artificial intelligence

Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input.

The AI journey starts with a single step, but too many companies take the wrong first step.

AltaML has announced a partnership with engineering and design firm Kleinfelder in which the two companies will pair 3D reality scans of facilities with artificial intelligence to look for potential problems and risks.

When the field emerged at the end of the 20th century, it was hoped that computers would be able to operate on their own, with human-like abilities—a capability known as generalized AI.

As deep learning matures and moves from the hype peak to its trough of disillusionment, it is becoming clear that it is missing some fundamental components.

Modeling immensely complex natural phenomena such as how subatomic particles interact or how atmospheric haze affects climate can take hours on even the fastest supercomputers. Now, work posted online shows how AI can easily produce emulators that can accelerate simulations by billions of times.

A system proposed by researchers at the University of California, Santa Barbara, uses hyperspectral imaging and machine learning to detect the specific wavelength of methane emissions.

The chip is less than 4.5 mm across and weighs less than 2 oz. Nonetheless, it is pushing the power of artificial intelligence to the edge.

MIT Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.

More energy and engineering companies participate in second Industrial VR/AR Forum.