Deep Learning and Neural Networks - Is That Fair?

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The overwhelming majority of recent progress in ai has been driven by neural networks and deep learning algorithms. Some older techniques that everybody takes for granted are actually hybrid models using classical tree search techniques enhanced with monte carlo techniques. And so there's a second caviet i would give you, m. The third cavit i would giveyou is y. Most of the progress has been with deep learning late, but most of the money has been there too.

Artificial intelligence is everywhere around us. Deep-learning algorithms are used to classify images, suggest songs to us, and even to drive cars. But the quest to build truly “human” artificial intelligence is still coming up short. Gary Marcus argues that this is not an accident: the features that make neural networks so powerful also prevent them from developing a robust common-sense view of the world. He advocates combining these techniques with a more symbolic approach to constructing AI algorithms.

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Gary Marcus received his Ph.D. in cognitive science from MIT. He is founder and CEO of Robust.AI, and was formerly a professor of psychology at NYU as well as founder of Geometric Intelligence. Among his books are Rebooting AI: Building Machines We Can Trust (with Ernest Davis).

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