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A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI.
To help them explain the shocking success of deep neural networks, researchers are turning to older but better-understood models of machine learning.
So-called topological quantum computing would avoid many of the problems that stand in the way of full-scale quantum computers. But high-profile missteps have led some experts to question whether the field is fooling itself.
Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.
The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult computational problem.
Melanie Mitchell has worked on digital minds for decades. She says they’ll never truly be like ours until they can make analogies.
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