Introduction Artificial Intelligence Pdf
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Introduction to Artificial Intelligence
This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated second edition also includes new material on deep learning. Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW); examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW); discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW); includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW). Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.
Demystifying Artificial Intelligence
Author: Emmanuel Gillain
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2024-07-22
This book is intended for business professionals that want to understand the fundamental concepts of Artificial Intelligence, their applications and limitations. Built as a collaborative effort between academia and the industry, this book bridges the gap between theory and business application, demystifying AI through fundamental concepts and industry examples. The reader will find here an overview of the different AI techniques to search, plan, reason, learn, adapt, understand and interact. The book covers the two traditional paradigms in AI: the statistical and data-driven AI systems, which learn and perform by ingesting millions of data points into machine learning algorithms, and the consciously modelled AI systems, known as symbolic AI systems, which use explicit symbols to represent the world and make conclusions. Rather than opposing those two paradigms, the book will also show how those different fields can complement each other. All royalties go to a charity. “Demystifying AI reveals its true power: not as a mysterious force, but as a tool for human progress, accessible to all who seek to understand it.” Dr. Barak Chizi, Chief Data & Analytics Officer, KBC Group
Artificial Intelligence in Architecture and the Built Environment
Imagine if every architect had an apprentice who could consistently observe and understand their intentions, take over routine tasks and monitor technical, environmental, and economic constraints. This apprentice would continually improve, freeing the architect to concentrate on truly creative work. This book outlines a plan to turn this vision into reality. It evaluates the development of artificial intelligence from its inception to the present, focusing on the last two decades of applying AI in architectural design and planning; the current state of architectural practice is also examined. Integrating architecture, computer science, AI, robotics, economics, law, neurobiology, and philosophy, the vision is built on three key premises: (i) authentic, poetic creativity that transcends parameterization and algorithmizing, (ii) innovative learning strategies and training approaches not yet applied concerning architectural design, and (iii) the convergence of architecture’s inherent spatiality with virtual reality technology and new theories of human thinking and intelligence, poised for implementation in machine learning.