To build a general artificial intelligence, we may need to know more about our own minds, argues the computer scientist Melanie Mitchell.
Samuel Velasco/Quanta Magazine
The thorny thought experiment has been turned into a real experiment — one that physicists use to probe the physics of information.
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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.
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Radio waves, longer and less energetic than visible light, give astronomers access to some of the most obscure physics in the cosmos.
Inside the symmetries of a crystal shape, a postdoctoral researcher has unearthed a counterexample to a basic conjecture about multiplicative inverses.
A patchwork of genomic differences in the placenta may explain the organ’s “live fast, die young” strategy and its connections to cancer.
Today’s long-anticipated announcement by Fermilab’s Muon g-2 team appears to solidify a tantalizing conflict between nature and theory. But a separate calculation, published at the same time, has clouded the picture.
To build a general artificial intelligence, we may need to know more about our own minds, argues the computer scientist Melanie Mitchell.
Long considered solved, David Hilbert’s question about seventh-degree polynomials is leading researchers to a new web of mathematical connections.
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