AI workflows fundamentally depend on real-time data movement: ingesting training data streams, feeding live data to models for inference and distributing predictions back to applications. But strip ...

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs ...

A new book by one of the founders of research on neural networks covers more than 40 years of scientific work that led to the ...

The parallel computing market was valued at USD 22.4 billion in 2024 and is projected to reach approximately USD 54.0 billion ...

UCLA researchers demonstrate diffractive optical processors as universal nonlinear function approximators using linear ...

This creates what you might call the AI workflow paradox: the faster we can generate code, the more critical it becomes to ...

Nvidia transformed itself with its CUDA software platform while creating a wide moat at the same time. Amazon and Apple's moats are built around customer loyalty. The abilities of Microsoft and ...

What if your workflows could process tens of thousands of files in parallel, never missing a beat? For many, scaling n8n workflows to handle such massive workloads ...

Oracle's data center revenue rose sharply last quarter as demand from AI developers for capacity continued to outstrip supply. Its remaining performance obligation rose by a remarkable 359% year over ...

Few topics in science are as fascinating and mind-bending as quantum computing and parallel universes. These concepts, once the exclusive domain of science fiction, are now being seriously explored by ...

With AI changing so fast, it’s a juggle for companies to ensure they can deliver the best performance now while also future-proofing for unknown AI models or a completely different approach to ...