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The influence of Vannevar Bush on the history and institutions of twentieth-century American science and technology is staggeringly vast. As a leading figure in the creation of the National Science Foundation, the organizer of the Manhattan Project, and an adviser to Presidents Roosevelt and Truman during and after World War II, he played an indispensable role in the mobilization of scientific innovation for a changing world. A polymath, Bush was a cofounder of Raytheon, a pioneer of computing technology, and a visionary who foresaw the personal computer and might have coined the term “web.”Edited by Bush's biographer, G. Pascal Zachary, this collection presents more than fifty of Bush's most important works across four decades. His subjects are as varied as his professional pursuits. Here are his thoughts on the management of innovation, the politics of science, research and national security, technology in public life, and the relationship of scientific advancement to human flourishing. It includes his landmark introduction to Science, the Endless Frontier, the blueprint for how government should support research and development, and much more. The works are as illuminating as they are prescient, from considerations of civil-military relations and the perils of the nuclear arms race to future encyclopedias and information overload, the Apollo program, and computing and consciousness. Together, these pieces reveal Bush as a major figure in the history of science, computerization, and technological development and a prophet of the information age.

This book addresses controversies concerning the epistemological foundations of data science: Is it a genuine science? Or is data science merely some inferior practice that can at best contribute to the scientific enterprise, but cannot stand on its own? The author proposes a coherent conceptual framework with which these questions can be rigorously addressed. Readers will discover a defense of inductivism and consideration of the arguments against it: an epistemology of data science more or less by definition has to be inductivist, given that data science starts with the data. As an alternative to enumerative approaches, the author endorses Federica Russo's recent call for a variational rationale in inductive methodology. Chapters then address some of the key concepts of an inductivist methodology including causation, probability and analogy, before outlining an inductivist framework. The inductivist framework is shown to be adequate and useful for an analysis of the epistemological foundations of data science. The author points out that many aspects of the variational rationale are present in algorithms commonly used in data science. Introductions to algorithms and brief case studies of successful data science such as machine translation are included. Data science is located with reference to several crucial distinctions regarding different kinds of scientific practices, including between exploratory and theory-driven experimentation, and between phenomenological and theoretical science. Computer scientists, philosophers and data scientists of various disciplines will find this philosophical perspective and conceptual framework of great interest, especially as a starting point for further in-depth analysis of algorithms used in data science.