Artificial intelligence (AI) Books

Book SynopsisAn in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions.  The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload chara

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Book SynopsisHow to stay in charge in a world populated by algorithms that beat us in chess, find us romantic partners, and tell us to “turn right in 500 yards.”Doomsday prophets of technology predict that robots will take over the world, leaving humans behind in the dust. Tech industry boosters think replacing people with software might make the world a better place—while tech industry critics warn darkly about surveillance capitalism. Despite their differing views of the future, they all seem to agree: machines will soon do everything better than humans. In How to Stay Smart in a Smart World, Gerd Gigerenzer shows why that’s not true, and tells us how we can stay in charge in a world populated by algorithms.Machines powered by artificial intelligence are good at some things (playing chess), but not others (life-and-death decisions, or anything involving uncertainty). Gigerenzer explains why algorithms often fail at finding us romantic part

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Book SynopsisSix classic science fiction stories and commentary that illustrate and explain key algorithms or principles of artificial intelligence.This book presents six classic science fiction stories and commentary that illustrate and explain key algorithms or principles of artificial intelligence. Even though all the stories were originally published before 1973, they help readers grapple with two questions that stir debate even today: how are intelligent robots programmed? and what are the limits of autonomous robots? The stories—by Isaac Asimov, Vernor Vinge, Brian Aldiss, and Philip K. Dick—cover telepresence, behavior-based robotics, deliberation, testing, human-robot interaction, the “uncanny valley,” natural language understanding, machine learning, and ethics. Each story is preceded by an introductory note, “As You Read the Story,” and followed by a discussion of its implications, “After You Have Read the Story.” Together with the

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Book SynopsisAn NPR 2023 "Books We Love" Pick One of the Next Big Idea Club's Must-Read Books An award-winning defense expert tells the story of today’s great power rivalry—the struggle to control artificial intelligence.Trade Review"Should be required reading for anyone interested in the future of the global economy or geopolitics." -- Thomas E. Ricks - New York Times Book Review"Scharre is a thoughtful, knowledgeable, and capable guide. He explains why AI matters and charts the areas that will determine which country gets the most out of its investments." -- Lawrence Freedman - Foreign Affairs"An invaluable primer to arguably the most important driver of change for our future. Scharre marshals fact after fact to explain not just the technology, but the trends soon to unfold and remake our world." -- P. W. Singer, author of Burn-In"Paul Scharre argues that the AI race between democratic and authoritarian states is well underway, and the stakes could not be higher: whoever wins will write the international rules of the next century. With revealing anecdotes, cogent analysis, and incisive insight, Scharre demystifies AI and its national security implications. If you read one book on AI this year, read this one!" -- Michèle Flournoy, former Under Secretary of Defense for Policy"How will AI change the balance of power between authoritarian states and democracies? This is one of the most important questions in geopolitics today. Authoritarians have already figured out how to use AI to their maximum advantage, and democrats must urgently do the same or risk losing the contest. First step: Read this book, a farsighted and comprehensive survey of the issues involved and the paths forward." -- Pedro Domingos, author of The Master Algorithm"America and its military are facing a major test when it comes to AI. The country that best incorporates artificial intelligence technology into its defense will have significant military advantages over its competitors. Four Battlegrounds is an essential book for everyone involved in American leadership and American defense, because it outlines the challenges we face and explains the key components that will determine our success in using this important new technology to support American power and American ideals." -- Admiral James Stavridis, 16th Supreme Allied Commander of NATO"In this riveting book on AI and power by one of the leading strategists of our time, Paul Scharre highlights an existential challenge: as Americans and Chinese militarize ever more powerful AI to avoid ceding control to each other, they risk ceding too much power to machines." -- Prof. Max Tegmark, MIT AI researcher and author of Life 3.0"A must-read guide to how the emerging artificial intelligence arms race will shape the geopolitical, economic, and political struggle between China and its authoritarian allies and the democratic West led by the United States and Europe." -- Martin Ford, author of Rule of the Robots and Rise of the Robots"A solid, well-organized account of the military applications of AI and of the race to take the lead global position." -- Kirkus Reviews"Technophiles and technophobes alike will be challenged and enlightened." -- Publishers Weekly"Readers knowledgeable about computer science will find it clarifying, while other will gain immense understand of an often opaque if important subject." -- James Pekoll - Booklist

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Book SynopsisThis book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved. Reviews: this book is easy to read and entertaining, and much can be learned from it. Even if you know just abouteverything about large-scale and temporal networks, the book is a worthwhile read; you will learn a lot about SNA literature, patents, the US Supreme Court, and European soccer. (Social Networks) a clear and accessible textbook, balancing symbolic maths, code, and visual explanations. The authors' enthusiasm for the subject matter makes it enjoyable to read (JASSS)Table of ContentsPreface xiii 1 Temporal and Spatial Networks 1 1.1 Modern Social Network Analysis 1 1.2 Network Sizes 3 1.3 Substantive Concerns 3 1.3.1 Citation Networks 3 1.3.2 Other Types of Large Networks 7 1.4 Computational Methods 10 1.5 Data for Large Temporal Networks 12 1.5.1 The Main Datasets 12 1.5.2 Secondary Datasets 14 1.6 Induction and Deduction 16 2 Foundations of Methods for Large Networks 18 2.1 Networks 18 2.1.1 Descriptions of Networks 20 2.1.2 Degrees 21 2.1.3 Descriptions of Properties 21 2.1.4 Visualizations of Properties 22 2.2 Types of Networks 22 2.2.1 Temporal Networks 23 2.2.2 Multirelational Networks 25 2.2.3 Two-mode Networks 28 2.3 Large Networks 28 2.3.1 Small and Middle Sized Networks 29 2.3.2 Large Networks 30 2.3.3 Complexity of Algorithms 30 2.4 Strategies for Analyzing Large Networks 32 2.5 Statistical Network Measures 33 2.5.1 Using Pajek and R Together 35 2.5.2 Fitting Distributions 35 2.6 Subnetworks 37 2.6.1 Clusters, Clusterings, Partitions, Hierarchies 37 2.6.2 Contractions of Clusters 38 2.6.3 Subgraphs 40 2.6.4 Cuts 42 2.7 Connectivity Properties of Networks 46 2.7.1 Walks 46 2.7.2 Equivalence Relations and Partitions 47 2.7.3 Connectivity 48 2.7.4 Condensation 49 2.7.5 Bow-tie Structure of the Web Graph 50 2.7.6 The Internal Structure of Strong Components 51 2.7.7 Bi-connectivity and -connectivity 51 2.8 Triangular and Short Cycle Connectivities 53 2.9 Islands 54 2.9.1 Defining Islands 55 2.9.2 Some Properties of Islands 56 2.10 Cores and Generalized Cores 57 2.10.1 Cores 58 2.10.2 Generalized Cores 59 2.11 Important Vertices in Networks 61 2.11.1 Degrees, Closeness, Betweenness and Other Indices 63 2.11.2 Clustering 65 2.11.3 Computing Further Indices Through Functions 66 2.12 Transition to Methods for Large Networks 68 3 Methods for Large Networks 69 3.1 Acyclic Networks 71 3.1.1 Some Basic Properties of Acyclic Networks 71 3.1.2 Compatible Numberings: Depth and Topological Order 72 3.1.3 Topological Orderings and Functions on Acyclic Networks 74 3.2 SPC Weights in Acyclic Networks 75 3.2.1 Citation Networks 75 3.2.2 Analysis of Citation Networks 76 3.2.3 Search Path Count Method 77 3.2.4 Computing SPLC and SPNP Weights 77 3.2.5 Implementation Details 78 3.2.6 Vertex Weights 78 3.2.7 General Properties of Weights 79 3.2.8 SPC Weights 80 3.3 Probabilistic Flow in Acyclic Network 81 3.4 Nonacyclic Citation Networks 82 3.5 Two-mode Networks from Data Tables 84 3.5.1 Multiplication of Two-mode Networks 85 3.6 Bibliographic Networks 88 3.6.1 Co-authorship Networks 88 3.6.2 Collaboration Networks 89 3.6.3 Other Derived Networks 92 3.7 Weights 94 3.7.1 Normalizations of Weights 94 3.7.2 -Rings 94 3.7.3 4-Rings and Analysis of Two-mode Networks 95 3.7.4 Two-mode Cores 96 3.8 Pathfinder 96 3.8.1 Pathfinder Algorithms 100 3.8.2 Computing the Closure Over the Pathfinder Semiring 101 3.8.3 Spanish Algorithms 101 3.8.4 A Sparse Network Algorithm 102 3.9 Clustering, Blockmodeling, and Community Detection 102 3.9.1 The Louvain Method and VOS 102 3.10 Clustering Symbolic Data 103 3.10.1 Symbolic Objects Described with Distributions 103 3.10.2 The Leaders Method 105 3.10.3 An AgglomerativeMethod 107 3.11 Approaches to Temporal Networks 107 3.11.1 Journeys -- Walks in Temporal Networks 108 3.11.2 Measures 110 3.11.3 Problems and Algorithms 111 3.11.4 Evolution 114 3.12 Levels of Analysis 114 3.13 Transition to Substantive Topics 116 4 Scientific Citation and Other Bibliographic Networks 117 4.1 The Centrality Citation Network 117 4.2 Preliminary Data Analyses 118 4.2.1 Temporal Distribution of Publications 119 4.2.2 Degree Distributions of the Centrality Literature 121 4.2.3 Types of Works 124 4.2.4 The Boundary Problem 126 4.3 Transforming a Citation Network into an Acyclic Network 128 4.3.1 Checking for the Presence of Cycles 128 4.3.2 Dealing with Cycles in Citation Networks 133 4.4 The Most ImportantWorks 134 4.5 SPC Weights 134 4.5.1 Obtaining SPC Weights and Drawing Main Paths 135 4.5.2 The Main Path of the Centrality Citation Network 135 4.6 Line Cuts 139 4.7 Line Islands 141 4.7.1 The Main Island 143 4.7.2 A Geophysics and Meteorology Line Island 145 4.7.3 An Optical Network Line Island 150 4.7.4 A Partial Summary of Main Path and Line Island Results 154 4.8 Other Relevant Subnetworks for a Bounded Network 155 4.9 Collaboration Networks 157 4.9.1 Macros for Collaboration Networks 158 4.9.2 An Initial Attempt of Analyses of Collaboration Networks 159 4.10 A Brief Look at the SNA Literature SN5 Networks 160 4.11 On the Centrality and SNA Collaboration Networks 173 References 173 5 Citation Patterns in Temporal United States Patent Data 175 5.1 Patents 175 5.2 Supreme Court Decisions Regarding Patents 179 5.2.1 Co-cited Decisions 179 5.2.2 Citations Between Co-cited Decisions 182 5.3 The 1976--2006 Patent Data 183 5.4 Structural Variables Through Time 184 5.4.1 Temporally Specific Networks 184 5.4.2 Shrinking Specific Patent Citation Networks 186 5.4.3 Structural Properties 187 5.5 Some Patterns of Technological Development 188 5.5.1 Structural Properties of Temporally Specific Networks 190 5.6 Important Subnetworks 193 5.6.1 Line Islands 194 5.6.2 Line Islands with Patents Tagged by Keywords 196 5.6.3 Vertex Islands 201 5.7 Citation Patterns 202 5.7.1 Patents from 1976, Cited Through to 2006 204 5.7.2 Patents from 1987, Cited Through to 2006 209 5.8 Comparing Citation Patterns for Two Time Intervals 211 5.9 Summary and Conclusions 214 6 The US Supreme Court Citation Network 216 6.1 Introduction 217 6.2 Co-cited Islands of Supreme Court Decisions 219 6.3 A Native American Line Island 222 6.3.1 Forced Removal of Native American Populations 222 6.3.2 RegulatingWhites on Native American Lands 224 6.3.3 Curtailing the Authority of Native American Courts 224 6.3.4 Taxing Native Americans and Enforcing External Laws 225 6.3.5 The Presence of Non-Native Americans on Native American Lands 226 6.3.6 Some Later Developments 227 6.3.7 A Partial Summary 227 6.4 A ‘Perceived Threats to Social Order’ Line Island 228 6.4.1 Perceived Threats to Social Order 228 6.4.2 The Structures of the Threats to Social Order Line Island 230 6.4.3 Decisions Involving Communists and Socialists 230 6.4.4 Restrictions of Labor Groups Organizing 236 6.4.5 Restrictions of African Americans Organizing 237 6.4.6 Jehovah’sWitnesses as a Perceived Threat 239 6.4.7 Obscenity as a Threat to Social Order 243 6.5 Other Perceived Threats 246 6.6 The Dred Scott Decision 250 6.6.1 Citations from Dred Scott 251 6.6.2 Citations to Dred Scott 253 6.6.3 Methodological Implications of Dred Scott 260 6.7 Further Reflections on the Supreme Court Citation Network 261 7 Football as the World’s Game 263 7.1 A Brief Historical Overview 264 7.2 Football Clubs 264 7.3 Football Players 266 7.4 Football in England 267 7.5 Player Migrations 268 7.6 Institutional Arrangements and the Organization of Football 269 7.7 Court Rulings 271 7.8 Specific Factors Impacting Football Migration 272 7.9 Some Arguments and Propositions 272 7.10 Some Preliminary Results 278 7.10.1 The Non-English Presence in the EPL 279 7.10.2 Player Fitness 289 7.10.3 Starting Clubs for English Players 292 7.10.4 General Features of the Top Five European Leagues 295 7.10.5 Flows of Footballers into the Top European Leagues 301 7.11 Player Ages When Recruited to the EPL 303 7.12 A Partial Summary of Results 305 8 Networks of Player Movements to the EPL 308 8.1 Success in the EPL 308 8.2 The Overall Presence of Other Countries in the EPL 311 8.3 Network Flows of Footballers Between Clubs to Reach the EPL 312 8.3.1 Moving Directly into the EPL from Local and Non-local Clubs 313 8.3.2 Direct Moves of Players to the EPL from Non-EPL Clubs 315 8.4 Moves from EPL Clubs 318 8.4.1 The 1992--1996 Time Slice Flows with at Least Three Moves 318 8.4.2 The 1997--2001 Time Slice Flows with at Least Three Moves 322 8.4.3 The 2002--2006 Time Slice Flows with at Least Three Moves 323 8.5 Moves Solely Within the EPL 324 8.5.1 Loans 324 8.5.2 Transfers 326 8.6 All Trails of Footballers to the EPL 330 8.6.1 Counted Features of Trails to the EPL 331 8.6.2 Clustering Player Trails 335 8.6.3 Interpreting the Clusters of Player Careers 350 8.7 Summary and Conclusions 350 9 Mapping Spatial Diversity in the United States of America 353 9.1 Mapping Nations as Spatial Units of the United States 354 9.1.1 The Counties of the United States 357 9.2 Representing Networks in Space 359 9.3 Clustering with a Relational Constraint 360 9.3.1 Conditions for Hierarchical Clustering Methods 361 9.3.2 Clustering with a Relational Constraint 363 9.3.3 An AgglomerativeMethod for Relational Constraints 365 9.3.4 Hierarchies 367 9.3.5 Fast Agglomerative Clustering Algorithms 368 9.4 Data for Constrained Spatial Clustering 369 9.4.1 Discriminant Analysis for Garreau’s Nations 369 9.5 Clustering the US Counties with a Spatial Relational Constraint 374 9.5.1 The Eight Garreau Nations in the USA 375 9.5.2 The Ten Woodard Nations in the USA 379 9.6 Summary 381 10 On Studying Large Networks 382 10.1 Substance 382 10.2 Methods, Techniques, and Algorithms 384 10.3 Network Data 385 10.4 Surprises and Issues Triggered by Them 388 10.5 FutureWork 390 10.6 Two Final Comments 393 Appendix: Data Documentation 395 A.1 Bibliographic Networks 395 A.1.1 Centrality Literature Networks 397 A.1.2 SNA Literature 399 A.2 Patent Data 400 A.3 Supreme Court Data 401 A.4 Football Data 403 A.4.1 Core Data 403 A.4.2 Ancillary Data 413 A.5 The USA Spatial County Network 415 References 419 Person Index 428 Subject Index 432

