Search

Summary: "An updated introduction for generalists to this powerful technology, its applications and possible future directions"-- Provided by publisher.

Summary: The Law of Artificial Intelligence is an essential practitioner's reference text examining how key areas of current civil and criminal law will apply to AI and examining emerging laws specific to the use of AI. It explains the fundamentals of AI technology, its development and terminology. The book also covers regulation, ethics and the use of AI within legal services and the administration of justice.

Summary: Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that it’s worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.

Summary: Saransh works at a life insurance company, as part of the Special Projects Group (SPG). Their current project is top-secret: the development of an Artificial Intelligence system that will leave 552 branch-level employees redundant overnight. Because of site-specific customizations, however, the system needs to collect information from the company's various branches. Thus, begins a cycle in which Saransh travels across the country, interviewing the very people that his machine will replace soon. Meanwhile, his conscientious ex-journalist girlfriend Jyoti repeatedly questions Saransh's complicity in the impending destruction of hundreds of lives. The Machine is Learning is a novel about twenty-first-century workplaces, love and the impact of technology in all of our lives. It interrogates a world order that accommodates guilt but offers no truly ethical course correction.

Abstract: Artificial Intelligence (AI) for security management explores terminologies of security and how AI can be applied to automate security processes. Additionally, the text provides detailed explanations and recommendations for how implement procedures. Practical examples and real-time use cases are evaluated and suggest appropriate algorithms based on the author's experiences. Threat and associated securities from the data, process, people, things (e.g., Internet of things), systems, and actions were used to develop security knowledge base, which will help readers to build their own knowledge base. This book will help the readers to start their AI journey on security and how data can be applied to drive business actions to build secure environment.

Summary: Many industries have been revolutionized by the widespread adoption of AI and machine learning. The programmatic availability of historical and real-time financial data in combination with techniques from AI and machine learning will also change the financial industry in a fundamental way. This practical book explains how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science how machine and deep learning algorithms can be applied to finance.

Summary: Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.' Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that's both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: -Improving time-to-value with infused AI models for common use cases -Optimizing knowledge work and business processes -Utilizing AI-based business intelligence and data visualization -Establishing a data topology to support general or highly specialized needs -Successfully completing AI projects in a predictable manner -Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.

Summary: Artificial Intelligence in Cancer: Diagnostic to Tailored Treatment provides theoretical concepts and practical techniques of AI and its applications in cancer management, building a roadmap on how to use AI in cancer at different stages of healthcare.

Summary: This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19. .

Summary: Artificial Intelligence (AI) gets your system to think smart and learn intelligently. This book is packed with some of the smartest trending examples with which you will learn the fundamentals of AI. By the end, you will have acquired the basics of AI by practically applying the examples in this book.

Summary: Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language processing rely on computer sciences use of big data. Aimed at those with a statistical background who want to use their strengths in pursuing AI research, the book: Covers broad AI topics in drug development, precision medicine, and healthcare. Elaborates on supervised, unsupervised, reinforcement, and evolutionary learning methods. Introduces the similarity principle and related AI methods for both big and small data problems. Offers a balance of statistical and algorithm-based approaches to AI. Provides examples and real-world applications with hands-on R code. Suggests the path forward for AI in medicine and artificial general intelligence. As well as covering the history of AI and the innovative ideas, methodologies and software implementation of the field, the book offers a comprehensive review of AI applications in medical sciences. In addition, readers will benefit from hands on exercises, with included R code.

Summary: This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.

Summary: "To understand the dynamic patterns of behaviours and interactions between athletes that characterise successful performance in different sports is an important challenge for all sport practitioners. This book guides the reader in understanding how an ecological dynamics framework for use of artificial intelligence (AI) can be implemented to interpret sport performance and the design of practice contexts. By examining how AI methodologies are utilised in team games, such as football, as well as individual sports, such as golf and climbing, this book provides a better understanding of the kinematic and physiological indicators that might better capture athletic performance by looking at the current state-of-the-art AI approaches. Artificial Intelligence in Sport Performance Analysis provides an all-encompassing perspective in an innovative approach that signals practical applications for both academics and practitioners in the fields of coaching, sports analysis, sport science as well as related subjects such as engineering, computer and data science, and statistics"-- Provided by publisher.

