There’s growing concern among AI experts that it may be years, if not decades, before self-driving systems can reliably avoid accidents. As self-trained systems grapple with the chaos of the real world, experts like NYU’s Gary Marcus are bracing for a painful recalibration in expectations, a correction sometimes called “AI winter.” That delay could have disastrous consequences for companies banking on self-driving technology, putting full autonomy out of reach for an entire generation.
It’s easy to see why car companies are optimistic about autonomy. Over the past ten years, deep learning — a method that uses layered machine-learning algorithms to extract structured information from massive data sets — has driven almost unthinkable progress in AI and the tech industry. It powers Google Search, the Facebook News Feed, conversational speech-to-text algorithms, and champion Go-playing systems. Outside the internet, we use deep learning to detect earthquakes, predict heart disease, and flag suspicious behavior on a camera feed, along with countless other innovations that would have been impossible otherwise.
Engineers can get creative in where the data comes from and how it’s structured, but it places a hard limit on how far a given algorithm can reach. The same algorithm can’t recognize an ocelot unless it’s seen thousands of pictures of an ocelot — even if it’s seen pictures of housecats and jaguars, and knows ocelots are somewhere in between. That process, called “generalization,” requires a different set of skills.
But deep learning requires massive amounts of training data to work properly, incorporating nearly every scenario the algorithm will encounter. Systems like Google Images, for instance, are great at recognizing animals as long as they have training data to show them what each animal looks like. Marcus describes this kind of task as “interpolation,” taking a survey of all the images labeled “ocelot” and deciding whether the new picture belongs in the group.
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