Ram D. Sriram – författare

Realizing the promise of digital technology will depend on the ability to share information across time and space from multiple devices, sources, systems, and organizations. The major barrier to progress is not technical; rather, it is in the failure of organizational demand and purchasing requirements. In contrast to many other industries, the purchasers of health care technologies have not marshaled their purchasing power to drive interoperability as a key requirement. Better procurement practices, supported by compatible interoperability platforms and architecture, will allow for better, safer patient care; reduced administrative workload for clinicians; protection from cybersecurity attacks; and significant financial savings across multiple markets.With funding support from the Gordon and Betty Moore Foundation, this National Academy of Medicine Special Publication represents a multi-stakeholder exploration of the path toward achieving large-scale interoperability through strategic acquisition of health information technology solutions and devices. In this publication, data exchanges over three environments are identified as critical to achieving interoperability: facility-to-facility (macro-tier); intra-facility (meso-tier); and at point-of-care (micro-tier). The publication further identifies the key characteristics of information exchange involved in health and health care, the nature of the requirements for functional interoperability in care processes, the mapping of those requirements into prevailing contracting practices, the specification of the steps necessary to achieve system-wide interoperability, and the proposal of a roadmap for using procurement specifications to engage those steps. The publication concludes with a series of checklists to be used by health care organizations and other stakeholders to accelerate progress in achieving system-wide interoperability.

When men of knowledge impart this knowledge, I do not mean they will convince your reason. I mean they will awaken in you the faith that it is so. - Sri Krishna, Bhagavadgita BACKGROUND The use of computers has led to significant productivity increases in the en­ gineering industry. Most ofthe computer-aided engineering applications were . restricted to algorithmic computations, such as finite element programs and circuit analysis programs. However, a number ofproblems encountered in en­ gineering are not amenable to purely algorithmic solutions. These problems are often ill-structured; the term ill-structured problems is used here to de­ note problems that do not have a clearly defined algorithmic solution. An experienced engineer deals with these ill-structured problems using his/her judgment and experience. The knowledge-based systems (KBS) technology, which emerged out of research in artificial intelligence (AI), offers a method­ ologyto solve these ill-structuredengineering problems. The emergenceofthe KBS technology can be viewed as the knowledge revolution: other important events that led to increased productivity are the industrial revolution (17th century); the invention of the transistor and associated developments (first half of the 20th century); and the world-wide web (towards the end of the 20th century). Kurzweil, in a lecture at M. LT on December 3, 1987, linked the progress of automation to two industrial revolutions: the first industrial PREFACE xxxii revolution leveraged our physical capabilities, whereas the second industrial revolution - the knowledge revolution - is expected leverage oUr mental ca­ pabilities.

This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for robotics and related areas.

This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for robotics and related areas.