Machine learning

Efforts to build a better digital “nose” suggest that our perception of scents reflects both the structure of aromatic molecules and the metabolic processes that make them.

A custom-built machine learning algorithm can predict when a complex system is about to switch to a wildly different mode of behavior.

Neural networks originally designed for language processing turn out to be great models of how our brains understand places.

Anima Anandkumar wants computer scientists to move beyond the matrix, among other challenges.

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.

Christopher Kanan is building algorithms that can continuously learn over time — the way we do.

Intelligent beings learn by interacting with the world. Artificial intelligence researchers have adopted a similar strategy to teach their virtual agents new tricks.

Physicists are building neural networks out of vibrations, voltages and lasers, arguing that the future of computing lies in exploiting the universe’s complex physical behaviors.

Researchers say we’re on the cusp of “GoPro physics,” where a camera can point at an event and an algorithm can identify the underlying physics equation.