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R 763.00
Enterprise Guide for Implementing Generative AI and Agentic AI
Generative AI and Agentic AI together are revolutionizing the technology landscape, with profound and far-reaching impacts across industries. Organizations are increasingly adopting these technologies to drive innovation, enhance unstructured content management, and improve problem-solving capabilities. With Agentic AI, enterprises are moving towards the development of intelligent systems that can plan, reason, and act with autonomy. While early proof-of-concepts (POCs) demonstrated the potential of these technologies, the current shift is toward responsible and scalable production implementations that leverage both generative and agentic capabilities.This book begins by guiding you through the technological evolution of AI, from early machine learning to today's large language models (LLMs) and agentic systems. It then explores a wide range of use cases across industries, highlighting how LLMs can support decision-making, and how Agentic AI enables dynamic, collaborative systems that act with autonomy and intent. This is followed by Design Patterns across the lifecycle of AI solution development, deployment and monitoring. Readers will then gain insights into the methodologies for developing and deploying Generative and Agentic AI solutions at an enterprise level. A featured implementation demonstrates how Agentic AI can be effectively put into action.The book also introduces essential concepts such as MLOps, LLMOps, and Responsible AI principles which are critical for transitioning the AI solutions from experimentation to production. These principles ensure that AI deployments are scalable, secure, ethical and compliant. The book concludes with key takeaways and best practices for developing, evaluating, deploying and scaling AI applications responsibly and effectively within enterprise settings. You Will: Understand key design patterns to develop, deploy and monitor a Generative AI solution effectively.Learn how to develop and implement a production-ready Agentic AI use case.Discover best practices for building scalable, secure, and enterprise-grade AI solutions.Understand how to assess and mitigate risks using Responsible AI principles and LLMOps best practices. This book is for: Enterprise Software Engineers and Architects
R 912.00
R 489.00
Computational Modeling of Narrative
The field of narrative (or story) understanding and generation is one of the oldest in natural language processing (NLP) and artificial intelligence (AI), which is hardly surprising, since storytelling is such a fundamental and familiar intellectual and social activity. In recent years, the demands of interactive entertainment and interest in the creation of engaging narratives with life-like characters have provided a fresh impetus to this field. This book provides an overview of the principal problems, approaches, and challenges faced today in modeling the narrative structure of stories. The book introduces classical narratological concepts from literary theory and their mapping to computational approaches. It demonstrates how research in AI and NLP has modeled character goals, causality, and time using formalisms from planning, case-based reasoning, and temporal reasoning, and discusses fundamental limitations in such approaches. It proposes new representations for embedded narratives and fictional entities, for assessing the pace of a narrative, and offers an empirical theory of audience response. These notions are incorporated into an annotation scheme called NarrativeML. The book identifies key issues that need to be addressed, including annotation methods for long literary narratives, the representation of modality and habituality, and characterizing the goals of narrators. It also suggests a future characterized by advanced text mining of narrative structure from large-scale corpora and the development of a variety of useful authoring aids. This is the first book to provide a systematic foundation that integrates together narratology, AI, and computational linguistics. It can serve as a narratology primer for computer scientists and an elucidation of computational narratology for literary theorists. It is written in a highly accessible manner and is intended for use by a broad scientific audience that includes linguists (computational and formal semanticists), AI researchers, cognitive scientists, computer scientists, game developers, and narrative theorists. Table of Contents: List of Figures / List of Tables / Narratological Background / Characters as Intentional Agents / Time / Plot / Summary and Future Directions
R 1,125.20
Hybrid Mind
The Hybrid Mind: The Human-AI Convergence explores the transformative potential of humans and AI systems collaborating to revolutionize risk management, oversight, and adaptive governance. Introducing Agentic AI—systems capable of autonomous decision-making while preserving human authority—the book outlines a hybrid inference architecture that combines Large and Small Language Models, machine learning, and probabilistic reasoning.Structured around the OODAF (Observe, Orient, Decide, Act, Feedback) loop, the book offers accessible explanations of intelligent system behavior and its implications. Use cases in finance, healthcare, cybersecurity, and SaaS environments demonstrate how hybrid intelligence enables scalable yet accountable operations, providing practical insights for the AI era.It introduces original frameworks, including Human ETHOS, the Experimentum Crucis Technology Matrix (ECTM), and the Hybrid Audit Model (HAM), that reimagine ethical design, system evaluation, and real-time auditability. Chapters explore topics such as feedback architecture, ethical interruption, decision traceability, and context framing, critical elements for governing AI with integrity.Designed for business leaders, technologists, and policymakers, The Hybrid Mind serves as both a strategic guide and a moral blueprint for the AI era. It equips readers to lead in a world where intelligence is shared, automation is consequential, and responsibility must be architected, not assumed.
