The list of things we’ve learned from chess-playing computers keeps getting longer. In 1985, Gary Kasparov was able to beat 32 computers in simultaneous games. By 1997, IBM’s Deep Blue was able to edge him in a series of games. While a single human can no longer beat a single computer, a team of humans and computers collaborating is an even more formidable competitor. Interestingly, neither the humans nor the computers need be chess masters. As with any good collaboration, the whole is much greater than the sum of the parts.
This isn’t a chess or technology blog, so what does this mean for business leaders?
Computers don’t out-think humans, they simply brute-force calculations for all the possible moves and countermoves. Since reasoning actually involves relatively little computation, computers are very, very good at it. Conversely, the things we take for granted such as recognizing a face are highly complex functions that involve neurological “black magic” that is difficult to reproduce with computers. Quoting Steve Pinker:
“The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard. The mental abilities of a four-year-old that we take for granted – recognizing a face, lifting a pencil, walking across a room, answering a question – in fact solve some of the hardest engineering problems ever conceived…. As the new generation of intelligent devices appears, it will be the stock analysts and petrochemical engineers and parole board members who are in danger of being replaced by machines. The gardeners, receptionists, and cooks are secure in their jobs for decades to come.”
In this light, the teaming of humans with their intuition and computers with their ability to do billions of rote calculations starts to make sense, and is instructive.
The lesson for leaders is to look for ways to combine humans and computers in ways to accentuate the strengths of each. The experience with chess teaches us that purely automated processes can outperform purely human processes, but that a human controlled process backed by computers checking for errors and consistency will outperform both. Recognizing this will have implications for how we make decisions. The first step is to understand how we make decisions, so we can learn how to leverage the relative strengths (and cover the weaknesses) of humans and computers respectively.