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By teaching machines to understand our true desires, one scientist hopes to avoid the potentially disastrous consequences of having them do what we command.
Machine learning and deep neural networks can capture and analyze the “language” of animal behavior in ways that go beyond what’s humanly possible.
By ignoring their goals, evolutionary algorithms have solved longstanding challenges in artificial intelligence.
In some ways, machine vision is superior to human vision. In other ways, it may never catch up.
The computer vision scientist Greg Johnson is building systems that can recognize organelles on sight and show the dynamics of living cells more clearly than microscopy can.
To researchers’ surprise, deep learning vision algorithms often fail at classifying images because they mostly take cues from textures, not shapes.
With the help of deep learning techniques, paleoanthropologists find evidence of long-lost branches on the human family tree.
A visual prank exposes an Achilles’ heel of computer vision systems: Unlike humans, they can’t do a double take.
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.