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R 2,901.00
Generative AI in Education
As 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-solving Who 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
R 1,140.00
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
Fuzzy Logic is a type of computational intelligence that recognises degrees of true and false, allowing decisions to be made without complete information. Fuzzy Logic is behind many of the computer-driven systems we use every day - from lifts to washing machines.;Data mining is the variety of techniques used for finding useful information in large databases, and is becoming increasingly important as the number of database-driven operations grows at a phenomenal rate. Many company customer service systems are data-base-driven, as are many web sites.;This work covers data mining from a computational intelligence viewpoint: how to use Fuzzy Logic to discover and reveal the data you want, making intelligent decisions that will take mining further than it has traditionally been able to go.;This book should interest researchers and practitioners working in organisations with large databases.
R 2,176.00
Seismic Reservoir Modeling
Seismic reservoir characterization aims to build 3-dimensional models of rock and fluid properties, including elastic and petrophysical variables, to describe and monitor the state of the subsurface for hydrocarbon exploration and production and for CO₂ sequestration. Rock physics modeling and seismic wave propagation theory provide a set of physical equations to predict the seismic response of subsurface rocks based on their elastic and petrophysical properties. However, the rock and fluid properties are generally unknown and surface geophysical measurements are often the only available data to constrain reservoir models far away from well control. Therefore, reservoir properties are generally estimated from geophysical data as a solution of an inverse problem, by combining rock physics and seismic models with inverse theory and geostatistical methods, in the context of the geological modeling of the subsurface. A probabilistic approach to the inverse problem provides the probability distribution of rock and fluid properties given the measured geophysical data and allows quantifying the uncertainty of the predicted results. The reservoir characterization problem includes both discrete properties, such as facies or rock types, and continuous properties, such as porosity, mineral volumes, fluid saturations, seismic velocities and density. Seismic Reservoir Modeling: Theory, Examples and Algorithms presents the main concepts and methods of seismic reservoir characterization. The book presents an overview of rock physics models that link the petrophysical properties to the elastic properties in porous rocks and a review of the most common geostatistical methods to interpolate and simulate multiple realizations of subsurface properties conditioned on a limited number of direct and indirect measurements based on spatial correlation models. The core of the book focuses on Bayesian inverse methods for the prediction of elastic petrophysical properties from seismic data using analytical and numerical statistical methods. The authors present basic and advanced methodologies of the current state of the art in seismic reservoir characterization and illustrate them through expository examples as well as real data applications to hydrocarbon reservoirs and CO₂ sequestration studies.
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R 860.00
Generative AI with SAP and Amazon Bedrock
Explore Generative AI and understand its key concepts, architecture, and tangible business use cases. This book will help you develop the skills needed to use SAP AI Core service features available in the SAP Business Technology Platform.You’ll examine large language model (LLM) concepts and gain the practical knowledge to unleash the best use of Gen AI. As you progress, you’ll learn how to get started with your own LLM models and work with Generative AI use cases. Additionally, you’ll see how to take advantage Amazon Bedrock stack using AWS SDK for ABAP.To fully leverage your knowledge, Generative AI with SAP and Amazon Bedrock offers practical step-by-step instructions for how to establish a cloud SAP BTP account model and create your first GenAIartifacts. This work is an important prerequisite for those who want to take full advantage of generative AI with SAP.What You Will LearnMaster the concepts and terminology of artificial intelligence and GenAIUnderstand opportunities and impacts for different industries with GenAIBecome familiar with SAP AI Core, Amazon Bedrock, AWS SDK for ABAP and develop your firsts GenAI projectsAccelerate your development skillsGain more productivity and time implementing GenAI use casesWho this Book Is ForAnyone who wants to learn about Generative AI for Enterprise and SAP practitioners who want to take advantage of AI within the SAP ecosystem to support their systems and workflows.