Summary: This contributed volume discusses diverse topics to demystify the rapidly emerging and evolving blockchain technology, the emergence of integrated platforms and hosted third-party tools, and the development of decentralized applications for various business domains. It presents various applications that are helpful for research scholars and scientists who are working toward identifying and pinpointing the potential of as well as the hindrances to this technology.

Summary: The book discusses topics such as cognitive computing and emotional intelligence, big data analysis, clinical decision support systems, deep learning, personal omics, digital health, predictive models, prediction of epidemics, drug discovery, precision nutrition and fitness. Additionally, there is a section dedicated to discuss and analyze AI products related to precision healthcare already available"--Publisher's description.

Summary: "This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering"-- Provided by publisher.

Summary: "Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use."--Page 4 of cover.

Summary: "This book lays a new foundation toward achieving artificial self-intelligence by future machines such as intelligent vehicles. Its chapters provide a broad coverage to the three key modules behind the design and development of intelligent vehicles for the ultimate purpose of actively ensuring driving safety as well as preventing accidents from all possible causes. Self-contained and unified in presentation, the book explains in details the fundamental solutions of vehicle's perception, vehicle's decision-making, and vehicle's action-taking in a pedagogic order. Besides the fundamental knowledge and concepts of intelligent vehicle's perception, decision and action, this book includes a comprehensive set of real-life application scenarios in which intelligent vehicles will play a major role or contribution. These case studies of real-life applications will help motivate students to learn this exciting subject. With concise and simple explanations, and boasting a rich set of graphical illustrations, the book is an invaluable source for both undergraduate and postgraduate courses, on artificial intelligence, intelligent vehicle, and robotics, which are offered in automotive engineering, computer engineering, electronic engineering, and mechanical engineering. In addition, the book will help strengthen the knowledge and skills of young researchers who want to venture into the research and development of artificial self-intelligence for intelligent vehicles of the future"-- Provided by publisher.

Summary: The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing. .

Summary: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website

Summary: All Indian Reprints of O'Reilly are printed in Grayscale. If you’re looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

Summary: This Open Access book explores questions such as why and how did the first biological cells appear? And then complex organisms, brains, societies and -now- connected human societies? Physicists have good models for describing the evolution of the universe since the Big Bang, but can we apply the same concepts to the evolution of aggregated matter -living matter included? The Amazing Journey analyzes the latest results in chemistry, biology, neuroscience, anthropology and sociology under the light of the evolution of intelligence, seen as the ability of processing information. The main strength of this book is using just two concepts used in physics -information and energy- to explain: The emergence and evolution of life: procaryotes, eukaryotes and complex organisms The emergence and evolution of the brain The emergence and evolution of societies (human and not) Possible evolution of our "internet society" and the role that Artificial Intelligence is playing.

Summary: Combining classical scenario thinking (the gentle art of perception) with the analytical power of big data and artificial intelligence, Real Time Strategypresents the decision making of the future which enables decision makers to develop dynamic strategies, monitor their validity, and react faster.

Summary: "In The Age of AI, researcher Jason Thacker explores how the prevalence of artificial intelligence shapes what it means to be human today - and how the fact that we are made in the image of God transforms everything about how we use it"-- Provided by publisher.

Summary: Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook. You will: Understand the techniques and methods utilized in ensemble learning Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias Enhance your machine learning architecture with ensemble learning.

Summary: "An accessible overview of the threats and opportunities inherent in automated decision making in academia, government, and industry"-- Provided by publisher.

Summary: This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.

Summary: "This book explores advancements in artificial intelligence with a focus on its application engineering"-- Provided by publisher.

Summary: "Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare"-- Provided by publisher.

Summary: "This book traces the evolution of AI in contemporary history. It analyses how AI is primarily being driven by 'capital' as the only 'factor of production' and its consequences for the global political economy. It further explores the dystopian prospect of mass unemployment by AI and takes up the ethical aspects of AI and its possible use in undermining natural and fundamental rights"--Back cover.

Summary: The book provides an advanced vision and trends of computational intelligence in cyberspace and cyber-enabled spaces. It reviews architectures and models, as well as state-of-the-art computational and interpretation capabilities for social, industrial, and multimedia applications. Cyber-enabled intelligence involves the design and development of intelligent and innovative application scenarios in social networks, computer vision, multimedia, and image processing. Application scenarios can also cover the applicability of intelligent sensing, data collection and predictive analysis in Internet of Things.

