通过超学习神经网络进行类似人类系统的概括
Brenden M Lake1, Marco Baroni2,3
1Department of Psychology and Center for Data Science, New York University, New York, NY, USA. brenden@nyu.edu.
Nature
|October 25, 2023
概括
神经网络可以通过优化其构成技巧来实现类似人类语言和思维的系统性. 构成性的元学习 (MLC) 方法使网络能够灵活地泛化,解决人工智能的长期挑战.
科学领域:
- 认知科学
- 人工智能
- 计算语言学
背景情况:
- 人类的认知依赖于系统的构成,使已知元素的新组合成为可能.
- 福多尔和皮利希恩的挑战是,人工神经网络缺乏这种系统性,限制了它们作为思维模型的可行性.
- 尽管取得了进展,但在神经网络中实现系统的概括仍然是一个持续的挑战.
研究的目的:
- 证明神经网络可以实现类似人类的系统性.
- 引入和评估组合性 (MLC) 的元学习方法.
- 将MLC的概括能力与其他模型和人类性能进行比较.
主要方法:
- 开发了组合性的元学习 (MLC) 方法,指导神经网络训练与多种组合任务.
- 使用指令学习模式进行人类行为实验.
- 评估了七种不同的模型,包括MLC,概率符号模型和标准神经网络,以系统的概括基准.
主要成果:
- MLC成功地实现了系统性和灵活性,超过了严格的符号模型和非系统的神经网络.
- 在对比中,MLC表现出类似人类的概括能力.
- 在多个基准中,MLC显著提高了机器学习系统的构成技能.
结论:
- 通过对神经网络进行优化,
- 这种方法为开发更有能力,更像人类的人工智能提供了可行的方法.
- 这项研究弥合了人造神经网络与人类思维和语言的系统性之间的差距.
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