发表机构
Allen Discovery Center at Tufts University; Wyss Institute for Biologically Inspired Engineering at Harvard University（塔夫茨大学艾伦发现中心; 哈佛大学威斯生物启发工程研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究表明可学习新奇性产生智能不同投影，基于廉价可微储层计算机给出闭式估计器。此估计器无监督可恢复复杂度分类，作目标能推动神经细胞自动机发展，作奖励可提升强化学习智能体表现，让智能相关研究有了共同定量基础。
AI 中文摘要
智能在不同领域有不同称谓：在统计与机器学习中是数据压缩，在动力系统中是通用计算，在智能体中是自适应行为。不同领域有各自目标，新奇性搜索与自由能原理常失效。原因是它们将学习者可转化为知识的惊奇和不可转化的惊奇视为一个量。本文表明可学习新奇性产生了智能的不同投影，给出基于廉价可微储层计算机的闭式估计器。该估计器无监督时可恢复复杂度分类，作为目标时能使神经细胞自动机进入孤子状态，还能组织图像编码器表示。作为强化学习智能体的内在奖励，可改善任务表现。
英文摘要
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 carries its own objective, and the two most influential drives often fail in mirror image: novelty search, which seeks surprise, is transfixed by a noisy television screen, while the free-energy principle, which avoids surprise, is most content in a dark room. Both failures have a single cause: each objective treats as one quantity the surprise a learner can convert into knowledge and the surprise it never can. Here we show that the learnable part of that information, which we call learnable novelty, yields the seemingly disparate projections of intelligence, and we give a closed-form estimator of it built on a cheap and differentiable reservoir computer. Used as a measure, with no supervision of any kind, 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 a regime of solitons, the traveling, colliding structures by which rule~110 computes, as well as organizes the representation of an image encoder around the ten digit classes of MNIST, fully unsupervised: no label ever enters training. Handed to a reinforcement-learning agent as an intrinsic reward, it supplies the exploration that task rewards lack, improving on the task baseline in nine of ten environments and collapsing in none. Complexity generation, abstraction, and exploration, ordinarily pursued with unrelated objectives in separate fields, thus emerge from ascent on one differentiable quantity, and the projections of intelligence gain a common quantitative footing.
Comments24 pages, 10 figures, 4 tables
