The Parameters of Educability

TL;DR

This paper discusses the key parameters influencing the design of educable systems, aiming to understand how these parameters affect the system's ability to acquire and apply knowledge, with broader implications for AI development.

Contribution

It introduces and analyzes the main parameters of educability models, highlighting their importance and the lack of universally optimal choices, advancing understanding of intelligent system design.

Findings

Parameters significantly influence educability models.

No universally optimal parameter choices exist.

Implications for AI system development and capabilities.

Abstract

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.