wired | Artificial intelligence wasn’t supposed to work this way. Until a few years ago, mainstream AI researchers assumed that to create intelligence, we just had to imbue a machine with the right logic. Write enough rules and eventually we’d create a system sophisticated enough to understand the world. They largely ignored, even vilified, early proponents of machine learning, who argued in favor of plying machines with data until they reached their own conclusions. For years computers weren’t powerful enough to really prove the merits of either approach, so the argument became a philosophical one. “Most of these debates were based on fixed beliefs about how the world had to be organized and how the brain worked,” says Sebastian Thrun, the former Stanford AI professor who created Google’s self-driving car. “Neural nets had no symbols or rules, just numbers. That alienated a lot of people.”
The implications of an unparsable machine language aren’t just philosophical. For the past two decades, learning to code has been one of the surest routes to reliable employment—a fact not lost on all those parents enrolling their kids in after-school code academies. But a world run by neurally networked deep-learning machines requires a different workforce. Analysts have already started worrying about the impact of AI on the job market, as machines render old skills irrelevant. Programmers might soon get a taste of what that feels like themselves.
Of course, humans still have to train these systems. But for now, at least, that’s a rarefied skill. The job requires both a high-level grasp of mathematics and an intuition for pedagogical give-and-take. “It’s almost like an art form to get the best out of these systems,” says Demis Hassabis, who leads Google’s DeepMind AI team. “There’s only a few hundred people in the world that can do that really well.” But even that tiny number has been enough to transform the tech industry in just a couple of years.
These forces have led technologist Danny Hillis to declare the end of the age of Enlightenment, our centuries-long faith in logic, determinism, and control over nature. Hillis says we’re shifting to what he calls the age of Entanglement. “As our technological and institutional creations have become more complex, our relationship to them has changed,” he wrote in the Journal of Design and Science. “Instead of being masters of our creations, we have learned to bargain with them, cajoling and guiding them in the general direction of our goals. We have built our own jungle, and it has a life of its own.” The rise of machine learning is the latest—and perhaps the last—step in this journey.