Summary: No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. An award-winning author and leading computer scientist reveals its turbulent history and the recent surge of successes, grand hopes, and emerging fears that surround AI.

Summary: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You'll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You'll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.

Summary: Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business.-- Provided by publisher

Summary: Deep learning has already achieved remarkable results in many fields. Now it's making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields.

Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.

Summary: With the resurgence of neural networks in the 2010s, understanding deep learning has become essential for machine learning practitioners and even many software engineers. This practical book provides a thorough introduction for data scientists and software engineers with previous exposure to machine learning. You'll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks function using a first principles approach. You'll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a detailed understanding of how these networks work mathematically, computationally, and conceptually, you'll be set up for success on future deep learning projects.

Summary: Artificial Intelligence in the Age of Neural Networks and Brain Computing is the comprehensive guide for neural network advances in artificial intelligence (AI). It covers the major, basic ideas of "brain-like computing" behind AI, providing a framework to deep learning and launching novel and intriguing paradigms as possible future alternatives. Following an introduction, initial chapters discuss revolutionary new brain-mind approaches alternative to deep learning, the brain-mind-computer trichotomy, pitfalls and opportunities in the development of AI systems. Subsequent chapters explore a deep learning approach to electrophysiological multivariate time series analysis, multiview learning in biomedical applications, and the evolution of deep neural networks. This is an essential companion to researchers, engineers, advance AI practitioners, postdoctoral students in computational intelligence and neural engineering, and the technically oriented public. It provides access to the latest up-to-date knowledge from top, global experts working on theory and cutting-edge applications in signal processing, speech recognition, games, adaptive control, and decision-making. -- From back cover.

Summary: With this groundbreaking text, discover how wireless artificial intelligence (AI) can be used to determine position at centimeter level, sense motion and vital signs, and identify events and people. Using a highly innovative approach that employs existing wireless equipment and signal processing techniques to turn multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real practical use cases developed for products and applications. Topics explored include indoor positioning and tracking, wireless sensing and analytics, wireless power transfer and energy efficiency, 5G and next-generation communications, and the connection of large numbers of heterogeneous IoT devices of various bandwidths and capabilities. Demo videos accompanying the book online enhance understanding of these topics. Providing a unified framework for wireless AI, this is an excellent text for graduate students, researchers, and professionals working in wireless sensing, positioning, IoT, machine learning, signal processing and wireless communications.

Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.

Summary: Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. ---------------------------------- Also the key selling points ? Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP ? Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP ? Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis.

Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.

Summary: "In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable. In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage. If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial. In a 2014 editorial co-authored with Stephen Hawking, Russell wrote, "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last." Solving the problem of control over AI is not just possible; it is the key that unlocks a future of unlimited promise"-- Provided by publisher.

This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.

Summary: "Artificial Intelligence is a disruptive technology across business and society. There are three long-term trends driving this AI revolution: the emergence of Big Data, the creation of cheaper and more powerful computers, and development of better algorithms for processing an learning from data. Deep learning is the subfield of Artificial Intelligence that focuses on creating large neural network models that are capable of making accurate data driven decisions. Modern neural networks are the most powerful computational models we have for analyzing massive and complex datasets, and consequently deep learning is ideally suited to take advantage of the rapid growth in Big Data and computational power. In the last ten years, deep learning has become the fundamental technology in computer vision systems, speech recognition on mobile phones, information retrieval systems, machine translation, game AI, and self-driving cars. It is set to have a massive impact in healthcare, finance, and smart cities over the next years. This book is designed to give an accessible and concise, but also comprehensive, introduction to the field of Deep Learning. The book explains what deep learning is, how the field has developed, what deep learning can do, and also discusses how the field is likely to develop in the next 10 years. Along the way, the most important neural network architectures are described, including autoencoders, recurrent neural networks, long short-term memory networks, convolutional networks, and more recent developments such as Generative Adversarial Networks, transformer networks, and capsule networks. The book also covers the two more important algorithms for training a neural network, the gradient descent algorithm and Backpropagation"-- Provided by publisher.

