This practical guide to artificial intelligence and its impact on industry dispels common myths and calls for cross-sector, collaborative leadership for the responsible design and embedding of AI in the daily work of businesses and oversight by boards. Artificial intelligence has arrived, and it's coming to a business near you. The disruptive impact of AI on the global economy—from health care to energy, financial services to agriculture, and defense to media—is enormous. Technology literacy is a must for traditional businesses, their boards, policy makers, and governance professionals. This is the first book to explain where AI comes from, why it has emerged as one of the most powerful forces in mergers and acquisitions and research and development, and what companies need to do to implement it successfully. It equips business leaders with a practical roadmap for competing and even thriving in the face of the coming AI revolution. The authors analyze competitive trends, provide industry and governance examples, and explain interactions between AI and other digital technologies, such as blockchain, cybersecurity, and the Internet of Things. At the same time, AI experts will learn how their research and products can increase the competitiveness of their businesses, and corporate boards will come away with a thorough knowledge of the AI governance, ethics, and risk questions to ask.

In the dynamic intersection of cutting-edge technology and evolving business strategies, the fusion of Artificial Intelligence (AI) and the Internet of Things (IoT) has given rise to a revolutionary paradigm known as Artificial Intelligence of Things (AIoT). This emerging technological powerhouse offers boundless possibilities while presenting formidable challenges, and organizations worldwide are wrestling with its integration into their core operations. Artificial Intelligence of Things (AIoT) for Productivity and Organizational Transition introduces the fundamental concepts underpinning AIoT integration and the evolving landscape of AIoT research and development, then delves deep into the theoretical foundations of AIoT, examining it through the lenses of economics, behavioral science, technology, psychology, and organizational theory. Within its pages, readers will discover insights into the tools, methods, design factors, user interfaces, and techniques driving AIoT innovation.

This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.

This book constitutes the refereed proceedings of the First Southern African Conference on Artificial Intelligence Research, SACAIR 2020, held in Muldersdrift, South Africa, in February 2021. Due to the COVID-19 pandemic the SACAIR 2020 has been postponed to February 2021. The 19 papers presented were thoroughly reviewed and selected from 53 submissions. They are organized on the topical sections on ​AI for ethics and society; AI in information systems, AI for development and social good; applications of AI; knowledge representation and reasoning; machine learning theory.

The ever-changing world of wearable technologies makes it difficult for experts and practitioners to keep up with the most recent developments. This handbook provides a solid understanding of the significant role that AI plays in the design and development of wearable technologies along with applications and case studies. Handbook of Artificial Intelligence and Wearables: Applications and Case Studies presents a deep understanding of AI and its involvement in wearable technologies. The book discusses the key role that AI plays and goes on to discuss the challenges and possible solutions. It highlights the more recent advances along with real-world approaches for the design and development of the most popular AI-enabled wearable devices such as smart fitness trackers, AI-enabled glasses, sports wearables, disease diagnostic devices, and more, complete with case studies. This book will be a valuable source for researchers, academics, technologists, industrialists, practitioners, and all people who wish to explore the applications of AI and the part it plays in wearable technologies.

This volume is the first in a series which deals with the challenge of AI issues, gives updates of AI methods and applications, and promotes high quality new ideas, techniques and methodologies in AI. This volume contains articles by 38 specialists in various AI subfields covering theoretical and application issues.

Gathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day.

With the constant evolution of technology, libraries must grapple with the urgent need to adapt or face obsolescence. The integration of artificial intelligence (AI) into library operations presents many new opportunities as well as a complex array of challenges. The traditional roles of libraries, as pillars of knowledge and information, are being reshaped by AI, compelling institutions to reassess their relevance in an ever-evolving digital landscape. The urgency of this intersection between libraries and AI is emphasized by the necessity to revolutionize outdated systems, and it is in this dynamic context that Applications of Artificial Intelligence in Libraries emerges as an essential guide. The book addresses the ethical implications of AI-enabled libraries, offering strategies for navigating privacy concerns and potential challenges in the implementation of AI. It serves as a strategic guide for evaluating the impact and effectiveness of AI initiatives, developing policies and practices centered around AI, and training librarians for the inevitable integration of AI into their roles. By fostering collaboration between librarians, researchers, and AI experts, this book aims to empower professionals to navigate the transformative journey that AI is ushering in for libraries, fostering innovation, collaboration, and the creation of more effective and user-centric library services.

Focusing on the cutting-edge applications of AI cognitive computing from neuromorphic to quantum cognition as applied to AI business analytics, this new volume explores AI’s importance in managing cognitive processes along with ontological modeling concepts for venturing into new business frontiers. The volume presents a selection of significant new accomplishments in the areas of AI cognitive computing ranging from neurocognition perception and decision-making in the human brain—combining neurocognitive techniques and effective computing—to basic facial recognition computing models. Topics include: Agent neurocomputing techniques for facial expression recognition Computing haptic motion and ontology epistemic Characterizations of morph schemas for visual analytics Learning and perceptive computing Functional and structural neuroimaging modeling Observed links between facial recognition and affective emotional processes Interaction of cognitive and emotional processes during social decision-making Neurocognitive processing of emotional facial expressions in individuals Neurocognitive affective system for emotive robot androids Virtual reality-based affect adaptive neuromorphic computing Executive surveys indicate that cognitive adoption is very important in business strategy for success and to remain competitive. Employing cognitive-based processes provides the way to get the right information in the right hands at the right time, which is the key to winning in the digital era and to driving business value that emphasizes competitive differentiation. Several chapters of the volume address the goal of using cognitive technology to improve search capabilities, to provide personalized customer service in business and in health and wellness, and to create better workflow management. Key features: Looks at the newest frontiers on very popular AI and analytics topics Discusses new techniques for visual analytics and data filtering Shows how AI and cognitive science merges with quantum neurocognitive computing Presents ontology models with ontology preservation data filtering techniques Provides a cross-transposition on AI and digitizations for business model innovations Artificial Intelligence and Computing Logic: Cognitive Technology for AI Business Analytics is a valuable resource that informs businesses and other enterprises the value of artificial intelligence and computing logic applications.

This book constitutes the refereed proceedings of the Third Mexican International Conference on Artificial Intelligence, MICAI 2004, held in Mexico City, Mexico in April 2004. The 94 revised full papers presented were carefully reviewed and selected from 254 submissions. The papers are organized in topical sections on applications, intelligent interfaces and speech processing, knowledge representation, logic and constraint programming, machine learning and data mining, multiagent systems and distributed AI, natural language processing, uncertainty reasoning, vision, evolutionary computation, modeling and intelligent control, neural networks, and robotics.