R 1,026.00
Cognitive Modeling
Cognitive Modeling is the first book to provide students with an easy-to understand introduction to the basic methods used to build and test cognitive models. Authors Jerome R. Busemeyer and Adele Diederich answer many of the questions that researchers face when beginning work on cognitive models, such as the following: What makes a cognitive model different from conceptual or statistical models? How do you develop such a model? How can you derive qualitatively different predictions between two cognitive models? Focusing on a few key representations, the authors introduce a basic problem in each chapter, illustrate the concept with three examples, and end with a summary of general principles, making this book by far the most accessible cognitive modeling book on the market. Key Features Emphasizes modeling by presenting the tools needed to build a cognitive model, rather than simply reviewing existing models of cognition Provides tutorial presentations of psychological, mathematical, statistical, and computational methods used in all areas of cognitive modeling Includes detailed examples applied to real cognitive models published in the literature in a variety of areas, including recognition, categorization, decision making, and learning Stresses the importance of designing the right conditions for evaluating models Addresses the issues of individual differences in cognitive modeling head-on Cognitive Modeling is ideal for students and researchers across the various domains of cognitive sciences, including perception, learning, decision making, and inference. It is intended for use in upper-level undergraduate and graduate courses such as Cognition/Cognitive Modeling, Cognitive Science, Cognitive Psychology, Quantitative Methods, and Mathematical Modeling in Psychology.
R 4,907.47
Colored Petri Nets for Modeling of Discrete Systems
This book offers a practical approach to modeling real-world discrete systems and performing analysis. The theory behind the book is the Colored Petri nets, and the tool used for simulations is general-purpose Petri net simulator (GPenSIM). The theory part is kept to a minimum, and more explanation is given to the GPenSIM functions and how they can be used for modeling, simulation, and performance analysis. Real-world industrial systems possess a large number of resources. Modeling these systems with Petri nets would become a problem as they result in huge Petri net models. This book offers a unique solution to this problem by moving resources away from the Petri net model and treating them as variables in the background. Also, the theory and practice put forward in the book help cost estimation of the systems (e.g., manufacturing subsystems).
R 6,594.00
Modeling for Insight
Praise for Modeling for Insight "Most books on modeling are either too theoretical or too focused on the mechanics of programming. Powell and Batt's emphasis on using simple spreadsheet models to gain business insight (which is, after all, the name of the game) is what makes this book stand head and shoulders above the rest. This clear and practical book deserves a place on the shelf of every business analyst." —Jonathan Koomey, PhD, Lawrence Berkeley National Laboratory and Stanford University, author of Turning Numbers into Knowledge: Mastering the Art of Problem Solving Most business analysts are familiar with using spreadsheets to organize data and build routine models. However, analysts often struggle when faced with examining new and ill-structured problems. Modeling for Insight is a one-of-a-kind guide to building effective spreadsheet models and using them to generate insights. With its hands-on approach, this book provides readers with an effective modeling process and specific modeling tools to become a master modeler. The authors provide a structured approach to problem-solving using four main steps: frame the problem, diagram the problem, build a model, and generate insights. Extensive examples, graduated in difficulty, help readers to internalize this modeling process, while also demonstrating the application of important modeling tools, including: Influence diagrams Spreadsheet engineering Parameterization Sensitivity analysis Strategy analysis Iterative modeling The real-world examples found in the book are drawn from a wide range of fields such as financial planning, insurance, pharmaceuticals, advertising, and manufacturing. Each chapter concludes with a discussion on how to use the insights drawn from these models to create an effective business presentation. Microsoft Office Excel and PowerPoint are used throughout the book, along with the add-ins Premium Solver, Crystal Ball, and Sensitivity Toolkit. Detailed appendices guide readers through the use of these software packages, and the spreadsheet models discussed in the book are available to download via the book's related Web site. Modeling for Insight is an ideal book for courses in engineering, operations research, and management science at the upper-undergraduate and graduate levels. It is also a valuable resource for consultants and business analysts who often use spreadsheets to better understand complex problems.
