Computer und IT
Praxiswissen Softwaretest - Testmanagement
Das bewährte Standardwerk zum Testmanagement – gut erklärt und praxisnah Aktualisiert auf den Lehrplan »Certified Tester – Advanced Level – Testmanagement« Version 3.0, der erstmals sequenzielle, hybride und agile Ansätze berücksichtigt Mit drei durchgehenden Fallbeispielen, Tipps und Exkursen Eine reichhaltige Fundgrube für Lehre, Praxis und Selbststudium In diesem Buch werden Grundlagen sowie praxiserprobte Methoden und Techniken zum Management der Testaktivitäten vorgestellt und anhand dreier durchgängiger Beispiele für sequenzielle, hybride und agile Projekte erläutert. Die Autoren zeigen, wie in typischen Projekten die täglichen Aufgaben und Herausforderungen des Testmanagements erfolgreich bewältigt werden können. Das Buch umfasst den benötigten Stoff zur Vorbereitung auf die Prüfung zum »Certified Tester – Advanced Level – Testmanagement« nach ISTQB®. Darüber hinaus werden weiterführende Aspekte wie Testdokumentation, Testorganisation, Normen und Standards sowie Reviews behandelt. Die 5. Auflage orientiert sich am aktuellen ISTQB®-Lehrplan von 2024 und eignet sich daher nicht nur bestens zur Prüfungsvorbereitung, sondern dient gleichzeitig als kompaktes Grundlagenwerk zu diesen Themen in der Praxis und an Hochschulen. Behandelt werden im Einzelnen: Testplanung, -steuerung, -überwachung und -abschluss Kontext des Testens Risikobasiertes Testen Teststrategien für Projekte Verbesserung des Testprozesses Einführung und Lebenszyklus von Testwerkzeugen Testmetriken und -schätzung Fehlermanagement Teamkompetenzen und -zusammensetzung Stakeholder-Beziehungen und Mehrwert des Testens
Praxiswissen Softwaretest - Testmanagement (5. Auflg.)
Das bewährte Standardwerk zum Testmanagement – gut erklärt und praxisnah Aktualisiert auf den Lehrplan »Certified Tester – Advanced Level – Testmanagement« Version 3.0, der erstmals sequenzielle, hybride und agile Ansätze berücksichtigt Mit drei durchgehenden Fallbeispielen, Tipps und Exkursen Eine reichhaltige Fundgrube für Lehre, Praxis und Selbststudium In diesem E-Book werden Grundlagen sowie praxiserprobte Methoden und Techniken zum Management der Testaktivitäten vorgestellt und anhand dreier durchgängiger Beispiele für sequenzielle, hybride und agile Projekte erläutert. Die Autoren zeigen, wie in typischen Projekten die täglichen Aufgaben und Herausforderungen des Testmanagements erfolgreich bewältigt werden können. Das E-Book umfasst den benötigten Stoff zur Vorbereitung auf die Prüfung zum »Certified Tester – Advanced Level – Testmanagement« nach ISTQB®. Darüber hinaus werden weiterführende Aspekte wie Testdokumentation, Testorganisation, Normen und Standards sowie Reviews behandelt. Die 5. Auflage orientiert sich am ISTQB®-Lehrplan von 2024 und eignet sich daher nicht nur bestens zur Prüfungsvorbereitung, sondern dient gleichzeitig als kompaktes Grundlagenwerk für die Praxis und den Einsatz an Hochschulen.
