提出超外推概念，让模型在数据之外的新维度上进行合理推断。
Interpolation, Extrapolation, Hyperpolation: Generalising into new dimensions
- 定义超外推：在数据所在子空间之外预测新位置的函数值
- 揭示当前AI缺乏根本创造力，源于无法有效超外推
- 关联艺术与科学中的创造性，拓展机器学习的认知边界
本文引入超外推（hyperpolation）的概念：一种从有限数据点中进行泛化的方法，与插值和外推并列。超外推是估计函数在现有数据子空间（或流形）之外的新位置的值。我们将证明超外推是可行的，并探讨其与艺术和科学中创造力的联系。同时分析超外推在机器学习中的作用，指出当前人工智能系统缺乏根本创造力，与其有限的超外推能力密切相关。
原文摘要 · Abstract (English)
This paper introduces the concept of hyperpolation: a way of generalising from a limited set of data points that is a peer to the more familiar concepts of interpolation and extrapolation. Hyperpolation is the task of estimating the value of a function at new locations that lie outside the subspace (or manifold) of the existing data. We shall see that hyperpolation is possible and explore its links to creativity in the arts and sciences. We will also examine the role of hyperpolation in machine learning and suggest that the lack of fundamental creativity in current AI systems is deeply connected to their limited ability to hyperpolate.
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