![]() This data set features hundreds of variables on patients, drawn from their test results, doctor visits, and so on. In 2015, a research group at Mount Sinai Hospital in New York was inspired to apply deep learning to the hospital’s vast database of patient records. The pictures were produced using a mid-level layer of the neural network. The artist Adam Ferriss created this image, and the one below, using Google Deep Dream, a program that adjusts an image to stimulate the pattern recognition capabilities of a deep neural network. How well can we expect to communicate-and get along with-intelligent machines that could be unpredictable and inscrutable? These questions took me on a journey to the bleeding edge of research on AI algorithms, from Google to Apple and many places in between, including a meeting with one of the great philosophers of our time. Will that also be possible with machines that think and make decisions differently from the way a human would? We’ve never before built machines that operate in ways their creators don’t understand. Sure, we humans can’t always truly explain our thought processes either-but we find ways to intuitively trust and gauge people. As the technology advances, we might soon cross some threshold beyond which using AI requires a leap of faith. Even the engineers who build these apps cannot fully explain their behavior. The computers that run those services have programmed themselves, and they have done it in ways we cannot understand. This might be impossible, even for systems that seem relatively simple on the surface, such as the apps and websites that use deep learning to serve ads or recommend songs. ![]() Starting in the summer of 2018, the European Union may require that companies be able to give users an explanation for decisions that automated systems reach. There’s already an argument that being able to interrogate an AI system about how it reached its conclusions is a fundamental legal right. “Whether it’s an investment decision, a medical decision, or maybe a military decision, you don’t want to just rely on a ‘black box’ method.” “It is a problem that is already relevant, and it’s going to be much more relevant in the future,” says Tommi Jaakkola, a professor at MIT who works on applications of machine learning. Deep learning, the most common of these approaches, represents a fundamentally different way to program computers. But banks, the military, employers, and others are now turning their attention to more complex machine-learning approaches that could make automated decision-making altogether inscrutable. If you could get access to these mathematical models, it would be possible to understand their reasoning. 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.Īlready, mathematical models are being used to help determine who makes parole, who’s approved for a loan, and who gets hired for a job. The system is so complicated that even the engineers who designed it may struggle to isolate the reason for any single action. 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 result seems to match the responses you’d expect from a human driver. 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. But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions. Getting a car to drive this way was an impressive feat. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it. ![]() The car didn’t follow a single instruction provided by an engineer or programmer. 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. Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. ![]()
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