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


Jason Collins


July 19, 2017

In preparation for my recent column in The Behavioral Scientist, which opened with the story of world chess champion Garry Kasparov’s defeat by the computer Deep Blue, I read Kasparov’s recently released Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins.

Despite the title and Kasparov’s interesting observations on the computer-human relationship, Deep Thinking is more a history of man versus machine in chess than a deep analysis of human or machine intelligence. Kasparov takes us from the earliest chess program, produced by Alan Turing on a piece of paper in 1952, through to a detailed account of Kasparov’s 1997 match against the computer Deep Blue, and then beyond.

Kasparov’s history provides an interesting sense of not just the process toward a machine defeating the world champion, but also when computers overtook the rest of us. In 1977 Kasparov had the machines ahead of all but the top 5% of humans. From the perspective of the average human versus machine, the battle is over decades before the machine is better than the best human. And even then the competition at the top levels is brief. As Kasparov puts it, we have:

Thousands of years of status quo human dominance, a few decades of weak competition, a few years of struggle for supremacy. Then, game over. For the rest of human history, as the timeline draws into infinity, machines will be better than humans at chess. The competition period is a tiny dot on the historical timeline.

As Kasparov also discusses, his defeat did not completely end the competition between humans and computers in chess. He describes a 1995 competition in what was called “freestyle chess”, whereby people were free to mix humans and machines as they see fit. To his surprise, the winners of this competition were not a grandmaster teamed with a computer, but a pair of amateur Americans using three computers at the same time. As Kasparov puts it, a weak human + machine + better process is superior to a strong human + machine + inferior process. There is still hope for the humans.

That hope, however, and the human-computer partnership, is also short-lived.  Kasparov notes that the algorithms will continue to improve and the hardware will get faster until the human partnership adds nothing to the mix. Kasparov’s position does not seem that different to my own.

One thing clear through Kasparov’s tale is that he does not consider chess to be the best forum for exploring machine intelligence. This was due to both the nature of chess itself, and the way in which those trying to develop a machine to defeat a world champion (particularly IBM) went about the task.

On the nature of chess, chess is just not complex enough. Its constraints - eight by eight board with sixteen pieces a side - meant that it was amenable to algorithms built using a combination of fixed human knowledge and brute force computational power. From the 1970s onward, developers of chess computers realised that this was the case, so much of the focus was on increasing computational power and refining algorithms for efficiency until they inevitably reach world champion standard.

The nature of these algorithms is best understood in the context of two search techniques described by Claude Shannon in 1949. Type A search is the process of going through every possible combination of moves deeper and deeper with each pass - one move deep, two moves deep and so on. The Type B search is more human-like, focusing on the few most promising moves and examining those in great depth. The development of Type B processes would provide more insight into machine intelligence.

The software that defeated Kasparov, along with most other chess software, used what Kasparov calls alpha-beta search. Alpha-beta search is a Type A approach that stops searching down any particular path whenever a move being examined has a lower value than the currently selected move. This process and increases in computational power were the keys to chess being vulnerable to the brute force attack. Although enormous amounts of work also went into Deep Blue’s openings and evaluation function, another few years would have seen Kasparov or his successor defeated by something far less highly tuned. His defeat was somewhat inevitable.

IBM’s approach to the contest also did not add much to the exploration of machine intelligence. As became clear to Kasparov in the lead up to the Deep Blue rematch (he had defeated Deep Blue in 1996), IBM was not interested in the science behind the enterprise, but simply wanted to win. It provided great advertising for IBM, but the machine logs of the contest were not made available and Deep Blue was later trashed. It’s an interesting contrast to IBM’s approach with Jeopardy winning Watson, which now seems to be everywhere.

As a result, Kasparov sees the AlphaGo project as a more interesting AI project than anything behind the top chess machines. The complexity of Go - a 19 by 19 board and 361 stones - requires the use of techniques such as neural networks. AlphaGo had to teach itself to play.

Even though Kasparov’s offerings on human and machine on intelligence are relatively thin, the chess history in itself makes the book worth reading. Kasparov’s story differs from some of the “myths” that have spread about that contest over the last 20 years, with Kasparov critical of many commentator’s interpretations of events.

One story Kasparov attacks is Nate Silver’s version in The Signal and the Noise (at which time Kasparov also takes a few swings at Silver’s understanding of chess). Silver’s story starts at the conclusion of game 1 of the match. When Kasparov considered his victory near complete, Deep Blue moved a rook in a highly unusual move - a move that turned out the be a “bug” in Deep Blue’s programming. As he did not understand it was a bug, Kasparov saw the move as a sign that the machine could see mate by Kasparov in 20 or more moves, and was seeking to delay this defeat. Kasparov was so impressed by the depth of Deep Blue’s calculations that it affected his play for the rest of the match and was the ultimate cause of his loss.

As Kasparov tells in his version, he simply discarded Deep Blue’s move as the type of inexplicable move computers tend to make when lost. Instead, his state of mind suffered most severely when he was defeated in game 2. Through game 2 he played an unnatural (to him) style of anti-computer chess, and overlooked a potential chance to draw the game through perpetual check (he was informed of his missed opportunity the next day). He simply wasn’t looking for opportunities that he thought a computer would have spotted.