A New Approach to Computation Reimagines Artificial Intelligence
By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficiently — than before.
Self-Taught AI Shows Similarities to How the Brain Works
Self-supervised learning allows a neural network to figure out for itself what matters. The process might be what makes our own brains so successful.
To Be Energy-Efficient, Brains Predict Their Perceptions
Results from neural networks support the idea that brains are “prediction machines” — and that they work that way to conserve energy.
A New Link to an Old Model Could Crack the Mystery of Deep Learning
To help them explain the shocking success of deep neural networks, researchers are turning to older but better-understood models of machine learning.
Latest Neural Nets Solve World’s Hardest Equations Faster Than Ever Before
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.
Artificial Neural Nets Finally Yield Clues to How Brains Learn
The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could.
A New Theorem Maps Out the Limits of Quantum Physics
The result highlights a fundamental tension: Either the rules of quantum mechanics don’t always apply, or at least one basic assumption about reality must be wrong.