At the heart of any discussion about the future of libraries is the future of librarians—and how well our instructional programs, especially the Master of Library Science (MLS) degree, prepare them for their careers. This book continues the critical conversations around preparing future librarians.

In this topical volume, the authors provide in-depth coverage of the vital relationship between electrochemistry and the morphology of thin films and surfaces. Clearly divided into four major sections, the book covers nanoscale dielectric films for electronic devices, superconformal film growth, electrocatalytic properties of transition metal macrocycles, and the use of synchrotron techniques in electrochemistry. All the chapters offer a concise introduction to the relevant topic, as well as supplying numerous references for easy access to further reading and the original literature. The result is must-have reading for electrochemists, physical and surface chemists and physicists, as well as materials scientists and engineers active in the field of spectroscopic methods in electrochemistry.

Information technology (IT) is widely understood to be the enabling technology of the 21st century. IT has transformed, and continues to transform, all aspects of our lives: commerce and finance, education, employment, energy, health care, manufacturing, government, national security, transportation, communications, entertainment, science, and engineering. IT and its impact on the U.S. economy-both directly (the IT sector itself) and indirectly (other sectors that are powered by advances in IT)-continue to grow in size and importance. In 1995, the National Research Council's Computer Science and Telecommunications Board (CSTB) produced the report Evolving the High Performance Computing and Communications Initiative to Support the Nation's Information Infrastructure. A graphic in that report, often called the "tire tracks" diagram because of its appearance, produced an extraordinary response by clearly linking government investments in academic and industry research to the ultimate creation of new information technology industries with more than $1 billion in annual revenue. Used in presentations to Congress and executive branch decision makers and discussed broadly in the research and innovation policy communities, the tire tracks figure dispelled the assumption that the commercially successful IT industry is self-sufficient, underscoring through long incubation periods of years and even decades. The figure was updated in 2002, 2003, and 2009 reports produced by the CSTB. With the support of the National Science Foundation, CSTB updated the tire tracks figure. Continuing Innovation in Information Technology includes the updated figure and a brief text based in large part on prior CSTB reports.

Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016, the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federally-funded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts.