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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.
For all their triumphs, AI systems can’t seem to generalize the concepts of “same” and “different.” Without that, researchers worry, the quest to create truly intelligent machines may be hopeless.
Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster.
Rediet Abebe uses the tools of theoretical computer science to understand pressing social problems — and try to fix them.
Avi Wigderson and László Lovász won for their work developing complexity theory and graph theory, respectively, and for connecting the two fields.
By harnessing randomness, a new algorithm achieves a fundamentally novel — and faster — way of performing one of the most basic computations in math and computer science.
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