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Computer Science

Abstractions blog

One-Way Salesman Finds Fast Path Home

October 5, 2017

The real-world version of the famous “traveling salesman problem” finally gets a good-enough solution.

Information dog bottleneck
Wired to Learn: The Next AI

New Theory Cracks Open the Black Box of Deep Learning

September 21, 2017

A new idea is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

Brain made of wires
Wired to Learn: The Next AI

A Brain Built From Atomic Switches Can Learn

September 20, 2017

A tiny self-organized mesh full of artificial synapses recalls its experiences and can solve simple problems. Its inventors hope it points the way to devices that match the brain’s energy-efficient computing prowess.

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

Clever Machines Learn How to Be Curious

September 19, 2017

Computer scientists are finding ways to code curiosity into intelligent machines.

Nash equilibrium maze
Game Theory

In Game Theory, No Clear Path to Equilibrium

July 18, 2017

John Nash’s notion of equilibrium is ubiquitous in economic theory, but a new study shows that it is often impossible to reach efficiently.

Abstractions blog

Why Quantum Computers Might Not Break Cryptography

May 15, 2017

A new paper claims that a common digital security system could be tweaked to withstand attacks even from a powerful quantum computer.

Q&A

How to Force Our Machines to Play Fair

November 23, 2016

The computer scientist Cynthia Dwork takes abstract concepts like privacy and fairness and adapts them into machine code for the algorithmic age.

Pencils Down: Experiments in Education

Do You Love or Hate Math and Science?

October 20, 2016

Quanta Magazine invites readers to share about their early math and science learning experiences and to explore the interactive survey results.

Pencils Down: Experiments in Education

The Art of Teaching Math and Science

October 11, 2016

The impasse in math and science instruction runs deeper than test scores or the latest educational theory. What can we learn from the best teachers on the front lines?