Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...

Caltech researchers have developed an innovative algorithm that allows neural networks to be updated with new information without losing previously acquired knowledge, mimicking the flexibility of ...

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 ...

How does the brain know that the same snake deserves calm curiosity at the zoo and a hasty retreat on a wilderness trail?

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...

Scientists in Spain have used genetic algorithms to optimize a feedforward artificial neural network for the prediction of energy generation of PV systems. Genetic algorithms use “parents” and ...

Neuro-symbolic AI combines neural networks with rules-based symbolic processing techniques to improve artificial intelligence systems' accuracy, explainability and precision. The neural aspect ...