A penetrating guide to artificial intelligence: what it is, what it does, and how it will change our lives At a breathtaking pace, artificial intelligence is getting better and faster at making complex decisions. AI can already identify malignant tumors on CT scans, give legal advice, out-bluff the best poker players in the world, and, with ever-increasing skill, drive our cars. In Who’s Afraid of AI?, award-winning author Thomas Ramge expertly explains how machines are learning to learn, and he questions what today’s explosion of AI capability could mean for tomorrow: Is it ethical to allow robots—endlessly patient—to replace human caregivers in providing comfort and companionship to the elderly? Since AI feeds on big data, can we prevent its misuse by corporations or the government? Will AI ever be capable of runaway self-improvement? And if “the singularity” does arrive, with AI’s intelligence exponentially outpacing our own, what will become of us when, in many ways, we’re obsolete?

This groundbreaking book explores the power of collaborative AI in amplifying human creativity and expertise. Written by two seasoned experts in data analytics, AI, and machine learning, the book offers a comprehensive overview of the creative process behind AI-powered content generation. It takes the reader through a unique collaborative process between human authors and various AI-based topic experts, created, prompted, and fine-tuned by the authors. This book features a comprehensive list of prompts that readers can use to create their own ChatGPT-powered topic experts. By following these expertly crafted prompts, individuals and businesses alike can harness the power of AI, tailoring it to their specific needs and fostering a fruitful collaboration between humans and machines. With real-world use cases and deep insights into the foundations of generative AI, the book showcases how humans and machines can work together to achieve better business outcomes and tackle complex challenges. Social and ethical implications of collaborative AI are covered and how it may impact the future of work and employment. Through reading the book, readers will gain a deep understanding of the latest advancements in AI and how they can shape our world. Converging Minds: The Creative Potential of Collaborative AI is essential reading for anyone interested in the transformative potential of AI-powered content generation and human-AI collaboration. It will appeal to data scientists, machine learning architects, prompt engineers, general computer scientists, and engineers in the fields of generative AI and deep learning. Chapter 1 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons [Attribution- No Derivatives (CC-BY -ND)] 4.0 license.

This book discuss the following potential trends and innovations that could shape the future of HRM: Data-Driven Decision Making: The use of data analytics and artificial intelligence in HRM is expected to continue growing. Remote Work and Hybrid Models: The COVID-19 pandemic accelerated the adoption of remote work. Skills Development and Lifelong Learning: Continuous learning and upskilling are becoming essential due to the rapid pace of technological advancements. Artificial Intelligence (AI) in Recruitment: AI can streamline and improve the recruitment process by automating tasks like resume screening, candidate sourcing, and initial interviews. Gig Economy and Contingent Workforce Management: As the gig economy expands, HRM will need to adapt to manage both traditional employees and contingent workers effectively, ensuring fairness and compliance. Diversity, Equity, and Inclusion (DEI): DEI initiatives are gaining prominence as organizations recognize the importance of creating inclusive workplaces. Employee Experience (EX): HRM is shifting towards focusing on enhancing the overall employee experience.

Defeat cancer before it develops. Prevent crime before it happens. Get the perfect job without having to know the right people. Algorithms turn long-wished-for dreams into reality. At the same time, they can weaken solidarity in healthcare systems, lead to discriminatory court judgements and exclude individuals from the labor market. Algorithms are already deeply determining our lives. This book uses illuminating examples to describe the opportunities and risks machine-based decision-making presents for each of us. It also offers specific suggestions for ensuring artificial intelligence serves society as it should.

Seit 2014 erscheinen die Bände der renommierten Wiener Reihe bei De Gruyter. Das äußere Layout der Bände wurde modernisiert, inhaltlich und personell jedoch ist das Profil der seit mehr als zwei Jahrzehnten erscheinenden Buchreihe von Kontinuität geprägt. Die Bände sind jeweils einer aktuellen philosophischen Fragestellung gewidmet. Eine internationale Autorenschaft und die Veröffentlichung fremdsprachiger Beiträge sind Elemente des Programms. Die Reihe will dazu beitragen, dogmatische Abgrenzungen zwischen philosophischen Schulen und Traditionen abzubauen.

The intention of this collection agrees with the purposes of the homonymous mini-symposium (MS) at ICIAM-2019, which were to overview the essentials of geometric calculus (GC) formalism, to report on state-of-the-art applications showcasing its advantages and to explore the bearing of GC in novel approaches to deep learning. The first three contributions, which correspond to lectures at the MS, offer perspectives on recent advances in the application GC in the areas of robotics, molecular geometry, and medical imaging. The next three, especially invited, hone the expressiveness of GC in orientation measurements under different metrics, the treatment of contact elements, and the investigation of efficient computational methodologies. The last two, which also correspond to lectures at the MS, deal with two aspects of deep learning: a presentation of a concrete quaternionic convolutional neural network layer for image classification that features contrast invariance and a general overview of automatic learning aimed at steering the development of neural networks whose units process elements of a suitable algebra, such as a geometric algebra. The book fits, broadly speaking, within the realm of mathematical engineering, and consequently, it is intended for a wide spectrum of research profiles. In particular, it should bring inspiration and guidance to those looking for materials and problems that bridge GC with applications of great current interest, including the auspicious field of GC-based deep neural networks.

The power of information -- Data alchemy -- Schumpeter's nightmare -- Data capitalism -- Might and machines -- Access rules -- Open data reloaded -- The end of data colonialism.