Sztuczna inteligencja
Ujjwal Ratan
While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics.This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You’ll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications.By the end of this book, you’ll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.
Mercury Learning and Information, Paul Boudreau
Artificial intelligence is reshaping the way projects are managed, offering unprecedented opportunities to improve efficiency, accuracy, and outcomes. This course begins with an introduction to AI’s role in project management, exploring how machine learning, natural language processing, and predictive algorithms can transform traditional approaches. You’ll learn about the key components of AI-driven projects and how to develop a strong business case for adopting these innovations.As you progress, the course delves into practical applications of AI in automating project tasks, analyzing data, and predicting results. Participants will gain hands-on experience with tools that leverage machine learning to forecast project success, improve productivity, and resolve potential failures. Additionally, you’ll discover how generative AI and large language models can enhance communication, planning, and decision-making throughout the project lifecycle.Finally, the course examines the broader implications of integrating AI into project management. You’ll explore strategies for acquiring AI solutions, implementing them within teams, and navigating the ethical challenges they present. By the end of the course, participants will have a clear understanding of how to leverage AI to optimize projects and stay competitive in a rapidly evolving technological landscape.
Richard D Avila, Imran Ahmad
Architecting AI Software Systems provides a definitive guide to building AI-enabled systems, emphasizing the balance between AI’s capabilities and traditional software architecture principles.As AI technologies gain widespread acceptance and are increasingly expected in future applications, this book provides architects and developers with the essential knowledge to stay competitive. It introduces a structured approach to mastering the complexities of AI integration, covering key architectural concepts and processes critical to building scalable and robust AI systems while minimizing development and maintenance risks. The book guides readers on a progressive journey, using real-world examples and hands-on exercises to deepen comprehension. It also includes the architecture of a fictional AI-enabled system as a learning tool. You will engage with exercises designed to reinforce your understanding and apply practical insights, leading to the development of key architectural products that support AI systems. This is an essential resource for architects seeking to mitigate risks and master the complexities of AI-enabled system development.By the end of the book, readers will be equipped with patterns, strategies and concepts necessary to architect AI-enabled systems across various domains.
Lars Malmqvist
Written for Salesforce architects who want quickly implementable AI solutions for their business challenges, Architecting AI Solutions on Salesforce is a shortcut to understanding Salesforce Einstein’s full capabilities – and using them.To illustrate the full technical benefits of Salesforce’s own AI solutions and components, this book will take you through a case study of a fictional company beginning to adopt AI in its Salesforce ecosystem. As you progress, you'll learn how to configure and extend the out-of-the-box features on various Salesforce clouds, their pros, cons, and limitations. You'll also discover how to extend these features using on- and off-platform choices and how to make the best architectural choices when designing custom solutions. Later, you'll advance to integrating third-party AI services such as the Google Translation API, Microsoft Cognitive Services, and Amazon SageMaker on top of your existing solutions. This isn’t a beginners’ Salesforce book, but a comprehensive overview with practical examples that will also take you through key architectural decisions and trade-offs that may impact the design choices you make.By the end of this book, you'll be able to use Salesforce to design powerful tailor-made solutions for your customers with confidence.
Artificial Intelligence and Expert Systems. Techniques and Applications for Problem Solving
Mercury Learning and Information, I. Gupta, G....
This book covers current AI applications and techniques for solving problems and accomplishing tasks. It introduces branches of AI such as formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic. It emphasizes expert systems, with sections on state space search, knowledge engineering, neural networks, fuzzy logic, and Prolog.It begins with an introduction to AI and its applications, setting the stage for foundational concepts. Readers are guided through state space search and heuristic search strategies, crucial for problem-solving in AI. The focus shifts to expert systems, covering their development life cycle, knowledge acquisition, and representation, providing a deep dive into emulating human decision-making.Later chapters cover neural networks and the learning process, essential for creating adaptive systems. Sections on fuzzy logic and fuzzy systems introduce methods for handling uncertainty in AI. Final chapters on programming in logic and advanced Prolog offer practical techniques for AI solutions. This approach equips readers with the skills to apply AI in various domains, enhancing their problem-solving abilities and understanding of intelligent systems.
Artificial Intelligence Basics. A Self-Teaching Introduction
Mercury Learning and Information, N. Gupta, R....
This book is designed as a self-teaching introduction to the fundamental concepts of artificial intelligence (AI). It begins with the history of AI, the Turing test, and early applications, providing a strong foundation. Later chapters cover the basics of searching, game playing, and knowledge representation. The journey continues with detailed explorations of expert systems and machine learning, equipping readers with essential AI techniques.As the course progresses, you will delve into separate programming chapters on Prolog and Python, learning how to implement AI concepts in these languages. These chapters offer practical coding experience, enhancing your understanding of AI programming. The book culminates with a comprehensive chapter on AI machines and robotics, showcasing numerous modern applications and providing a glimpse into the future of AI technology.Understanding these AI concepts is crucial as they form the basis of many modern technologies and applications. This book ensures a smooth transition from a beginner to a proficient AI practitioner, equipped with both theoretical knowledge and practical skills. By the end of the book, you will have a thorough understanding of AI's history, core principles, and practical implementations, ready to apply this knowledge to real-world problems and projects.
Artificial Intelligence Engines. A Tutorial Introduction to the Mathematics of Deep Learning
James V Stone
This book is a comprehensive guide to the mathematics behind artificial intelligence engines, taking readers from foundational concepts to advanced applications. It begins with an introduction to artificial neural networks, exploring topics like perceptrons, linear associative networks, and gradient descent. Practical examples accompany each chapter, making complex mathematical principles accessible, even for those with limited prior knowledge.The book's detailed structure covers key algorithms like backpropagation, Hopfield networks, and Boltzmann machines, advancing to deep restricted Boltzmann machines, variational autoencoders, and convolutional neural networks. Modern topics such as generative adversarial networks, reinforcement learning, and capsule networks are explored in depth. Each section connects theory to real-world AI applications, helping readers understand how these techniques are used in practice.Ideal for students, researchers, and AI enthusiasts, the book balances theoretical depth with practical insights. Basic mathematical knowledge or foundation is recommended, allowing readers to fully engage with the content. This book serves as an accessible yet thorough resource for anyone eager to dive deeper into artificial intelligence and machine learning.
Artificial Intelligence. Ethical, social, and security impacts for the present and the future
IT Governance Publishing, Dr. Julie E. Mehan
This book offers an in-depth exploration of Artificial Intelligence (AI), from its origins to the ethical and societal challenges it presents today. It provides a comprehensive understanding of AI’s impact on human interaction, collaboration, privacy, and security. Through analyzing both opportunities and risks, the book emphasizes the ethical concerns surrounding AI, such as bias, privacy violations, and security threats.Chapters explore AI’s transformative role in cybersecurity, misinformation, and human-machine collaboration, highlighting its implications for job markets and human relationships. Real-world examples illustrate how AI can drive progress or cause harm. The ethical dilemmas around AI, including its use in surveillance and decision-making, are thoroughly examined, presenting challenges central to modern technology.Looking ahead, the book offers a forward-thinking perspective on AI’s future, discussing emerging trends and the need for responsible policy-making. It concludes by addressing how society can prepare for AI’s continued growth, offering strategies for navigating the evolving landscape. With practical insights and deep analysis, this book helps readers grasp AI’s profound implications for our future.