Summary:In the past five years, there has been an acceleration in the use of artificial intelligence (AI) in materials science. AI now pervades the entire materials science workflow from new hypothesis generation through knowledge extraction. In fact, when combined with automated synthesis and characterization robots and the state-of-the-art data infrastructure tools, AI closed-loop autonomous materials research systems become tangible possibilities. Such systems would use existing materials data to build AI derived testable hypotheses, identify appropriate materials to test these hypotheses, synthesize and characterize them, perform automated knowledge extraction and then begin the cycle anew without the need for human intervention. This presentation will discuss the bleeding edge of autonomous materials research including the 2018 Materials Accelerator Platform report and the autonomous systems predating it and inspired by it. It will also discuss recent NIST efforts at building multiple autonomous measurement systems to target structure and functionality in corrosion-resistant alloys.