R 4,264.00
Grundlagen Der Generativen Syntax
Das Buch führt grundlegend in die neuere generative Syntax ein. Ausgehend von Chomskys (1981) Prinzipien- und Parametertheorie werden die Weiterwicklung des Parameterbegriffs (Roberts 2019) und das Minimalistische Programm (Chomsky 2005) behandelt, das wegen der angenommenen Interaktion unterschiedlicher Module sprachlichen Wissens mit anderen Modellen wie der Optimalitätstheorie kompatibel ist. Der Band fasst zentrale Entwicklungsschritte syntaktischer Theoriebildung zusammen und diskutiert deren Anwendbarkeit auf die vergleichende Analyse romanischer Daten. Behandelt werden Themenfelder wie Wortstellung und Informationsstruktur, Pronomina, Adjektivstellung, sog. quirky subjects und flektierte Infinitive. Zudem werden Kaynes (1994) Antisymmetriehypothese und Rizzis (1997) Feinstruktur der Linken Satzperipherie diskutiert. Die Darstellung orientiert sich an in der universitären Lehre erprobten Konzepten und erlaubt sowohl die Verwendung des Bandes in Anfängerkursen als auch die vertiefende Nutzung durch Fortgeschrittene. Die Neuauflage bezieht neben dem Französischen, Italienischen und Spanischen auch das Portugiesische mit ein, wobei die Darstellung der europäischen und brasilianischen Varietät gleichberechtigt verpflichtet ist.
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Theory Of Cognitive Chasms: Delivering Business Value From Genai
This comprehensive book provides a research-backed framework explaining why GenAI adoptions routinely fail to deliver business value across enterprises. Based on a grounded theory approach, it identifies four major failure modes, or the Cognitive Chasms, that represent exponential risks to adoption.The scope details the complete causal framework, including antecedents, moderators, consequents and the core mediator, Cognitive Decision-Making, which marks the boundary condition of successful GenAI adoptions. The volume also proposes a Multistage Scaling Strategy (a dynamic roadmap for enterprise adoption) and essential Cognitive Mindsets (attitudes and beliefs integral to GenAI adoption) to successfully cross the cognitive chasms.Serving as a prescriptive field manual, this useful handbook benefits professionals, researchers, academics, postgraduate and graduate students in the fields of AI, technology/information management, organization behavior and business strategy and management.
R 5,056.00
Generative AI for Transformational Management
The business world today is changing at a breakneck pace. Traditional management practices need help keeping up with the uncertainties and complexities of the digital age. Leaders face a lot of pressure to innovate, adapt, and drive transformative change within their organizations. However, they need more than just conventional wisdom to navigate this terrain. A deep understanding of emerging technologies like artificial intelligence (AI) and their practical applications in management is essential. Generative AI for Transformational Management offers a compelling solution to these challenges. This book provides a roadmap for leveraging AI to drive organizational transformation by exploring the intersection of generative AI and visionary leadership. By examining real-world case studies and practical applications, readers can learn how AI can be integrated into leadership practices to promote innovation and proactive decision-making and effectively navigate the complexities of the digital age. This book goes beyond theoretical exploration to be a practical guide that empowers leaders to harness AI's full potential in driving transformative outcomes. By providing a comprehensive understanding of generative AI and its impact on management, Generative AI for Transformational Management equips readers with the knowledge and tools they need to thrive in today's dynamic business environment.
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R 764.00
Speech Prosody in Speech Synthesis: Modeling and generation of prosody for high quality and flexible speech synthesis
The volume addresses issues concerning prosody generation in speech synthesis, including prosody modeling, how we can convey para- and non-linguistic information in speech synthesis, and prosody control in speech synthesis (including prosody conversions). A high level of quality has already been achieved in speech synthesis by using selection-based methods with segments of human speech. Although the method enables synthetic speech with various voice qualities and speaking styles, it requires large speech corpora with targeted quality and style.Accordingly, speech conversion techniques are now of growing interest among researchers. HMM/GMM-based methods are widely used, but entail several major problems when viewed from the prosody perspective; prosodic features cover a wider time span than segmental features and their frame-by-frame processing is not always appropriate. The book offers a good overview of state-of-the-art studies on prosody in speech synthesis.
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R 672.00
Regulating Hate Speech Created by Generative AI
Regulating Hate Speech Created by Generative AI explores the new hybrid space of Human Machine Interaction (HMI) in which hate speech is represented and computed through algorithms and AI generative systems. The book is exploratory because there are still many problem-solving challenges to be faced. It is also innovative because it is not assuming that solutions lie only in technological advancements but on a broader scale. In this sense, large language models can and are being considered from a holistic view (i.e., from the different dimensions and layers of regulatory and legal governance). Highlights of the book include:Generative AI and social engines of hateAn introduction to generative Artificial Intelligence application, trends, and ethicsThe mechanics and validation of generative AI outcomesAn evaluation of Generative AI for hate speech detectionBest practices and key considerations for AI regulationUsing GenAI capabilities for early detection of threats in the digital environment.This book is a hard look at ways to regulate generative AI to reduce online hate and secure justice in a digital environment. Featuring research and offering practical guidelines, the book examines guidelines for regulating generative AI models, so they do not contribute to online hate disinformation and imagery.
