Generative design helps engineers explore design, delivering 30-50% faster time-to-market, 10-50% weight reductions, and up ...

Researchers have proposed a unifying mathematical framework that helps explain why many successful multimodal AI systems work ...

The topic of AI and its implications for orthopedic surgeons became of high personal importance when Bill Gates predicted that AI would replace physicians and others within the next decade. As an ...

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...

Antibiotic resistance is turning routine infections into life threatening events, and the traditional pipeline for new drugs ...

Formula 1 teams live in a world where a single misjudged flick of carbon fiber can decide a championship. Aerodynamicists are ...

Before artificial intelligence started running complex systems and influencing everyday decisions, one question kept ...

Autostereoscopic displays enable glasses-free 3D visualization, transforming industries by improving interaction with ...

Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.

Ralph Wigum keeps coding work moving by reading prior outputs, ideal for greenfield specs and batch cleanup, giving steady, measurable progress.

Think back to middle school algebra, like 2 a + b. Those letters are parameters: Assign them values and you get a result. In ...