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"OH WATERS, TEEM WITH MEDICINE TO KEEP MY BODY SAFE FROM HARM, SO THAT I MAY LONG SEE THE SUN." - Rig Veda
A number of years ago, a team of research scientists tried to improve the design of a certain kind of computer circuit. They created a simple task that the circuit needed to solve and then tried to evolve a potential solution. After many generations, the team eventually found a successful circuit design. But here’s the interesting part: there were parts of it that were disconnected from the main part of the circuit, but were essential for its function. Essentially, the evolutionary program took advantage of weird physical and electromagnetic phenomena that no engineer would ever think of using in order to make the circuit complete its task. In the words of the researchers: ‘Evolution was able to exploit this physical behaviour, even though it would be difficult to analyse.’
This evolutionary technique yielded a novel technological system, one that we have difficulty understanding, because we would never have come up with something like this on our own. In chess, a realm where computers are more powerful than humans and have the ability to win in ways that the human mind can’t always understand, these types of solutions are known as ‘computer moves’ — the moves that no human would ever do, the ones that are ugly but still get results. As the American economist Tyler Cowen noted in his book Average Is Over (2013), these types of moves often seem wrong, but they are very effective. Computers have exposed the fact that chess, at least when played at the highest levels, is too complicated, with too many moving parts for a person — even a grandmaster — to understand.
While we can’t actually control the weather or understand it in all of its nonlinear details, we can predict it reasonably well, adapt to it, and even prepare for it. And when the elements deliver us something unexpected, we muddle through as best as we can. So, just as we have weather models, we can begin to make models of our technological systems, even somewhat simplified ones. Playing with a simulation of the system we’re interested in — testing its limits and fiddling with its parameters, rather than understanding it completely — can be a powerful path to insight, and is a skill that needs cultivation.
We also need interpreters of what’s going on in these systems, a bit like TV meteorologists. Near the end of Average Is Over, Cowen speculates about these future interpreters. He says they ‘will hone their skills of seeking out, absorbing, and evaluating this information… They will be translators of the truths coming out of our networks of machines… At least for a while, they will be the only people left who will have a clear notion of what is going on.’