The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists.

The objective of this book is to contribute to the development of the intelligent information and database systems with the essentials of current knowledge, experience and know-how. The book contains a selection of 40 chapters based on original research presented as posters during the 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016) held on 14–16 March 2016 in Da Nang, Vietnam. The papers to some extent reflect the achievements of scientific teams from 17 countries in five continents. The volume is divided into six parts: (a) Computational Intelligence in Data Mining and Machine Learning, (b) Ontologies, Social Networks and Recommendation Systems, (c) Web Services, Cloud Computing, Security and Intelligent Internet Systems, (d) Knowledge Management and Language Processing, (e) Image, Video, Motion Analysis and Recognition, and (f) Advanced Computing Applications and Technologies. The book is an excellent resource for researchers, those working in artificial intelligence, multimedia, networks and big data technologies, as well as for students interested in computer science and other related fields.

From fundamental to advanced strategies, unlock useful insights for creating innovative, user-centric websites while navigating the evolving landscape of AI ethics and security Key Features Explore AI's role in web development, from shaping projects to architecting solutions Master advanced AI strategies to build cutting-edge applications Anticipate future trends by exploring next-gen development environments, emerging interfaces, and security considerations in AI web development Purchase of the print or Kindle book includes a free PDF eBook Book Description If you're a web developer looking to leverage the power of AI in your projects, then this book is for you. Written by an AI and ML expert with more than 15 years of experience, AI Strategies for Web Development takes you on a transformative journey through the dynamic intersection of AI and web development, offering a hands-on learning experience.The first part of the book focuses on uncovering the profound impact of AI on web projects, exploring fundamental concepts, and navigating popular frameworks and tools. As you progress, you'll learn how to build smart AI applications with design intelligence, personalized user journeys, and coding assistants. Later, you'll explore how to future-proof your web development projects using advanced AI strategies and understand AI's impact on jobs. Toward the end, you'll immerse yourself in AI-augmented development, crafting intelligent web applications and navigating the ethical landscape.Packed with insights into next-gen development environments, AI-augmented practices, emerging realities, interfaces, and security governance, this web development book acts as your roadmap to staying ahead in the AI and web development domain. What you will learn Build AI-powered web projects with optimized models Personalize UX dynamically with AI, NLP, chatbots, and recommendations Explore AI coding assistants and other tools for advanced web development Craft data-driven, personalized experiences using pattern recognition Architect effective AI solutions while exploring the future of web development Build secure and ethical AI applications following TRiSM best practices Explore cutting-edge AI and web development trends Who this book is for This book is for web developers with experience in programming languages and an interest in keeping up with the latest trends in AI-powered web development. Full-stack, front-end, and back-end developers, UI/UX designers, software engineers, and web development enthusiasts will also find valuable information and practical guidelines for developing smarter websites with AI. To get the most out of this book, it is recommended that you have basic knowledge of programming languages such as HTML, CSS, and JavaScript, as well as a familiarity with machine learning concepts.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

The digital age has witnessed the meteoric rise of artificial intelligence (AI), a paradigm-shifting technology that has redefined the boundaries of computation and decision-making. Initially, AI's journey began with basic rule-based systems, evolving into the current digital age is dominated by complex machine learning and deep learning models. The digital AI presence and progression has brought with it a myriad of ethical challenges, necessitating a rigorous examination of AI's role in complex and interconnected systems. Ethical Dimensions of AI Development notes that the core of these challenges are issues of privacy, transparency, and validity. AI's ability to process vast datasets can intrude on individual privacy, while opaque algorithmic decision-making processes can obscure transparency. Addressing these ethical concerns is crucial to fostering trust and ensuring the responsible use of AI technologies in society. Covering topics such as accountability, discrimination, and privacy and security, this book is an essential resource for AI researchers and developers, data scientists, ethicists, policy makers, legal professionals, technology industry leaders, and more.

