Your cart is empty
In a global age of disruption, future organisational change is not avoidable. Organisational effects will be immediate and transformational across companies, and due consideration and preparation ahead of these changes may be paramount for the survival of organisations and their leaders of tomorrow. Leading and Managing Change in the Age of Disruption and Artificial Intelligence modernises the topics of management, leadership, and organisational change to inform those leading and managing organisations into the future. The book covers modern disruptions ranging from Trump and other geo-political changes, to Brexit, new currencies, trade wars, and even knowledge mobility. It also considers the broad scope of potential impacts posed by artificial intelligence. With insights and strategies that the modern manager of the future can implement in their daily work, this book provides critical thinking that will future proof organisations with practical models. It will interest and inform managers and leaders across small and large organisations and will also prove useful and thought-provoking to those studying in business related disciplines such as management, leadership, and organisational change.
We are very pleased to present to you this LNCS volume, the proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). PPSN is one of the most respected and highly regarded c- ference series in evolutionary computation, and indeed in natural computation aswell.Thisbiennialeventwas?rstheldinDortmundin1990,andtheninBr- sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006) and again in Dortmund in 2008. PPSN 2010 received 232 submissions. After an extensive peer review p- cess involving more than 180 reviewers, the program committee chairs went through all the review reports and ranked the papers according to the revi- ers'comments. Each paper wasevaluated by at least three reviewers.Additional reviewers from the appropriate branches of science were invoked to review into disciplinary papers. The top 128 papers were ?nally selected for inclusion in the proceedings and presentation at the conference. This represents an acceptance rate of 55%, which guarantees that PPSN will continue to be one of the c- ferences of choice for bio-inspired computing and metaheuristics researchers all over the world who value the quality over the size of a conference. The papers included in the proceedingsvolumes covera wide range of topics, fromevolutionarycomputationto swarmintelligence, frombio-inspiredcomp- ing to real-world applications. Machine learning and mathematical games s- portedbyevolutionaryalgorithmsaswellasmemetic,agent-orientedsystemsare also represented. They all are the latest and best in natural computation. The proceedings are composed of two volumes divided into nine thematic sections.
In May 1997, the world watched as Garry Kasparov, the greatest chess player in the world, was defeated for the first time by the IBM supercomputer Deep Blue. It was a watershed moment in the history of technology: machine intelligence had arrived at the point where it could best human intellect. It wasn't a coincidence that Kasparov became the symbol of man's fight against the machines. Chess has long been the fulcrum in development of machine intelligence; the hoax automaton 'The Turk' in the 18th century and Alan Turing's first chess program in 1952 were two early examples of the quest for machines to think like humans -- a talent we measured by their ability to beat their creators at chess. As the pre-eminent chessmaster of the 80s and 90s, it was Kasparov's blessing and his curse to play against each generation's strongest computer champions, contributing to their development and advancing the field. Like all passionate competitors, Kasparov has taken his defeat and learned from it. He has devoted much energy to devising ways in which humans can partner with machines in order to produce results better than either can achieve alone. During the twenty years since playing Deep Blue, he's played both with and against machines, learning a great deal about our vital relationship with our most remarkable creations. Ultimately, he's become convinced that by embracing the competition between human and machine intelligence, we can spend less time worrying about being replaced and more thinking of new challenges to conquer. In this breakthrough book, Kasparov tells his side of the story of Deep Blue for the first time -- what it was like to strategize against an implacable, untiring opponent -- the mistakes he made and the reasons the odds were against him. But more than that, he tells his story of AI more generally, and how he's evolved to embrace it, taking part in an urgent debate with philosophers worried about human values, programmers creating self-learning neural networks, and engineers of cutting edge robotics.
First Published in 1986. Routledge is an imprint of Taylor & Francis, an informa company.
First published in 1986. Routledge is an imprint of Taylor & Francis, an informa company.
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.
This book gathers a collection of the latest research, applications, and proposals, introducing readers to innovations and concepts from diverse environments and systems. As such, it will provide students and professionals alike with not only cutting-edge information, but also new inspirations and potential research directions. Each chapter focuses on a specific aspect of applied decision making, e.g. in complex systems, computational intelligence, security, and ubiquitous computing.
