With massive amounts of
computational power, machines can now recognize objects and translate speech in
real time. Artificial intelligence is finally getting smart. Deep-learning
software attempts to mimic the activity in layers of neurons in the neocortex,
the wrinkly 80 percent of the brain where thinking occurs. The software learns,
in a very real sense, to recognize patterns in digital representations of
sounds, images, and other data. The basic idea—that software can simulate the
neocortex’s large array of neurons in an artificial neural network—is decades
old, and it has led to as many disappointments as breakthroughs. But because of
improvements in mathematical formulas and increasingly powerful computers,
computer scientists can now model many more layers of virtual neurons than ever
before.
With this greater depth, they are
producing remarkable advances in speech and image recognition. Last June, a
Google deep-learning system that had been shown 10 million images from YouTube
videos proved almost twice as good as any previous image recognition effort at
identifying objects such as cats. Google also used the technology to cut the
error rate on speech recognition in its latest Android mobile software. In
October, Microsoft researchers demonstrated of speech software that transcribed
his spoken words into English text with an error rate of 7 percent, translated
them into Chinese-language text, and then simulated his own voice uttering them
in Mandarin. Also researchers identified molecules that could lead to new
drugs. The group used deep learning to zero in on the molecules most likely to
bind to their targets.
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