Intelligence from Learnable Novelty

Intelligence appears under different names in different fields: as data compression in statistics and machine learning, as universal computation in dynamical systems, and as adaptive behavior in agents. Each field has its own foundational theory, and the two most influential theories fail in mirror image: novelty search, seeking surprise, is transfixed by a noisy TV; the free-energy principle is content in a dark room. Both failures have a single cause: each objective treats as one quantity the information a learner can convert into knowledge and the information it never can. Here we show that the learnable part, which we call learnable novelty, yields the seemingly disparate projections of intelligence, and we give a fast, differentiable, closed-form estimator built on a reservoir computer. Used as a measure, without supervision, the estimator recovers decades of complexity classification, ranking the Turing-complete rule 110 highest among the elementary cellular automata. Used as an objective, its gradient carries a neural cellular automaton from simple dynamics into solitons, the traveling, colliding structures by which rule 110 computes, and organizes an image encoder around the ten digit classes of MNIST; no label ever enters training. Handed to a reinforcement-learning agent as an intrinsic reward, it improves on the task baseline in nine of ten environments and collapses in none. Complexity generation, abstraction, and exploration, ordinarily pursued in separate fields, thus emerge from ascent on one differentiable quantity, and the projections of intelligence gain a common footing.