R 7,970.30
Generative Complexity in a Complex Generative World
This book introduces a refreshing approach to twenty-first-century scientific approach in an age, which is also known as the Century of Complexity. It deals with the deep problem of complexity, being operative from the bottom-up. The current lack of understanding of complexity has led scholars into the so-called embarrassment of complexity. A long overdue paradigm shift is necessary to address complexity as generative complexity and brings readers to the edge of a scientific revolution: that is, a generative revolution in the Century of Complexity. The book offers a radical shift of paradigm from the paradigm of simplifying into the new generative paradigm of complexifying about processes that develop from the bottom-up. The book links complex generative reality with a corresponding radical new generative nature of order and explores new fronts in science. This book explores innovative concepts of interaction, of causality, of the unit of study, and of reality itself and enables readers to see complexity as generative, emergent complexity as being operative from the bottom-up. The book discusses and suggests solutions for the problem of complexity in this Century of Complexity. The author provides a new understanding of complexity based on a generative flux of forces and relations. The book aims to bring about a fundamental and foundational change in how we view and ‘do’ science for an interdisciplinary audience of academics ranging from social science and humanities to economy and biology.
R 3,550.68
Modeling and Simulation of Complex Systems
Robert Siegfried presents a framework for efficient agent-based modeling and simulation of complex systems. He compares different approaches for describing structure and dynamics of agent-based models in detail. Based on this evaluation the author introduces the “General Reference Model for Agent-based Modeling and Simulation” (GRAMS). Furthermore he presents parallel and distributed simulation approaches for execution of agent-based models –from small scale to very large scale. The author shows how agent-based models may be executed by different simulation engines that utilize underlying hardware resources in an optimized fashion.
R 3,663.00
Modeling and Managing Shallow Lake Eutrophication
In the late 1970s. the adverse effects of man-made eutrophication became manifest in many countries. which explains. perhaps. why there was such a broad interest when the former Resources and Environment Area of the International Institute for Applied Systems Analysis (IIASA) organized a workshop on the subject. There was such an enthusiasm among the partici pants that two further workshops were quickly organized. one on deep and the other on shallow lake eutrophication problems. The organization of these meetings was extremely stimulating. and the round table discussions among scientists from both West and East remain thought provoking for those who took part. The general feeling emerged that the complexity and multifaceted nature of the problem, even though perhaps not fully recognized at that time. clearly demanded a systems analysis approach. No wonder. then. that the request made by the Hungarian Member Organization of nASA to adopt Lake Balaton as a "real life laboratory" for an nASA case study fell on fertile ground. the more so since it appeared that shallow lake eutrophication had received less attention and was less well understood than that of deep lakes. And so the nASA Lake Balaton Case Study began. with the appointment of Gerrit van Straten as the first leader of the project.
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Credit Risk Modeling
Credit risk is today one of the most intensely studied topics in quantitative finance. This book provides an introduction and overview for readers who seek an up-to-date reference to the central problems of the field and to the tools currently used to analyze them. The book is aimed at researchers and students in finance, at quantitative analysts in banks and other financial institutions, and at regulators interested in the modeling aspects of credit risk. David Lando considers the two broad approaches to credit risk analysis: that based on classical option pricing models on the one hand, and on a direct modeling of the default probability of issuers on the other. He offers insights that can be drawn from each approach and demonstrates that the distinction between the two approaches is not at all clear-cut. The book strikes a fruitful balance between quickly presenting the basic ideas of the models and offering enough detail so readers can derive and implement the models themselves. The discussion of the models and their limitations and five technical appendixes help readers expand and generalize the models themselves or to understand existing generalizations.The book emphasizes models for pricing as well as statistical techniques for estimating their parameters. Applications include rating-based modeling, modeling of dependent defaults, swap- and corporate-yield curve dynamics, credit default swaps, and collateralized debt obligations.
R 5,027.00
