Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

Neuroscience continually strives to unravel the intricate relationship between neural network morphology, spiking dynamics, and their resulting functional ...

AI accelerators are gaining traction in high-performance electronics design, driven by the need for efficiency and ...

Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to ...

Abstract: Digital in-memory compute (IMC) architectures allow for a balance of the high accuracy and precision necessary for many machine learning applications, with high data reuse and parallelism to ...

Designers are utilizing an array of programmable or configurable ICs to keep pace with rapidly changing technology and AI.

Next Generation Non Volatile Memory Market is anticipated to be USD 37.7 billion by 2033. It is estimated to record a steady ...

Real-world test of Apple's latest implementation of Mac cluster computing proves it can help AI researchers work using ...

A new technical paper titled “A Tensor Compiler for Processing-In-Memory Architectures” was published by researchers at ...

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...

Monoclonal antibody (mAb) manufacturing must continually improve to keep up with increasing demands. To do this, biomanufacturers can deploy machine learning tools to augment traditional process ...

Google Research on November 7, 2025, introduced a new machine learning paradigm called Nested Learning, designed to solve catastrophic forgetting in AI models. This long-standing problem causes models ...