通过人工智能引导人类直觉,推进数学
Alex Davies1, Petar Veličković2, Lars Buesing2
1DeepMind, London, UK. adavies@deepmind.com.
Nature
|December 2, 2021
概括
机器学习帮助数学家通过识别模式和指导猜测制定来发现新的定理. 这种人工智能辅助的方法加速了纯数学研究的突破.
科学领域:
- 纯粹的数学
- 人工智能
背景情况:
- 数学传统上依赖于对猜测和定理的模式发现.
- 自20世纪60年代以来,计算机一直在帮助数学家发现模式和构建推测.
- 值得注意的例子包括伯奇和斯温纳顿-戴尔猜测,一个千年奖问题.
研究的目的:
- 展示机器学习的方法,以帮助数学家发现新的推测和定理.
- 提出使用机器学习指导数学直觉和发现的框架.
主要方法:
- 使用机器学习来识别数学对象之间的潜在模式和关系.
- 使用归因技术来理解这些发现的模式.
- 使用这些洞察力来引导人类的直觉, 并制定新的数学猜测.
主要成果:
- 机器学习引导框架的成功应用在纯数学当前的研究问题上.
- 发现结的代数和几何结构之间的新联系.
- 通过对称组的组合不变假设预测的候选算法的识别.
结论:
- 机器学习可以极大地帮助我们发现纯数学中的基本结果.
- 拟议的框架有助于数学家和人工智能之间的合作.
- 这种协同方法可以为开放的数学问题做出令人惊和有意义的贡献.
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