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Foundations Built for a General Theory of Neural Networks
Neural networks can be as unpredictable as they are powerful. Now mathematicians are beginning to reveal how a neural network’s form will influence its function.
A New Approach to Understanding How Machines Think
Neural networks are famously incomprehensible, so Been Kim is developing a “translator for humans.”
Machine Learning Confronts the Elephant in the Room
A visual prank exposes an Achilles’ heel of computer vision systems: Unlike humans, they can’t do a double take.
New AI Strategy Mimics How Brains Learn to Smell
Machine learning techniques are commonly based on how the visual system processes information. To beat their limitations, scientists are drawing inspiration from the sense of smell.
‘Functional Fingerprint’ May Identify Brains Over a Lifetime
A unique neurological “functional fingerprint” allows scientists to explore the influence of genetics, environment and aging on brain connectivity.
To Make Sense of the Present, Brains May Predict the Future
A controversial theory suggests that perception, motor control, memory and other brain functions all depend on comparisons between ongoing actual experiences and the brain’s modeled expectations.
To Build Truly Intelligent Machines, Teach Them Cause and Effect
Judea Pearl, a pioneering figure in artificial intelligence, argues that AI has been stuck in a decades-long rut. His prescription for progress? Teach machines to understand the question why.
Machine Learning’s ‘Amazing’ Ability to Predict Chaos
In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.
Why Artificial Intelligence Like AlphaZero Has Trouble With the Real World
The latest artificial intelligence systems start from zero knowledge of a game and grow to world-beating in a matter of hours. But researchers are struggling to apply these systems beyond the arcade.