s underlying operations are “syntactic” may say less about its supposed inability to mimic the human brain than John Searle imagines. In fact, in his long career he has never yet given an account of what having mental images, having the capacity to “see” with our minds when we understand something, actually amounts to — nor any reason to think that the basic operations in brains aren’t syntactic, too.
What if a sufficiently complex and layered computer program, using the same basic syntactic processes available to all computers, could develop and use representational models of its world and its various internal systems and components (the way we’re aware of the elements of our world and all our aches and pains and other somatic sensations)? What if this were then integrated with a “Watson”-like natural language program and the same massive database of stored inputs? Why should we think that that system, now able to image itself and the world, as well as the myriads of relations obtaining between these different layers of representation, would not be able to understand what it means to play and win games, too?
Searle’s reassurance aside, the fact that “Watson” 1.0 can only beat Jeopardy! contestants in an uncomprehending way really says nothing about what some future “Watson” 7.0 — or higher — might accomplish. And maybe that ought to be no more worrying to us than putting men on the moon, decoding the human genome or discovering antibiotics turned out to be.