Hopfield Neural Networks (HNNs) constitute a class of recurrent neural networks that function as associative memory systems, adept at converging towards stable states that represent optimised ...

Neural networks are emerging as transformative tools in the field of material sciences by providing new avenues for constitutive modelling. Integrating advanced algorithms with physics-based insights, ...

Artificial intelligence startup Anthropic PBC says it has come up with a way to get a better understanding of the behavior of the neural networks that power its AI algorithms. Because neural networks ...

In labs that once focused on fruit flies and mouse neurons, researchers are now turning their instruments and intuitions on ...

With most computer programs—even complex ones—you can meticulously trace through the code and memory usage to figure out why that program generates any specific behavior or output. That’s generally ...

A new technical paper titled “Impact of Strain on Sub-3 nm Gate-all-Around CMOS Logic Circuit Performance Using a Neural Compact Modeling Approach” was published by researchers at Hanyang University ...

Artificial intelligence might now be solving advanced math, performing complex reasoning, and even using personal computers, but today’s algorithms could still learn a thing or two from microscopic ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or ...

In the 90s, a video game craze took over the youth of the world — but unlike today’s games that rely on powerful PCs or consoles, these were simple, standalone devices with monochrome screens, each ...