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The biggest problem in AI? Machines have no common sense. | Gary Marcus

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Correlation doesn't equal causation — we all know this. Well, except robots.

- There are a lot of people in the tech world who think that if we collect as much data as much, and run a lot of statistics, that we will be able to develop robots where artificial "intelligence" organically emerges.

- However, many A.I.'s that currently exist aren't close to being "intelligent," it's difficult to even program common sense into them. The reason for this is because correlation doesn't always equal causation — robots that operate on correlation alone may have skewed algorithms in which to operate in the real world.

- When it comes to performing simple tasks, such as opening a door, we currently don't know how to encode that information — the varied process that is sometimes required in differing situations, i.e. jiggling the key, turning the key just right — into a language that a computer can understand.

Dr. Gary Marcus is the director of the NYU Infant Language Learning Center, and a professor of psychology at New York University. He is the author of "The Birth of the Mind," "The Algebraic Mind: Integrating Connectionism and Cognitive Science," and "Kluge: The Haphazard Construction of the Human Mind." Marcus's research on developmental cognitive neuroscience has been published in over forty articles in leading journals, and in 1996 he won the Robert L. Fantz award for new investigators in cognitive development. Marcus contributed an idea to Big Think's "Dangerous Ideas" blog, suggesting that we should develop Google-like chips to implant in our brains and enhance our memory. His latest book is Rebooting AI: Building Artificial Intelligence We Can Trust (https://amzn.to/2kCk8wn)

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