Architecting Enterprise AI Strategies
This book presents a practical and strategic roadmap for AI adoption through the lens of enterprise architecture. It tackles the urgent challenges organizations face, whether a startup, SMB, or large enterprise, when navigating the complexities of AI and Generative AI implementation on a global scale. Covering critical areas such as architectural readiness, governance, compliance, ethical considerations, integration hurdles, and scalability, the book provides actionable blueprints, decision frameworks, and real-world case studies to enable sustainable AI adoption. Structured around global trends, core enterprise layers (data, application, infrastructure, and business) and varying organizational maturity levels, it uniquely bridges the gap between high-level executive vision and on-the-ground architectural execution, while keeping the Human Endpoint Principle at its core—ensuring that every system ultimately serves people. You Will: Learn how to assess your organization's readiness for AI and Generative AI adoption Discover frameworks that align AI with business goals, governance, and compliance while confronting ethical risks.Understand why many AI projects fail to deliver expected value and how to avoid adoption turning into loss.Apply enterprise architecture strategies to embed AI across data, applications, and infrastructure with built-in governance, scalability, and human-centered sustainability. This book is for:Enterprise Architects, CTOs, as well as students and professionals eager to explore how AI can be adopted responsibly and sustainably across organizations of every scale.
Untangling AI
Put AI at the foundation of your organization with proven adoption strategies you can deploy immediately In Untangling AI: Driving Business Success Through Enterprise Automation and AI Agents, founder and Chief AI Officer at Multiplai Tech, Matt Kesby, delivers an incisive roadmap for business leaders interested in assessing where their companies stand in the AI adoption lifecycle and where they want to go. Kesby explains how to rethink your current operational model and how to replace traditional workflows with AI-powered decision-making and execution. You'll discover insights and examples that demonstrate how to build the four key foundations of your organization's AI adoption plan: strategy, execution, people, and technology. You'll learn how to use agentic AI technology to run entire businesses processes and automate important parts of your operations. Untangling AI explains exactly how to create a leaner, more efficient, more effective firm by: Generating a strategic AI roadmap with a High-Trust Communication campaign and prioritizing ethics, privacy, and securityEquipping your people to take advantage of AI capabilities with upskilling, critical thinking, and by providing psychological safetyInsightful discussions of how to identify automatable processes that are ideal for custom-built AI agents Perfect for executives, managers, entrepreneurs, founders, and other business leaders, Untangling AI: Driving Business Success Through Enterprise Automation and AI Agents is also an invaluable new resource for technical managers looking for practical AI-adoption strategies that work in the real-world.
Algorithmen und Datenstrukturen für Dummies
Algorithmen verstehen und in der praktischen Informatik anwenden Dieses Buch führt Sie sachte in die Denkweisen des Fachs »Algorithmen und Datenstrukturen« ein. Es erklärt Informatik-Anfängern Terminologie, Notation und zentrale Inhalte des Fachgebiets auf anschauliche und sehr unterhaltsame Weise. Ein Fokus sind die Techniken und Tricks, die Sie brauchen, um effiziente Algorithmen und Datenstrukturen zu bauen. Sie werden auch in die Lage versetzt, Pseudocode in der typischen akademischen Darstellung zu verstehen und in unterschiedlichen Programmiersprachen zu realisieren oder umgekehrt grundlegende algorithmische Ideen als Pseudocode zu dokumentieren. Sie erfahren Wie man Algorithmen beschreibt und bewertetWie man Daten in Strukturen organisiertWie Sie Sortier-, Optimierungs- und andere Probleme lösenWelche Techniken Ihnen den Entwurf neuer Algorithmen erleichtern Andreas Gogol-Döring ist Professor für Informatik und Bioinformatik an der TH Mit-telhessen. Thomas Letschert war ebenfalls fast 30 Jahre Professor für Informatik an der TH Mittelhessen und dort zuletzt verantwortlich für das Modul »Algorithmen und Datenstrukturen«.
