AI initiatives don’t stall because models aren’t good enough, but because data architecture lags the requirements of agentic systems.

Ranjith Rajasekharan says The right workload in the wrong environment is worse than no workload at all. Smarter placement isn ...

Objective To determine whether a full-scale randomised control trial (RCT) assessing the efficacy and cost-effectiveness of a ...

The world’s top chipmaker wants open source AI to succeed—perhaps because closed models increasingly run on its rivals’ ...

Invent 2025 signals the shift from AI experimentation to AI architecture. Seven insights for business leaders navigating ...

Francisco Javier Arceo explored Feast, the open-source feature store designed to address common data challenges in the AI/ML ...

Instead of creating a false binary between thoughts and emotions, we need a practical framework for understanding how they ...

The model, called SHARP, can reconstruct a photorealistic 3D scene from a single image in under a second. Here are some examples.

C compiler, LustreC, into a generator of both executable code and associated specification. Model-based design tools are ...

Bioprocessing data is often scattered across electronic laboratory notebooks (ELNs), laboratory information management systems (LIMS), instruments, spreadsheets, and legacy systems that don’t talk to ...

AI has moved long beyond hype. Most enterprises now expect tangible value from AI - fewer manual tasks, better decisions, and ...

Scientifically, artistically, and environmentally speaking, there’s a lot to take into account.