Summary: A classic for its insights on complex systems, design, and artificial intelligence, and its contribution to our understanding of human intelligence. -- Information from publisher.

Summary: While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.

Summary: HR professionals need to get to grips with artificial intelligence and the way it's changing the world of work. From using natural language processing to ensure job adverts are free from bias and gendered language to implementing chatbots to enhance the employee experience, AI has created a variety of opportunities for the HR function. Artificial Intelligence for HR empowers HR professionals to leverage this potential and use AI to improve efficiency and develop a talented and productive workforce. Outlining the current technology landscape as well as the latest AI developments, this book ensures that HR professionals fully understand what AI is and what it means for HR in practice. Covering everything from recruitment and retention to employee engagement and learning and development, Artificial Intelligence for HR outlines the value AI can add to HR. It also features discussions on the challenges that can arise from AI and how to deal with them, including data privacy, algorithmic bias and how to develop the skills of a workforce with the rise of automation, robotics and machine learning in order to make it more human, not less. Packed with practical advice, research and case studies from global organisations including Uber, IBM and Unilever, this book will equip HR professionals with the

Summary: Learning for the Age of Artificial Intelligence is a richly informed argument for curricular change to educate people towards achievement and success as intelligent machine systems proliferate. Describing eight key competences, this comprehensive volume prepares educational leaders, designers, researchers, and policymakers to effectively rethink the knowledge, skills, and environments that students need to thrive and avoid displacement in today's technology-enhanced culture and workforce. Essential insights into school operations, machine learning, complex training and assessment, and economic challenges round out this cogent, relatable discussion about the imminent evolution of the education sector.

Summary: Why aren't the most powerful new technologies being used to solve the world's most important problems: hunger, poverty, conflict, employment, disease? In Link, Dr. Lorien Pratt answers these questions by exploring the solution that is emerging worldwide to take Artificial Intelligence to the next level: Decision Intelligence.

Summary: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You'll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. You will: Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas.

Summary: This book discusses emerging technologies in the field of the Internet of Things and big data, an area that will be scaled in next two decades. Written by a team of leading experts, it is the only book focusing on the broad areas of both the Internet of things and big data. The thirteen chapters present real-time experimental methods and theoretical explanations, as well as the implementation of these technologies through various applications. Offering a blend of theory and hands-on practices, the book enables graduate, postgraduate and research students who are involved in real-time project scaling techniques to understand projects and their execution. It is also useful for senior computer students, researchers and industry workers who are involved in experimenting with the Internet of Things and big data technologies, helping them to solve the real-time problem. Moreover, the chapters covering cutting-edge technologies help multidisciplinary researchers who are bridging the gap of two different outset real-time problems.

Subject: Artificial Intelligence (AI) is changing all aspects of communications and journalism as automatic processes are being introduced into all facets of classical journalism: investigation, content production, and distribution. Traditional human roles in these fields are being replaced by automatic processes and robots. The first section of this book focuses on a discussion of AI, the new emerging field of robot journalism, and the opportunities that AI limitations create for human journalists. The second section offers examples of the new journalism storytelling that empower human journalists using new technologies, new applications, and AI tools. While this book focuses on journalism, the discussion and conclusions are relevant to all content creators, including professionals in the advertising industry, which is a major main source of support for journalism.

Summary: Can games measure intelligence? How will artificial intelligence inform games of the future? In Playing Smart, Julian Togelius explores the connections between games and intelligence to offer a new vision of future games and game design. Video games already depend on AI. We use games to test AI algorithms, challenge our thinking, and better understand both natural and artificial intelligence. In the future, Togelius argues, game designers will be able to create smarter games that make us smarter in turn, applying advanced AI to help design games. In this book, he tells us how. Games are the past, present, and future of artificial intelligence. In 1948, Alan Turing, one of the founding fathers of computer science and artificial intelligence, handwrote a program for chess. Today we have IBM's Deep Blue and DeepMind's AlphaGo, and huge efforts go into developing AI that can play such arcade games as Pac-Man. Programmers continue to use games to test and develop AI, creating new benchmarks for AI while also challenging human assumptions and cognitive abilities. Game design is at heart a cognitive science, Togelius reminds us—when we play or design a game, we plan, think spatially, make predictions, move, and assess ourselves and our performance. By studying how we play and design games, Togelius writes, we can better understand how humans and machines think. AI can do more for game design than providing a skillful opponent. We can harness it to build game-playing and game-designing AI agents, enabling a new generation of AI-augmented games. With AI, we can explore new frontiers in learning and play.