Create applications using Industry 4.0. Discover how artificial intelligence (AI) and machine learning (ML) capabilities can be enhanced using the Internet of things (IoT) and secured using Blockchain, so your latest app can be not just smarter but also more connected and more secure than ever before. This book covers the latest easy-to-use APIs and services from Microsoft, including Azure IoT, Cognitive Services APIs, Blockchain as a Service (BaaS), and Machine Learning Studio. As you work through the book, you'll get hands-on experience building an example solution that uses all of these technologies-an IoT suite for a smart healthcare facility. Hosted on Azure and networked using Azure IoT, the solution includes centralized patient monitoring, using Cognitive Services APIs for face detection, recognition, and tracking. Blockchain is used to create trust-based security and inventory management. Machine learning is used to create predictive solutions to proactively improve quality of life. By the end of the book, you'll be confident creating richer and smarter applications using these technologies. What You'll Learn Know the technologies underpinning Industry 4.0 and AI 2.0 Develop real-time solutions using IoT in Azure Bring the smart capabilities of AI 2.0 into your application using a simple API call Host and manage your solution on Azure Understand Blockchain as a Service Capture and analyze data on the fly Make predictions using existing data Who This Book Is For Novice and intermediate .NET developers and architects who want to learn what it takes to create a modern or next-generation application
Weighing in from the cutting-edge frontiers of science, today's most forward-thinking minds explore the rise of "machines that think." Stephen Hawking recently made headlines by noting, "The development of full artificial intelligence could spell the end of the human race." Others, conversely, have trumpeted a new age of "superintelligence" in which smart devices will exponentially extend human capacities. No longer just a matter of science-fiction fantasy (2001, Blade Runner, The Terminator, Her, etc.), it is time to seriously consider the reality of intelligent technology, many forms of which are already being integrated into our daily lives. In that spirit, John Brockman, publisher of Edge. org ("the world's smartest website" - The Guardian), asked the world's most influential scientists, philosophers, and artists one of today's most consequential questions: What do you think about machines that think?
The last century has seen enormous leaps in the development of digital technologies, and most aspects of modern life have changed significantly with their widespread availability and use. Technology at various scales - supercomputers, corporate networks, desktop and laptop computers, the internet, tablets, mobile phones, and processors that are hidden in everyday devices and are so small you can barely see them with the naked eye - all pervade our world in a major way. Computers and Society: Modern Perspectives is a wide-ranging and comprehensive textbook that critically assesses the global technical achievements in digital technologies and how are they are applied in media; education and learning; medicine and health; free speech, democracy, and government; and war and peace. Ronald M. Baecker reviews critical ethical issues raised by computers, such as digital inclusion, security, safety, privacy,automation, and work, and discusses social, political, and ethical controversies and choices now faced by society. Particular attention is paid to new and exciting developments in artificial intelligence and machine learning, and the issues that have arisen from our complex relationship with AI.
This book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems. It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson-Cowan model. In turn, two properties that are vital in nature are added to the CNN to help it more accurately deliver mimetic behavior: randomness of connection, and the presence of different dynamics (excitatory and inhibitory) within the same network. It uses an ID matrix to determine the location of excitatory and inhibitory neurons, and to reconfigure the network to optimize its topology. The book demonstrates that reconfiguring a single-layer CNN is an easier and more flexible solution than the procedure required in a multilayer CNN, in which excitatory and inhibitory neurons are separate, and that the key CNN criteria of a spatially invariant template and local coupling are fulfilled. In closing, the application of the authors' neuron population model as a feature extractor is exemplified using odor and electroencephalogram classification.
This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research, future challenges and applications in various domains, such as aerospace and mechanical applications, structure health and seismic hazard, electromagnetic energy (its impact on systems and humans) and global environmental state change. Written by leading international experts from different fields, the book demonstrates the unifying property of UQ theme that can be profitably adopted to solve problems of different domains. The collection in one place of different methodologies for different applications has the great value of stimulating the cross-fertilization and alleviate the language barrier among areas sharing a common background of mathematical modeling for problem solution. The book is designed for researchers, professionals and graduate students interested in quantitatively assessing the effects of uncertainties in their fields of application. The contents build upon the workshop "Uncertainty Modeling for Engineering Applications" (UMEMA 2017), held in Torino, Italy in November 2017.
