NEURAL networks, like the ones grabbing headlines for winning boardgames or driving cars, depend on huge amounts of computing hardware. That in turn means a colossal amount of power: the next wave may consume millions of watts each.
That’s one reason why some suggest we rethink what we want computers to be. Reducing the precision with which they analyse problems, and putting up with the odd “error”, can cut zeroes off their energy consumption (see ““). And it has precedent in the human brain – an unrivalled piece of hardware using electrical fluctuations and requiring a million times less power than a computer.
Introducing error will also make computers better at handling the real world. Neural networks, loosely modelled on the brain, capture some of its problem-solving capacity. AlphaGo surprised opponents and designers alike with Go moves described by some as “intuitive”.
Computers that play Go, understand human language and behaviour, spot patterns across databases of genetic information or forecast economic and climate trends do not deal in absolutes. It is their rough guesses that will bring us closest to comprehensive artificial intelligence.
This article appeared in print under the headline “To err should be inhuman”
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