Dr. James McCaffrey of Microsoft Research says that when quantum computing becomes generally available, evolutionary algorithms for training huge neural networks could become a very important and ...

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

For about a decade, computer engineer Kerem Çamsari employed a novel approach known as probabilistic computing. Based on probabilistic bits (p-bits), it’s used to solve an array of complex ...

As the world races to build artificial superintelligence, one maverick bioengineer is testing how much unprogrammed intelligence may already be lurking in our simplest algorithms to determine whether ...

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AI R&D runs on a cycle of hypothesis, experiment, and analysis — each step demanding substantial manual engineering effort. A new framework from researchers at SII-GAIR aims to close that bottleneck ...

Algorithms have taken on an almost mythical significance in the modern world. They determine what you see on social media and when browsing online, help form people’s belief systems, and impact the ...

For better accountability, we should shift the focus from the design of these systems to their impact. Describing a decision-making system as an “algorithm” is often a way to deflect accountability ...

The goal of a numerical optimization problem is to find a vector of values that minimizes some cost function. The most fundamental example is minimizing the Sphere Function f(x0, x1, .. xn) = x0^2 + ...