Generic discussion of AI advancements and societal risks

weight them to reach a certain goal. This method in some sense mimics how we learn as children. The results from these new approaches are amazing. Such a deep-learning program was used to teach a computer to play Go, a game that only a few years ago was thought to be beyond the reach of AI because it was so hard to calculate how well you were doing. It seemed that top Go players relied a great deal on intuition and a feel for position, so proficiency was thought to require a particularly human kind of intelligence. But the AlphaGo program produced by DeepMind, after being trained on thousands of high-level Go games played by humans and then millions of games with itself, was able to beat the top human players in short order. Even more amazingly, the related AlphaGo Zero program, which learned from scratch by playing itself, was stronger than the version trained initially on human games! It was as though the humans had been preventing the computer from reaching its true potential. The same method has recently been generalized: Starting from scratch, within just twenty-four hours, an equivalent AlphaZero chess program was able to beat today’s top “conventional” chess programs, which in turn have beaten the best humans. Progress has not been restricted to games. Computers are significantly better at image and voice recognition and speech synthesis than they used to be. They can detect tumors in radiographs earlier than most humans. Medical diagnostics and personalized medicine will improve substantially. Transportation by self-driving cars will keep us all safer, on average. My grandson may never have to acquire a driver’s license, because driving a car will be like riding a horse today—a hobby for the few. Dangerous activities, such as mining, and tedious repetitive work will be done by computers. Governments will offer better targeted, more personalized and efficient public services. AI could revolutionize education by analyzing an individual pupil’s needs and enabling customized teaching, so that each student can advance at an optimal rate. Along with these huge benefits, of course, will come alarming risks. With the vast amounts of personal data, computers will learn more about us than we may know about ourselves; the question of who owns data about us will be paramount. Moreover, data-based decisions will undoubtedly reflect social biases: Even an allegedly neutral intelligent system designed to predict loan risks, say, may conclude that mere membership in a particular minority group makes you more likely to default on a loan. While this is an obvious example that we could correct, the real danger is that we are not always aware of biases in the data and may simply perpetuate them. Machine learning may also perpetuate our own biases. When Netflix or Amazon tries to tell you what you might want to watch or buy, this is an application of machine learning. Currently such suggestions are sometimes laughable, but with time and more data they will get increasingly accurate, reinforcing our prejudices and likes and dislikes. Will we miss out on the random encounter that might persuade us to change our views by exposing us to new and conflicting ideas? Social media, given its influence on elections, is a particularly striking illustration of how the divide between people on different sides of the political spectrum can be accentuated. We may have already reached the stage where most governments are powerless to resist the combined clout of a few powerful multinational companies that control us and our digital future. The fight between dominant companies today is really a fight for control over our data. They will use their enormous influence to prevent regulation of data, because their interests lie in unfettered control of it. Moreover, they have the 129 HOUSE_OVERSIGHT_016932