More than 13,000 artificial intelligence mavens flocked to Vancouver this week for the world's leading academic AI conference, NeurIPS. The venue included a maze of colorful corporate booths aiming to lure recruits for projects like software that plays doctor. Google handed out free luggage scales and socks depicting the colorful bikes employees ride on its campus.
Tuesday night, Google and Uber hosted well-lubricated, over-subscribed parties. At a bleary 8:30 the next morning, one of Google's top researchers gave a keynote with a sobering message about AI's future.
Blaise Aguera y Arcas praised the revolutionary technique known as deep learning that has seen teams like his get phones to recognize faces and voices. He also lamented the limitations of that technology, which involves designing software called artificial neural networks that can get better at a specific task by experience or seeing labeled examples of correct answers.
"We're kind of like the dog who caught the car," Aguera y Arcas said. Deep learning has rapidly knocked down some longstanding challenges in AI—but it doesn't immediately seem well suited to many that remain. Problems that involve reasoning or social intelligence, such as weighing up a potential hire in the way a human would, are still out of reach, he said. "All of the models that we have learned how to train are about passing a test or winning a game with a score, [but] so many things that intelligences do aren't covered by that rubric at all," he said.
Hours later, one of the three researchers seen as the godfathers of deep learning also pointed to the limitations of the technology he had helped bring into the world. Yoshua Bengio, director of Mila, an AI institute in Montreal, recently shared the highest prize in computing with two other researchers for starting the deep learning revolution.
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