Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike abstractions or analogies. This paper reviews the advantages and limitations of several approaches toward this goal, including symbolic methods, deep learning, and probabilistic program induction. The paper concludes with several proposals for designing challenge tasks and evaluation measures in order to make quantifiable and generalizable progress in this area.

中文翻译：

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人工智能中的抽象与类比

概念抽象和类比制定是人类学习，推理并强大地将其知识适应新领域的能力的关键能力。尽管构建具有这些功能的AI系统的研究已有很长的历史，但是当前的AI系统都无法与形成类似人的抽象或类比的能力相提并论。本文回顾了实现此目标的几种方法的优缺点，包括符号方法，深度学习和概率程序归纳。本文最后提出了一些设计挑战性任务和评估措施的建议，以便在这一领域取得可量化和可概括的进展。