Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper appearing in Patterns. Though large language models ...

Discovering and characterizing new materials is important for unlocking advances in fields like clean energy, advanced ...

Engineers develop a system that captures all the elements of trial and error in material design, enabling reliable ...

The Centre of Intelligence of Things, (CIoTh), at the University of Greater Manchester, has announce the publication of two new research papers focused on autism related innovation, developed using ...

Scientists have found a way to make AI much better at predicting complex, chaotic systems by tapping into the unique power of ...

What if auditors could predict when errors are more likely to occur in financial reporting? Instead of simply improving ...

Neuroscientist Vivienne Ming argues in her new book that the biggest risk of artificial intelligence is people using it too ...

Artificial intelligence has moved from the back office to the witness stand, becoming the foundation for expert testimony and ...

Researchers used computational tools and catalyst design to overcome the surface energy barrier limiting hydrogen release ...

This study presents valuable findings by reanalyzing previously published MEG and ECoG datasets to challenge the predictive nature of pre-onset neural encoding effects. The evidence supporting the ...

A former Snowflake data scientist who refined multi-billion-dollar forecasts is now building AI models that outperform Claude ...

Artificial Intelligence is no longer a futuristic concept. It’s a tool that marketers rely on every day. Businesses generate ...