Summary: TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.

Summary: Build next-generation Artificial Intelligence systems with Java About This Book Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Who This Book Is For This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus. What You Will Learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms In Detail In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. Style and approach An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Artificial Intelligence for big data using Java

Summary: This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

Summary: Build smart applications using cutting-edge technologies such as rules engines, code automation frameworks, and natural language processing (NLP). This book provides step-by-step instructions on how to port nine rules engines (CLIPS, JRuleEngine, DTRules, Zilonis, TermWare, Roolie, OpenRules, JxBRE, and JEOPS) to the Android platform. You'll learn how to use each rules engine to build a smart application with sample code snippets so that you can get started with programming smart applications immediately. Build Android-Based Smart Applicationsalso describes porting issues with other popular rules engines (Drools, JLisa, Take, and Jess). This book is a step-by-step guide on how to generate a working smart application from requirement specifications. It concludes by showing you how to generate a smart application from unstructured knowledge using the Stanford POS (Part of Speech) tagger NLP framework. You will: Evaluate the available rules engines to see which rules engine is best to use for building smart applications Build smart applications using rules engines Create a smart application using NLP Automatically generate smart application from requirement specifications.

Summary: Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots. Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download. With Facebook opening up its Messenger platform for developers, followed by Microsoft opening up Skype for development, a new channel has emerged for brands to acquire, engage, and service customers on chat with chatbots. You will: Work with the bot development life cycle Master bot UX design Integrate into the bot ecosystem Maximize the business and monetization potential for bots.

Summary: Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. You will: Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms .

Summary: This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading.

Summary: The best hope for peace and prosperity in our world is the expansion of information, and, as such, Artificial Intelligence (AI) was created to process an infinite amount of information. As men and women continue to perfect AI, monitoring its evolution can be both enlightening and unnerving. This book showcases the immense utility of AI and its superhuman characteristics. Without a doubt, patents play an important role in the remarkable progression of AI, exposing pioneering innovations that stimulate future improvements. From 1987 to 2017, at least one hundred fifty patents with the phrase artificial intelligence in the title were granted by the United States Patent and Trademark Office. This important book provides an easy-to-read summary of such patents. Within many of the summaries, there are inventor profiles and news articles that are insightful and thought-provoking. Pioneering inventors hail from China, Denmark, France, Germany, Italy, Japan, Korea, New Zealand, Russia, and Taiwan. Prominent organizations include Amazon, Disney, Ford, IBM, Intel, Microsoft, and Sony. Throughout the book, diverse quotes present the emotional impact of Artificial Intelligence. In reverence to Alan Mathison Turing (1912-1954), widely considered the father of AI, this book explores fascinating aspects of computing machinery that can process information to the nth power in a blink.

Summary: In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.

Summary: Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases.

Summary: This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.

Summary: Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.

Summary: This comprehensive reference text discusses the fundamental concepts of artificial intelligence and its applications in a single volume. Artificial Intelligence: Fundamentals and Applications presents a detailed discussion of basic aspects and ethics in the field of artificial intelligence and its applications in areas, including electronic devices and systems, consumer electronics, automobile engineering, manufacturing, robotics and automation, agriculture, banking, and predictive analysis. Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, manufacturing engineering, pharmacy, and healthcare, this text: Discusses advances in artificial intelligence and its applications. Presents the predictive analysis and data analysis using artificial intelligence. Covers the algorithms and pseudo-codes for different domains. Discusses the latest development of artificial intelligence in the field of practical speech recognition, machine translation, autonomous vehicles, and household robotics. Covers the applications of artificial intelligence in fields, including pharmacy and healthcare, electronic devices and systems, manufacturing, consumer electronics, and robotics.

Summary: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! You will: Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering.

Summary: Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms. The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the authors active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples. You will: Understand and explore quantum computing and quantum machine learning, and their application in science and industry Explore various data training models utilizing quantum machine learning algorithms and Python libraries Get hands-on and familiar with applied quantum computing, including freely available cloud-based access Be familiar with techniques for training and scaling quantum neural networks Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive.

