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In this comprehensive and cutting-edge volume, Qureshi and Jeon bring together experts from around the world to explore the potential of artificial intelligence models in research and discuss the potential benefits and the concerns and challenges that the rapid development of this field has raised. The international chapter contributor group provides a wealth of technical information on different aspects of AI, including key aspects of AI, deep learning and machine learning models for AI, natural language processing and computer vision, reinforcement learning, ethics and responsibilities, security, practical implementation, and future directions. The contents are balanced in terms of theory, methodologies, and technical aspects, and contributors provide case studies to clearly illustrate the concepts and technical discussions throughout. Readers will gain valuable insights into how AI can revolutionize their work in fields including data analytics and pattern identification, healthcare research, social science research, and more, and improve their technical skills, problem-solving abilities, and evidence-based decision-making. Additionally, they will be cognizant of the limitations and challenges, the ethical implications, and security concerns related to language models, which will enable them to make more informed choices regarding their implementation. This book is an invaluable resource for undergraduate and graduate students who want to understand AI models, recent trends in the area, and technical and ethical aspects of AI. Companies involved in AI development or implementing AI in various fields will also benefit from the book’s discussions on both the technical and ethical aspects of this rapidly growing field.
The traditional educational landscape often struggles to keep pace with the rapid advancements in technology and the evolving needs of both students and educators. This challenge has given rise to a crucial question; how can we effectively harness the full potential of next-generation educational technologies to shape a brighter future for education? A solution to this very question can be found within the pages ofReshaping Learning with Next Generation Educational Technologies. This book delves deep into the convergence of artificial intelligence (AI), disruptive technologies, and cutting-edge educational practices, revealing their transformative power. Through practical examples, visionary insights, and thought-provoking analyses, it provides a roadmap for educators, researchers, and professionals to navigate this changing educational landscape. It's a call to action, urging academia to seize the transformative potential of these groundbreaking technologies.
Artificial Intelligence is reshaping our world, and at the forefront of this revolution are Generative AI and Large Language Models (LLMs). This book, "Generative AI and Large Language Models: Revolutionizing the Future," offers an in-depth exploration of these groundbreaking technologies, delving into their foundations, development, and profound implications for various industries and society as a whole. Starting with a historical overview of AI, the book traces the evolution of machine learning and deep learning, setting the stage for understanding the rise of generative AI. Readers will discover the inner workings of LLMs, from their advanced neural network architectures to the massive datasets and computational power required for their training. Key models, such as the Generative Pre-trained Transformer (GPT) series, are examined in detail, showcasing their remarkable capabilities in natural language processing and beyond. The book also addresses the ethical and social challenges posed by these powerful technologies. Issues such as bias, fairness, and privacy are discussed, alongside the need for transparent and accountable AI systems. Through real-world applications and case studies, readers will see how generative AI is transforming fields like healthcare, finance, content creation, and more. Looking ahead, the book explores future trends and innovations, highlighting potential advancements and the ongoing research aimed at enhancing AI's efficiency and multimodal capabilities. It envisions a future where AI and humans collaborate more closely, driving progress and innovation across all domains. "Generative AI and Large Language Models: Revolutionizing the Future" is an essential read for anyone interested in the cutting-edge of AI technology. Whether you are a researcher, practitioner, or simply curious about the future of AI, this book provides a comprehensive and accessible guide to the transformative power of generative AI and LLMs.
In today’s world, smart healthcare supports the out-of-hospital concept, which transforms and offers higher care standards. This is accomplished with individual requirements with the help of public opinion. Moreover, smart healthcare systems are generally designed to sense individual health status data, which can be forwarded to clinical professionals for interpretation. Swarm intelligence analysis is a valuable tool for categorizing public opinion into different sentiments. Dynamics of Swarm Intelligence Health Analysis for the Next Generation discusses the role of behavioral activity in the evolution of traditional medical systems to intelligent systems. It further focuses on the economic, social, and environmental impacts of swarm intelligence smart healthcare systems. Covering topics such as healthcare data analytics, clustering algorithms, and the internet of medical things, this premier reference source is an excellent resource for healthcare professionals, hospital administrators, IT managers, policymakers, educators and students of higher education, researchers, and academicians.
