你不能和大自然玩20个问题并赢得redux
Bradley C Love1, Robert M Mok2
1Experimental Psychology, University College London, London, UK b.love@ucl.ac.uk https://bradlove.org/.
The Behavioral and brain sciences
|December 6, 2023
概括
这项研究反对用更简单的玩具模型取代具有深度学习能力的模型. 评估任务执行模型至关重要,而不仅仅是遵守实验室检查清单.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 机器学习 机器学习
背景情况:
- 当前的科学模型往往是不完美的,因为不完全的理解.
- 目标文章建议用玩具模型取代合格的深度学习模型.
- 这种方法是根据实验室发现的检查清单进行评估的.
研究的目的:
- 批评深度学习模型被玩具模型所替代的建议.
- 为持续使用和开发有能力的人工智能模型辩论.
- 强调功能性能在模型评估中的重要性.
主要方法:
- 对目标文章提出的方法的分析.
- 深度学习能力与玩具模型评估的比较.
- 在AI评估的背景下考虑Alan Newell的20个问题批评.
主要成果:
- 拟议的方法有可能导致科学建模中的不一致性.
- 对实验室检查清单进行模型评估可能会忽视基本的功能能力.
- 深度学习模型展示了对象识别任务的能力.
结论:
- 抛弃有能力的深度学习模型以更简单的替代方案是不建议的.
- 任何模型的主要测试应该是其执行预期任务的能力.
- 需要对AI模型评估采取平衡的方法,考虑理论和实践性能.
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