Understanding Artificial Intelligence


Book Description

Drawn from the pages of Scientific American and collected here for the first time, this work contains updated and condensed information, made accessible to a general popular science audience, on the subject of artificial intelligence.




Artificial Intelligence and Scientific Method


Book Description

Artificial Intelligence and Scientific Method examines the remarkable advances made in the field of AI over the past twenty years, discussing their profound implications for philosophy. Taking a clear, non-technical approach, Donald Gillies shows how current views on scientific method are challenged by this recent research, and suggests a new framework for the study of logic. Finally, he draws on work by such seminal thinkers as Bacon, Gödel, Popper, Penrose, and Lucas, to address the hotly contested question of whether computers might become intellectually superior to human beings.




Artificial Intelligence for Materials Science


Book Description

Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.




Artificial Intelligence


Book Description

Artificial intelligence has long been a mainstay of science fiction and increasingly it feels as if AI is entering our everyday lives, with technology like Apple's Siri now prominent, and self-driving cars almost upon us. But what do we actually mean when we talk about 'AI'? Are the sentient machines of 2001 or The Matrix a real possibility or will real-world artificial intelligence look and feel very different? What has it done for us so far? And what technologies could it yield in the future? AI expert Yorick Wilks takes a journey through the history of artificial intelligence up to the present day, examining its origins, controversies and achievements, as well as looking into just how it works. He also considers the future, assessing whether these technologies could menace our way of life, but also how we are all likely to benefit from AI applications in the years to come. Entertaining, enlightening, and keenly argued, this is the essential one-stop guide to the AI debate.




Artificial Intelligence Science And Technology - Proceedings Of The 2016 International Conference (Aist2016)


Book Description

The 2016 International Conference on Artificial Intelligence Science and Technology (AIST2016) was held in Shanghai, China, from 15th to 17th July, 2016.AIST2016 aims to bring together researchers, engineers, and students to the areas of Artificial Intelligence Science and Technology. AIST2016 features unique mixed topics of artificial intelligence and application, computer and software, communication and network, information and security, data mining, and optimization.This volume consists of 101 peer-reviewed articles by local and foreign eminent scholars which cover the frontiers and state-of-art development in AI Technology.




Artificial Intelligence in Society


Book Description

The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises.




Introduction to Artificial Intelligence


Book Description

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.




Artificial Intelligence


Book Description

After decades of basic research and more promises than impressive applications, artificial intelligence (AI) is starting to deliver benefits. A convergence of advances is motivating this new surge of AI development and applications. Computer capability as it has evolved from high throughput and high performance computing systems is increasing. AI models and operations research adaptations are becoming more mature, and the world is breeding big data not only from the web and social media but also from the Internet of Things. Organizations around the world have been realizing that there are substantial performance gains and increases in productivity for the use of AI and predictive analytics techniques. Their use is bringing a new era of breakthrough innovation and opportunities. This book, compiles research insights and applications in diverse areas such as manufacturing, supply chain management, pricing, autonomous vehicles, healthcare, ecommerce, and aeronautics. Using classical and advanced tools in AI such as deep learning, particle swarm optimization, support vector machines and genetic programming among others. This is a very distinctive book which discusses important applications using a variety of paradigms from AI and outlines some of the research to be performed. The work supersedes similar books that do not cover as diversified a set of sophisticated applications. The authors present a comprehensive and articulated view of recent developments, identifies the applications gap by quoting from the experience of experts, and details suggested research areas. Artificial Intelligence: Advances in Research and Applications guides the reader through an intuitive understanding of the methodologies and tools for building and modeling intelligent systems. The book's coverage is broad, starting with clustering techniques with unsupervised ensemble learning, where the optimal combination strategy of individual partitions is robust in comparison to the selection of an algorithmic clustering pool. This is followed by a case in a parallel-distributed simulator using deep learning for its configuration. Chapter Three presents a case for autonomous vehicles. Chapter Four discusses the novel use of genetic algorithms with support vector machines. Chapters Five through Thirteen focus on the applications. The book discusses how the use of AI can allow for productivity development and other benefits not just for businesses, but also for economies. Finally, you can find an interesting investigation of the transhuman dimension of AI.




AI for Data Science


Book Description

Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and Julia code. Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world. The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity. The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache's MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA). Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline. Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS). Chapter 12 covers other considerations complementary to the AI topics covered, including Big Data concepts, Data Science specialization areas, and useful data resources to experiment on. A comprehensive glossary is included, as well as a series of appendices covering Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative Adversarial Networks. There is also an appendix on the business aspects of AI in data science projects, and an appendix on how to use the Docker image to access the book's data and code. The field of AI is vast, and can be overwhelming for the newcomer to approach. This book will arm you with a solid understanding of the field, plus inspire you to explore further.




The Sciences of the Artificial


Book Description

The Sciences of the Artificialreveals the design of an intellectual structure aimed at accommodating those empirical phenomena that are "artificial" rather than "natural." The goal is to show how empirical sciences of artificial systems are possible, even in the face of the contingent and teleological character of the phenomena, their attributes of choice and purpose. Developing in some detail two specific examples—human psychology and engineering design—Professor Simon describes the shape of these sciences as they are emerging from developments of the past 25 years. "Artificial" is used here in a very specific sense: to denote systems that have a given form and behavior only because they adapt (or are adapted), in reference to goals or purposes, to their environment. Thus, both man-made artifacts and man himself, in terms of his behavior, are artificial. Simon characterizes an artificial system as an interface between two environments—inner and outer. These environments lie in the province of "natural science," but the interface, linking them, is the realm of "artificial science." When an artificial system adapts successfully, its behavior shows mostly the shape of the outer environment and reveals little of the structure or mechanisms of the inner. The inner environment becomes significant for behavior only when a system reaches the limits of its rationality and adaptability, and contingency degenerates into necessity.