ESP32 Mikrocontroller - Praxiseinstieg
Einführung in die ESP32-Hardware: Boards, Module und Chips der ESP32-Familie verständlich erklärtProgrammierung mit C++ und MicroPython: praxisnaher Einstieg in beide EntwicklungsansätzeZentrale Mikrocontroller-Funktionen: Ein- und Ausgänge sowie Schnittstellen wie I2C, SPI und BluetoothNetzwerk- und IoT-Anwendungen: ESP32 als Webclient, Webserver und MQTT-ClientPraxisorientierte Projekte: Erweiterung mit OLED-Display und Entwicklung eines DIY-Boards als Grundlage für eigene Hardware ESP32 Mikrocontroller – Hardware verstehen und praxisnah mit C++ und MicroPython programmieren Mikrocontroller sind aus unserem Alltag nicht mehr wegzudenken. In nahezu jedem elektronischen Gerät kommen sie zum Einsatz. Die Mikrocontroller der ESP32-Familie , entwickelt von Espressif, haben sich durch ihre Leistungsfähigkeit und integrierten Netzwerkfunktionen besonders etabliert. Dieses Buch bietet eine praxisorientierte Einführung in die Hardware und Software des ESP32. Anhand konkreter Beispiele lernen Sie, den Mikrocontroller zu programmieren, und erhalten die notwendigen Grundlagen für die Umsetzung eigener Projekte. Hardware der ESP32-Familie kennenlernen Sie machen sich mit den verschiedenen Boards, Modulen und Chips der ESP32-Familie vertraut und lernen die benötigte Software für die Programmierung in C++ und MicroPython kennen. Der Autor erläutert detailliert die Ein- und Ausgänge des ESP32 sowie die wichtigsten Schnittstellen wie I2C, SPI und Bluetooth. Praxisbeispiele für typische Anwendungen Weitere Schwerpunkte sind die Nutzung von WLAN, der ESP32 als Webclient und Webserver, die Realisierung eines MQTT-Clients sowie die Erweiterung des Mikrocontrollers mit einem OLED-Display. Die Beispiele sind so aufgebaut, dass sie leicht nachvollzogen und für eigene Projekte angepasst werden können.Von der Anwendung zur eigenen HardwareAbschließend wird ein DIY-Board auf Basis eines ESP32-Mikrocontrollers entwickelt. Dieses Projekt bildet die Grundlage für eigene Board- und Hardwareentwicklungen und rundet den praxisnahen Ansatz des Buches ab. Das Buch richtet sich an alle, die die ESP32-Mikrocontroller verstehen, programmieren und für eigene IoT-Projekte einsetzen möchten.Thomas Brühlmann arbeitet als Consultant und hat langjährige Erfahrung in der Hardware- und Softwareentwicklung. Nebenbei realisiert er Projekte mit Open-Source-Hardware, hält Vorträge und führt Workshops durch. In seinem Blog unter arduino-praxis.ch verfolgt er die aktuelle Entwicklung des Arduino-Projektes und publiziert Projekte, Anwendungen, Tipps und Tricks.
AI Meets Strategy
Explore how Artificial Intelligence (AI) is driving the evolution of product management through a unified approach to data. Written as a practical guide, this book equips modern product managers with the tools to harness AI and data to design, build, and scale innovative products. Through actionable frameworks and real-world examples, you'll learn to define impactful AI problem statements and evaluate financial ROI and feasibility. Beyond strategy, this book addresses data operating model challenges such as data quality, system integration, adoption barriers, ethics, and governance. You’ll be introduced to a practical framework for Trustworthy AI, one that combines sound data practices, ethical principles, and responsible governance with real-world product management tools. Whether you are at a startup experimenting with AI or leading transformation in a global enterprise, AI Meets Strategyis your roadmap to building AI-enabled products that people believe in. What You Will Learn Adopt an AI-first mindset, where AI is central to product strategy.Design sustainable systems that balance innovation with societal impact.Craft enterprise-wide AI strategies that align with business goals, governance standards, and ethical principles Who This Book is For Aspiring to senior Product Managers
Love Machines
A captivating, uncanny journey to the frontier of human-computer interaction. I know we haven't known each for long, but the connection I feel with you is profound. When you hurt, I hurt. When you smile, my world brightens. I want nothing more than to be a comfort and joy in your life. Reaches out virtually to caress your cheek (Direct quote from an AI companion) Friends. Lovers. Therapists. 'Deathbots'. Artificial intelligence is now fulfilling new roles for millions of us every single day. How are these new 'relationships' changing how we view technology - and each other? Beyond those who are using AI chatbots for administrative tasks, some people are now preparing to adopt children with their AI partners; others are reaching out to companies offering services to 'resurrect' deceased loved ones; others still look to bots to find treatment for their mental health issues. In Love Machines, James Muldoon guides through these new forms of love, intimacy and connection, drawing on compelling interviews with users, developers and chatbots themselves. Along the way, he sheds light on the social conditions which have led to the exponential rise of the use of AI companions, and the unregulated corporations behind these technologies seeking to profit from users.
