Listing 1 - 2 of 2 |
Sort by
|
Choose an application
Based on current and projected breakthroughs in biological, genetic, and digital technologies—and their possible convergences—contemporary transhumanism confronts the Christian faith with the question: can finite beings be saved from suffering, illness and death? Transhumanists emphatically embrace this possibility as they offer their concrete visions of a future self-redemption through science, medicine, and technology. Transhumanism aims to take control of the evolutionary process and to steer it into a better future for humanity, or rather, their artificial successors. This book is a comprehensive and constructive critique of the transhumanist agenda and its underlying sociotechnical imaginary, worldview, and anthropology. For this task, it draws on theological resources of Christian tradition(s) in novel ways that serve to render the Christian faith plausible in a digital age. In developing a theology that explores the creative potential of “perfected finitude” (Vollendlichkeit) from an eschatological perspective, it contributes to a “theology of technology”. Das transhumanistische Anliegen, den Menschen in physischer und psychischer Hinsicht zu verbessern, hat eine lange Geschichte. Neu in der Gegenwart sind die Gestaltungspotentiale und Handlungsspielräume, die durch biologische, genetische und digitale Technologien eröffnet werden. Sie nötigen den Menschen zur Entscheidung: Wie kann, soll und will er sich als der „neue Mensch“ (homo novus) in Zukunft bestimmen (lassen)? In der Auseinandersetzung mit dieser Frage werden die Anliegen des Transhumanismus aus der Perspektive des christlichen Glaubens konstruktiv und kritisch diskutiert und mit einer zeit- gemäßen Techniktheologie konfrontiert, welche die Potentiale einer eschato- logischen „Vollendlichkeit“ von Mensch und Schöpfung entfaltet.
Choose an application
Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.
Listing 1 - 2 of 2 |
Sort by
|