Download Free Stanford University Bulletin Book in PDF and EPUB Free Download. You can read online Stanford University Bulletin and write the review.

1913/15 contains reports of chancellor and treasurer; 1919/24, reports of treasurer and comptroller; 1924- reports of treasurer, comptroller, departments, committees and the publications of the faculty.
The technology controlling United States nuclear weapons predates the Internet. Updating the technology for the digital era is necessary, but it comes with the risk that anything digital can be hacked. Moreover, using new systems for both nuclear and non-nuclear operations will lead to levels of nuclear risk hardly imagined before. This book is the first to confront these risks comprehensively. With Cyber Threats and Nuclear Weapons, Herbert Lin provides a clear-eyed breakdown of the cyber risks to the U.S. nuclear enterprise. Featuring a series of scenarios that clarify the intersection of cyber and nuclear risk, this book guides readers through a little-understood element of the risk profile that government decision-makers should be anticipating. What might have happened if the Cuban Missile Crisis took place in the age of Twitter, with unvetted information swirling around? What if an adversary announced that malware had compromised nuclear systems, clouding the confidence of nuclear decision-makers? Cyber Threats and Nuclear Weapons, the first book to consider cyber risks across the entire nuclear enterprise, concludes with crucial advice on how government can manage the tensions between new nuclear capabilities and increasing cyber risk. This is an invaluable handbook for those ready to confront the unique challenges of cyber nuclear risk.
Nobel Laureate Steven Weinberg explains the foundations of modern physics in historical context for undergraduates and beyond.
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Much contemporary political philosophy has been a debate between utilitarianism on the one hand and Kantian, or rights-based ethic has recently faced a growing challenge from a different direction, from a view that argues for a deeper understanding of citizenship and community than the liberal ethic allows. The writings collected in this volume present leading statements of rights-based liberalism and of the communitarian, or civic republican alternatives to that position. The principle of selection has been to shift the focus from the familiar debate between utilitarians and Kantian liberals in order to consider a more powerful challenge ot the rights-based ethic, a challenge indebted, broadly speaking, to Aristotle, Hegel, and the civic republican tradition. Contributors include Isaiah Berlin, John Rawls, Alasdair MacIntyre.
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.