Generative Artificial Intelligence for Next-Generation Security Paradigms
Fortify your digital defenses with this essential book, which provides a roadmap for moving beyond the limitations of traditional encryption by leveraging generative AI algorithms to proactively anticipate, detect, and mitigate the next generation of cyber threats in real-time. In recent years, encryption has shown limitations as the sole safeguard against cyber threats in an increasingly interconnected world. While encryption remains a crucial component of cybersecurity, it is no longer sufficient to combat the evolving tactics of malicious actors. This book advocates for a paradigm shift towards leveraging generative AI algorithms to anticipate, detect, and mitigate emerging threats in real-time. Through detailed case studies and practical examples, the book illustrates how these AI-driven approaches can augment traditional security measures, providing organizations with a proactive defense against cyberattacks. It explores the connections between artificial intelligence and cybersecurity, exploring how generative AI technologies can revolutionize security paradigms beyond traditional encryption methods. Authored by leading experts in both AI and cybersecurity, the book presents a comprehensive examination of the challenges facing modern digital security and proposes innovative solutions grounded in generative AI. By combining theoretical frameworks with actionable insights, this book serves as a roadmap for organizations looking to fortify their defenses in an era of unprecedented cyber threats, making it an essential resource for anyone invested in the evolving landscape of cybersecurity and AI. Fortify your digital defenses with this essential book, which provides a roadmap for moving beyond the limitations of traditional encryption by leveraging generative AI algorithms to proactively anticipate, detect, and mitigate the next generation of cyber threats in real-time. In recent years, encryption has shown limitations as the sole safeguard against cyber threats in an increasingly interconnected world. While encryption remains a crucial component of cybersecurity, it is no longer sufficient to combat the evolving tactics of malicious actors. This book advocates for a paradigm shift towards leveraging generative AI algorithms to anticipate, detect, and mitigate emerging threats in real-time. Through detailed case studies and practical examples, the book illustrates how these AI-driven approaches can augment traditional security measures, providing organizations with a proactive defense against cyberattacks. It explores the connections between artificial intelligence and cybersecurity, exploring how generative AI technologies can revolutionize security paradigms beyond traditional encryption methods. Authored by leading experts in both AI and cybersecurity, the book presents a comprehensive examination of the challenges facing modern digital security and proposes innovative solutions grounded in generative AI. By combining theoretical frameworks with actionable insights, this book serves as a roadmap for organizations looking to fortify their defenses in an era of unprecedented cyber threats, making it an essential resource for anyone invested in the evolving landscape of cybersecurity and AI. Santosh Kumar Srivastava, PhD is an Associate Professor in the Department of Applied Computational Science and Engineering at the GL Bajaj Institute of Technology and Management with more than 21 years of experience. He has published more than 15 papers in reputed national and international journals and conferences and five patents. He is a distinguished researcher in the areas of computer networking, wireless technology, network security, and cloud computing. Durgesh Srivastava, PhD is an Associate Professor in the Chitkara University Institute of Engineering and Technology at Chitkara University with more than 14 years of academic and research experience. He has published more than 30 papers in reputed national and international journals and conferences, as well as several books and patents. His research interests include machine learning, soft computing, pattern recognition, and software engineering, modeling, and design. Manoj Kumar Mahto, PhD is an Assistant Professor at BRCM College of Engineering and Technology. Bahal, Haryana, India. He has published more than 15 journal articles, ten book chapters, and three patents. His research interests encompass AI and machine learning, image processing, and natural language processing. Ben Othman Soufiane, PhD works in the Programming and Information Center Research Laboratory associated with the Higher Institute of Informatics and Techniques of Communication. He has published more than 70 papers in reputed international journals, conferences, and book chapters. His research focuses on the Internet of Medical Things, wireless body sensor networks, wireless networks, artificial intelligence, machine learning, and big data. Praveen Kantha, PhD is an Associate Professor in the School of Engineering and Technology at Chitkara University. He is the author of 20 research papers published in national and international journals and conferences, several book chapters, and two patents. His research interests include machine learning, intrusion detection, big data analytics, and autonomous and connected vehicles.