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Book SynopsisGenetic and Evolutionary Computation: Medical Applications provides an overview of the range of GEC techniques being applied to medicine and healthcare in a context that is relevant not only for existing GEC practitioners but also those from other disciplines, particularly health professionals. There is rapidly increasing interest in applying evolutionary computation to problems in medicine, but to date no text that introduces evolutionary computation in a medical context. By explaining the basic introductory theory, typical application areas and detailed implementation in one coherent volume, this book will appeal to a wide audience from software developers to medical scientists. Centred around a set of nine case studies on the application of GEC to different areas of medicine, the book offers an overview of applications of GEC to medicine, describes applications in which GEC is used to analyse medical images and data sets, derive advanced models, and suggest diagnoses and Table of ContentsAbout the Editors. List of Contributors. 1 Introduction. 2 Evolutionary Computation: A Brief Overview (Stefano Cagnoni and Leonardo Vanneschi). 2.1 Introduction. 2.2 Evolutionary Computation Paradigms. 2.2.1 Genetic Algorithms. 2.2.2 Evolution Strategies. 2.2.3 Evolutionary Programming. 2.2.4 Genetic Programming. 2.2.5 Other Evolutionary Techniques. 2.2.6 Theory of Evolutionary Algorithms. 2.3 Conclusions. 3 A Review of Medical Applications of Genetic and Evolutionary Computation (Stephen L. Smith). 3.1 Medical Imaging and Signal Processing. 3.1.1 Overview. 3.1.2 Image Segmentation. 3.1.3 Image Registration, Reconstruction and Correction. 3.1.4 Other Applications. 3.2 Data Mining Medical Data and Patient Records. 3.3 Clinical Expert Systems and Knowledge-based Systems. 3.4 Modelling and Simulation of Medical Processes. 3.5 Clinical Diagnosis and Therapy. 4 Applications of GEC in Medical Imaging. 4.1 Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial-based Shape Deformation (Chris McIntosh and Ghassan Hamarneh). 4.1.1 Introduction. 4.1.1.1 Statistically Constrained Localized and Intuitive Deformations. 4.1.1.2 Genetic Algorithms. 4.1.2 Methods. 4.1.2.1 Population Representation. 4.1.2.2 Encoding the Weights for GAs. 4.1.2.3 Mutations and Crossovers. 4.1.2.4 Calculating the Fitness of Members of the GA Population. 4.1.3 Results. 4.1.4 Conclusions. 4.2 Feature Selection for the Classification of Microcalcifications in Digital Mammograms using Genetic Algorithms, Sequential Search and Class Separability (Santiago E. Conant-Pablos, Rolando R. Hernández-Cisneros, and Hugo Terashima-Marín). 4.2.1 Introduction. 4.2.2 Methodology. 4.2.2.1 Pre-processing. 4.2.2.2 Detection of Potential Microcalcifications (Signals). 4.2.2.3 Classification of Signals into Microcalcifications. 4.2.2.4 Detection of Microcalcification Clusters. 4.2.2.5 Classification of Microcalcification Clusters into Benign and Malignant. 4.2.3 Experiments and Results. 4.2.3.1 From Pre-processing to Signal Extraction. 4.2.3.2 Classification of Signals into Microcalcifications. 4.2.3.3 Microcalcification Clusters Detection and Classification. 4.2.4 Conclusions and Future Work. 4.3 Hybrid Detection of Features within the Retinal Fundus using a Genetic Algorithm (Vitoantonio Bevilacqua, Lucia Cariello, Simona Cambo, Domenico Daleno, and Giuseppe Mastronardi). 4.3.1 Introduction. 4.3.2 Acquisition and Processing of Retinal Fundus Images. 4.3.2.1 Retinal Image Acquisition. 4.3.2.2 Image Processing. 4.3.3 Previous Work. 4.3.4 Implementation. 4.3.4.1 Vasculature Extraction. 4.3.4.2 A Genetic Algorithm for Edge Extraction. 4.3.4.3 Skeletonization Process. 4.3.4.4 Experimental Results. 5 New Analysis of Medical Data Sets using GEC. 5.1 Analysis and Classification ofMammography Reports using Maximum Variation Sampling (Robert M. Patton, Barbara G. Beckerman, and Thomas E. Potok). 5.1.1 Introduction. 5.1.2 Background. 5.1.3 Related Works. 5.1.4 Maximum Variation Sampling. 5.1.5 Data. 5.1.6 Tests. 5.1.7 Results & Discussion. 5.1.8 Summary. 5.2 An Interactive Search for Rules in Medical Data using Multiobjective Evolutionary Algorithms (Daniela Zaharie, D. Lungeanu, and Flavia Zamfirache). 5.2.1 Medical Data Mining. 5.2.2 Measures for Evaluating the Rules Quality. 5.2.2.1 Accuracy Measures. 5.2.2.2 Comprehensibility Measures. 5.2.2.3 Interestingness Measures. 5.2.3 Evolutionary Approaches in Rules Mining. 5.2.4 An Interactive Multiobjective Evolutionary Algorithm for Rules Mining. 5.2.4.1 Rules Encoding. 5.2.4.2 Reproduction Operators. 5.2.4.3 Selection and Archiving. 5.2.4.4 User Guided Evolutionary Search. 5.2.5 Experiments in Medical Rules Mining. 5.2.5.1 Impact of User Interaction. 5.2.6 Conclusions. 5.3 Genetic Programming for Exploring Medical Data using Visual Spaces (Julio J. Valdés, Alan J. Barton, and Robert Orchard). 5.3.1 Introduction. 5.3.2 Visual Spaces. 5.3.2.1 Visual Space Realization. 5.3.2.2 Visual Space Taxonomy. 5.3.2.3 Visual Space Geometries. 5.3.2.4 Visual Space Interpretation Taxonomy. 5.3.2.5 Visual Space Characteristics Examination. 5.3.2.6 Visual Space Mapping Taxonomy. 5.3.2.7 Visual Space Mapping Computation. 5.3.3 Experimental Settings. 5.3.3.1 Implicit Classical Algorithm Settings. 5.3.3.2 Explicit GEP Algorithm Settings. 5.3.4 Medical Examples. 5.3.4.1 Data Space Examples. 5.3.4.2 Semantic Space Examples. 5.3.5 Future Directions. 6 Advanced Modelling, Diagnosis and Treatment using GEC. 6.1 Objective Assessment of Visuo-spatial Ability using Implicit Context Representation Cartesian Genetic Programming (Michael A. Lones and Stephen L. Smith). 6.1.1 Introduction. 6.1.2 Evaluation of Visuo-spatial Ability. 6.1.3 Implicit Context Representation CGP. 6.1.4 Methodology. 6.1.4.1 Data Collection. 6.1.4.2 Evaluation. 6.1.4.3 Parameter Settings. 6.1.5 Results. 6.1.6 Conclusions. 6.2 Towards an Alternative to Magnetic Resonance Imaging for Vocal Tract Shape Measurement using the Principles of Evolution (David M. Howard, Andy M. Tyrrell, and Crispin Cooper). 6.2.1 Introduction. 6.2.2 Oral Tract Shape Evolution. 6.2.3 Recording the Target Vowels. 6.2.4 Evolving Oral Tract Shapes. 6.2.5 Results. 6.2.5.1 Oral Tract Areas. 6.2.5.2 Spectral Comparisons. 6.2.6 Conclusions. 6.3 How Genetic Algorithms can Improve Pacemaker Efficiency (Laurent Dumas and Linda El Alaoui). 6.3.1 Introduction. 6.3.2 Modeling of the Electrical Activity of the Heart. 6.3.3 The Optimization Principles. 6.3.3.1 The Cost Function. 6.3.3.2 The Optimization Algorithm. 6.3.3.3 A New Genetic Algorithm with a Surrogate Model. 6.3.3.4 Results of AGA on Test Functions. 6.3.4 A Simplified Test Case for a Pacemaker Optimization. 6.3.4.1 Description of the Test Case. 6.3.4.2 Numerical Results. 6.3.5 Conclusion. 7 The Future for Genetic and Evolutionary Computation in Medicine: Opportunities, Challenges and Rewards. 7.1 Opportunities. 7.2 Challenges. 7.3 Rewards. 7.4 The Future for Genetic and Evolutionary Computation in Medicine. Appendix: Introductory Books and Useful Links. Index.

