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.
SelfAI: A self-directed framework for long-horizon scientific discovery
University of Electronic Science and Technology of China（电子科技大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Xian Jiaotong University（西安交通大学）；Yale University（耶鲁大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Monash University（莫纳什大学）
Conformational Rank Conditioned Committees for Machine Learning-Assisted Directed Evolution
Pomona College（波莫纳学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Cambridge（剑桥大学）；University of California, Irvine（加州大学尔湾分校）；University of California, Los Angeles（加州大学洛杉矶分校）；Avery Bio Inc（艾维生物公司）
Cyber Academia-Chemical Engineering (CA-ChemE): A Living Digital Town for Self-Directed Research Evolution and Emergent Scientific Discovery
State Key Laboratory of Heavy Oil Processing, College of Carbon Neutrality Future Technology, China University of Petroleum (Beijing), Beijing 102249, China（石油加工国家重点实验室，碳中和未来技术学院，中国石油大学（北京），北京102249）
Self-Directed Learning of Convex Labelings on Graphs
Moscow Institute of Physics and Technology (MIPT)（莫斯科物理技术学院）；TU Wien（维也纳技术大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；CENTAI Institute（CENTAI研究所）
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tencent（腾讯）
