Learnable Novelty Unifies Intelligence Across Fields
A new preprint introduces 'learnable novelty' as a unified, differentiable measure of intelligence that connects data compression, computation, and adaptive behavior. The authors present a closed-form estimator based on a differentiable reservoir computer, showing its effectiveness in complexity classification, unsupervised representation learning, and as an intrinsic reward for reinforcement learning agents. Their results suggest that complexity generation, abstraction, and exploration can all emerge from optimizing this single objective.
Why it matters: This work proposes a single, differentiable objective that could unify approaches to intelligence across multiple fields, potentially simplifying and advancing research in artificial intelligence.
Full story at: arXiv Machine Learning ↗