提出面向概念的语言框架，助力机器理解与发现知识。
Learning Machines: In Search of a Concept Oriented Language
- 通过类比人类智能，构建概念导向的机器语言框架。
- 强调机器需具备知识发现与决策能力，方称真正智能。
- 适合研究通用人工智能与认知计算的学者参考。
在数据与数字革命之后，下一代智能机器的核心需求是什么？它们如何实现记忆、学习与发现？何为智能的判定标准？这些问题指向具备知识发现、决策与概念处理能力的智能系统。本文回顾历史贡献，通过类比人类智能，探讨上述问题，并提出一种通用的概念导向语言框架，旨在推动机器向更高层次的认知能力演进。
原文摘要 · Abstract (English)
What is the next step after the data/digital revolution? What do we need the most to reach this aim? How machines can memorize, learn or discover? What should they be able to do to be qualified as "intelligent"? These questions relate to the next generation "intelligent" machines. Probably, these machines should be able to handle knowledge discovery, decision-making and concepts. In this paper, we will take into account some historical contributions and discuss these different questions through an analogy to human intelligence. Also, a general framework for a concept oriented language will be proposed.
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