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But voters, media, and watchdog organizations can do their own data mining and expose politicians who cross the line. The arms race is not just between candidates but among all participants in the democratic process. The larger outcome is that democracy works better because the bandwidth of communication between voters and politicians increases enormously. In these days of high-speed Internet, the amount of information your elected representatives get from you is still decidedly nineteenth century: a hundred bits or so every two years, as much as fits on a ballot.
Without it, programmers become the bottleneck holding up progress. With it, the pace of progress picks up. If you’re a lazy and not-too-bright computer scientist, machine learning is the ideal occupation, because learning algorithms do all the work but let you take all the credit. On the other hand, learning algorithms could put us out of our jobs, which would only be poetic justice. By taking automation to new heights, the machine-learning revolution will cause extensive economic and social changes, just as the Internet, the personal computer, the automobile, and the steam engine did in their time.
The goal of AI is to teach computers to do what humans currently do better, and learning is arguably the most important of those things: without it, no computer can keep up with a human for long; with it, the rest follows. In the information-processing ecosystem, learners are the superpredators. Databases, crawlers, indexers, and so on are the herbivores, patiently munging on endless fields of data. Statistical algorithms, online analytical processing, and so on are the predators. Herbivores are necessary, since without them the others couldn’t exist, but superpredators have a more exciting life.