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Book SynopsisAn expert guide for applying data mining with uncertain reasoning to a wide range of uses This volume presents a holistic view of data mining by integrating this diverse and exciting field with uncertain reasoning. It treats a wide range of issues and examines the state of the art in both fields while summarizing vital concepts that can normally only be found in various separate resources. The author concentrates on practical aspects of data mining-such as infrastructure and overall processes-but also discusses some selected algorithms and performance-related issues. Several important topics are addressed specifically, such as bridging the fields of machine learning and data mining and the discovery of influential association rules. In addition, the author discusses data warehousing as an enabling technique for data mining. Case studies are included throughout to illustrate important concepts. Data Mining and Uncertain Reasoning is a practical reference for pTable of ContentsWhat This Book Is About. Basics of Data Mining. Enabling Techniques and Advanced Features of Data Mining. Dealing with Uncertainty in Manipulation of Data. Data Mining Tasks for Knowledge Discovery. Bayesian Networks and Artificial Neural Networks. Uncertain Reasoning Techniques for Data Mining. Data Mining Lifecycle with Uncertainty Handling: Case Studies and Software Tools. Intelligent Conceptual Query Answering with Uncertainty: Basic Aspects and Case Studies. References. Index.

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Book SynopsisFrom Oxford''s leading AI researcher comes a fun and accessible tour through the history and future of one of the most cutting edge and misunderstood field in science: Artificial IntelligenceThe somewhat ill-defined long-term aim of AI is to build machines that are conscious, self-aware, and sentient; machines capable of the kind of intelligent autonomous action that currently only people are capable of. As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine scientific breakthroughs that have made AI systems possible in the past decade that the founders of the field would have hailed as miraculous. Driverless cars and automated translation tools are just two examples of AI technologies that have become a practical, everyday reality in the past few years, and which will have a huge impact on our w

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Book SynopsisMany organizations recognize the value and benefits Artificial Intelligence (AI) can bring if implemented correctly. This topic is outlined in the authors’ previous book, Artificial Intelligence: Evolution and Revolution. A long-standing challenge that many organizations continue to face is preparing for AI and making sure that their data and assets are accessible, manageable, and governed and are of the right quality so that they can be consumed by new and existing AI applications in order to infuse AI across the enterprise to help drive smarter business outcomes. Over the years, numerous paradigms and efforts have attempted to address the complexities of managing sprawling and disparate data silos, but all seemed to have fallen short of their promises and expectations. Organizations need the flexibility to put their data and assets where it makes most business sense, whether that’ s on premises or in the private or public cloud. This book attempts to explain the concepts and values that a data fabric approach can deliver to both technical and business communities.

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Book SynopsisBig Data Big Design defines and explores what every designer needs to know about artificial intelligence (AI) and machine learning (ML), all the while inspiring designers to harness this technology and establish leadership via thoughtful, human-centered design. It’s not just about the algorithms, it’s about what we do with the data once received. Ellen lupton says, “ Important and accessible!” Readers will explore the principles and cultural context of Ai and ML, as well as gain an understanding of the design opportunities and pitfalls that arise as designers incorporate predictive algorithms into their practice. Designers will walk away from this portable, friendly book inspired by practical and theoretical knowledge that will allow them to make thoughtful decisions as this technology unfolds.

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Book SynopsisCountless books, news reports, and opinion pieces have announced the impending arrival of artificial intelligence, with most claiming that it will upend our world, revolutionizing not just work but society overall. Yet according to political philosopher and historian David Runciman, we’ve actually been living with a version of AI for 300 years because states and corporations are robots, too. In The Handover, Runciman explains our current situation through the history of these “artificial agents” we created to rescue us from our all-too-human limitations—and demonstrates what this radical new view of our recent past means for our collective future. From the United States and the United Kingdom to the East India Company, Standard Oil, Facebook, and Alibaba, states and corporations have gradually, and then much more rapidly, taken over the planet. They have helped to conquer poverty and eliminate disease, but also unleashed global wars and environmental degradation. As Runciman demonstrates, states and corporations are the ultimate decision-making machines, defined by their ability to make their own choices and, crucially, to sustain the consequences of what has been chosen. And if the rapid spread of the modern state and corporation has already transformed the conditions of human existence, new AI technology promises the same. But what happens when AI interacts with other kinds of artificial agents, the inhuman kind represented by states and corporations? Runciman argues that the twenty-first century will be defined by increasingly intense battles between state and corporate power for the fruits of the AI revolution. In the end, it is not our own, human relationship with AI that will determine our future. Rather, humanity’s fate will be shaped by the interactions among states, corporations, and thinking machines. With clarity and verve, The Handover presents a brilliantly original history of the last three centuries and a new understanding of the immense challenges we now face.Trade Review"[W]itty and refined . . . Runciman’s basic argument, which unfolds in the elegantly shaggy manner of a Peripatetic seminar, is that the alignment problem is not in fact an anomaly, and that the coming singularity might best be historicized as the Second Singularity. . . . he turns a standard argumentative form on its head. It’s not that we can look to the past to help us solve the alignment problems of the future. It’s that the alignment problems of the future help clarify our existing sense that everything is intractable and wrong. . . . Runciman’s point is that the alliance between even a democratic government and a safe-ish A.I. could derail civilization." -- Gideon Lewis-Kraus - New Yorker"Ingenious . . . a well-informed and provocative read about the essence of political power." -- John Thornhill - Financial Times"[A] searching meditation on creeping dehumanization . . . Runciman’s approach to these issues is less technological than social and psychological . . . The result is a shrewd and stimulating look at society’s drive toward an inhuman perfection." -- Publishers Weekly, starred review"A thoughtful, learned contribution to the fevered conversation now surrounding AI." -- Kirkus Reviews"Amid a headlong international panic about a looming robot insurrection, David Runciman offers a searching history of earlier takeovers by other artificial creatures of our own making—states and corporations—and a stirring call for a new and fortified commitment to all that is human." -- Jill Lepore, author of These Truths: A History of the United States"David Runciman is always fascinating." -- Adam Tooze"One of the great modern writers of democracy." -- Anne Applebaum"Surely one of the most luminously intelligent [writers] on politics to have been published for many years." -- New Statesman (UK)

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Book Synopsis With the idea of “deep learning” having now become the key to this new generation of solutions, major technological players in the business intelligence sector have taken an interest in the application of Big Data. In this book, the author explores the recent technological advances associated with digitized data flows, which have recently opened up new horizons for AI. The reader will gain insight into some of the areas of application of Big Data in AI, including robotics, home automation, health, security, image recognition and natural language processing. Table of ContentsList of Figures ix Preface xiii Introduction xxi Chapter 1. What is Intelligence? 1 1.1. Intelligence 1 1.2. Business Intelligence 2 1.3. Artificial Intelligence 5 1.4. How BI has developed 6 1.4.1. BI 1.0 7 1.4.2. BI 2.0 8 1.4.3. And beyond 11 Chapter 2. Digital Learning 13 2.1. What is learning? 13 2.2. Digital learning 14 2.3. The Internet has changed the game 16 2.4. Big Data and the Internet of Things will reshuffle the cards 18 2.5. Artificial Intelligence linked to Big Data will undoubtedly be the keystone of digital learning 21 2.6. Supervised learning 22 2.7. Enhanced supervised learning 24 2.8. Unsupervised learning 28 Chapter 3. The Reign of Algorithms 33 3.1. What is an algorithm? 34 3.2. A brief history of AI 34 3.2.1. Between the 1940s and 1950s 35 3.2.2. Beginning of the 1960s 36 3.2.3. The 1970s 37 3.2.4. The 1980s 37 3.2.5. The 1990s 38 3.2.6. The 2000s 38 3.3. Algorithms are based on neural networks, but what does this mean? 39 3.4. Why do Big Data and AI work so well together? 42 Chapter 4. Uses for Artificial Intelligence 47 4.1. Customer experience management 48 4.1.1. What role have smartphones and tablets played in this relationship? 50 4.1.2. CXM is more than just a software package 51 4.1.3. Components of CXM 53 4.2. The transport industry 55 4.3. The medical industry 58 4.4. “Smart” personal assistant (or agent) 60 4.5. Image and sound recognition 62 4.6. Recommendation tools 65 4.6.1. Collaborative filtering (a “collaborative” recommendation mode) 66 Conclusion 71 Appendices 75 Appendix 1. Big Data 77 Appendix 2. Smart Data 83 Appendix 3. Data Lakes 89 Appendix 4. Some Vocabulary Relevant to 93 Appendix 5. Comparison Between Machine Learning and Traditional Business Intelligence 101 Appendix 6. Conceptual Outline of the Steps Required to Implement a Customization Solution based on Machine Learning 103 Bibliography 107 Glossary 111 Index 115

