Last year, a strange self-driving
car was released onto the quiet roads of Monmouth County, New Jersey. The
experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t
look different from other autonomous cars, but it was unlike anything
demonstrated by Google, Tesla, or General Motors, and it showed the rising
power of artificial intelligence. The car didn’t follow a single instruction
provided by an engineer or programmer. Instead, it relied entirely on an
algorithm that had taught itself to drive by watching a human do it. Getting a
car to drive this way was an impressive feat. But it’s also a bit unsettling,
since it isn’t completely clear how the car makes its decisions. Information
from the vehicle’s sensors goes straight into a huge network of artificial
neurons that process the data and then deliver the commands required to operate
the steering wheel, the brakes, and other systems. The result seems to match
the responses you would expect from a human driver.
The system is so complicated that
even the engineers who designed it may struggle to isolate the reason for any
single action. And you can’t ask it: there is no obvious way to design such a
system so that it could always explain why it did what it did. The mysterious
mind of this vehicle points to a looming issue with artificial intelligence.
The car’s underlying AI technology, known as deep learning, has proved very powerful
at solving problems in recent years, and it has been widely deployed for tasks
like image captioning, voice recognition, and language translation. There is
now hope that the same techniques will be able to diagnose deadly diseases,
make million-dollar trading decisions, and do countless other things to
transform whole industries. But this won’t happen—or shouldn’t happen—unless we
find ways of making techniques like deep learning more understandable to their
creators and accountable to their users. Otherwise it will be hard to predict
when failures might occur—and it’s inevitable they will. That’s one reason
Nvidia’s car is still experimental.
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