Saturday, July 24, 2021

A Review of Garry Kasparov’s Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins

A repost from 2017.

Mr. Kasparov was a pretty good chess player:

"From 1986 until his retirement in 2005, Kasparov was ranked world No. 1 for 225 out of 228 months. His peak rating of 2851,achieved in 1999, was the highest recorded until being surpassed by Magnus Carlsen in 2013." -Wikipedia
But he should probably also be known for his twitter feed, last seen in our post:

The Challenges And Triumphs Of Expanding A Family-Owned Winery
Tell me about it.*
*****
*Just kidding, despite making installment payments that could have bought France, I don't own a vineyard.

I wanted a chance to reprise one of the all-time greatest retweet comments, this one from former chess World Champion Garry Kasparov in response to The Onion: Prompting a world-weary:

And from the Los Angeles Review of Books, June 29, 2017 the headline story:

A Brutal Intelligence: AI, Chess, and the Human Mind

CHESS IS THE GAME not just of kings but of geniuses. For hundreds of years, it has served as standard and symbol for the pinnacles of human intelligence. Staring at the pieces, lost to the world, the chess master seems a figure of pure thought: brain without body. It’s hardly a surprise, then, that when computer scientists began to contemplate the creation of an artificial intelligence in the middle years of the last century, they adopted the chessboard as their proving ground. To build a machine able to beat a skilled human player would be to fabricate a mind. It was a compelling theory, and to this day it shapes public perceptions of artificial intelligence. But, as the former world chess champion Garry Kasparov argues in his illuminating new memoir Deep Thinking, the theory was flawed from the start. It reflected a series of misperceptions — about chess, about computers, and about the mind.

At the dawn of the computer age, in 1950, the influential Bell Labs engineer Claude Shannon published a paper in Philosophical Magazine called “Programming a Computer for Playing Chess.” The creation of a “tolerably good” computerized chess player, he argued, was not only possible but would also have metaphysical consequences. It would force the human race “either to admit the possibility of a mechanized thinking or to further restrict [its] concept of ‘thinking.’” He went on to offer an insight that would prove essential both to the development of chess software and to the pursuit of artificial intelligence in general. A chess program, he wrote, would need to incorporate a search function able to identify possible moves and rank them according to how they influenced the course of the game. He laid out two very different approaches to programming the function. “Type A” would rely on brute force, calculating the relative value of all possible moves as far ahead in the game as the speed of the computer allowed. “Type B” would use intelligence rather than raw power, imbuing the computer with an understanding of the game that would allow it to focus on a small number of attractive moves while ignoring the rest. In essence, a Type B computer would demonstrate the intuition of an experienced human player.

When Shannon wrote his paper, he and everyone else assumed that the Type A method was a dead end. It seemed obvious that, under the time restrictions of a competitive chess game, a computer would never be fast enough to extend its analysis more than a few turns ahead. As Kasparov points out, there are “over 300 billion possible ways to play just the first four moves in a game of chess, and even if 95 percent of these variations are terrible, a Type A program would still have to check them all.” In 1950, and for many years afterward, no one could imagine a computer able to execute a successful brute-force strategy against a good player. “Unfortunately,” Shannon concluded, “a machine operating according to the Type A strategy would be both slow and a weak player.”  
Type B, the intelligence strategy, seemed far more feasible, not least because it fit the scientific zeitgeist. Fascination with digital computers intensified during the 1950s, and the so-called “thinking machines” began to influence theories about the human mind. Many scientists and philosophers came to assume that the brain must work something like a digital computer, using its billions of networked neurons to calculate thoughts and perceptions. Through a curious kind of circular logic, this analogy in turn guided the early pursuit of artificial intelligence: if you could figure out the codes that the brain uses in carrying out cognitive tasks, you’d be able to program similar codes into a computer. Not only would the machine play chess like a master, but it would also be able to do pretty much anything else that a human brain can do. In a 1958 paper, the prominent AI researchers Herbert Simon and Allen Newell declared that computers are “machines that think” and, in the near future, “the range of problems they can handle will be coextensive with the range to which the human mind has been applied.” With the right programming, a computer would turn sapient.

¤ It took only a few decades after Shannon wrote his paper for engineers to build a computer that could play chess brilliantly. Its most famous victim: Garry Kasparov.
One of the greatest and most intimidating players in the history of the game, Kasparov was defeated in a six-game bout by the IBM supercomputer Deep Blue in 1997. Even though it was the first time a machine had beaten a world champion in a formal match, to computer scientists and chess masters alike the outcome wasn’t much of a surprise. Chess-playing computers had been making strong and steady gains for years, advancing inexorably up the ranks of the best human players. Kasparov just happened to be in the right place at the wrong time.

But the story of the computer’s victory comes with a twist. Shannon and his contemporaries, it turns out, had been wrong. It was the Type B approach — the intelligence strategy — that ended up being the dead end. Despite their early optimism, AI researchers utterly failed in getting computers to think as people do. Deep Blue beat Kasparov not by matching his insight and intuition but by overwhelming him with blind calculation. Thanks to years of exponential gains in processing speed, combined with steady improvements in the efficiency of search algorithms, the computer was able to comb through enough possible moves in a short enough time to outduel the champion. Brute force triumphed. “It turned out that making a great chess-playing computer was not the same as making a thinking machine on par with the human mind,” Kasparov reflects. “Deep Blue was intelligent the way your programmable alarm clock is intelligent.”

The history of computer chess is the history of artificial intelligence....

...MUCH MORE 

HT: Rough Type who we will be visiting again tomorrow.

Previously on Mr. Kasparov;
How human traders will beat the machines

And on Claude Shannon the Bell Labs polymath genius:
"Claude Shannon, the Las Vegas Shark"
"How Information Got Re-Invented"
The Bit Bomb: The True Nature of Information
"How did Ed Thorp Win in Blackjack and the Stock Market?"
How Big Data and Poker Playing Bots Are Taking the Luck Out of Gambling

There was also a shout out to Shannon from the quants at Ruffer in July 17's Ruffer Review: "Navigating information" 

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