As we build our platforms and applications following a human-computer symbiosis approach, we keep an ear to the ground for interesting examples that illuminate new techniques or validate our approach in some empirical way.

One of the areas that we’re interested is in the overall friction of analysis systems. The systems that we build are built on commodity hardware — we’re not building faster computers and yet we can deliver orders-of-magnitude better performance on analysis tasks than existing solutions. How do we do this? By building software in such a way that it reduces the friction experienced at the boundaries between the computing power, the analyst, and the source data.

Chess as analysis laboratory

Chess is, at its heart, a predictive venture. The player attempts to anticipate their opponent’s moves, planning their own moves accordingly, with the straightforward goal of finding a sequence of piece moves that force checkmate.

This game is, in its ideal form, analysis. (The moves made are the logical extension of the analysis.) The data are clean, the problem is well-defined and everyone plays by the same rules. There are even well-defined metrics for ranking chess players by skill — a better chess player is a better chess-game analyst.

In the realm of evaluation of analysis systems, this is as about as good as it gets in terms of designing controlled experiments to study the relative strengths of different analysis systems.

Garry Kasparov, widely considered to be the greatest chess player of all time, recently wrote a review of Diego Rasskin Gutman’s book, Chess Metaphors: Artificial Intelligence and the Human Mind.

The review is excellent and covers a lot of ground. However, one particular anecdote stood out as a very interesting example of human-computer symbiosis (emphasis added):

In 2005, the online chess-playing site Playchess.com hosted what it called a “freestyle” chess tournament in which anyone could compete in teams with other players or computers. Normally, “anti-cheating” algorithms are employed by online sites to prevent, or at least discourage, players from cheating with computer assistance. (I wonder if these detection algorithms, which employ diagnostic analysis of moves and calculate probabilities, are any less “intelligent” than the playing programs they detect.)

Lured by the substantial prize money, several groups of strong grandmasters working with several computers at the same time entered the competition. At first, the results seemed predictable. The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming.

The surprise came at the conclusion of the event. The winner was revealed to be not a grandmaster with a state-of-the-art PC but a pair of amateur American chess players using three computers at the same time. Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

After the jump, we look at this finding in a more generalized way and map it onto the Palantir approach.
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