Can We Distinguish Machine Learning from Human Learning?

TL;DR

This paper explores the fundamental differences between human and machine learning by focusing on tasks where their relative difficulty is reversed, proposing a rule-based framework to analyze and compare their learning processes.

Contribution

It introduces a novel approach of studying learning under human-created rules to rigorously compare human and AI learning, aiming to identify interesting task pairs and underlying principles.

Findings

Proposes a framework for learning under human-created rules.

Identifies the potential for interesting task pairs where difficulty is reversed.

Suggests a long-term goal of understanding what makes task pairs salient.

Abstract

What makes a task relatively more or less difficult for a machine compared to a human? Much AI/ML research has focused on expanding the range of tasks that machines can do, with a focus on whether machines can beat humans. Allowing for differences in scale, we can seek interesting (anomalous) pairs of tasks T, T'. We define interesting in this way: The "harder to learn" relation is reversed when comparing human intelligence (HI) to AI. While humans seems to be able to understand problems by formulating rules, ML using neural networks does not rely on constructing rules. We discuss a novel approach where the challenge is to "perform well under rules that have been created by human beings." We suggest that this provides a rigorous and precise pathway for understanding the difference between the two kinds of learning. Specifically, we suggest a large and extensible class of learning tasks, formulated as learning under rules. With these tasks, both the AI and HI will be studied with rigor and precision. The immediate goal is to find interesting groundtruth rule pairs. In the long term, the goal will be to understand, in a generalizable way, what distinguishes interesting pairs from ordinary pairs, and to define saliency behind interesting pairs. This may open new ways of thinking about AI, and provide unexpected insights into human learning.