Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research

Self-assembly: from crystals to cells

Self-assembly (SA) is the process in which a system's components-be it molecules, polymers, colloids, or macroscopic particles-organize into ordered and/or functional structures without human intervention. The main challenge in SA research is the ability to "program'' the properties of the individual pieces such that they organize into a desired structure. Although a general strategy for doing so is still elusive, heuristic rules can be formulated that guide design of SA under various conditions and thermodynamic constraints. This Review examines SA in both the equilibrium and non-equilibrium/dynamic systems and discusses different SA modalities: energy driven, entropy-driven, templated, and field-directed. Non-equilibrium SA is discussed as a route to reconfigurable ("adaptive'') materials, and its connection to biological systems is emphasized