Unsupervised Learning
Summary
Unsupervised learning is a branch of machine learning that aims to discover patterns and structures in data without explicit labeling or guidance. Recent advancements in this field have drawn inspiration from physics-based strategies such as divide-and-conquer, Occam’s razor, unification, and lifelong learning. A novel paradigm has emerged that focuses on learning and manipulating “theories” – parsimonious models that predict future outcomes based on past observations and define their domains of accuracy. This approach employs techniques like generalized-mean-loss to encourage specialization, differentiable description length objectives to simplify learned theories, and theory unification to continuously refine and expand knowledge. When applied to complex physics environments, such as those involving gravity, electromagnetism, and chaotic systems, this method has demonstrated significant improvements in learning speed and prediction accuracy compared to traditional neural networks, often recovering exact integer and rational parameters. This innovative approach to unsupervised learning shows promise in handling diverse and complex environments, potentially leading to more robust and interpretable AI systems.