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’d expect from a human driver. But what if one day it did something unexpected—crashed 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’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.
Thought provoking (and remarkably similar in spirit to our article, "Self-Driving Cars - Second Level Changes") article from Ben Evans of Andreessen Horowitz.
There are two foundational technology changes rolling through the car industry at the moment; electric and autonomy. Electric is happening right now, largely as a consequence of falling battery prices, while autonomy, or at least full autonomy, is a bit further off - perhaps 5-10 years, depending on how fast some pretty hard computer science problems get solved. Both of these will cycle into essentially the entire global stock of (today) around 1.1bn cars over a period of decades, subject to all sorts of variables, and both of them completely remake the car industry and its suppliers, as well as parts of the tech industry.
Both electric and autonomy have profound consequences beyond the car industry itself. Half of global oil production today goes to gasoline, and removing that demand will have geopolitical as well as industrial consequences. Over a million people are killed in car accidents every year around the world, mostly due to human error, and in a fully autonomous world all of those (and many more injuries) will also go away.
However, it's also useful, and perhaps more challenging, to think about second and third order consequences. Moving to electric means much more than replacing the gas tank with a battery, and moving to autonomy means much more than ending accidents. Quite what those consequences would be is much harder to predict: as the saying goes, it was easy to predict mass car ownership but hard to predict Wal-mart, and the broader consequences of the move to electric and autonomy will come in some very widely-spread industries, in complex interlocked ways. Still, we can at least point to where some of the changes might come. I can't tell you what will happen to car repairs, commercial real-estate or buses - I'm not an expert on any of those, and neither can anyone who is - but I can suggest that something will happen, and probably something big. Hence, this post is not a description of what will happen, but of where it might, and why, with some links to further reading.