Summary: Big Data' refers to a new class of data, to which 'big' doesn't quite do it justice. Much like an ocean is more than simply a deeper swimming pool, big data is fundamentally different to traditional data and needs a whole new approach. Packed with examples and case studies, this clear, comprehensive book will show you how to accumulate and utilise 'big data' in order to develop your business strategy. Big Data Demystified is your practical guide to help you draw deeper insights from the vast information at your fingertips; you will be able to understand customer motivations, speed up production lines, and even offer personalised experiences to each and every customer. With 20 years of industry experience, David Stephenson shows how big data can give you the best competitive edge, and why it is integral to the future of your business. "--Publisher's description.

Summary: Use Microsoft Azure to optimally design your data solutions and save time and money. Scenarios are presented covering analysis, design, integration, monitoring, and derivatives. This book is about data and provides you with a wide range of possibilities to implement a data solution on Azure, from hybrid cloud to PaaS services. Migration from existing solutions is presented in detail. Alternatives and their scope are discussed. Five of six chapters explore PaaS, while one focuses on SQL Server features for cloud and relates to hybrid cloud and IaaS functionalities. What You'll Learn: Know the Azure services useful to implement a data solution Match the products/services used to your specific needs Fit relational databases efficiently into data design Understand how to work with any type of data using Azure Hybrid and Public cloud features Use non-relational alternatives to solve even complex requirements Orchestrate data movement using Azure services Approach analysis and manipulation according to the data life cycle.

Summary: Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics. The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. You will: See machine learning in OBIEE Master the fundamentals of machine learning and how it pertains to BI and advanced analytics Gain an introduction to Oracle R Enterprise Discover the practical considerations of implementing machine learning with OBIEE.

Summary: "The AI delusion demonstrates why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, or that black boxes should be trusted"--Back dust jacket.

Summary: 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.

Summary: This book constitutes the refereed proceedings of the Second International Symposium on Artificial Life and Intelligent Agents, ALIA 2016, held in Birmingham, UK, in June 2016. The 8 revised full papers and three revised short papers presented together with two demo papers were carefully reviewed and selected from 25 submissions. The papers are organized in topical sections on modelling; robotics; bio-inspired problem solving; human-like systems; applications and games.

Summary: Learn how Artificial Intelligence (AI) is being applied in the fashion industry. With an application focused approach, this book provides real-world examples, breaks down technical jargon for non-technical readers, and provides an educational resource for fashion professionals. The book investigates the ways in which AI is impacting every part of the fashion value chain starting with product discovery and working backwards to manufacturing. Artificial Intelligence for Fashion walks you through concepts, such as connected retail, data mining, and artificially intelligent robotics. Each chapter contains an example of how AI is being applied in the fashion industry illustrated by one major technological theme. There are no equations, algorithms, or code. The technological explanations are cumulative so you'll discover more information about the inner workings of artificial intelligence in practical stages as the book progresses.

Summary: "A bold challenge to our obsession with efficiency--and a new understanding of how to benefit from the powerful potential of serendipity Algorithms, multitasking, sharing economy, life hacks: our culture can't get enough of efficiency. One of the great promises of the Internet and big data revolutions is the idea that we can improve the processes and routines of our work and personal lives to get more done in less time than ever before. There is no doubt that we're performing at higher scales and going faster than ever, but what if we're headed in the wrong direction? The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of platforms can in fact lead to wasted efforts, missed opportunities, and above all an inability to break out of established patterns. Edward Tenner offers a smarter way to think about efficiency, showing how we can combine artificial intelligence and our own intuition, leaving ourselves and our institutions open to learning from the random and unexpected"-- Provided by publisher.

