提出人类独特认知能力的计算模型，揭示机器可学习的关键参数。
The Parameters of Educability
- 定义'可教育性'为获取与应用知识的能力，构建计算模型。
- 指出教育系统有远超传统计算机和机器学习的多种参数配置。
- 探讨参数选择对智能系统设计的启示，适合人工智能研究者参考。
可教育性模型是一种近期提出的计算模型，旨在描述人类在地球上所有生物中独有的、能创造先进文明的认知能力。可教育性被定义为获取并应用知识的能力，既用于描述人类能力，也作为机器可实现的理想目标。尽管该模型期望具有数学上严谨的定义，但在具体构建实例时需做出多项决策，这些决策被称为‘参数’。标准计算机有两个关键参数：内存容量和时钟频率，二者无最优组合；类似地，机器学习系统也有两个核心参数：学习算法和训练数据集，同样不存在普遍最优选择。而可教育系统包含的参数数量远超上述两类系统。本文简要讨论了可教育系统的主要参数及其广泛影响。
原文摘要 · Abstract (English)
The educability model is a computational model that has been recently proposed to describe the cognitive capability that makes humans unique among existing biological species on Earth in being able to create advanced civilizations. Educability is defined as a capability for acquiring and applying knowledge. It is intended both to describe human capabilities and, equally, as an aspirational description of what can be usefully realized by machines. While the intention is to have a mathematically well-defined computational model, in constructing an instance of the model there are a number of decisions to make. We call these decisions {\it parameters}. In a standard computer, two parameters are the memory capacity and clock rate. There is no universally optimal choice for either one, or even for their ratio. Similarly, in a standard machine learning system, two parameters are the learning algorithm and the dataset used for training. Again, there are no universally optimal choices known for either. An educable system has many more parameters than either of these two kinds of system. This short paper discusses some of the main parameters of educable systems, and the broader implications of their existence.
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