Recognising the artifice in artificial intelligence

Nearly 20 years ago, after a chess-playing computer called Deep Blue beat the world grandmaster Garry Kasparov, I wrote an article about why humans would long remain the champions in the game of Go.

“It may be a hundred years before a computer beats humans at Go — maybe even longer,” Piet Hut, an astrophysicist and Go enthusiast at the Institute for Advanced Study in Princeton, New Jersey, told me in 1997. “If a reasonably intelligent person learned to play Go, in a few months he could beat all existing computer programs. You don’t have to be a Kasparov.”

That was the prevailing wisdom. Last month, after a Google computer programme called AlphaGo defeated the Go master Lee Se-dol, I asked Piet for his reaction. “I was way off, clearly, with my prediction,” he replied in an email. “It’s really stunning.”

At the time, his pessimism seemed well founded. While Deep Blue had been trained and programmed by IBM with some knowledge about chess, its advantage lay primarily in what computer scientists call brute-force searching. At each step of the game, Deep Blue would rapidly look ahead, exploring a maze of hypothetical moves and countermoves and counter-countermoves. Then it would make the choice that its algorithms ranked as the best. No living brain could possibly move so fast.

Vast possibilities

But in Go, an ancient board game renowned for its complexity, the ever-forking space of possibilities is so much vaster that sheer electronic speed was not nearly enough. Capturing in a computer something closer to human intuition — the ability to seek and respond to meaningful patterns — seemed crucial and very far away.

In AlphaGo, learning algorithms, called deep neural nets, were trained using a database of millions of moves made in the past by human players. Then it refined this knowledge by playing one split-second game after another against itself. Tweak by algorithmic tweak, it became ever more adept at the game. By combining this insensate learning, which amounts to many human lifetimes of experience, with a technique called Monte Carlo tree search, named for the ability to randomly sample a universe of possible moves, AlphaGo prevailed.

That was an enormous victory. But the glory goes not to the computer programme but to the human brains that pulled it off. At the end of the tournament in Seoul, South Korea, 15 of them took the stage. They represented just a fraction of the number of people it took to invent and execute all of the technologies involved. Lee Se-dol was playing against an army.
Back in 1997 I wrote, “To play a decent game of Go, a computer must be endowed with the ability to recognise subtle, complex patterns and to draw on the kind of intuitive knowledge that is the hallmark of human intelligence.” Defeating a human Go champion, I wrote, “will be a sign that artificial intelligence is truly beginning to become as good as the real thing.”

That doesn’t seem so true anymore. Ingenious learning algorithms combined with ‘big data’ have led to impressive accomplishments — what has even been called bottled intuition. But artificial intelligence is far from rivaling the fluidity of the human mind.

“Humans can learn to recognise patterns on a Go board — and patterns related to faces and patterns in language — and even patterns of patterns,” said Melanie Mitchell, a computer scientist at Portland State University and the Santa Fe Institute. “This is what we do every second of every day. But AlphaGo only recognises patterns related to Go boards and has no ability to generalise beyond that — even to games similar to Go but with different rules.

“Also, it takes millions of training examples for AlphaGo to learn to recognise patterns,” she continued, “whereas it only seems to take humans a few.” Computer scientists are experimenting with programmes that can generalise far more efficiently. But the squishy neural nets in our heads — shaped by half a billion years of evolution and given a training set as big as the world — can still hold their own against ultra-high-speed computers designed by teams of humans, programmed for a single purpose and given an enormous head start.

“It was a regrettable game, but I enjoyed it,” Lee said during the award ceremony. (Regret, enjoy — these words do not compute.) He added that the contest “clearly showed my weaknesses, but not the weakness of humanity.” Picking up the plaque and bouquet he had been given as consolation prizes, he laughed nervously and stumbled from the stage. Several days later, he said he would like a rematch.

The New York Times