神经网络,人工智能以及建模的目标
Walter Veit1, Heather Browning2
1Department of Philosophy, University of Bristol, Bristol, UK wrwveit@gmail.com https://walterveit.com/.
The Behavioral and brain sciences
|December 6, 2023
概括
深度神经网络 (DNN) 在模仿人类认知方面表现有前途. 然而,这项研究反对将它们的预测能力与人工智能研究中的真正解释能力混为一谈.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 深度神经网络 (DNN) 在各种应用中取得了显著的成功,经常模仿人类的认知过程.
- 人们认为DNN与人类智能之间的相似性导致它们被用作理解认知的模型.
- 人工智能和认知科学的一个关键辩论是,DNN在多大程度上真正解释或仅仅预测认知现象.
研究的目的:
- 批判性地检查深度神经网络的预测和解释能力之间的融合.
- 在人工智能和认知科学的背景下分析科学建模的不同目标.
- 要区分预测行为的模型和解释潜在机制的模型.
主要方法:
- 在人工智能研究中建模目标的概念分析.
- 在深度神经网络中对预测准确度与解释深度进行比较检查.
- 对人工智能和人类认知的当前观点的文献综述.
主要成果:
- DNN擅长预测,经常与人类在特定任务上的表现相匹配.
- DNN的内部运作并不一定能为认知功能提供机械解释.
- 一个模型的预测成功与它解释现象的能力之间必须保持区别.
结论:
- 不应该把DNN的预测成功等同于对人类智能的真正解释能力.
- 了解建模的具体目标对于解释人工智能研究结果至关重要.
- 未来的研究应该专注于开发人工智能模型,为认知过程提供更深入的解释性见解.
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