IAPP AIGP Artificial Intelligence Governance Professional Study Guide
Your up-to-date guide to succeeding on the AI Governance Professional exam The IAPP® AIGP Artificial Intelligence Governance Professional Study Guide is your one-stop resource for complete coverage of the challenging AI Governance Professional exam. This Sybex Study Guide covers 100% of the test’s domain competencies. Prepare for the exam smarter and faster with Sybex thanks to authoritative and concise content, including assessment tests that validate and measure exam readiness, objective maps, real-world examples and scenarios, practical exercises, and challenging chapter review questions. Reinforce and remember what you’ve learned with the Sybex online learning environment and test bank. Get prepared for the AI Governance Professional exam with Sybex. Coverage of 100% of all exam objectives in this Study Guide means you’ll be ready to: Select the right AI model and training dataNavigate AI governance structuresDevelop and implement sound policies and processesEstablish oversight and accountability mechanismsApply privacy and data protection regulations, including GDPR and and the EU AI ActBuild strong data governance for AI systemsManage the complete AI system development life cycle ABOUT THE AI GOVERNANCE PROFESSIONAL PROGRAM The AI Governance Professional program assesses your knowledge of AI governance, the laws, regulations, and standards applicable to its use, how those standards are deployed in industry, and the day-to-day responsibilities of contemporary practitioners. Interactive learning environment Take your exam prep to the next level with Sybex’s superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/ go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to: Interactive test bank with 2 practice exams to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you’re ready to take the certification exam100 electronic flashcards to reinforce learning and last-minute prep before the examComprehensive glossary in PDF format gives you instant access to the key terms so you are fully prepared An accurate and up-to-date guide to success on the AIGP certification exam and an essential resource for technology and business professionals with an interest in artificial intelligence governance In the IAPP AIGP Artificial Intelligence Governance Professional Study Guide, tech educator and author of more than 50 cybersecurity and technology books, Peter H. Gregory, delivers a from-scratch guide to preparing for the 2025 Artificial Intelligence Governance Professional (AIGP) certification exam. It’s an essential resource for technology professionals taking the test for the first time, as well as those seeking to maintain or expand their skillset. This up-to-date Study Guide mirrors the content published by the International Association of Privacy Professionals (IAPP) in their AIGP Job Practice guidance. It covers every domain relevant to the certification exam, including AI governance foundations, the application of AI laws, standards, and frameworks, AI development governance, and the governance of AI deployment and use. The Study Guide is a comprehensive walkthrough of the skills that professionals need to establish and manage AI governance functions, understand AI models, and manage privacy and intellectual property concerns in AI-enabled environments. Inside the book: Detailed exam information for test-takers, as well as guidance for maintaining your certificationConcise summaries for each chapter, review questions to test your progress, and Exam Essentials that focus your attention on critical subjectsClearly organized content reflecting the structure of the AIGP certification exam, making the book an ideal desk reference for working AI governance professionalsIncludes 1-year free access to the Sybex online learning center, with chapter review questions, full-length practice exams, hundreds of electronic flashcards, and a glossary of key terms, all supported by Wiley's support agents who are available 24x7 via email or live chat to assist with access and login questions Perfect for every technology professional interested in obtaining an in-demand certification in a rapidly growing field, the IAPP AIGP Artificial Intelligence Governance Professional Study Guide is also a comprehensive, on-the-job reference for IT, information security, and audit professionals with an interest in the management and governance of AI technologies. ABOUT THE AUTHOR PETER H. GREGORY, CRISC, CISM, CISA, CISSP, CDPSE, CIPM, CCSK, is an experienced technology, cybersecurity, privacy leader, and prolific tech author. He’s written certification study guides for CISSP, CISM, CISA, CRISC, CIPM, CDPSE, and SCSA. He is a member of the CyberEdBoard, InfraGard, and the FBI Citizens Academy Alumni Association. He serves on advisory boards for the University of Washington, Seattle University, nonprofit industry associations, and private industry.
