防止智能机器的不良行为
Philip S Thomas1, Bruno Castro da Silva2, Andrew G Barto3
1University of Massachusetts, Amherst, MA, USA. pthomas@cs.umass.edu.
概括
我们开发了一个新的框架来设计机器学习 (ML) 算法, 这种方法成功地防止了实验机器系统的危险行为,促进了更安全的AI应用.
科学领域:
- 人工智能
- 机器学习工程
背景情况:
- 智能机器和机器学习 (ML) 算法在各种应用中越来越普遍,从数据分析到复杂任务执行.
- 确保机器学习系统不会表现出有害或不必要的行为是人工智能开发的关键挑战.
研究的目的:
- 为设计机器学习算法提出一个通用和灵活的框架,简化不良行为的规范和调节.
- 展示该框架在创建更安全的机器学习系统中的实际可行性.
主要方法:
- 开发一种新的ML算法设计框架.
- 基于拟议的框架实施ML算法.
- 对设计的ML算法与标准算法进行实验性评估,以评估危险行为的排除.
主要成果:
- 建议的框架有效地简化了定义和控制不良的ML行为.
- 使用该框架设计的ML算法成功排除了在实验中观察到的标准ML算法的危险行为.
- 这种框架被证明可以用于创建更安全的ML应用程序.
结论:
- 开发的框架为设计负责任和安全的机器学习算法提供了一种简化方法.
- 这项工作有助于机器学习的安全和负责任的应用,
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