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Book Synopsis“I love my robot lawn mowers, my laptop, wifi, Google, Facetime, Whatsapp and the possibility of drone postal deliveries and more.. Yet worries nag about being overwhelmed by an artificial intelligence revolution whose ethical and moral parameters are less clear than its rampant profiteering from and monetising of your lives and mine. This hugely informative book shakes us out of our massage armchairs and demands that we engage immediately with these galloping advances so we can shape them to the benefit of the many and not leave them to the enrichment of the few at the awful cost of the impoverishment of swathes of humanity”. Mary McAleese, former President of Ireland. "Robots, Ethics and The Future of Jobs is a wakeup call for political, civic, media and church leaders, urging a response to the deepening and accelerating pace of technological change and its potential consequences. Artificial Intelligence, robotics, drones, the internet of things and 3D printing are the building blocks of the 4th industrial revolution. These technologies offer great potential but also carry real risks and are reaching into every corner of our lives, civilian and military. Who will win and who will lose? Who will set the rules and the ethical boundaries within which they should develop and operate? Will the displaced be included, if so, how; or ignored and, if so, with what political, social and economic consequences? That these questions cannot be avoided and should not be postponed - and that we do not need to wait for change to happen because it is already upon us - are central messages of this thought provoking text." Pat Cox, former President European Parliament.Trade Review‘...required reading for all those who take their faith seriously. This book needs to be widely available, read, and its contents put into practice and earthed.’ Catholic South West Feb21 -- Denis Blackledge SJ * Catholic South West Diocesan magazine *‘...an accessible and thought-provoking look at the potential of new technology to bring about seismic societal change and a call for discourse and decisions to ensure the best outcomes for humanity.’ ICN Feb21 * Independent Catholic News website *‘...this important book...deals with complex ideas ...in refreshingly simple language...I would highly recommend it.’ Brendan Hoban, Western People, Feb21 -- Brendan Hoban * Western People *How should we respond ethically to these developments? All this, and much more, is explored by the author...' -- Nigel Waugh * Church Review *The book ranges widely... it is not limited to truckers and retailers, but also to high-end economic work, from factories in Bangladesh to stockbrokers in New York... an engaging introduction to the subject’ The Irish Catholic Apr21 -- Ruadhán Jones * The Irish Catholic *'...this is the best introduction to these questions yet written for a popular Irish audience. It is accessible to a reader who is coming to these topics completely fresh. McDonagh will deepen your understanding and prompt you to question your assumptions.' -- Kevin Hargaden * Jesuit Centre for Faith & Justice *‘McDonagh draws on the experience of the COVID-19 pandemic to spotlight how people might react to being out of work on a mass scale. [This book] highlights the need to address the potential repercussions of these developing technologies.’ -- Sarah MacDonald * National Catholic Reporter *‘...timely, accessible, well-researched and thought-provoking: this is a thoroughly readable and enjoyable book. It addresses some of the key challenges facing twenty-first century society and should be essential reading for anyone in public life or in education.’ -- Pat O'Mahony * Doctrine & Life *‘...intelligent, critically reflective, and honest. McDonagh writes with clarity and charity. The book is a pleasure to read.’ -- Christopher West * Search Journal *‘clear and accessible, it is an engaging read and a good introduction to the topic for the lay person’ Studies: An Irish Quarterly Review -- Margaret McGaley * Studies: An Irish Quarterly Review *‘...highly informative, provocative and challenging...a must-read, especially for those in leadership roles. A book critical for our times’ The Furrow Oct 21 -- Suzanne Mulligan * The Furrow *

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Book SynopsisMachining, as a reliable manufacturing process, still offers unmatched capabilities in producing high quality three-dimensional parts from metals, polymers, ceramics, wood and composites. Advances in computational modeling and optimization methods enabled researchers to develop cost effective and high throughput modern machining processes. This book aims to provide recent advances intelligent machining for modern manufacturing engineering. It includes six chapters that provide basic fundamentals, modern machining processes, analytical and mechanistic modeling approaches, finite element modeling and systems based modeling, recent optimization methods and case studies.

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Book SynopsisThe new digital media offers us an unprecedented memory capacity, an ubiquitous communication channel and a growing computing power. How can we exploit this medium to augment our personal and social cognitive processes at the service of human development? Combining a deep knowledge of humanities and social sciences as well as a real familiarity with computer science issues, this book explains the collaborative construction of a global hypercortex coordinated by a computable metalanguage. By recognizing fully the symbolic and social nature of human cognition, we could transform our current opaque global brain into a reflexive collective intelligence.Table of ContentsAcknowledgements xv Chapter 1. General Introduction 1 1.1. The vision: to enhance cognitive processes 2 1.2. A transdisciplinary intellectual adventure 5 1.3. The result: toward hypercortical cognition 27 1.4. General plan of this book 35 PART 1. THE PHILOSOPHY OF INFORMATION 37 Chapter 2. The Nature of Information 41 2.1. Orientation 41 2.2. The information paradigm 45 2.3. Layers of encoding 56 2.4. Evolution in information nature 66 2.5. The unity of nature 69 Chapter 3. Symbolic Cognition 75 3.1. Delimitation of the field of symbolic cognition76 3.2. The secondary reflexivity of symbolic cognition 78 3.3. Symbolic power and its manifestations 80 3.4. The reciprocal enveloping of the phenomenal world and semantic world 82 3.5. The open intelligence of culture 84 3.6. Differences between animal and human collective intelligence 85 Chapter 4. Creative Conversation 89 4.1. Beyond “collective stupidity” 89 4.2. Reflexive explication and sharing of knowledge 92 4.3. The symbolic medium of creative conversation 103 Chapter 5. Toward an Epistemological Transformation of the Human Sciences 113 5.1. The stakes of human development 113 5.2. Critique of the human sciences 120 5.3. The threefold renewal of the human sciences 125 5.4. The Ouroboros 133 Chapter 6. The Information Economy 135 6.1. The symbiosis of knowledge capital and cognitive labor 136 6.2. Toward scientific self-management of collective intelligence 140 6.3. Flows of symbolic energy 144 6.4. Ecosystems of ideas and the semantic information economy 148 6.5. The semantic information economy in the digital medium 154 PART 2. MODELING COGNITION 159 Chapter 7. Introduction to the Scientific Knowledge of the Mind 161 7.1. Research program 161 7.2. The mind in nature 165 7.3. The three symbolic functions of the cortex 171 7.4. The IEML model of symbolic cognition. 176 7.5. The architecture of the Hypercortex 184 7.6. Overview: toward a reflexive collective intelligence 187 Chapter 8. The Computer Science Perspective: Toward a Reflexive Intelligence 189 8.1. Augmented collective intelligence 189 8.2. The purpose of automatic manipulation of symbols: cognitive modeling and self-knowledge 194 8.3. The means of automatic manipulation of symbols: beyond probabilities and logic 202 Chapter 9. General Presentation of the IEML Semantic Sphere 207 9.1. Ideas 208 9.2. Concepts 213 9.3. Unity and calculability 217 9.4. Symmetry 220 9.5. Internal coherence 225 9.6. Inexhaustible complexity 230 Chapter 10. The IEML Metalanguage 235 10.1. The problem of encoding concepts 235 10.2. Text units 238 10.3. Circuits of meaning 241 10.4. Between text and circuits 244 Chapter 11. The IEML Semantic Machine 253 11.1. Overview of the functions involved in symbolic cognition 253 11.2. Requirements for the construction of the IEML semantic machine 258 11.3. The IEML textual machine (S) 261 11.4. The STAR (Semantic Tool for Augmented Reasoning) linguistic engine (B) 264 11.5. The conceptual machine (T) 267 11.6. Conclusion 270 Chapter 12. The Hypercortex 275 12.1. The role of media and symbolic systems in cognition 275 12.2. The digital medium 277 12.3. The evolution of the layers of addressing in the digital medium 284 12.4. Between the Cortex and the Hypercortex 289 12.5. Toward an observatory of collective intelligence 291 12.6. Conclusion: the computability and interoperability of semantic and hermeneutic functions 296 Chapter 13. Hermeneutic Memory 299 13.1. Toward a semantic organization of memory 299 13.2. The layers of complexity of memory 302 13.3. Radical hermeneutics 304 13.4. The hermeneutics of information 308 13.5. The hermeneutics of knowledge 312 13.6. Wisdom 317 13.7. Collective interpretation games 318 Chapter 14. The Perspective of the Humanities: Toward Explicit Knowledge 323 14.1. Context 323 14.2. Methodology: the digital humanities 327 14.3. Epistemology: explicating symbolic cognition 331 Chapter 15. Observing Collective Intelligence 341 15.1. The semantic sphere as a mirror of concepts 341 15.2. The structure of the cognitive image 346 15.3. The two eyes of reflexive observation 350 Bibliography 353 Index 377