Summary: A novel approach to decision engineering, with a verified framework for modeling human reasoning Soft Computing Evaluation Logic provides an in-depth examination of evaluation decision problems and presents comprehensive guidance toward the use of the Logic Scoring of Preference (LSP) method in modeling complex decision criteria. Fully aligned with current developments in computational intelligence, the discussion covers the design and use of LSP criteria for evaluation and comparison in diverse areas, such as search engines, medical conditions, real estate, space management, habitat mitigation projects in ecology, and land use and residential development suitability maps, with versatile transfer to other similar decision-modeling contexts. Human decision making is rife with fuzziness, imprecision, uncertainty, and half-truths-yet humans make evaluation decisions every day. In this book, such decision processes are observed, analyzed, and modeled. The result is graded logic, a soft computing mathematical infrastructure that provides both formal logic and semantic generalizations of classical Boolean logic. Graded logic is used for logic aggregation in the context of evaluation models consistent with observable properties of human reasoning. The LSP method, based on graded logic and logic aggregation, is a vital component of an industrial-strength decision engineering framework. Thus, the book: Provides detailed theoretical background for graded logic Provides a theory of logic aggregators Explains the LSP method for designing complex evaluation criteria and their use Shows techniques for evaluation, comparison, and selection of complex systems, as well as the cost/suitability analysis, optimization, sensitivity analysis, tradeoff analysis, and missingness-tolerant aggregation Includes a survey of available LSP software tools, including ISEE, ANSY and LSP.NT. With quantitative modeling of human reasoning, novel approaches to modeling decision criteria, and a verified decision engineering framework applicable to a broad array of applications, this book is an invaluable resource for graduate students, researchers, and practitioners working within the decision engineering realm

Summary: This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations.

Summary: Originally published in 1995, this volume is the direct result of a conference in which a number of leading researchers from the fields of artificial intelligence and biology gathered to examine whether there was any ground to assume that a new AI paradigm was forming itself and what the essential ingredients of this new paradigm were. A great deal of scepsis is justified when researchers, particularly in the cognitive sciences, talk about a new paradigm. Shifts in paradigm mean not only new ideas but also shifts in what constitutes good problems, what counts as a result, the experimental practice to validate results, and the technological tools needed to do research. Due to the complexity of the subject matter, paradigms abound in the cognitive sciences -- connectionism being the most prominent newcomer in the mid-1980s. This workshop group was brought together in order to clarify the common ground, see what had been achieved so far, and examine in which way the research could move further. This volume is a reflection of this important meeting. It contains contributions which were distributed before the workshop but then substantially broadened and revised to reflect the workshop discussions and more recent technical work. Written in polemic form, sometimes criticizing the work done thus far within the new paradigm, this collection includes research program descriptions, technical contributions, and position papers.

Summary: This book deals with artificial intelligence (AI) and its several applications. It is not an organic text that should be read from the first page onwards, but rather a collection of articles that can be read at will (or at need). The idea of this work is indeed to provide some food for thoughts on how AI is impacting few verticals (insurance and financial services), affecting horizontal and technical applications (speech recognition and blockchain), and changing organizational structures (introducing new figures or dealing with ethical issues). The structure of the chapter is very similar, so I hope the reader won’t find difficulties in establishing comparisons or understanding the differences between specific problems AI is being used for. The first chapter of the book is indeed showing the potential and the achievements of new AI techniques in the speech recognition domain, touching upon the topics of bots and conversational interfaces. The second and thirds chapter tackle instead verticals that are historically data-intensive but not data-driven, i.e., the financial sector and the insurance one. The following part of the book is the more technical one (and probably the most innovative), because looks at AI and its intersection with another exponential technology, namely the blockchain. Finally, the last chapters are instead more operative, because they concern new figures to be hired regardless of the organization or the sector, and ethical and moral issues related to the creation and implementation of new type of algorithms. .

Summary: Artificial intelligence comes of age AI in the enterprise What are companies doing today? What's your cognitive strategy? AI tasks, organizational structures, and business processes Jobs and skills in a world of smart machines A technological foundation for AI Managing the organizational, social, and ethical implications of AI.

