神经网络用于抽象和推理.
Mikel Bober-Irizar1, Soumya Banerjee2
1University of Cambridge, Cambridge, UK.
Scientific reports
|November 13, 2024
概括
研究人员为抽象和推理体 (ARC) 探索了新的AI方法,这是广泛概括的具有挑战性的基准. 虽然神经网络和大型语言模型显示出希望,但它们仍然落后于手工制作的规则,这表明各种策略是人工智能推理的关键.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 认知科学 认知科学
背景情况:
- 人工智能 (AI) 长期以来一直致力于复制人类的抽象和推理,使系统能够从最小的例子中学习.
- 尽管神经网络取得了进展,但在训练数据之外实现广泛的概括仍然是人工智能的重大挑战.
- 抽象和推理库 (ARC) 是为了严格测试AI在抽象视觉推理任务上的广泛概括能力而开发的.
研究的目的:
- 调查解决抽象和推理集团 (ARC) 任务的新方法,重点是广泛的概括.
- 评估最近神经网络进步和神经符号方法的有效性,与现有的手工制作的ARC解决方案相比.
- 探索大型语言模型 (LLM) 和整体方法在应对ARC带来的挑战方面的潜力.
主要方法:
- 调整了DreamCoder神经符号推理解决器,引入感知抽象和推理语言 (PeARL) 以提高ARC任务性能.
- 开发了一个新的识别模型和编码/增强方案,使大型语言模型 (LLM) 能够处理ARC任务.
- 进行了综合分析,将各种AI系统结合起来,以评估协同性能并确定单个系统的优势.
主要成果:
- 适应的DreamCoder系统,包括PeARL和一个新的识别模型,显著改进了ARC之前的神经符号方法.
- 大型语言模型 (LLM) 证明了解决ARC任务子集的能力,补充了其他最先进的解决方案的性能.
- 与单个系统相比,整体模型取得了优异的结果,突出了结合各种AI策略来进行复杂推理的潜在好处.
结论:
- 目前基于神经网络的方法,包括LLM,在ARC基准上与传统的手工解决方案相比,仍然表现不佳.
- 多种方法,可能受到人类认知策略的启发,可能对于在ARC等广泛的概括任务上实现强大的性能至关重要.
- arckit Python 库的发布旨在促进人工智能抽象和推理的未来研究和开发.
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