The Most Human Human (15 page)

At first glance it would seem, of course, that no two subjects could possibly be further apart than an underground society of pickup artists and supercomputer chess. What on earth do these two narratives have to do with each other—and what do they have to do with asserting myself as human in the Turing test?

The answer is surprising, and it hinges on what chess players call “getting out of book.” We’ll look at what that means in chess and in conversation, how to make it happen, and what the consequences are if you don’t.

At one point in his career, the famous twentieth-century French artist Marcel Duchamp gave up art, in favor of something he felt was even more expressive, more powerful: something that “has all the beauty of art—and much more.” It was chess. “I have come to the personal conclusion,” Duchamp wrote, “that while all artists are not chess players, all chess players are artists.”

The scientific community, by and large, seemed to agree with that sentiment. Douglas Hofstadter’s 1980 Pulitzer Prize–winning
Gödel, Escher, Bach
, written at a time when computer chess was over twenty-five years old, advocates “the conclusion that profoundly insightful chess-playing draws intrinsically on central facets of the human condition.” “All of these elusive abilities … lie so close to the core of human nature itself,” Hofstadter says, that computers’ “mere brute-force … [will] not be able to circumvent or shortcut that fact.”

Indeed,
Gödel, Escher, Bach
places chess alongside things like music and poetry as one of the most uniquely and expressively human activities of life. Hofstadter argues, rather emphatically, that a world-champion chess program would need so much “
general
intelligence” that it wouldn’t even be appropriate to call it a
chess
program at all. “I’m bored with chess. Let’s talk about poetry,” he imagines it responding to a request for a game. In other words, world-champion chess means passing the Turing test.

This was the esteem in which chess, “the game of kings,” the mandatory part of a twelfth-century knight’s training after “riding, swimming, archery, boxing, hawking, and verse writing,” the game played by political and military thinkers from Napoleon, Franklin, and Jefferson to Patton and Schwarzkopf, was held, from its modern origins in fifteenth-century Europe up through the 1980s. Intimately bound to and inseparable from the human condition; expressive and subtle as art. But already by the 1990s, the tune was changing. Hofstadter: “The first time I … saw … a graph [of chess machine ratings over time] was in an article in
Scientific American
 … and I vividly remember thinking to myself, when I looked at it, ‘Uh-oh! The handwriting is on the wall!’ And so it was.”
¹

Indeed, it wasn’t long before IBM was ready to propose a meeting in 1996 between their Deep Blue machine and Garry Kasparov, the reigning world champion of chess, the highest-rated player of all time, and some say the greatest who ever lived.

Kasparov accepted: “To some extent, this match is a defense of the whole human race. Computers play such a huge role in society. They are everywhere. But there is a frontier that they must not cross. They must not cross into the area of human creativity.”

Long story short: Kasparov stunned the nation by losing the very first game—while the IBM engineers toasted themselves over dinner, he had a kind of late-night existential crisis, walking the icy Philadelphia streets with one of his advisers and asking, “Frederic, what if this thing is invincible?” But he hit back, hard, winning three of the next five games and drawing the other two, to win the match with an entirely convincing 4–2 score. “The sanctity of human intelligence seemed to dodge a bullet,” reported the
New York Times
at the match’s end, although I think that might be a little overgenerous. The machine had drawn blood. It had proven itself formidable. But ultimately, to borrow an image from David Foster Wallace, it was “like watching an extremely large and powerful predator get torn to pieces by an even larger and more powerful predator.”

IBM and Kasparov agreed to a rematch a year later in Manhattan, and in 1997 Kasparov sat down to another six-game series with a
new
version of the machine: faster—twice as fast, in fact—sharper, more complex. And this time, things didn’t go quite so well. In fact, by the morning of the sixth, final game of the rematch, the score is tied, and Kasparov has the black pieces: it’s the computer’s “serve.” And then, with the world watching, Kasparov plays what will be the
quickest loss of his entire career
. A machine defeats the world champion.

Kasparov, of course, immediately proposes a 1998 “best out of
three” tiebreaker match for all the marbles—“I personally guarantee I will tear it in pieces”—but as soon as the dust settles and the press walks away, IBM quietly cuts the team’s funding, reassigns the engineers, and begins to slowly take Deep Blue apart.

When something happens that creates a cognitive dissonance, when two of our beliefs are shown to be incompatible, we’re still left with the choice of which one to reject. In academic philosophy circles this has a famous joke:

There’s always more than one way to revise our beliefs.

So what happened after the Deep Blue match?

Most people were divided between two conclusions: (1) accept that the human race was done for, that intelligent machines had finally come to be and had ended our supremacy over all creation (which, as
you can imagine, essentially no one was prepared to do), or (2) what most of the scientific community chose, which was essentially to throw chess, the game Goethe called “a touchstone of the intellect,” under the bus. The
New York Times
interviewed the nation’s most prominent thinkers on AI immediately after the match, and our familiar Douglas Hofstadter, seeming very much the tickled corpse, says, “My God, I used to think chess required thought. Now, I realize it doesn’t.”

