baby learn


How babies learn – and why robots can't compete

#artificialintelligence

Deb Roy and Rupal Patel pulled into their driveway on a fine July day in 2005 with the beaming smiles and sleep-deprived glow common to all first-time parents. Roy was an AI and robotics expert at MIT, Patel an eminent speech and language specialist at nearby Northeastern University. For years, they had been planning to amass the most extensive home-video collection ever. From the ceiling in the hallway blinked two discreet black dots, each the size of a coin. Further dots were located over the open-plan living area and the dining room. There were 25 in total throughout the house – 14 microphones and 11 fish-eye cameras, part of a system primed to launch on their return from hospital, intended to record the newborn's every move. It had begun a decade earlier in Canada – but in fact Roy had built his first robots when he was just was six years old, back in Winnipeg in the 1970s, and he'd never really stopped. As his interest turned into a career, he wondered about android brains. What would it take for the machines he made to think and talk? "I thought I could just read the literature on how kids do it, and that would give me a blueprint for building my language and learning robots," Roy told me. Over dinner one night, he boasted to Patel, who was then completing her PhD in human speech pathology, that he had already created a robot that was learning the same way kids learn.


Teaching machines to understand video could be the key to giving them common sense

#artificialintelligence

Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. That's what would allow them to acquire common sense, in the end.


Teaching machines to understand video could be the key to giving them common sense

#artificialintelligence

Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. That's what would allow them to acquire common sense, in the end.


Teaching machines to understand video could be the key to giving them common sense

#artificialintelligence

Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. That's what would allow them to acquire common sense, in the end.


Facebook's AI Chief: Machines Could Learn Common Sense from Video

MIT Technology Review

Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). It is why Google and Facebook now let you search inside your photos, and it has unlocked new applications for facial recognition. Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. He says there's still progress to be made--and that it could lead to software with common sense.