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Algorithms that use the brain’s communication signal can now work on analog neuromorphic chips, which closely mimic our energy-efficient brains.

Two recent collaborations between mathematicians and DeepMind demonstrate the potential of machine learning to help researchers generate new mathematical conjectures.

Two researchers show that for neural networks to be able to remember better, they need far more parameters than previously thought.

If only scientists understood exactly how electrons act in molecules, they’d be able to predict the behavior of everything from experimental drugs to high-temperature superconductors. Following decades of physics-based insights, artificial intelligence systems are taking the next leap.

Two teams have shown how quantum approaches can solve problems faster than classical computers, bringing physics and computer science closer together.

By using hypernetworks, researchers can now preemptively fine-tune artificial neural networks, saving some of the time and expense of training.

For years, intermediate measurements made it hard to quantify the complexity of quantum algorithms. New work establishes that those measurements aren’t necessary after all.

A new result shows that quantum information can theoretically be protected from errors just as well as classical information can.

Mathematicians and computer scientists answered major questions in topology, set theory and even physics, even as computers continued to grow more capable.