Other academics seemed eager to kick chess when it was down. “From a purely mathematical point of view, chess is a trivial game,” says philosopher and UC Berkeley professor John Searle. (There are ten thousand billion billion billion billion possible games of chess for every atom in the universe.) As the
New York Times
explained:

In
Gödel, Escher, Bach
, Hofstadter writes, “Once some mental function is programmed, people soon cease to consider it as an essential ingredient of ‘real thinking.’ ” It’s a great irony, then, that he was among the first to throw chess out of the boat.

If you had to imagine one human being completely unable to accept
either
of these conclusions—(a) humankind is doomed, or (b) chess is trivial—and you’re imagining that this person’s name is “Garry Kasparov,” you’re right. Whose main rhetorical tear after the match, as you can well imagine, was,
That didn’t count
.

Garry Kasparov may have lost the final game, he says. But Deep Blue didn’t win it.

Strangely enough, it’s
this
argument that I’m the most interested in, and the one I want to talk about. What seems at first to mean simply that he made an uncharacteristic blunder (which he did) actually has a very deep and altogether different meaning behind it. Because I think he means it
literally
.

Well, if Deep Blue didn’t win it, who—or what—did?

This
is the question that starts to take us into the really weird and interesting territory.

To answer it, we need to get into some briefly technical stuff about how chess computers work;
²
hopefully I can demystify a few things without going into soul-crushing detail.

Almost all computer chess programs work essentially the same way. To make a chess program, you need three things: (1) a way to represent the board, (2) a way to generate legal moves, and (3) a way to pick the best move.

Computers can only do one thing: math. Fortunately for them, a shockingly high percentage of life can be translated into math. Music is represented by air pressure values over time, video is represented by red, blue, and green intensity values over time, and a chessboard is just a grid (in computer jargon: “array”) of numbers, representing what piece, if any, is on that square.
³
Compared to encoding a song,
or a film: piece of cake. As is often true in computer science, there are nifty tricks you can do, and clever corners you can cut, to save time and space—in some cases, astonishingly much—but those don’t concern us here.

Once the computer has a chessboard it can understand in its own language (numbers), it figures out what the legal moves are from a given position. This is also simple, in fact, rather boringly straightforward, and involves a process like: “Check the first square. If empty, move on. If not empty, check what kind of piece it is. If a rook, see if it can move one square left. If yes, check to see if it can move another square left, and so on. If not, see if it can move one square right …” Again, there are some clever and ingenious ways to speed this up, and if you’re trying to take down a world champion, they become important—for example, Deep Blue’s creator, IBM electrical engineer Feng-hsiung Hsu, designed Deep Blue’s thirty-six-thousand-transistor move generator
by hand
—but we are not touching
that
level of detail with a ten-foot pole. If shaving off microseconds doesn’t matter to you, then anything that tells you the moves will do the trick.

Okay, so we can represent the board and we can figure out what moves are possible. Now we need an algorithm to help us decide what move to make. The idea is this:

You’re starting to get the sense that this is a rather circular definition. Or not circular, exactly, but what computer scientists call
recursive
. A function that calls itself. This particular function, which
calls itself, you might say, in reverse—what move makes things best, given the move that makes things worst, given the move that makes things best, etc.—is called a minimization-maximization algorithm, or “minimax algorithm,” and it crops up virtually everywhere in the theory and the AI of games.

Well, if you’re writing a program for tic-tac-toe, for instance, this isn’t a problem. Because the game only has nine possible first moves, eight possible second moves, seven possible third moves, and so on. So that’s nine factorial: 9! = 362,880. That may seem like a big number, but that’s kid stuff to a computer. Deep Blue, and this was fifteen years ago, could look at 300,000,000 positions
per second
.
⁴

The idea is that if your “search tree” goes all the way to the end, then the positions resolve into win, loss, and draw, the results filter back up, and then you move. The thing about chess, though, is that the search tree
doesn’t bottom out
. Searching the whole thing (10
⁹⁰
years was Claude Shannon’s famous estimate) would take
considerably
longer than the lifetime (a paltry 13.73 × 10
⁹
years) of the universe.

So you have to pull it up short. There are very sophisticated ways of doing this, but the easiest is just to specify a maximum search depth at which point you just have to call off the dogs. (Calling off the search in some lines before you call it off in others is called
pruning.
) So how do you evaluate the position if you can’t look any further ahead and the game’s not over? You use something called a
heuristic
, which—barring the ability to consider any further moves or countermoves—is a kind of static guesstimate of how good that position seems, looking at things like who has more pieces, whose king is safer, and things like that.
⁵

That’s it: represent the board, find moves and search through the replies, evaluate their outcomes with a heuristic, and use minimax to pick the best. The computer can then play chess.

There is, however, one other major add-on that top computer programs use, and this is what I want to talk about.

Computer programmers have a technique called “memoization,” where the results of frequently called functions are simply stored and recalled—much like the way most math-savvy people will, when asked, respond that 12 squared is 144, or that 31 is prime, without actually crunching the numbers. Memoization is frequently a big time-saver in software, and it’s used in chess software in a very particular way.