人工智能时代的科学发现
Hanchen Wang1,2,3,4, Tianfan Fu5, Yuanqi Du6
1Department of Engineering, University of Cambridge, Cambridge, UK.
Nature
|August 2, 2023
概括
人工智能通过自我监督和几何深度学习等先进方法加速科学发现. 在数据质量和理解人工智能的局限性方面仍然存在挑战.
科学领域:
- 综合计算和数据科学
- 生物医学信息学
- 材料科学
背景情况:
- 人工智能 (AI) 越来越多地成为科学研究的组成部分,增强了传统的方法.
- 人工智能有助于产生假设,实验设计,数据解释和发现新见解.
- 最近的进展显著扩大了人工智能的科学探索能力.
研究的目的:
- 检查过去十年科学发现中的关键人工智能突破.
- 在科学过程的各个阶段讨论人工智能的应用.
- 确定科学中人工智能的持续挑战和未来方向.
主要方法:
- 对未标记数据的培训模型进行自我监督学习的审查.
- 分析几何深度学习,利用数据结构提高准确性.
- 从多种数据设计分子和蛋白质的生成性AI的探索.
主要成果:
- 自主监督学习可以在大量未标记的数据集上进行训练.
- 通过使用数据结构来提高模型性能.
- 通过分析多模式数据,生成性AI可以创建新型设计 (例如药物,蛋白质).
结论:
- 人工智能方法显著增强跨学科的科学发现过程.
- 关键的挑战包括改善数据质量,管理和理解人工智能的局限性.
- 基础算法开发对于自主科学理解和人工智能创新至关重要.
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