走向人类层面的概念学习:对AI算法进行模式基准测试
Andreas Holzinger1,2, Anna Saranti1,2, Alessa Angerschmid1,2
1Human-Centered AI Lab, University of Natural Resources & Life Sciences Vienna, Vienna, Austria.
Patterns (New York, N.Y.)
|August 21, 2023
概括
当前的人工智能 (AI) 在模式识别方面表现出色,但在类似人类的概念学习方面扎. 本文审查了AI概念学习基准和数据集,突出了可解释机器智能的未来研究方向.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 现代人工智能 (AI) 在标准模式识别方面表现出高度熟练,这在很大程度上归因于广泛的数据可用性和复杂的机器学习算法.
- 人工智能的模式识别能力和人类层面的概念学习之间仍然存在很大的差异,特别是在处理不确定性和从有限的例子中概括方面.
研究的目的:
- 提供当前人工智能方法论的全面概述,用于对比概念学习,推理和概括.
- 批判性地评估用于评估AI概念学习的诊断和基准数据集的最新情况.
- 确定和讨论可解释机器智能和概念分析领域未来有希望的研究途径.
主要方法:
- 人工智能概念学习研究的文献综述.
- 分析现有的基准数据集 (例如,CLEVR,RAVEN) 用于概念学习评估.
- 讨论AI在模式分析和机器智能的进展.
主要成果:
- 人工智能在模式识别方面的成功与其在类似人类的概念学习方面的局限性形成对比.
- 现有的基准数据集被目录,并讨论了它们用于诊断AI概念学习限制的实用性.
- 强调需要先进的实验环境和数据集,以推进可解释的AI.
结论:
- 弥合人工智能模式识别和人类概念学习之间的差距需要新的方法和强大的评估方法.
- 专门的基准数据集的开发和使用对于推动机器智能和可解释AI的进步至关重要.
- 未来的研究应该专注于创建能够更类似人类概念学习,概括和在不确定性下推理的人工智能系统.
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