Explainable AI
2 reportsExplainable AI is an artificial intelligence method for learning patterns, generating outputs, making predictions, or controlling systems. Claims about capability are tested through computational complexity and optimization method, with attention to scale, failure modes, comparability, and operating conditions.
A source-based view of Explainable AI considers failure modes, together with learning objective and data requirements. Reporting on failure modes draws on ablation studies and is then checked against replication on different datasets; the comparison must account for the fact that headline accuracy can hide distribution shifts, bias, or unstable behavior.
A source-based view of Explainable AI considers failure modes, together with learning objective and data requirements. Reporting on failure modes draws on ablation studies and is then checked against replication on different datasets; the comparison must account for the fact that headline accuracy can hide distribution shifts, bias, or unstable behavior.
Curiosity-Driven AI Robots Show Faster Language Learning in Simulation
Researchers at the Okinawa Institute of Science and Technology have built virtual robots that learn language tasks more efficiently by rewarding curiosity, revealing new patterns of play-like behavior and adaptability in controlled experiments
China Details AI Roadmap for Safer Nuclear Energy Operations
At the World Artificial Intelligence Conference, Chinese researchers presented a multi-layered AI integration plan for advanced nuclear energy systems, aiming to address safety and operational challenges across the full reactor lifecycle