…The car didn\u2019t 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.<\/p>\n
Getting a car to drive this way was an impressive feat. But it\u2019s also a bit unsettling, since it isn\u2019t completely clear how the car makes its decisions. Information from the vehicle\u2019s 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\u2019d expect from a human driver. But what if one day it did something unexpected\u2014crashed into a tree, or sat at a green light? As things stand now, it might be difficult to find out why. 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\u2019t ask it: there is no obvious way to design such a system so that it could always explain why it did what it did.<\/p>\n
The mysterious mind of this vehicle points to a looming issue with artificial intelligence. The car\u2019s 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.<\/p>\n
But this won\u2019t happen\u2014or shouldn\u2019t happen\u2014unless 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\u2014and it\u2019s inevitable they will.<\/p>\n