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John Pavlus

Contributing Writer

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Latest Articles

Q&A

His Artificial Intelligence Sees Inside Living Cells

By John Pavlus
July 24, 2019
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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.

Q&A

Curious About Consciousness? Ask the Self-Aware Machines

By John Pavlus
July 11, 2019
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Consciousness is a famously hard problem, so Hod Lipson is starting from the basics: with self-aware robots that can help us understand how we think.

Abstractions blog

Sum-of-Three-Cubes Problem Solved for ‘Stubborn’ Number 33

By John Pavlus
March 26, 2019
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A number theorist with programming prowess has found a solution to 33 = x³ + y³ + z³, a much-studied equation that went unsolved for 64 years.

Photo of Been Kim
Q&A

A New Approach to Understanding How Machines Think

By John Pavlus
January 10, 2019
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Neural networks are famously incomprehensible, so Been Kim is developing a “translator for humans.”

Q&A

How to Build a Robot That Wants to Change the World

By John Pavlus
November 1, 2017
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And not destroy humanity in the process.

Digital question marks surrounded by vines
Wired to Learn: The Next AI

Clever Machines Learn How to Be Curious

By John Pavlus
September 19, 2017
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Computer scientists are finding ways to code curiosity into intelligent machines.

The mathematician Ken Ono in his office at Emory University in Atlanta.
Q&A

A Life Inspired by an Unexpected Genius

By John Pavlus
May 19, 2016
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Greatness was always expected of Ken Ono. But as he struggled with his parents’ expectations, he found solace and inspiration in the life of two-time college dropout — and mathematical genius — Srinivasa Ramanujan, the subject of the film The Man Who Knew Infinity.

Q&A

Searching for the Algorithms Underlying Life

By John Pavlus
January 28, 2016
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The biological world is computational at its core, argues computer scientist Leslie Valiant.

Computer Science

A New Map Traces the Limits of Computation

By John Pavlus
September 29, 2015
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A major advance in computational complexity reveals deep connections between the classes of problems that computers can — and can’t — possibly do.


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