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WILL COMPUTERS BECOME OUR OVERLORDS?
Venki Ramakrishnan

Venki Ramakrishnan is a scientist at the Medical Research Council Laboratory of Molecular Biology, Cambridge University; recipient of the Nobel Prize in Chemistry (2009); current president of the Royal Society; and the author of Gene Machine: The Race to Discover the Secrets of the Ribosome.

A former colleague of mine, Gérard Bricogne, used to joke that carbon-based intelligence was simply a catalyst for the evolution of silicon-based intelligence. For quite a long time, both Hollywood movies and scientific Jeremiahs have been predicting our eventual capitulation to our computer overlords. We all await the singularity, which always seems to be just over the horizon.
In a sense, computers have already taken over, facilitating virtually every aspect of our lives—from banking, travel, and utilities to the most intimate personal communication. I can see and talk to my grandson in New York for free. I remember when I first saw the 1968 movie 2001: A Space Odyssey, the audience laughed at the absurdly cheap cost of a picturephone call from space: $1.70, at a time when a long-distance call within the U.S. was $3 per minute.
However, the convenience and power of computers is also something of a Faustian bargain, for it comes with a loss of control. Computers prevent us from doing things we want. Try getting on a flight if you arrive at the airport and the airline computer systems are down, as happened not so long ago to British Airways at Heathrow. The planes, pilots, and passengers were all there; even the air-traffic controls were working. But no flights for that airline were allowed to take off. Computers also make us do things we don’t want—by generating mailing lists and print labels to send us all millions of pieces of unwanted mail, which we humans have to sort, deliver, and dispose of.
But you ain’t seen nothing yet. In the past, we programmed computers using algorithms we understood at least in principle. So when machines did amazing things like beating world chess champion Garry Kasparov, we could say that the victorious programs were designed with algorithms based on our own understanding—using, in this instance, the experience and advice of top grandmasters. Machines were simply faster at doing brute-force calculations, had prodigious amounts of memory, and were not prone to errors. One article described Deep Blue’s victory not as that of a computer, which was just a dumb machine, but as the victory of hundreds of programmers over Kasparov, a single individual.
That way of programming is changing dramatically. After a long hiatus, the power of machine learning has taken off. Much of the change came when programmers, rather than trying to anticipate and code for every possible contingency, allowed computers to train themselves on data, using deep neural networks based on models of how our own brains learn. They use probabilistic methods to “learn” from large quantities of data; computers can recognize patterns and come up with conclusions on their own. A particularly powerful method is called reinforcement learning, by which the computer learns, without prior input, which variables are important and how much to

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