Scientific AI refers to the application of artificial intelligence methods to accelerate scientific discovery and understanding across disciplines such as physics, biology, chemistry, and engineering. It integrates machine learning with domain knowledge, simulations, and experimental data to model complex systems, identify patterns, and generate hypotheses. Scientific AI enables tasks such as drug discovery, climate modeling, materials design, and data-driven experimentation, improving efficiency and insight in research workflows.

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Beyond Explainability: How We Are Redefining Interpretability in AI

AI interpretability has long been the focus, but what if it’s only part of the story? New research introduces model semantics, a framework for understanding what AI systems truly represent and how their internal structures connect to real-world phenomena.