AlphaZero: Chess belongs to the machines, now more than ever before – Implications please?

Steven Strogatz in the NYTimes, 12.26.18. In the old days (1997) IBM's Deep Blue beat Kasparov:

For better and worse, it played like a machine, brutally and materialistically. It could out-compute Mr. Kasparov, but it couldn’t outthink him. In Game 1 of their match, Deep Blue greedily accepted Mr. Kasparov’s sacrifice of a rook for a bishop, but lost the game 16 moves later. The current generation of the world’s strongest chess programs, such as Stockfish and Komodo, still play in this inhuman style. They like to capture the opponent’s pieces. They defend like iron. But although they are far stronger than any human player, these chess “engines” have no real understanding of the game. They have to be tutored in the basic principles of chess.

These principles, which have been refined over decades of human grandmaster experience, are programmed into the engines as complex evaluation functions that indicate what to seek in a position and what to avoid: how much to value king safety, piece activity, pawn structure, control of the center, and more, and how to balance the trade-offs among them. Today’s chess engines, innately oblivious to these principles, come across as brutes: tremendously fast and strong, but utterly lacking insight.

And now:

All of that has changed with the rise of machine learning. By playing against itself and updating its neural network as it learned from experience, AlphaZero discovered the principles of chess on its own and quickly became the best player ever. Not only could it have easily defeated all the strongest human masters — it didn’t even bother to try — it crushed Stockfish, the reigning computer world champion of chess. In a hundred-game match against a truly formidable engine, AlphaZero scored twenty-eight wins and seventy-two draws. It didn’t lose a single game.

Most unnerving was that AlphaZero seemed to express insight. It played like no computer ever has, intuitively and beautifully, with a romantic, attacking style. It played gambits and took risks. In some games it paralyzed Stockfish and toyed with it. While conducting its attack in Game 10, AlphaZero retreated its queen back into the corner of the board on its own side, far from Stockfish’s king, not normally where an attacking queen should be placed.

Yet this peculiar retreat was venomous: No matter how Stockfish replied, it was doomed. It was almost as if AlphaZero was waiting for Stockfish to realize, after billions of brutish calculations, how hopeless its position truly was, so that the beast could relax and expire peacefully, like a vanquished bull before a matador. Grandmasters had never seen anything like it. AlphaZero had the finesse of a virtuoso and the power of a machine. It was humankind’s first glimpse of an awesome new kind of intelligence.

Hmmmm.... Really?

Note:

Tellingly, AlphaZero won by thinking smarter, not faster; it examined only 60 thousand positions a second, compared to 60 million for Stockfish. It was wiser, knowing what to think about and what to ignore. By discovering the principles of chess on its own, AlphaZero developed a style of play that “reflects the truth” about the game rather than “the priorities and prejudices of programmers,” Mr. Kasparov wrote in a commentary accompanying the Science article.

And now for the implications:

The question now is whether machine learning can help humans discover similar truths about the things we really care about: the great unsolved problems of science and medicine, such as cancer and consciousness; the riddles of the immune system, the mysteries of the genome.

The early signs are encouraging. Last August, two articles in Nature Medicine explored how machine learning could be applied to medical diagnosis. In one, researchers at DeepMind teamed up with clinicians at Moorfields Eye Hospital in London to develop a deep-learning algorithm that could classify a wide range of retinal pathologies as accurately as human experts can.[...]

The other article concerned a machine-learning algorithm that decides whether a CT scan of an emergency-room patient shows signs of a stroke, an intracranial hemorrhage or other critical neurological event. For stroke victims, every minute matters; the longer treatment is delayed, the worse the outcome tends to be. (Neurologists have a grim saying: “Time is brain.”) The new algorithm flagged these and other critical events with an accuracy comparable to human experts — but it did so 150 times faster. A faster diagnostician could allow the most urgent cases to be triaged sooner, with review by a human radiologist.

What is frustrating about machine learning, however, is that the algorithms can’t articulate what they’re thinking.

H/t Tyler Cowen.