Self-directed Machine Learning

TL;DR

This paper introduces Self-directed Machine Learning (SDML), a framework inspired by human self-directed learning, enabling machines to autonomously select tasks, data, models, and evaluation metrics through self-awareness, aiming to advance towards artificial general intelligence.

Contribution

The paper proposes a novel SDML framework guided by self-awareness, with a mathematical formulation and case studies demonstrating autonomous learning processes without human intervention.

Findings

SDML enables autonomous task and data selection.

Self-awareness improves learning performance.

Potential applications in artificial general intelligence.

Abstract

Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation metrics, which is labor-intensive, time-consuming, and cannot learn autonomously like humans. In education science, self-directed learning, where human learners select learning tasks and materials on their own without requiring hands-on guidance, has been shown to be more effective than passive teacher-guided learning. Inspired by the concept of self-directed human learning, we introduce the principal concept of Self-directed Machine Learning (SDML) and propose a framework for SDML. Specifically, we design SDML as a self-directed learning process guided by self-awareness, including internal awareness and external awareness. Our proposed SDML process benefits from self task selection, self data selection, self model selection, self optimization strategy selection and self evaluation metric selection through self-awareness without human guidance. Meanwhile, the learning performance of the SDML process serves as feedback to further improve self-awareness. We propose a mathematical formulation for SDML based on multi-level optimization. Furthermore, we present case studies together with potential applications of SDML, followed by discussing future research directions. We expect that SDML could enable machines to conduct human-like self-directed learning and provide a new perspective towards artificial general intelligence.