Advances in AI have spurred high interest in the technology, but the road to making machines intelligent remains a long one, said MIT's Josh Tenenbaum at the EmTech conference.
CAMBRIDGE -- Current progress in machine intelligence is newsworthy, but it's often talked about out of context. MIT's Josh Tenenbaum described it this way: Advances in deep learning are powering machines to accurately recognize patterns, but human intelligence is not just about pattern recognition, it's about modeling the world.
By that, Tenenbaum, a professor of cognitive science and computation, was referring to abilities that humans possess such as understanding what they see, imagining what they haven't seen, problem-solving, planning, and building new models as they learn.
That's why, in the interest of advancing AI even further, Tenenbaum is turning to the best source of information on how humans build models of the world: children.
"Imagine if we could build a machine that grows into intelligence the way a person does -- that starts like a baby and learns like a child," he said during his presentation at EmTech 2018, an emerging technology conference hosted by MIT Technology Review.
Tenenbaum called the project a "moonshot," one of several that researchers at MIT are exploring as part of the university's new MIT Quest for Intelligence initiative to advance the understanding of human and machine intelligence. The "learning moonshot" is a collaborative effort by MIT colleagues, including AI experts, as well as those in early childhood development and neuroscience. The hope is to use how children learn as a blueprint to build a machine intelligence that's truly capable of learning, Tenenbaum said.
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