Summary: "From AI to Robotics: Mobile, Social, and Sentient Robots is a journey into the world of agent-based robotics and it covers a number of interesting topics, both in the theory and practice of the discipline. The book traces the earliest ideas for autonomous machines to the mythical lore of ancient Greece and ends the last chapter with a debate on a prophecy set in the apparent future, where human beings and robots/technology may merge to create superior beings -- the era of transhumanism. Throughout the text, the work of leading researchers is presented in depth, which helps to paint the socio-economic picture of how robots are transforming our world and will continue to do so. This work is presented along with the influences and ideas from futurists, such as Asimov, Moravec, Lem, Vinge, and of course Kurzweil. The book furthers the discussion with concepts of Artificial Intelligence and how it manifests in robotic agents. Discussions across various topics are presented in the book, including control paradigm, navigation, software, multi-robot systems, swarm robotics, robots in social roles, and artificial consciousness in robots. These discussions help to provide an overall picture of current day agent-based robotics and its prospects for the future. Examples of software and implementation in hardware are covered in Chapter 5 to encourage the imagination and creativity of budding robot enthusiasts. The book addresses several broad themes, such as AI in theory versus applied AI for robots, concepts of anthropomorphism, embodiment and situatedness, extending theory of psychology and animal behavior to robots, and the proposal that in the future, AI may be the new definition of science. Behavior-based robotics is covered in Chapter 2 and retells the debate between deliberative and reactive approaches. The text reiterates that the effort of modern day robotics is to replicate human-like intelligence and behavior, and the tools that a roboticist has at his or her disposal are open source software, which is often powered by crowd-sourcing. Open source meta-projects, such as Robot Operating System (ROS), etc. are briefly discussed in Chapter 5. The ideas and themes presented in the book are supplemented with cartoons, images, schematics and a number of special sections to make the material engaging for the reader. Designed for robot enthusiasts -- researchers, students, or the hobbyist, this comprehensive book will entertain and inspire anyone interested in the exciting world of robots."

Summary: This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.

Summary: This book gathers extended versions of the best papers presented at the 8th IEEE conference on Intelligent Systems, held in Sofia, Bulgaria on September 4-6, 2016, which are mainly related to theoretical research in the area of intelligent systems. The main focus is on novel developments in fuzzy and intuitionistic fuzzy sets, the mathematical modelling tool of generalized nets and the newly defined method of intercriteria analysis. The papers reflect a broad and diverse team of authors, including many young researchers from Australia, Bulgaria, China, the Czech Republic, Iran, Mexico, Poland, Portugal, Slovakia, South Korea and the UK.

Summary: How deep learning-from Google Translate to driverless cars to personal cognitive assistants-is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

Summary: This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.

Summary: This book describes efficient and effective techniques for harnessing the power of Linked Data by tackling the various aspects of managing its growing volume: storing, querying, reasoning, provenance management and benchmarking. To this end, Chapter 1 introduces the main concepts of the Semantic Web and Linked Data and provides a roadmap for the book. Next, Chapter 2 briefly presents the basic concepts underpinning Linked Data technologies that are discussed in the book. Chapter 3 then offers an overview of various techniques and systems for centrally querying RDF datasets, and Chapter 4 outlines various techniques and systems for efficiently querying large RDF datasets in distributed environments. Subsequently, Chapter 5 explores how streaming requirements are addressed in current, state-of-the-art RDF stream data processing. Chapter 6 covers performance and scaling issues of distributed RDF reasoning systems, while Chapter 7 details benchmarks for RDF query engines and instance matching systems. Chapter 8 addresses the provenance management for Linked Data and presents the different provenance models developed. Lastly, Chapter 9 offers a brief summary, highlighting and providing insights into some of the open challenges and research directions. Providing an updated overview of methods, technologies and systems related to Linked Data this book is mainly intended for students and researchers who are interested in the Linked Data domain. It enables students to gain an understanding of the foundations and underpinning technologies and standards for Linked Data, while researchers benefit from the in-depth coverage of the emerging and ongoing advances in Linked Data storing, querying, reasoning, and provenance management systems. Further, it serves as a starting point to tackle the next research challenges in the domain of Linked Data management.

Summary: This book provides a comprehensive picture of mobile big data starting from data sources to mobile data driven applications. Mobile Big Data comprises two main components: an overview of mobile big data, and the case studies based on real-world data recently collected by one of the largest mobile network carriers in China. In the first component, four areas of mobile big data life cycle are surveyed: data source and collection, transmission, computing platform and applications. In the second component, two case studies are provided, based on the signaling data collected in the cellular core network in terms of subscriber privacy evaluation and demand forecasting for network management. These cases respectively give a vivid demonstration of what mobile big data looks like, and how it can be analyzed and mined to generate useful and meaningful information and knowledge. This book targets researchers, practitioners and professors relevant to this field. Advanced-level students studying computer science and electrical engineering will also be interested in this book as supplemental reading.