What beliefs are, what they do for us, how we come to hold them, and how to evaluate them. Our beliefs constitute a large part of our knowledge of the world. We have beliefs about objects, about culture, about the past, and about the future. We have beliefs about other people, and we believe that they have beliefs as well. We use beliefs to predict, to explain, to create, to console, to entertain. Some of our beliefs we call theories, and we are extraordinarily creative at constructing them. Theories of quantum mechanics, evolution, and relativity are examples. But so are theories about astrology, alien abduction, guardian angels, and reincarnation. All are products (with varying degrees of credibility) of fertile minds trying to find explanations for observed phenomena. In this book, Nils Nilsson examines beliefs: what they do for us, how we come to hold them, and how to evaluate them. We should evaluate our beliefs carefully, Nilsson points out, because they influence so many of our actions and decisions. Some of our beliefs are more strongly held than others, but all should be considered tentative and changeable. Nilsson shows that beliefs can be quantified by probability, and he describes networks of beliefs in which the probabilities of some beliefs affect the probabilities of others. He argues that we can evaluate our beliefs by adapting some of the practices of the scientific method and by consulting expert opinion. And he warns us about "belief traps"-holding onto beliefs that wouldn't survive critical evaluation. The best way to escape belief traps, he writes, is to expose our beliefs to the reasoned criticism of others.
This book discusses the basic requirements and constraints in building a brain-computer interaction system. These include the technical requirements for building the signal processing module and the acquisition module. The major aspects to be considered when designing a signal acquisition module for a brain-computer interaction system are the human brain, types and applications of brain-computer systems, and the basics of EEG (electroencephalogram) recording. The book also compares the algorithms that have been and that can be used to design the signal processing module of brain-computer interfaces, and describes the various EEG-acquisition devices available and compares their features and inadequacies. Further, it examines in detail the use of Emotiv EPOC (an EEG acquisition module developed by Emotiv) to build a complete brain-computer interaction system for driving robots using a neural network classification module.
These volumes constitute the Proceedings of the 6th International Workshop on Soft Computing Applications, or SOFA 2014, held on 24-26 July 2014 in Timisoara, Romania. This edition was organized by the University of Belgrade, Serbia in conjunction with Romanian Society of Control Engineering and Technical Informatics (SRAIT) - Arad Section, The General Association of Engineers in Romania - Arad Section, Institute of Computer Science, Iasi Branch of the Romanian Academy and IEEE Romanian Section. The Soft Computing concept was introduced by Lotfi Zadeh in 1991 and serves to highlight the emergence of computing methodologies in which the accent is on exploiting the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solution cost. Soft computing facilitates the use of fuzzy logic, neurocomputing, evolutionary computing and probabilistic computing in combination, leading to the concept of hybrid intelligent systems. The combination of such intelligent systems tools and a large number of applications introduce a need for a synergy of scientific and technological disciplines in order to show the great potential of Soft Computing in all domains. The conference papers included in these proceedings, published post conference, were grouped into the following area of research: * Image, Text and Signal Processing Intelligent Transportation Modeling and Applications Biomedical Applications Neural Network and ApplicationsKnowledge-Based Technologies for Web Applications, Cloud Computing, Security, Algorithms and Computer Networks Knowledge-Based Technologies Soft Computing Techniques for Time Series Analysis Soft Computing and Fuzzy Logic in Biometrics Fuzzy Applications Theory and Fuzzy Control Business Process Management Methods and Applications in Electrical Engineering The volumes provide useful information to professors, researchers and graduated students in area of soft computing techniques and applications, as they report new research work on challenging issues.
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You'll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov's Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You'll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There's also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google's Deep Mind and see scenarios where reinforcement learning can be used. What You'll Learn Absorb the core concepts of the reinforcement learning process Use advanced topics of deep learning and AI Work with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using Python Who This Book Is For Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.
This book provides a comprehensive overview of modern computer-based techniques for analyzing the structure, properties and dynamics of biomolecules and biomolecular processes. It is organized in four main parts; the first one deals with methodology of molecular simulations; the second one with applications of molecular simulations; the third one introduces bioinformatics methods and the use of experimental information in molecular simulations; the last part reports on selected applications of molecular quantum mechanics. This second edition has been thoroughly revised and updated to include the latest progresses made in the respective field of research.
