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COLUMN SIX: A.I.’s fatal flaw

 

BY COLIN BREZICKI
Special to the VOICE

 

To make robots practical, flaws must be removed. 
To make robots endearing, flaws must be added.
—Khang Nguyen

We’ve come to love our mathematical certainties, and why not? We want to be assured of things, and in an algorithmic world if that means following the yellow brick code, we’ll do it. There’s safety in numbers, we’re told, and they never lie; so we’ll fall in and be counted. Unknowns make us nervous, and digital certainty is a big improvement on fate’s fickle finger, which tends to be the middle one anyway.

Half a century ago a school friend introduced me to the spell of numbers. Ziggy arrived as a late addition to our Grade 6 class because his family had just fled the Soviet invasion of Hungary. He struggled with English but he was absolutely fluent in math. Ziggy spoke the multiplication table while the rest of us just memorized it. We quit at 12×12 because that’s as far as the test ever went; but Ziggy could compute to infinity and he quickly became a legend around the school.

When we asked him to multiply serious numbers, like 758 times 857, he produced the answer in seconds. At first we’d check it with pencils and much erasing, but after a while we didn’t bother—Ziggy nailed it every time.

He did square roots in his head when he was bored, and he would sit at his desk mumbling about something called “the next one.” Our teacher explained that Ziggy was working on the next prime number.

O-kay, we said to ourselves.

He was a digital prodigy, a nascent Alan Turing who believed that numbers were the key to truth and beauty, to all ye know on earth and all ye need to know. He didn’t actually say that because he was only 11 (another prime number), but you just knew from the way he gazed into the middle distance that this was his credo.

He had taken an interest in hockey, but this had nothing to do with who would follow Wayne Gretzky or Mario Lemieux as the greatest player ever, mainly because Wayne and Mario weren’t even born yet. One day I discovered that Ziggy had turned his analytical powers from finding the next prime number to calculating the exact moment in a hockey game when Gordie Howe would score a goal.

Seriously. And without a computer or a calculator, because they weren’t born yet either.

He had drawn up a massive chart listing every player in the NHL (only six teams back then, so, unlike now, he was dealing with a finite number), along with their scoring stats, all faithfully entered each morning as he pored over the box scores. Against each scorer’s name, he would log the precise time of each goal. His spreadsheet grew daily as figures accumulated, and by the time he had gathered the stats of two entire hockey seasons it would cover a small skating rink.

Scoring stats were the only reason Ziggy followed hockey at all, as some people now watch the NFL only because they’re in the office pool. The league was just another numbers game for our budding Johannes Kepler.

With only six teams and sixty games in the schedule, the project was manageable for a probability fanatic with a brain the size of an anvil.

Ziggy believed his stats would one day enable him to determine the minute and second when a player like Howe would be most likely to score. When I told him it was no big deal because Gordie Howe was good enough to score in any second of any game, he just stared at me like he was trying to determine what species I belonged to.

Pretty soon a Saturday night came around when the Leafs were hosting Detroit—the odds of that happening back then were very short—so I phoned Ziggy on a whim and asked him when Howe would score a goal. He called me back after consulting his chart. “Second period at 11:32.”

Hockey Night in Canada didn’t come on till nine o’clock, when the game was usually half over, and I was anxious I might miss the apocalyptic moment. At last, after mercifully brief intros by TV host Ward Cornell, and a plug for Esso by Murray Westgate, they cut to the live action—eight minutes of the second period gone and the score tied 1-1.

I stopped breathing when Howe stepped onto the ice for a shift just after the 11-minute mark. Could it happen? If he scored 30 seconds later (could I hold my breath that long?) life would forever change: the earth would stand still, mountains would slide into the sea, the graves would open and there would then be nothing left remarkable beneath the visiting moon.

Howe stole the puck from George Armstrong and headed up the ice. He crossed the blue line, split the defence, and fired the puck at the top corner of the net, straight into Eddie Chadwick’s outstretched glove. A scuffle followed in front of the net, and at exactly 11:32 the referee blew his whistle and sent Howe to the penalty box for elbowing Bert Olmstead.

Like, instead.

The earth wobbled a little, but remained on its axis. And I breathed again. Ziggy went back to the drawing board, but, man, that was close.

In his own way, Ziggy pioneered an analytics industry that now underpins every sport on the planet—and pretty well everything else in our lives. He was an early quantifier, a pioneer of Big Data, with none of the generating capacity of today’s technology, who believed he had been put into this world to illuminate a dark corner of the unknowable.

Did he foresee the day we would become dazzled, if not blinded, by analytics?

Hockey analysts now pore over 50 categories of stats in computing a player’s worth, down to the precise minute, second and degree—everything from turnover differential to short-handed assists to shots missed while on a shift with more left-handed teammates than right who can chirp in two languages at once.

We seem bent on finding ways to measure whatever immeasurables remain, having long established that standard deviation tests can reduce to a number the universe contained in a human brain.

As we progress by quantum leaps and bounds to our ultimate end—reducing to algorithms everything that is calculable—is it impertinent to ask what will happen to the things we can’t compute?

The skeptics already wonder, is artificial intelligence really that smart, or have we quietly lowered our expectations of what smart is?

According to Nicholas Carr in The Glass Cage, there’s a simple question any child can answer that has so far stumped A.I. It goes like this: the bowling ball crashed through the table because it was made of Styrofoam: what was made of Styrofoam, the bowling ball or the table?

It appears that no computer, not even one endowed with voice recognition or real-time translation or other Deep Learning apps can answer it.

Carr explains, with mind-boggling simplicity, that A.I. has no intrinsic knowledge of the world. I would add that A.I. has no knowledge of ambiguity (or, for that matter, irony, nuance, subtext and metaphor).

The it, in ‘it was made of Styrofoam’, is ambiguous, and the question impossible to answer unless one has an intrinsic experience of bowling balls, styrofoam, and language, and can intuit that it has to be the table.

So, is there truth only in numbers, or in George Canning’s remark that he could prove anything by numbers except the truth?

Can machines compute emotional intelligence? What about the spontaneous, unpredictable, unexpected, whimsical and everything else in life that runs against the odds? The things that make life immeasurably interesting and human, warts and all.

Warts and all is the point, I believe.

I remember the moment in Curious Incident of the Dog in the Night Time, when young Christopher, himself a kind of Ziggy with his math expertise, remarks that prime numbers are like life—they’re what’s left when you take away the pattern.

I can imagine Ziggy now as an actuary at the very top of his game, crunching the numbers for insurance companies and pension firms to calculate the day a particular combination of diagnostics—a human person—will die. If he has worked that into an exact science I hope he has the good sense to keep it to himself. Gordie Howe’s appointed time has come and gone, but I’m not sure I want to know, along with a whole bunch of other things I don’t want to know, the moment when I step onto the ice for my last shift.

There are so many things in life, death being only one of them, that make us human, and perishable, and are best left alone to surprise us.

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