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Book SynopsisThis book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields. For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which metaheuristics are adapted, while the second tackles various transportation problems related to the organization of production systems. Many real-world applications are presented by the authors, making this an invaluable resource for researchers and students in engineering, economics, mathematics and computer science. Contents 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times, Mansour Eddaly, Bassem Jarboui, Radhouan Bouabda, Patrick Siarry and Abdelwaheb Rebaï. 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems, Imed Kacem. 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints, Hanen Akrout, Bassem Jarboui, Patrick Siarry and Abdelwaheb Rebaï. 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags, Emna Dhouib, Jacques Teghem, Daniel Tuyttens and Taïcir Loukil. 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search, Marie-Eléonore Marmion. 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints, Nadia Chaaben, Racem Mellouli and Faouzi Masmoudi. 7. Models and Methods in Graph Coloration for Various Production Problems, Nicolas Zufferey. 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties, Mustapha Ratli, Rachid Benmansour, Rita Macedo, Saïd Hanafi, Christophe Wilbaut. 9. Metaheuristics for Biobjective Flow Shop Scheduling, Matthieu Basseur and Arnaud Liefooghe. 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem, Caroline Gagné, Arnaud Zinflou and Marc Gravel. 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance, Ali Berrichi and Farouk Yalaoui. 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling, Fouzia Ounnar, Patrick Pujo and Afef Denguir. 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem, Olfa Dridi, Saoussen Krichen and Adel Guitouni. 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context, Tienté Hsu, Gilles Gonçalves and Rémy Dupas. 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities, Virginie André, Nathalie Grangeon and Sylvie Norre. 16. Vehicle Routing Problems with Scheduling Constraints, Rahma Lahyani, Frédéric Semet and Benoît Trouillet. 17. Metaheuristics for Job Shop Scheduling with Transportation, Qiao Zhang, Hervé Manier, Marie-Ange Manier. About the Authors Bassem Jarboui is Professor at the University of Sfax, Tunisia. Patrick Siarry is Professor at the Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), University of Paris-Est Créteil, France. Jacques Teghem is Professor at the University of Mons, Belgium.Table of ContentsIntroduction and Presentation xv Bassem JARBOUI, Patrick SIARRY and Jacques TEGHEM Chapter 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times 1 Mansour EDDALY, Bassem JARBOUI, Radhouan BOUABDA, Patrick SIARRY and Abdelwaheb REBAÏ 1.1. Introduction 1 1.2. Mathematical formulation 3 1.3. Estimation of distribution algorithms 5 1.3.1. Estimation of distribution algorithms proposed in the literature 6 1.4. The proposed estimation of distribution algorithm 8 1.4.1. Encoding scheme and initial population 8 1.4.2. Selection 9 1.4.3. Probability estimation 9 1.5. Iterated local search algorithm 10 1.6. Experimental results 11 1.7. Conclusion 15 1.8. Bibliography 15 Chapter 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems 19 Imed KACEM 2.1. Introduction 19 2.2. Flexible job shop scheduling problems 19 2.3. Genetic algorithms for some related sub-problems 25 2.4. Genetic algorithms for the flexible job shop problem 31 2.4.1. Codings 31 2.4.2. Mutation operators 34 2.4.3. Crossover operators 38 2.5. Comparison of codings 42 2.6. Conclusion 43 2.7. Bibliography 43 Chapter 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints 45 Hanen AKROUT, Bassem JARBOUI, Patrick SIARRY and Abdelwaheb REBAÏ 3.1. Introduction 45 3.2. Overview of the literature 47 3.2.1. Single-solution metaheuristics 47 3.2.2. Population-based metaheuristics 49 3.2.3. Hybrid approaches 49 3.3. Description of the problem 50 3.4. GRASP 52 3.5. Differential evolution 53 3.6. Iterative local search 55 3.7. Overview of the NEW-GRASP-DE algorithm 55 3.7.1. Constructive phase 56 3.7.2. Improvement phase 57 3.8. Experimental results 57 3.8.1. Experimental results for the Reeves and Heller instances 58 3.8.2. Experimental results for the Taillard instances 60 3.9. Conclusion 62 3.10. Bibliography 64 Chapter 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags 69 Emna DHOUIB, Jacques TEGHEM, Daniel TUYTTENS and Taïcir LOUKIL 4.1. Introduction 69 4.2. Description of the problem 70 4.2.1. Flowshop with time lags 70 4.2.2. A bicriteria hierarchical flow shop problem 71 4.3. The proposed metaheuristics 73 4.3.1. A simulated annealing metaheuristics 74 4.3.2. The GRASP metaheuristics 77 4.4. Tests 82 4.4.1. Generated instances 82 4.4.2. Comparison of the results 83 4.5. Conclusion 94 4.6. Bibliography 94 Chapter 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search 97 Marie-Eléonore MARMION 5.1. Introduction 97 5.2. Neutrality in a combinatorial optimization problem 98 5.2.1. Landscape in a combinatorial optimization problem 99 5.2.2. Neutrality and landscape 102 5.3. Study of neutrality in the flow shop problem 106 5.3.1. Neutral degree 106 5.3.2. Structure of the neutral landscape 108 5.4. Local search exploiting neutrality to solve the flow shop problem 112 5.4.1. Neutrality-based iterated local search 113 5.4.2. NILS on the flow shop problem 116 5.5. Conclusion 122 5.6. Bibliography 123 Chapter 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints 127 Nadia CHAABEN, Racem MELLOULI and Faouzi MASMOUDI 6.1. Introduction 127 6.2. Overview of the literature 128 6.3. Overview of the problem and notations used 131 6.4. Mathematical formulations 133 6.4.1. First formulation (MILP1) 133 6.4.2. Second formulation (MILP2) 135 6.4.3. Third formulation (MILP3) 137 6.5. A genetic algorithm: model and methodology 139 6.5.1. Coding used for our algorithm 139 6.5.2. Generating the initial population 140 6.5.3. Selection operator 142 6.5.4. Crossover operator 142 6.5.5. Mutation operator 144 6.5.6. Insertion operator 144 6.5.7. Evaluation function: fitness 144 6.5.8. Stop criterion 145 6.6. Verification and validation of the genetic algorithm 145 6.6.1. Description of benchmarks 145 6.6.2. Tests and results 146 6.7. Conclusion 148 6.8. Bibliography 148 Chapter 7. Models and Methods in Graph Coloration for Various Production Problems 153 Nicolas ZUFFEREY 7.1. Introduction 153 7.2. Minimizing the makespan 155 7.2.1. Tabu algorithm 155 7.2.2. Hybrid genetic algorithm 157 7.2.3. Methods prior to GH 158 7.2.4. Extensions 159 7.3. Maximizing the number of completed tasks 160 7.3.1. Tabu algorithm 161 7.3.2. The ant colony algorithm 162 7.3.3. Extension of the problem 164 7.4. Precedence constraints 165 7.4.1. Tabu algorithm 168 7.4.2. Variable neighborhood search method 169 7.5. Incompatibility costs 171 7.5.1. Tabu algorithm 173 7.5.2. Adaptive memory method 175 7.5.3. Variations of the problem 177 7.6. Conclusion 178 7.7. Bibliography 179 Chapter 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties 183 Mustapha RATLI, Rachid BENMANSOUR, Rita MACEDO, Saïd HANAFI, Christophe WILBAUT 8.1. Introduction 183 8.2. Properties and particular cases 185 8.3. Mathematical models 188 8.3.1. Linear models with precedence variables 188 8.3.2. Linear models with position variables 192 8.3.3. Linear models with time-indexed variables 194 8.3.4. Network flow models 197 8.3.5. Quadratic models 197 8.3.6. A comparative study 199 8.4. Heuristics 203 8.4.1. Properties 207 8.4.2. Evaluation 209 8.5. Metaheuristics 211 8.6. Conclusion 217 8.7. Acknowledgments 218 8.8. Bibliography 218 Chapter 9. Metaheuristics for Biobjective Flow Shop Scheduling 225 Matthieu BASSEUR and Arnaud LIEFOOGHE 9.1. Introduction 225 9.2. Metaheuristics for multiobjective combinatorial optimization 226 9.2.1. Main concepts 227 9.2.2. Some methods 229 9.2.3. Performance analysis 232 9.2.4. Software and implementation 237 9.3. Multiobjective flow shop scheduling problems 238 9.3.1. Flow shop problems 239 9.3.2. Permutation flow shop with due dates 240 9.3.3. Different objective functions 241 9.3.4. Sets of data 241 9.3.5. Analysis of correlations between objectives functions 242 9.4. Application to the biobjective flow shop 243 9.4.1. Model 244 9.4.2. Solution methods 246 9.4.3. Experimental analysis 246 9.5. Conclusion 249 9.6. Bibliography 250 Chapter 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem 253 Caroline GAGNÉ, Arnaud ZINFLOU and Marc GRAVEL 10.1. Introduction 253 10.2. Industrial car sequencing problem 255 10.3. Pareto strategies for solving the CSP 260 10.3.1. PMSMO 260 10.3.2. GISMOO 264 10.4. Numerical experiments 268 10.4.1. Test sets 269 10.4.2. Performance metrics 270 10.5. Results and discussion 271 10.6. Conclusion 279 10.7. Bibliography 280 Chapter 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance 283 Ali BERRICHI and Farouk YALAOUI 11.1. Introduction 283 11.2. State of the art on the joint problem 285 11.3. Integrated modeling of the joint problem 287 11.4. Concepts of multi-objective optimization 291 11.5. The particle swarm optimization method 292 11.6. Implementation of MOPSO algorithms 294 11.6.1. Representation and construction of the solutions 294 11.6.2. Solution Evaluation 295 11.6.3. The proposed MOPSO algorithms 298 11.6.4. Updating the velocities and positions 299 11.6.5. Hybridization with local searches 300 11.7. Experimental results 302 11.7.1. Choice of test problems and configurations 302 11.7.2. Experiments and analysis of the results 303 11.8. Conclusion 310 11.9. Bibliography 311 Chapter 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling 315 Fouzia OUNNAR, Patrick PUJO and Afef DENGUIR 12.1. Introduction 315 12.2. Methods for solving multicriteria scheduling 316 12.2.1. Optimization methods 316 12.2.2. Multicriteria decision aid methods 318 12.2.3. Choice of the multicriteria decision aid method 319 12.3. Presentation of the AHP method 320 12.3.1. Phase 1: configuration 320 12.3.2. Phase 2: exploitation 321 12.4. Evaluation of metaheuristics for the configuration of AHP 322 12.4.1. Local search methods 323 12.4.2. Population-based methods 324 12.4.3. Advanced metaheuristics 326 12.5. Choice of metaheuristic 326 12.5.1. Justification of the choice of genetic algorithms 326 12.5.2. Genetic algorithms 328 12.6. AHP optimization by a genetic algorithm 330 12.6.1. Phase 0: configuration of the structure of the problem 331 12.6.2. Phase 1: preparation for automatic configuration 332 12.6.3. Phase 2: automatic configuration 334 12.6.4. Phase 3: preparation of the exploitation phase 335 12.7. Evaluation of G-AHP 336 12.7.1. Analysis of the behavior of G-AHP 336 12.7.2. Analysis of the results obtained by G-AHP 342 12.8. Conclusions 343 12.9. Bibliography 344 Chapter 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem 349 Olfa DRIDI, Saoussen KRICHEN and Adel GUITOUNI 13.1. Introduction 349 13.2. Description and formulation of the problem 350 13.3. Literature review 353 13.3.1. Exact methods 354 13.3.2. Approximate methods 355 13.4. A multicriteria genetic algorithm for the MMSAP 356 13.4.1. Encoding variables 357 13.4.2. Genetic operators 358 13.4.3. Parameter settings 359 13.4.4. The GA 360 13.5. Experimental study 361 13.5.1. Diversification of the approximation set based on the diversity indicators 364 13.6. Conclusion 369 13.7. Bibliography 369 Chapter 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context 373 Tienté HSU, Gilles GONÇALVES and Rémy DUPAS 14.1. Introduction 373 14.2. Dynamic vehicle route management 375 14.2.1. The vehicle routing problem with time windows 377 14.3. Platform for the solution of the DVRPTW 382 14.3.1. Encoding a chromosome 384 14.4. Treating uncertainties in the orders 386 14.5. Treatment of traffic information 392 14.6. Conclusion 397 14.7. Bibliography 398 Chapter 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities 401 Virginie ANDRÉ, Nathalie GRANGEON and Sylvie NORRE 15.1. Knowledge model 403 15.1.1. Fixed resources and mobile resources 403 15.1.2. Modelling the activities in steps 404 15.1.3. The problem to be solved 406 15.1.4. Illustrative example 407 15.2. Solution procedure 410 15.3. Proposed approach 413 15.3.1. Metaheuristics 414 15.3.2. Simulation model 421 15.4. Implementation and results 422 15.4.1. Impact on the work mode 423 15.4.2. Results of the set of modifications to the teaching hospital 425 15.4.3. Preliminary study of the choice of shifts 428 15.5. Conclusion 430 15.6. Bibliography 431 Chapter 16. Vehicle Routing Problems with Scheduling Constraints 433 Rahma LAHYANI, Frédéric SEMET and Benoît TROUILLET 16.1. Introduction 433 16.2. Definition, complexity and classification 435 16.2.1. Definition and complexity 435 16.2.2. Classification 436 16.3. Time-constrained vehicle routing problems 438 16.3.1. Vehicle routing problems with time windows 438 16.3.2. Period vehicle routing problems 441 16.3.3. Vehicle routing problem with cross-docking 443 16.4. Vehicle routing problems with resource availability constraints 448 16.4.1. Multi-trip vehicle routing problem 448 16.4.2. Vehicle routing problem with crew scheduling 450 16.5. Conclusion 452 16.6. Bibliography 453 Chapter 17. Metaheuristics for Job Shop Scheduling with Transportation 465 Qiao ZHANG, Hervé MANIER, Marie-Ange MANIER 17.1. General flexible job shop scheduling problems 466 17.2. State of the art on job shop scheduling with transportation resources 468 17.3. GTSB procedure 474 17.3.1. A hybrid metaheuristic algorithm for the GFJSSP 474 17.3.2. Tests and results 480 17.3.3. Conclusion for GTSB 489 17.4. Conclusion 491 17.5. Bibliography 491 List of Authors 495 Index 499