For a period of some fifteen years following completion of my internship training in clinical psychology (1950-1951) at the Washington University School of Medicine and my concurrent successful navigation through that school's neuroanatomy course, clinical work in neuropsychology for me and the psychologists of my generation consisted almost exclusively of our trying to help our physician colleagues differentiate patients with neurologic disorders from those with psychiatric disorders. In time, experience led all of us from the several disciplines involved in this enterprise to the conclusion that the crude diagnostic techniques available to us circa 1945-1965 had garnered little valid information on which to base such complex, differential diagnostic decisions. It now is gratifying to look back and review the remarkable progress that has occurred in the field of clinical neuropsychology in the four decades since I was a graduate student. In the late 1940s such pioneers as Ward Halstead, Alexander Luria, George Yacorzynski, Hans-Lukas Teuber, and Arthur Benton already were involved in clinical studies that, by the late 1960s, would markedly have improved the quality of clinical practice. However, the only psychological tests that the clinical psychologist of my immediate post Second Wodd War generation had as aids for the diagnosis of neurologically based conditions involving cognitive deficit were such old standbys as the Wechsler-Bellevue, Rorschach, Draw A Person, Bender Gestalt, and Graham Kendall Memory for Designs Test.

Artificial Intelligence and Machine Learning for Sustainable Development is a comprehensive exploration of how artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing the field of sustainable development. The book examines cutting-edge innovations, practical applications, and potential challenges in harnessing AI and ML to address global sustainability issues. It offers insights into how these technologies can optimize resource management, improve environmental monitoring, enhance decision-making processes, and promote equitable, eco-friendly solutions. This book would be of special interest to researchers, policymakers, and practitioners seeking to leverage cutting-edge technology for a more sustainable future.

This book presents recent research on interactive collaborative learning. We are currently witnessing a significant transformation in the development of education and especially post-secondary education. To face these challenges, higher education has to find innovative ways to quickly respond to these new needs. On the one hand, there is a pressure by the new situation in regard to the COVID pandemic. On the other hand, the methods and organizational forms of teaching and learning at higher educational institutions have changed rapidly in recent months. Scientifically based statements as well as excellent experiences (best practice) are absolutely necessary. These were the aims connected with the 24th International Conference on Interactive Collaborative Learning (ICL2021), which was held online by Technische Universität Dresden, Germany, on 22–24 September 2021. Since its beginning in 1998, this conference is devoted to new approaches in learning with a focus on collaborative learning in Higher Education. Nowadays, the ICL conferences are a forum of the exchange of relevant trends and research results as well as the presentation of practical experiences in Learning and Engineering Pedagogy. In this way, we try to bridge the gap between ‘pure’ scientific research and the everyday work of educators. This book contains papers in the fields of Teaching Best Practices Research in Engineering Pedagogy Engineering Pedagogy Education Entrepreneurship in Engineering Education Project-Based Learning Virtual and Augmented Learning Immersive Learning in Healthcare and Medical Education. Interested readership includes policymakers, academics, educators, researchers in pedagogy and learning theory, schoolteachers, learning industry, further and continuing education lecturers, etc

In a world where business is increasingly influenced by rapid technological advancements and shifting societal norms, the definition of success has undergone a fundamental transformation. Profitability, once the sole measure of achievement, now shares the stage with sustainability as a critical component of business performance. As we transition into Society 5.0—a hyper-connected era where technology and human values converge—redefining success to include sustainability becomes not just desirable but essential. Sustainable Development, Humanities, and Social Sciences for Society 5.0 explores this paradigm shift, presenting a forward-thinking perspective on the intersection of technological innovation and sustainable business practices. This comprehensive volume bridges the gap between emerging technologies and the imperative for businesses to thrive in a way that benefits society and the environment. Through a curated collection of insights, strategies, and case studies, the book offers a valuable resource for understanding how to achieve continuous good performance while making a positive impact in the age of Society 5.0.