Computer scientists at MIT are spearheading an effort to make designing and training artificial neural networks a lot faster and more efficient by powering through a paradox in their implementation.

Imagine a computer the size of a solar system, powered by a star and built for speed. It sounds impressive, but it may not be as good of an idea as it may seem.

A new set of chips shows a lot of promise in drastically reducing the vast amount of energy used by artificial neural networks.

Technophobes and worried Singularitarians concerned about a violent AI need to stop worrying about the machines and start worrying about who's programming them.

Big Think's informative and educational Q&A feature once again produces answers that fail to inform or educate.

We've reached a phase in computer development when everything old is new again. And this approach can work. Up to a point.

One of the biggest limitations on the computing power of our spacecraft is shielding them from radiation.

The NSA wants to scan the internet in real time and store whatever it can catch for later. But are they ready for the challenge that poses?

How accurate are the comparisons between how our brains work and how computers calculate? A trio of researchers decided to find out.

Michio Kaku talks quantum computing at Big Think. It doesn't go well. It doesn't go well at all.

Is it possible to simulate and compute our way to solving the mysteries of the universe and actually get it right in the end?