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Book SynopsisThis book presents a theory of consciousness which is unique and sustainable in nature, based on physiological and cognitive-linguistic principles controlled by a number of socio-psycho-economic factors. In order to anchor this theory, which draws upon various disciplines, the author presents a number of different theories, all of which have been abundantly studied by scientists from both a theoretical and experimental standpoint, including models of social organization, ego theories, theories of the motivational system in psychology, theories of the motivational system in neurosciences, language modeling and computational modeling of motivation. The theory presented in this book is based on the hypothesis that an individual’s main activities are developed by self-motivation, managed as an informational need. This is described in chapters covering self-motivation on a day-to-day basis, the notion of need, the hypothesis and control of cognitive self-motivation and a model of self-motivation which associates language and physiology. The subject of knowledge extraction is also covered, including the impact of self-motivation on written information, non-transversal and transversal text-mining techniques and the fields of interest of text mining. Contents: 1. Consciousness: an Ancient and Current Topic of Study. 2. Self-motivation on a Daily Basis. 3. The Notion of Need. 4. The Models of Social Organization. 5. Self Theories. 6. Theories of Motivation in Psychology. 7. Theories of Motivation in Neurosciences. 8. Language Modeling. 9. Computational Modeling of Motivation. 10. Hypothesis and Control of Cognitive Self-Motivation. 11. A Model of Self-Motivation which Associates Language and Physiology. 12. Impact of Self-Motivation on Written Information. 13. Non-Transversal Text Mining Techniques. 14. Transversal Text Mining Techniques. 15. Fields of Interest for Text Mining. About the Authors Nicolas Turenne is a researcher at INRA in the Science and Society team at the University of Paris-Est Marne la Vallée in France. He specializes in knowledge extraction from texts with theoretical research into relational and stochastic models. His research topics also concern the sociology of uses, food and environmental sciences, and bioinformatics.Table of ContentsIntroduction xi Acknowledgements xiii Chapter 1. Consciousness: an Ancient and Current Topic of Study 1 1.1. Multidisciplinarity of the subject 1 1.2. Terminological outlook 2 1.3. Theological point of view 4 1.4. Notion of belief and autonomy 5 1.5. Scientific schools of thought 6 1.6. The question of experience 7 Chapter 2. Self-motivation on a Daily Basis 9 2.1. In news blogs 9 2.2. Marketing 9 2.3. Appearance 10 2.4. Mystical experiences 11 2.5. Infantheism 11 2.6. Addiction 11 Chapter 3. The Notion of Need 15 3.1. Hierarchy of needs 15 3.1.1. Level-1 needs 16 3.1.2. Level-3 needs 17 3.2. The satiation cycle 18 Chapter 4. The Models of Social Organization 21 4.1. The entrepreneurial model 21 4.2. Motivational and ethical states 23 Chapter 5. Self Theories 29 Chapter 6. Theories of Motivation in Psychology 33 6.1. Behavior and cognition 33 6.2. Theory of self-efficacy 34 6.3. Theory of self-determination 38 6.4. Theory of control 39 6.5. Attribution theory 39 6.6. Standards and self-regulation 42 6.7. Deviance and pathology 47 6.8. Temporal Motivation Theory 48 6.9. Effect of objectives 49 6.10. Context of distance learning 49 6.11. Maintenance model 49 6.12. Effect of narrative 49 6.13. Effect of eviction 50 6.14. Effect of the teacher–student relationship 50 6.15. Model of persistence and change 50 6.16. Effect of the man–machine relationship 51 Chapter 7. Theories of Motivation in Neurosciences 53 7.1. Academic literature on the subject 53 7.2. Psychology and Neurosciences 53 7.3. Neurophysiological theory 54 7.4. Relationship between the motivational system and the emotions 56 7.5. Relationship between the motivational system and language 58 7.6. Relationship between the motivational system and need 59 Chapter 8. Language Modeling 61 8.1. Issues surrounding language 61 8.2. Interaction and language 61 8.3. Development and language 62 8.4. Schools of thought in linguistic sciences 62 8.5. Semantics and combination 68 8.6. Functional grammar 68 8.7. Meaning-Text Theory 69 8.8. Generative lexicon 70 8.9. Theory of synergetic linguistics 70 8.10. Integrative approach to language processing 71 8.11. New spaces for date production 73 8.12. Notion of ontology 75 8.13. Knowledge representation 76 Chapter 9. Computational Modeling of Motivation 81 9.1. Notion of a computational model 81 9.2. Multi-agent systems 81 9.3. Artificial self-organization 85 9.4. Artificial neural networks 87 9.5. Free will theorem 88 9.6. The probabilistic utility model 89 9.7. The autoepistemic model 91 Chapter 10. Hypothesis and Control of Cognitive Self-Motivation 93 10.1. Social groups 93 10.2. Innate self-motivation 95 10.3. Mass communication 96 10.4. The Cost–Benefit ratio 97 10.5. Social representation 98 10.6. The relational environment 99 10.7. Perception 100 10.8. Identity 100 10.9. Social environment 101 10.10. Historical antecedence 102 10.11. Ethics 102 Chapter 11. A Model of Self-Motivation which Associates Language and Physiology 105 11.1. A new model 105 11.2. Architecture of a self-motivation subsystem 106 11.3. Level of certainty 108 11.4. Need for self-motivation 108 11.5. Notion of motive 109 11.6. Age and location 113 11.7. Uniqueness 113 11.8. Effect of spontaneity 114 11.9. Effect of dependence 114 11.10. Effect of emulation 115 11.11. Transition of belief 115 11.12. Effect of individualism 117 11.13. Modeling of the groups of beliefs 117 Chapter 12. Impact of Self-Motivation on Written Information 123 12.1. Platform for production and consultation of texts 123 12.2. Informational measure of the motives of self-motivation 124 12.2.1. Intra-phrastic extraction 125 12.2.2. Inter-phrastic extraction 126 12.2.3. Meta-phrastic extraction 128 12.3. The information market 129 12.4. Types of data 130 12.5. The outlines of text mining 133 12.6. Software economy 139 12.7. Standards and metadata 139 12.8. Open-ended questions and challenges for text-mining methods 140 12.9. Notion of lexical noise 141 12.10. Web mining 143 12.11. Mining approach 145 Chapter 13. Non-Transversal Text Mining Techniques 147 13.1. Constructivist activity 147 13.2. Typicality associated with the data 148 13.3. Specific character of text mining 148 13.4. Supervised, unsupervised and semi-supervised techniques 149 13.5. Quality of a model 149 13.6. The scenario 149 13.7. Representation of a datum 150 13.8. Standardization 151 13.9. Morphological preprocessing 152 13.10. Selection and weighting of terminological units 153 13.11. Statistical properties of textual units: lexical laws 154 13.12. Sub-lexical units 155 13.14. Shallow parsing or superficial syntactic analysis 157 13.15. Argumentation models 158 Chapter 14. Transversal Text Mining Techniques 159 14.1. Mixed and interdisciplinary text mining techniques 159 14.1.1. Supervised, unsupervised and semi-supervised techniques 159 14.2. Techniques for extraction of named entities 160 14.3. Inverse methods 163 14.4. Latent Semantic Analysis 164 14.5. Iterative construction of sub-corpora 165 14.6. Ordering approaches or ranking method 165 14.7. Use of ontology 166 14.8. Interdisciplinary techniques 167 14.9. Information visualization techniques 167 14.10. The k-means technique 168 14.11. Naive Bayes classifier technique 169 14.12. The k-nearest neighbors (KNN) technique 170 14.13. Hierarchical clustering technique 171 14.14. Density-based clustering techniques 172 14.15. Conditional fields 175 14.16. Nonlinear regression and artificial neural networks 176 14.17. Models of multi-agent systems (MASs) 177 14.18. Co-clustering models 178 14.19. Dependency models 179 14.20. Decision tree technique 179 14.21. The Support Vector Machine (SVM) technique 180 14.22. Set of frequent items 182 14.23. Genetic algorithms 184 14.24. Link analysis with a theoretical graph model 184 14.25. Link analysis without a graph model 185 14.26. Quality of a model 186 14.27. Model selection 189 Chapter 15. Fields of Interest for Text Mining 191 15.1. The avenues in text mining 191 15.1.1. Organization 191 15.1.2. Discovery 193 15.2. About decision support 194 15.3. Competitive intelligence (vigilance) 195 15.4. About strategy 197 15.5. About archive management 200 15.6. About sociology and the legal field 203 15.7. About biology 215 15.8. About other domains 219 Conclusion 221 Bibliography 225 Index 267

