A book came out this year that profoundly resonated with me and my experience. Range: How Generalists Triumph in a Specialized World by David Epstein challenges a deeply-held belief, which is this:
To get on in life you have to get a head start. You need to specialise early. You must get your 10,000 hours of practice to gain mastery of any skill.
This case for the 10,000 hours was laid out clearly by Malcolm Gladwell in his book Outliers: The Story of Success which came out in 2009. The 10,000-hour thesis is appealing. Many of us have a deeply-held belief that practice and persistence are more important to achieving outstanding results than was natural gifting or sheer talent. And getting that 10,000 hours of practice, becoming that high-achiever, means we have to specialise, and the earlier the better.
However, now it seems that Gladwell is delighted to be proven wrong. Epstein assembles research and historical biography that questions whether this kind of commitment to early specialisation is really true or even helpful.
I would recommend Range. It is one of the best books I have read so far this year. It explores a wide range of fields and topics from scientific research to athletic achievement to predicting engineering failure on spacecraft to education.
It gives me hope; hope for myself and humankind. Let me take just one theme from this book.
An early chapter tells how in 1940’s Hungary, the young Lazlo Polgar, convinced his future wife to have children with him that he would coach to become outstanding chess players. In fact, his three girls did just that. Polgar was ruthless in training his daughters and giving them an extreme head start. Later, his daughters formed three of the four-woman Hungarian team which defeated the until-then-invincible Russian team. All this seems to endorse the 10,000 hours thesis.
Then, along comes the era of Artificial Intelligence during the career of perhaps the greatest chess player, Gary Kasparov. Kasparov acknowledged that his ability was learned from memorising repeatable patterns of play, tactics of chess moves. His ability to see a pattern on a chessboard and to anticipate the next few moves of his opponent gave him an outstanding record as a chess Grand Master… until he was matched against IBM’s supercomputer Deep Blue in 1997.
Kasparov was defeated by the computer. It seemed that the age of machine supremacy, Artificial Intelligence, was dawning. Soon, we would all be replaceable by AI, not just those of us who worked with our hands, but also the thinkers, the knowledge workers. This is a common narrative these days.
Epstein drew my attention to the work of the New Zealand psychologist, Robin Hogarth, who says that there are kind learning environments, where the rules are clear, feedback is immediate, and proficiency is about learning repeatable patterns. Chess seems to be once such case of a kind learning environment. There is a saying that “chess is 99 per cent tactics.” Kasparov wondered about that 1%. A computer can store and retrieve a lot of patterns, but …roll the clock forward a few years and, yes, AI had advanced even further.
Now we see the emergence of “freestyle chess”— hybrid teams, humans and computers — competing against the best of the best chess supercomputers, Hydra. The machine lost the competition because Kasparov concluded that humans on the team were best at “coaching” multiple computers on what to examine, and then synthesising that information for an overall strategy.
In the end, Kasparov did figure out a way to beat the computer: by outsourcing tactics, the part of human expertise that is most easily replaced, the part that he and Polgar prodigies spent years honing.
David Epstein Range, pages 23 & 24
Hogarth compares the kind learning environment with its opposite, the wicked domain. Here the rules are unclear, incomplete, or absent. There might not be repetitive patterns, and feedback is not immediate and clear but is often delayed, inaccurate, or both.
Maybe we humans truly shine in wicked environments.
As with my previous article about Hans Rosling, perhaps hope for us arises in complexity. Despite the Terminator stories, there will be no Skynet that becomes self-aware at a certain point in the near future. There is an invitation for us to become more fully human and to delegate to machines the repetitive. We grow as we engage with the volatile, uncertain, complex and ambiguous world around us.
What do you think about the domain within which you work? Is it a kind learning environment, or a wicked one?
Leave your comment below.
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