This book discusses data communication and computer networking, communication technologies and the applications of IoT (Internet of Things), big data, cloud computing and healthcare informatics. It explores, examines and critiques intelligent data communications and presents inventive methodologies in communication technologies and IoT. Aimed at researchers and academicians who need to understand the importance of data communication and advanced technologies in IoT, it offers different perspectives to help readers increase their knowledge and motivates them to conduct research in the area, highlighting various innovative ideas for future research.
An exploration of embodied intelligence and its implications points toward a theory of intelligence in general; with case studies of intelligent systems in ubiquitous computing, business and management, human memory, and robotics. How could the body influence our thinking when it seems obvious that the brain controls the body? In How the Body Shapes the Way We Think, Rolf Pfeifer and Josh Bongard demonstrate that thought is not independent of the body but is tightly constrained, and at the same time enabled, by it. They argue that the kinds of thoughts we are capable of have their foundation in our embodiment-in our morphology and the material properties of our bodies. This crucial notion of embodiment underlies fundamental changes in the field of artificial intelligence over the past two decades, and Pfeifer and Bongard use the basic methodology of artificial intelligence-"understanding by building"-to describe their insights. If we understand how to design and build intelligent systems, they reason, we will better understand intelligence in general. In accessible, nontechnical language, and using many examples, they introduce the basic concepts by building on recent developments in robotics, biology, neuroscience, and psychology to outline a possible theory of intelligence. They illustrate applications of such a theory in ubiquitous computing, business and management, and the psychology of human memory. Embodied intelligence, as described by Pfeifer and Bongard, has important implications for our understanding of both natural and artificial intelligence.
The book gathers high-quality research papers presented at the International Conference on Advanced Computing and Intelligent Engineering (ICACIE 2017). It includes technical sections describing progress in the fields of advanced computing and intelligent engineering, and is primarily intended for postgraduate students and researchers working in Computer Science and Engineering. However, researchers working in Electronics will also find the book useful, as it addresses hardware technologies and next-gen communication technologies.
"Incomplete Information System and Rough Set Theory: Models and Attribute Reductions" covers theoretical study of generalizations of rough set model in various incomplete information systems. It discusses not only the regular attributes but also the criteria in the incomplete information systems. Based on different types of rough set models, the book presents the practical approaches to compute several reducts in terms of these models. The book is intended for researchers and postgraduate students in machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, and granular computing.
Dr. Xibei Yang is a lecturer at the School of Computer Science and Engineering, Jiangsu University of Science and Technology, China; Jingyu Yang is a professor at the School of Computer Science, Nanjing University of Science and Technology, China.
This book constitutes the refereed proceedings of the 6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011, held in Banff, Canada, in September 2011. The 89 revised full papers presented together with 3 keynote lectures and 1 invited tutorial session were carefully reviewed and selected from 229 submissions. The papers are organized in topical sections on attribute reduction and feature selection, generalized rough set models, machine learning with rough and hybrid techniques, knowledge technology and intelligent systems and applications.
This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.
This book provides an interdisciplinary collection of views on the ethical challenges and opportunities of workplaces in the Internet of things. Current developments within Ubiquitous computing (ubicomp) systems designed for the workplace are introduced and philosophical, organizational and socio-ethical considerations of ubicomp in workplaces are provided. Suggestions regarding the rules that should be respected in order to favor an adequate implementation of ubicomp in the workplace are offered, considering both intra-organizational but also wider societal concerns. The interdisciplinary collection of contributions invites the reader to engage in the discussion of ubicomp in everyday working environments.
You may like...
The Creativity Code - How AI is Learning…
Marcus du Sautoy Paperback (1)
Novacene - The Coming Age of…
James Lovelock Hardcover (1)
Autonomy - The Quest to Build the…
Lawrence D. Burns, Christopher Shulgan Paperback
We have been harmonised - Life in…
Kai Strittmatter Paperback (1)
The Elements of Statistical Learning…
Trevor Hastie, Robert Tibshirani, … Hardcover
The Technology Trap - Capital, Labor…
Carl Benedikt Frey Hardcover
Rage Inside the Machine
Robert Elliott Smith Hardcover
Superminds - The Surprising Power of…
Thomas W. Malone Paperback (1)
Inhuman Power - Artificial Intelligence…
Nick Dyer-Witheford, Atle Mikkola Kjosen, … Paperback
Superintelligence - Paths, Dangers…
Nick Bostrom Paperback (2)