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Book Synopsis'Artificial intelligence is going to have a massive impact on everyone’s lives... an accessible and sensible read that helps demystify AI' Deborah Meaden, entrepreneur and star of Dragon's Den'Nigel Toon is a visionary leader in the field of artificial intelligence... a must-read' Marc Tremblay, Distinguished Engineer, MicrosoftThose who understand how AI thinks are about to win big.We are used to thinking of computers as being a step up from calculators - very good at storing information, and maybe even at playing a logical game like chess. But up to now they haven't been able to think in ways that are intuitive, or respond to questions as a human might. All that has changed, dramatically, in the past few years.Our search engines are becoming answer engines. Artificial intelligence is already revolutionising sectors from education to healthcare to the creative arts. But how does an AI understand sentiment or context? How does it play and win games that have an almost infinite number of moves? And how can we work with AI to produce insights and innovations that are beyond human capacity, from writing code in an instant to unfolding the elaborate 3D puzzles of proteins?We stand at the brink of a historic change that will disrupt society and at the same time create enormous opportunities for those who understand how AI thinks. Nigel Toon shows how we train AI to train itself, so that it can paint images that have never existed before or converse in any language. In doing so he reveals the strange and fascinating ways that humans think, too, as we learn how to live in a world shared by machine intelligences of our own creation.Trade ReviewFew books are more timely than How AI Thinks, an accessible guide that walks the reader through the technology’s developmental history right back to the days before the computer... This is a fascinating read. -- Simon Hunt * Evening Standard *I believe that AI is going to have a massive impact on everyone’s lives; it’s such a hugely important topic that we can’t just leave it to technologists and governments to think about. Business people, teachers, students and parents - everyone needs to learn more about it. In How AI Thinks, Nigel Toon provides us with an accessible and sensible read that helps demystify AI and lets us all understand more about this incredibly powerful tool. -- Deborah Meaden, entrepreneur and star of Dragon's DenNigel Toon is not only a visionary leader in the field of artificial intelligence, but also a captivating storyteller who takes us on a journey through his own fascinating history and the evolution of our young industry. He has a gift for explaining complex concepts in simple terms, making this book accessible and engaging for anyone interested in AI. He also offers a prescriptive and optimistic view of the future of AI, showing how it can transform our lives and society for the better. This book is a must-read for anyone who wants to understand the past, present and future of artificial intelligence. -- Marc Tremblay PhD, Distinguished Engineer, MicrosoftAn insightful, informative, inspiring book which takes the reader on a journey of discovery, it ultimately paints a hopeful and reasoned vision of how humanity can move on from a position of fear and trepidation, and embrace AI, deriving profound benefit from all it makes possible. Nigel has a skill in taking highly technical content and making AI not just comprehensible, but also engaging. -- Professor Evelyn Welch, Vice-Chancellor and President, University of BristolAs a business leader, it was great to have all the strands that have created AI pulled together. Nigel Toon synthesizes everything so clearly, simply and in such an inspiring way. How AI Thinks delivers the perspective that leaders and politicians need so that they can regulate AI well. -- Sir Andrew MacKenzie, Chairman of Shell

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Book Synopsis1.Transforming Software Development: From Traditional Methods to Generative Artificial Intelligence.- 2.Case Study: Transforming Operational and Organizational Efficiency Using Artificial Intelligence and Machine Learning.- 3.Revolutionizing Software Development: The Transformative Influence of Machine Learning Integrated SDLC Model.- 4.Generative Coding: Unlocking Ontological AI.- 5.Case Studies: Machine Learning Approaches for Software Development Effort Estimation.- 6.Hybridizing Metaheuristics and Analogy-based Methods with Ensemble Learning for Improved Software Cost Estimation.- 7.A Review on Detection of Design Pattern in Source Code Using Machine Learning Techniques.- 8.Machine Learning Techniques for the Measurement of Software Attributes.- 9.An Effective Analysis of New Metaheuristic Algorithms and its Performance Comparison.- 10.Empowering Software Security: Leveraging Machine Learning for Anomaly Detection and Threat Prevention.- 11.Sentiment Analysis on Movie Reviews Using the Convolutional LSTM (Co-LSTM) Model.- 12.An Overview of AI Workload Optimization Techniques.- 13.Opportunity Discovery for Effective Innovation Using Artificial Intelligence.- 14.Applications of Machine Learning Algorithms in Open Innovation.

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Book SynopsisThis book highlights new trends and challenges in intelligent systems, which play an important part in the digital transformation of many areas of science and practice. It includes papers offering a deeper understanding of the human-centred perspective on artificial intelligence, of intelligent value co-creation, ethics, value-oriented digital models, transparency, and intelligent digital architectures and engineering to support digital services and intelligent systems, the transformation of structures in digital businesses and intelligent systems based on human practices, as well as the study of interaction and the co-adaptation of humans and systems. All papers were originally presented at the International KES Conference on Human Centred Intelligent Systems 2020 (KES HCIS 2020), held on June 17–19, 2020, in Split, Croatia.

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Book SynopsisThis beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will Learn Understand what anomaly detection is, why it it is important, and how it is applied Grasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorch Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.Table of Contents

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Book SynopsisThis book will teach you the underlying concepts of large language models (LLMs), as well as the technologies associated with them. The book starts with an introduction to the rise of conversational AIs such as ChatGPT, and how they are related to the broader spectrum of large language models. From there, you will learn about natural language processing (NLP), its core concepts, and how it has led to the rise of LLMs. Next, you will gain insight into transformers and how their characteristics, such as self-attention, enhance the capabilities of language modeling, along with the unique capabilities of LLMs. The book concludes with an exploration of the architectures of various LLMs and the opportunities presented by their ever-increasing capabilities—as well as the dangers of their misuse. After completing this book, you will have a thorough understanding of LLMs and will be ready to take your first steps in implementing them into your own projects. What You Will Learn Grasp the underlying concepts of LLMs Gain insight into how the concepts and approaches of NLP have evolved over the years Understand transformer models and attention mechanisms Explore different types of LLMs and their applications Understand the architectures of popular LLMs Delve into misconceptions and concerns about LLMs, as well as how to best utilize them Who This Book Is For Anyone interested in learning the foundational concepts of NLP, LLMs, and recent advancements of deep learningTable of ContentsChapter 1: Introduction.- Chapter 2: NLP Through the Ages.- Chapter 3: Transformers.- Chapter 4: What Makes LLMs Large?.- Chapter 5: Popular LLMs.- Chapter 6: Threats, Opportunities, and Misconceptions.

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Book SynopsisUnderstand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation. This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies. The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements. What You Will LearnDevelop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processingKnow the significance of design patterns in creating robust intelligent solutionsUnderstand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systemsBe aware of the ethical implications of scalable AI systemsWho This Book Is ForAI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience

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Book SynopsisThis transformative book explores the power of artificial intelligence (AI) in revolutionizing the utilities industry. It covers crucial topics such as intelligent grids, decentralized energy resources, customer engagement, electric vehicle integration, and more, providing a comprehensive and practical guide to successfully navigate the energy transition. In today's world, the urgency of addressing climate change and transitioning to sustainable energy systems is undeniable. With approximately 60 percent of global greenhouse gas emissions attributed to the energy sector, utilities play a vital role in achieving sustainability goals. The traditional utility business model faces disruption from renewable energy, changing consumer expectations, and regulatory shifts. Embracing AI emerges as a key solution to optimize operations, enhance grid reliability, and meet evolving customer demands. Through compelling case studies and industry-specific use cases, you will discover how AI drives innovation, improves operational efficiency, and contributes to a greener and more sustainable world. As the demand for cleaner and more sustainable energy practices grows, this book demonstrates how AI can support utilities in meeting these demands, making them more resilient, agile, and customer-centric. Whether you're a seasoned industry expert or a curious student, this book equips you with the knowledge and insights to embrace sustainability, navigate the complex energy landscape, leverage AI to shape a positive future, and join the movement towards a greener world, empowered by AI's potential in the utilities industry. What You Will LearnUnderstand the challenges and opportunities for utilities in the context of climate change, energy poverty, and the evolving business landscapeDiscover how rapid transformation is needed in the utilities sector to overcome challenges and leverage opportunities for a sustainable futureGain insight into the role of technology, particularly artificial intelligence (AI), as a critical tool for utilities in their transformation journeyBe aware of how AI can be applied in building the future utility industry, including its potential impact on energy efficiency, intelligent energy ecosystems, community engagement, and new business modelsGain knowledge of the adoption of AI and machine learning technologies in the utility industry, including the current state, barriers, significant influencing factors, and an AI adoption maturity model for utilitiesRecognize the sustainability imperative for utilities and how AI can help in achieving sustainable energy practicesBecome familiar with the transformation of power generation, microgrids, intelligent transmission and distribution systems, utilities retail, mobility through electric vehicles, and the integration of distributed energy resources(DER) using AIGain insight into the potential of AI in addressing challenges and driving innovation in the energy ecosystem, such as optimizing power generation assets, enhancing grid intelligence, improving customer service, and enabling clean energy awareness in the metaverseWho This Book Is ForProfessionals and decision makers in the global utilities industry who want to leverage artificial intelligence (AI) technologies to transform their operations and address challenges and opportunities in the energy sector. This book may also appeal to researchers, academics, and students in the fields of energy engineering, environmental science, data analytics, and AI who want to gain a deeper understanding of AI in the utilities sector and its implications for sustainable energy systems.

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Book SynopsisExplore the capabilities of ChatGPT and gain insight on how to utilize this AI tool in your daily tasks, and marketing endeavors. This book introduces ChatGPT, covering its architecture, training process, and applications across various fields. Start by delving into the benefits of integrating ChatGPT into everyday routines, emphasizing its potential to streamline tasks, optimize time management, and provide valuable insights that can revolutionize individuals' work approaches. You'll then look more closely at ChatGPT's mechanics, its capabilities, limitations, and unique features. The book also outlines the best practices for utilizing ChatGPT, offering practical tips, techniques, and strategies to enhance output quality and reliability, while minimizing errors and maximizing results. You'll focus on ChatGPT's relevance in marketing tasks, such as generating product descriptions, creating email templates, automating social media posts, and addressing customer inquiries. The book concludes by exploring techniques for marketing with ChatGPT, including integration with other tools, data analysis, reporting, and customizing ChatGPT to meet specific marketing needs. In the end, you'll have the knowledge and skills needed to leverage ChatGPT's AI marketing capabilities and to harness its power for success in the digital age. What You'll LearnUnderstand the concepts and workings of ChatGPT, its architecture, and the training processApply the best practices for ChatGPTCreate email templates and automate social media posts using ChatGPTUse ChatGPT for data analysis and reportingWho This Book Is ForMarketing professionals, business owners and entrepreneurs, content creators, and customer service representatives

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Book SynopsisThis book will help you comprehend the impact of artificial intelligence (AI) on information security, data privacy, and data security. The book starts by explaining the basics and setting the goals for a complete understanding of how AI, Information Security, Data Privacy, and Data Security all connect.

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Book SynopsisChapter 1: Introduction to Machine Learning Applications Development and the UN SDGs.- Chapter 2: Utilizing Machine Learning Algorithms for Power generation prediction and classification in Wind Farms.- Chapter 3: Crop Recommendation System Using Machine Learning Algorithms for achieving SDGs 2, 9, and 12.- Chapter 4: Aligning Manufacturing Emissions with SDGs 9 and 13 Using Machine Learning Algorithms.- Chapter 5: Water Potability Testing Using Machine Learning.- Applying Machine Learning for Air Quality Monitoring Targeting SDG 3 and 13.- Chapter 7: Clustering the Development of Worldwide Internet Connectivity with Unsupervised Learning for SDGs 7, 9, and 11.

