We’re summoning the demon. That’s what Elon Musk, serial entrepreneur on a cosmic scale, said about AI research to an audience at MIT last October.
“I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it’s probably that. … In all those stories where there’s the guy with the pentagram and the holy water, it’s like, he’s sure he can control the demon.
“Doesn’t work out.”
The British astrophysicist and brief historian of time Stephen Hawking broadly agrees. In December, he told the BBC, “The development of full artificial intelligence could spell the end of the human race.”
Wow. That’s heavy. But this isn’t the first time that AI has seen a lot of hype. And last time, it wasn’t the human race that disappeared.
“Artificial intelligence has gone through different stages,” said Wei Lu, an associate professor of electrical engineering and computer science at U-M. “It has died twice already!”
AI’s deaths – or its winters, as insiders more generally refer to the field’s downturns – are characterized by disillusionment, criticism and funding cuts following periods of excitement, money and high expectations. The first full-scale funeral, around 1974, was caused in part by a scathing report on the field as well as drastically reduced funding from what is now the Defense Advanced Research Projects Agency (DARPA). The second, around 1987, also involved heavy cuts from DARPA.
But hey, it’s now summertime for AI, and there is serious money at play. Google snapped up the company DeepMind for a reported $400 million and Facebook has started its own AI lab. Ray Kurzweil, futurist and AI don for Google, said machines could match human intelligence by 2029.
Technology certainly seems smarter than it was a decade ago. Most smartphones can now listen and talk. Computers are getting much better at interpreting images and video. Facebook, for instance, can recognize your face if you’re tagged in enough photos. These advances are largely thanks to machine learning, the technique of writing algorithms that can be “trained” to recognize images or sounds by analyzing many examples (see The Perception Problem sidebar with this story).
In spite of the AI optimism (to the point of existential pessimism), the field might best be described as a hot mess. Hot because it’s a whirlwind of activity – you’ve got self-driving cars and virtual assistants. You’ve got artificial neural networks parsing images, audio and video. Computer giants are starting to make special chips to run the artificial neural networks.
But the challenge of organizing these pieces into an intelligent system has taken a back seat to the development of the new techniques. That’s the mess part.
John Laird, the John L. Tishman Professor of Engineering at U-M, is one of those trying to bring human-like intelligence back to the fore in AI. Current AI systems are great at the tasks for which they have been programmed, but they are missing our flexibility. He cites IBM’s Jeopardy-winning Watson. Drawing on an extensive stockpile of knowledge, it’s tops for answering questions (or questioning answers, if you prefer).
“But you can’t teach it Tic Tac Toe,” Laird pointed out. (Some solace for human champion Ken Jennings.)
Humans can learn how to do new things in a variety of ways – through conversations, demonstrations and reading, to name a few. No one has to go in and hand-code our neurons. But how do you get computers to learn like that? Researchers come at it in a variety of ways.
Lu’s pioneering work with a relatively new electrical component feeds into a bottom-up strategy, with circuits that can emulate the electrical activity of our neurons and synapses. Build a brain, and the mind will follow. Laird, on the other hand, is going straight for the mind. He is a leader in cognitive architectures – systems inspired by psychology, with memories and processing behaviors designed to mirror the functional aspects of how humans learn.
These aren’t the only approaches (after all, who said the human brain was the ideal model for intelligence?), but these opposing philosophies represent a central question in the development of AI: if it is going to live up to its reputation, how brain-like does AI have to be?