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Experts explain the biggest obstacles to creating human-like robots

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Artificial intelligence (AI) became a scientific field almost 60 years ago. Ever since then, researchers have tried to achieve human-level smarts or better.

Yet even with recent feats of computational genius — for example, Google DeepMind beating a human player in the game Go — AI scientists say they still have a long road ahead.

Tech Insider spoke with AI researchers, computer scientists, and roboticists around the world about what it is going to take to build a machine that's able to think, work, and feel like a human.

Scroll down to see their lightly edited responses.

Bart Selman said computers need to learn how to understand the world like a human.

"The big obstacle, though it's not an obstacle because I think it will just take time, is the computer has to learn more about the way we see the world.

"It's very hard to understand the world from a human perspective. Intelligence relies on the way we view the world as humans, and the way we think about the world.

"Computers are just starting to be able to hear and starting to being able to see images. Those are tremendous improvements in the field in the last five years.

"We're doing that by having computers read millions of texts and pages from the web, by hooking them up to cameras and moving them around human environments."

Commentary from Bart Selman, a computer scientist at Cornell University.



This experience of the world will foster more intelligent AI, Peter Norvig says.

"AI needs to experience living in the world.

"We are very good at gathering data and developing algorithms to reason with that data. But that reasoning is only as good as the data, which, for the AI we have now, is one step removed from reality.

"Reasoning will be improved as we develop systems that continuously sense and interact with the world, as opposed to learning systems that passively observe information that others have chosen."

Commentary from Peter Norvig, director of research at Google.



To do that, Yoshua Bengio says computers should be trained to learn like children.

"Right now, all of the impressive progress we've made is mostly due to supervised learning, where we take advantage of large quantities of data that have already been annotated by humans.

"This supervised learning thing is not how humans learn.

"Before two years of age, a child understands the visual world through experiencing it, moving their head and looking around.

"There's no teacher that tells the child, 'in the image that's currently in your retina, there's a cat, and furthermore it's at this location' and for each pixel of the image say 'this is background and this is cat.' Humans are able to learn just by observation and experience with the world.

"In comparison to human learning, AI researchers are not doing that great."

Commentary from Yoshua Bengio, a computer scientist at University of Montreal.



See the rest of the story at Business Insider

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