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Book SynopsisChapter 1: Evolution and Significance of Large Language Models.- Chapter 2: What Are Large Language Models?.- Chapter 3: Python for LLMs.- Chapter 4: Python and Other Programming Approaches.- Chapter 5: Basic overview of the components of the LLM architectures.- Chapter 6: Applications of LLMs in Python.- Chapter 7: Harnessing Python 3.11 and Python Libraries for LLM Development.

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Book SynopsisThis book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others. The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications. By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing. What You Will LearnBe able to answer the question: What are Large Language Models?Understand techniques such asprompt engineering, fine-tuning, RAG, and vector databasesKnowthe best practices for effective implementationKnow the metrics and frameworks essential for evaluating the performance of Large Language ModelsWho This Book Is ForAn essential resource for AI-ML developers and enthusiasts eager to acquire practical, hands-on experience in this domain; also applies to individuals seeking a technical understanding of Large Language Models (LLMs) and those aiming to build applications using LLMs

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Book SynopsisAs artificial intelligence (AI) rapidly transforms education, tools like ChatGPT and Claude are revolutionizing the way we teach and learn. This book is a groundbreaking book that empowers parents and students to navigate this exciting new frontier, filling a critical gap in the current literature. As the first comprehensive guide to generative AI in education designed for parents and students, Generative AI in Education is positioned to become an indispensable resource. It provides the knowledge and strategies needed to effectively integrate AI into their learning journeys, transforming educational outcomes and preparing students for success in a rapidly changing world.You'll gain a deep understanding of how tools like ChatGPT and Claude work, and how they can be leveraged to support learning across various subjects and grade levels. You'll then see how to create clear, specific, and engaging prompts that elicit valuable responses from AI-powered tools. This book contains all the techniques for tailoring prompts to different learning objectives, styles, and contexts, and how they can use AI tools to support reading comprehension, writing skills, problem-solving, and creative thinking.What You Will LearnApply generative AI in educationCraft effective prompts for personalized learning experiencesUtilize AI tools to support learning, creativity, and problem-solvingWho This Book is ForParents and students who are eager to harness the power of generative AI to enhance learning experiences and prepare for success in an AI-driven future

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Book SynopsisPart 1: Defining Your AI Application.- Chapter 1: Human Flexibility and AI Specialization.- Chapter 2: Meta Systems.- Chapter 3: Prediction Machines.- Part 2: Designing Your AI Application.- Chapter 4: Anatomy of an AI Application.- Chapter 5: Data, Machine Learning, and Reasoners.- Chapter 6: Large Language Models (LLMs).- Chapter 7: AI Agents.- Part 3: Maintaining Your AI Application.- Chapter 8: Testing Your Enterprise AI Application.- Chapter 9: Testing automation for enterprise ai applications.- Chapter 10: Security.- Chapter 11: Information Curation.- Part 4: AI Enabled Teams.- Chapter 12: Remote Work and Reskilling.- Chapter 13: Expert Personas.- Chapter 14: The Role of the AI Handler.- Chapter 15: Legal and Ethical Considerations.

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Book SynopsisThis is a comprehensive exploration into the world of Artificial Intelligence, designed to bridge the gap between theoretical concepts and practical, real-world applications. This book unravels the mystique of AI, breaking down its components into understandable elements. From the early dawn of AI's inception to its current state of rapid evolution, we cover the essential building blocks necessary for leveraging AI in business, and personal development, and understanding its broader impacts on society. Through an engaging conversational format, readers are guided through the intricacies of AI, covering topics such as machine learning, AI governance,, data security, and the ethical challenges facing AI today. This book is an invaluable resource for those looking to understand the fundamentals of AI, its practical applications, and its significant implications for the future. After reading this book, you will be able to integrate AI into your business strategies and learn the intricacies of AI advancements. What You Will Learn:Key concepts and definitions within AI, including types of AI, machine learning, and neural networks and how they are utilized in AI apps like M365 CopilotPractical applications of AI for personal and business growth, focusing on the pillars of using AI to evolve these fronts effectively and sustainablyHow AI is transforming businesses and what organizational shifts must be made to realize the valueNavigating the challenges and ethical considerations in AI to ensure informed and responsible usageWho This Book Is For:Professionals looking to integrate AI into their business strategies or organizations.

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Book SynopsisThis comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible. As advances in Generative AI such as large language models accelerate, optimizing these resource-intensive workloads for efficiency and alignment with human values grows increasingly urgent. The book starts with the concept of Generative AI and its wide-ranging applications, while also delving into the environmental impact of AI workloads and the growing importance of adopting sustainable AI practices. It then delves into the fundamentals of efficient AI workload management, providing insights into understanding AI workload characteristics, measuring performance, and identifying bottlenecks and inefficiencies. Hardware optimization strategies are explored in detail, covering the selection of energy-efficient hardware, leveraging specialized AI accelerators, and optimizing hardware utilization and scheduling for sustainable operations. You are also guided through software optimization techniques tailored for Generative AI, including efficient model architecture, compression, and quantization methods, and optimization of software libraries and frameworks. Data management and preprocessing strategies are also addressed, emphasizing efficient data storage, cleaning, preprocessing, and augmentation techniques to enhance sustainability throughout the data life cycle. The book further explores model training and inference optimization, cloud and edge computing strategies for Generative AI, energy-efficient deployment and scaling techniques, and sustainable AI life cycle management practices, and concludes with real-world case studies and best practicesBy the end of this book, you will take away a toolkit of impactful steps you can implement to minimize the environmental harms and ethical risks of Generative AI. For organizations deploying any type of generative model at scale, this essential guide provides a blueprint for developing responsible AI systems that benefit society. What You Will LearnUnderstand how Generative AI can be more energy-efficient through improvements such asmodel compression, efficient architecture, hardware optimization, and carbon footprint trackingKnowthe techniques to minimize data usage, includingevaluation, filtering, synthesis, few-shot learning, and monitoring data demands over timeUnderstand spanning efficiency, data minimization, and alignment for comprehensive responsibilityKnow the methods for detecting, understanding, and mitigating algorithmic biases, ensuring diversity in data collection, and monitoring model fairnessWho This book Is ForProfessionals seeking to adopt responsible and sustainable practices in their Generative AI work; leaders and practitioners who need actionable strategies and recommendations that can be implemented directly in real-world systems and organizational workflows; ML engineers and data scientists building and deploying Generative AI systems in industry settings; and researchers developing new generative AI techniques, such as at technology companies or universities

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Book SynopsisChapter 1: Decoding Ecommerce: Business Models for Delivering Value.- Chapter 2: Ecommerce Platform: Journey from Click to Conversion.- Chapter 3: Ecommerce Merchandising: Presenting Curated Products.- Chapter 4: Ecommerce Search: Matching Query to Products.- Chapter 5: Recommendations: Creating Curated Choices.- Chapter 6: Ranking Algorithms: The Science of Sorting.- Chapter 7: Personalization: AI-crafted Personalized Experiences.- Chapter 8: Efficiency: Efficient Ecommerce Deliveries.

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Book SynopsisUnlock the future of software development and empower yourself to elevate your Python applications by harnessing the power of AI as this field continues to grow and evolve. Perfect for beginner to intermediate Python programmers, this book breaks down the essentials of using ChatGPT and OpenAI APIs. You'll start with the basics, learning to authenticate, send prompts, generate responses, test in the Playground, and handle errors with ease. Each chapter includes hands-on exercises that bring concepts to life, demonstrating different API functionalities and practical applications. You'll master models like GPT-4o, GPT-4, GPT-3.5, Whisper, and DALL-E, enabling you to enhance your applications with cutting-edge AI. Discover how generative AI tools like ChatGPT can automate tedious tasks rather than replace jobs. Leverage ChatGPT's powerful Natural Language Processing (NLP) capabilities to handle various formats of unstructured text within your Python apps. Quickly see how easy it is to use ChatGPT as your AI-pair programmer, boosting your productivity and speed. This step-by-step guide will have you creating intelligent chatbots that can automatically process messages from Slack or Discord. With Beginning ChatGPT for Python, you'll master the ChatGPT and OpenAI APIs, building intelligent applications that offer a personalized and engaging user experience. What You'll LearnConnect with the ChatGPT and OpenAI APIs and send effective prompts. Harness parameters like temperature and top_p to create unique and engaging responses from ChatGPT. Create an intelligent assistant bot for Slack that automates tasks and enhances productivity. Develop a bot that can moderate conversations and manage communities on Discord. Add context to your prompts to get more accurate and relevant responses. Who This Book Is ForPython developers and enthusiasts who aspire to employ OpenAI and ChatGPT in the creation of intelligent applications to enhance productivity.

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Book SynopsisChapter 1: Unraveling the Basics of Generative AI.- Chapter 2: Exploring the Transformative Potential of Generative AI.- Chapter 3: Revolutionizing Content: Generative AI in Marketing and Advertising.- Chapter 4: Elevating Customer Interactions with Generative AI.- Chapter 5: Streamlining Operations through Generative AI.- Chapter 6: Pioneering Products with Generative AI.- Chapter 7: Charting the Course: Strategies for Successful Generative AI Implementation.- Chapter 8: Navigating Risks and Legalities of Generative AI.- Chapter 9: Quantifying Success: Evaluating the ROI of Generative AI Initiatives.- Chapter 10: Looking Ahead: Preparing for the Future of Generative AI.

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Book SynopsisAs leaders plan to make significant investments to harness the power of foundational models such as ChatGPT, they need to understand the changes in organizational behaviors required for the successful implementation of such systems. The size, complexity and nature of this new wave of technologies requires a refresh in roles and responsibilities in conventional IT organizations. This book reveals practical and no-nonsense guidance on how to leverage generative AI to transform your business processes and organizational structures to achieve breakthroughs in efficiency, effectiveness and competitive advantage. Written in a lively, engaging, and often humorous style, this work provides practical insights and timely survival skills for leaders with anonymous but real-world experiences and case studies. If you're looking to understand how large language foundation models such as ChatGPT are reshaping managerial roles and organizational structures, and how they can leverage this knowledge to survive and thrive in this brave new world then Organizing for Generative AI and the Productivity Revolution is the book for you. What You Will Learn Review the key changes in current state roles and responsibilities that are required to successfully deploy generative AI systemsExamine the organizational reporting structures and associated incentives that form a strong generative AI systemUnderstand the financial, regulatory, and operational risks created by organizational behavioral issues that arise when organizations build and deploy large language modelsCompare the strategic differences in emerging versus traditional organizational behaviors, incentives, roles and responsibilitiesWho This Book Is ForExecutives and team leaders at enterprises large and small. The book addresses an important topic: how to set up an organization for success, particularly in Generative AI. Generative AI brings new challenges to organizations in terms of how to structure the organization for success, mitigating risks, software development lifecycle, and tracking ROI. I could think of no better person to tackle these issues than Arthur O'Connor, who has extensive experience in technology within some of the largest enterprises in Wall Street, complemented by his academic background. He brings an insightful and unique perspective for technology leaders who want to set up their organizations for success in Generative AI.- Joseph Sabelja, Generative AI Firmwide Platform Lead, J P Morgan

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