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Machine learning

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Where We See Shapes, AI Sees Textures

July 1, 2019

To researchers’ surprise, deep learning vision algorithms often fail at classifying images because they mostly take cues from textures, not shapes.

How Artificial Intelligence Is Changing Science

March 11, 2019

The latest AI algorithms are probing the evolution of galaxies, calculating quantum wave functions, discovering new chemical compounds and more. Is there anything that scientists do that can’t be automated?

Foundations Built for a General Theory of Neural Networks

January 31, 2019

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.

Q&A

A New Approach to Understanding How Machines Think

January 10, 2019

Neural networks are famously incomprehensible, so Been Kim is developing a “translator for humans.”

Machine Learning Confronts the Elephant in the Room

September 20, 2018

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

September 18, 2018

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.

A Poet of Computation Who Uncovers Distant Truths

August 1, 2018

The theoretical computer scientist Constantinos Daskalakis has won the Rolf Nevanlinna Prize for explicating core questions in game theory and machine learning.

How Artificial Intelligence Can Supercharge the Search for New Particles

July 23, 2018

In the hunt for new fundamental particles, physicists have always had to make assumptions about how the particles will behave. New machine learning algorithms don’t.

Q&A

To Build Truly Intelligent Machines, Teach Them Cause and Effect

May 15, 2018

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.

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