Chatbots powered by artificial intelligence (AI) have captivated the academic world as tools for human-like interaction across various settings. Within the realm of education, AI-powered chatbots, such as ChatGPT, hold the potential to revolutionize teaching, learning, and research processes. By simulating human conversation through vast data and machine learning algorithms, generative AI has unveiled new opportunities for personalized and adaptive learning experiences. Facilitating Global Collaboration and Knowledge Sharing in Higher Education With Generative AI delves into the promising prospects and challenges of applying generative AI in education while employing a critical interdisciplinary perspective. The book offers comprehensive insights into the transformative effects of generative AI on teaching, learning, and research. However, the application of generative AI in education also brings ethical, pedagogical, and technical challenges to the forefront. Concerns over privacy, data protection, and the impact of automation on human interaction and creativity demand thorough examination and practical solutions. Intended for educators, researchers, and administrators in higher education institutions, as well as policymakers and industry professionals at the intersection of AI and higher education. The book encompasses a wide range of themes, including the impact of AI-generated content on student engagement and performance in online learning environments, ethical implications of automating education through AI-powered chatbots, personalization of learning experiences for diverse student populations, and the challenges of integrating generative AI into traditional classroom settings.
Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. How Machine Learning is Innovating Today's World is a timely book and presents a diverse collection of 25 chapters that delve into the remarkable ways that ML is transforming various fields and industries. It provides a comprehensive understanding of the practical applications of ML techniques. The wide range of topics include: An analysis of various tokenization techniques and the sequence-to-sequence model in natural language processing explores the evaluation of English language readability using ML models a detailed study of text analysis for information retrieval through natural language processing the application of reinforcement learning approaches to supply chain management the performance analysis of converting algorithms to source code using natural language processing in Java presents an alternate approach to solving differential equations utilizing artificial neural networks with optimization techniques a comparative study of different techniques of text-to-SQL query conversion the classification of livestock diseases using ML algorithms ML in image enhancement techniques the efficient leader selection for inter-cluster flying ad-hoc networks a comprehensive survey of applications powered by GPT-3 and DALL-E recommender systems' domain of application reviews mood detection, emoji generation, and classification using tokenization and CNN variations of the exam scheduling problem using graph coloring the intersection of software engineering and machine learning applications explores ML strategies for indeterminate information systems in complex bipolar neutrosophic environments ML applications in healthcare, in battery management systems, and the rise of AI-generated news videos how to enhance resource management in precision farming through AI-based irrigation optimization. Audience The book will be extremely useful to professionals, post-graduate research scholars, policymakers, corporate managers, and anyone with technical interests looking to understand how machine learning and artificial intelligence can benefit their work.
In the realm of education, the challenge lies in effectively utilizing Artificial Intelligence to transform medical learning. Artificial Intelligence Applications Using ChatGPT in Education: Case Studies and Practices, authored by Muhammad Shahzad Aslam and Saima Nisar, offers insights into this issue. With expertise in Medical and Health Education, and Health Informatics, the authors explore AI's potential in reshaping medical education. Traditional medical education struggles to keep up with expanding knowledge and evolving medical science, leaving educators and students overwhelmed by vast information. Ethical concerns, such as plagiarism, further complicate matters. A solution is needed that blends technology with effective teaching. Artificial Intelligence Applications Using ChatGPT in Education: Case Studies and Practices proposes such a solution. By harnessing ChatGPT's capabilities as an AI chatbot, the book suggests a self-guided learning tool. Backed by case studies, the authors demonstrate how ChatGPT can become a personalized tutor, helping students grasp complex medical concepts at their own pace. The book also delves into the ethical aspects of AI integration, ensuring responsible use in academia.
Explore the inner workings of AI along with its limitations and future developments and create your first transparent and trustworthy neuro-symbolic AI system Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand symbolic and statistical techniques through examples and detailed explanations Explore the potential of neuro-symbolic AI for future developments using case studies Discover the benefits of combining symbolic AI with modern neural networks to build transparent and high-performance AI solutions Book Description Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI along with the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches. You'll begin by exploring the decline of symbolic AI and the recent neural network revolution, as well as their limitations. The book then delves into the importance of building trustworthy and transparent AI solutions using explainable AI techniques. As you advance, you'll explore the emerging field of neuro-symbolic AI, which combines symbolic AI and modern neural networks to improve performance and transparency. You'll also learn how to get started with neuro-symbolic AI using Python with the help of practical examples. In addition, the book covers the most promising technologies in the field, providing insights into the future of AI. Upon completing this book, you will acquire a profound comprehension of neuro-symbolic AI and its practical implications. Additionally, you will cultivate the essential abilities to conceptualize, design, and execute neuro-symbolic AI solutions. What you will learn Gain an understanding of the intuition behind neuro-symbolic AI Determine the correct uses that can benefit from neuro-symbolic AI Differentiate between types of explainable AI techniques Think about, design, and implement neuro-symbolic AI solutions Create and fine-tune your first neuro-symbolic AI system Explore the advantages of fusing symbolic AI with modern neural networks in neuro-symbolic AI systems Who this book is for This book is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to explore the emerging field of neuro-symbolic AI and discover how to build transparent and trustworthy AI solutions. A basic understanding of AI concepts and familiarity with Python programming are needed to make the most of this book.