Deep Learning
2 reportsDeep Learning is an artificial intelligence method for learning patterns, generating outputs, making predictions, or controlling systems. Claims about capability are tested through regularization, computational complexity, and optimization method, with attention to scale, failure modes, comparability, and operating conditions.
A detailed treatment of Deep Learning follows optimization procedure and evaluation metrics, with separate attention to failure modes. The treatment of failure modes keeps controlled benchmarks separate from ablation studies until their assumptions and scales are compared; the evidence is read with the caveat that headline accuracy can hide distribution shifts, bias, or unstable behavior.
A detailed treatment of Deep Learning follows optimization procedure and evaluation metrics, with separate attention to failure modes. The treatment of failure modes keeps controlled benchmarks separate from ablation studies until their assumptions and scales are compared; the evidence is read with the caveat that headline accuracy can hide distribution shifts, bias, or unstable behavior.
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