Artificial Intelligence
3 reportsArtificial Intelligence research evaluates how an engineered or computational system works, performs, fails, and compares with alternatives. Claims about capability are tested through training data, generative AI, and model robustness, with attention to scale, failure modes, comparability, and operating conditions.
Interpretation of evaluation benchmarks is tested with AI safety and training data. The discussion therefore addresses which data shape model behavior and how bias is measured, comparing technical gains with tradeoffs that a single metric can conceal.
Interpretation of evaluation benchmarks is tested with AI safety and training data. The discussion therefore addresses which data shape model behavior and how bias is measured, comparing technical gains with tradeoffs that a single metric can conceal.
AI in Particle Physics: Discovery Without Full Understanding
Artificial intelligence is now central to particle physics, accelerating data analysis and experiment design. But as AI systems identify patterns beyond human intuition, researchers face new challenges in transparency, reproducibility, and scientific interpretation
World Models Aim to Simulate Reality but Face Technical Barriers
Researchers are developing world models-AI systems designed to simulate aspects of the physical world. These models promise new capabilities beyond language, but their accuracy and reliability remain unsettled
NVIDIA's RoboLab Targets Real-World Robot Policy Evaluation Limits
NVIDIA has released RoboLab, an open-source simulation platform designed to benchmark and analyze general-purpose robot policies. The system aims to address persistent gaps in evaluating robotic models before real-world deployment