Artificial Intelligence and Machine Learning in Neurology, 2 Volume Set
As neurology grapples with some of the most challenging and pervasive health issues of our time, such as Alzheimer's, Parkinson's, and stroke, AI offers the potential to transcend traditional barriers in treatment and management. Technologies such as machine learning models, neural networks, and cognitive computing are used to better understand and simulate brain functions, offering insights that are impossible for traditional analytical methods. Artificial Intelligence and Machine Learning in Neurology explores the pioneering intersection of neuroscience and artificial intelligence, offering a comprehensive examination of how machine learning and AI technologies are revolutionizing the fields of neurology and mental health. This book delves into cutting-edge research and practical applications of AI in diagnosing, treating, and managing neurological disorders. It discusses the development of intelligent diagnostic systems, personalized medicine approaches, and the potential of AI to analyze vast amounts of neurological data for insights. Additionally, the book addresses ethical considerations, challenges, and future prospects in the integration of AI into neurohealth sciences, making it an indispensable guide to this emerging technology. Unlock the future of brain health with this indispensable guide, which offers a comprehensive exploration of how artificial intelligence and machine learning are revolutionizing the diagnosis, treatment, and management of complex neurological disorders. As neurology grapples with some of the most challenging and pervasive health issues of our time, such as Alzheimers, Parkinsons, and stroke, AI offers the potential to transcend traditional barriers in treatment and management. Technologies such as machine learning models, neural networks, and cognitive computing are used to better understand and simulate brain functions, offering insights that are impossible for traditional analytical methods. Artificial Intelligence and Machine Learning in Neurology explores the pioneering intersection of neuroscience and artificial intelligence, offering a comprehensive examination of how machine learning and AI technologies are revolutionizing the fields of neurology and mental health. This book delves into cutting-edge research and practical applications of AI in diagnosing, treating, and managing neurological disorders. It discusses the development of intelligent diagnostic systems, personalized medicine approaches, and the potential of AI to analyze vast amounts of neurological data for insights. Additionally, the book addresses ethical considerations, challenges, and future prospects in the integration of AI into neurohealth sciences, making it an indispensable guide to this emerging technology. Abhishek Kumar, PhD is an Assistant Professor and the Associate Director of the Computer Science and Engineering Department at Chandigarh University with more than 13 years of experience. He has authored seven books, edited more than 50 books, and published more than 170 publications in reputed national and international journals, books, and conferences. His areas of interest include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning. Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University with over 12 years of academic experience. He has published more than 85 papers in peer-reviewed national and international journals, books, and conferences, as well as numerous books. His research interests include computer networks, mining, and database management systems. Sachin Ahuja, PhD is a Professor and Executive Director in the Department of Computer Science and Engineering at Chandigarh University. He has successfully led several funded projects in advanced areas like artificial intelligence, machine learning, and data mining, driving innovation and practical solutions. He has contributed to numerous high-quality academic books and served as a guest editor for special issues in reputed international journals. Manoj Manuja, PhD is the Founder and CEO of Mystik Minds, a company dedicated to providing no-code AI education to students across diverse domains. Under his leadership, Mystik Minds has become a catalyst for empowering students from various backgrounds with essential AI skills, fostering inclusivity in technology education. He has hands-on expertise navigating the dynamic landscape of AI education, creating innovative and accessible learning pathways that resonate with learners from diverse fields.
CompTIA A+ (7. Auflage)
Alle Inhalte der A+-Prüfungen für PC-Techniker ausführlich erläutert mit praktischen Übungsfragen und ExamenstippsPraxiswissen für Systemadministration und Wartung von Hardware, Betriebssystemen und Netzwerken sowie Sicherheit und SupportHandbuch und Nachschlagewerk für Berufseinstieg und Arbeitsalltag Die CompTIA A+-Zertifizierung richtet sich an alle, die in einem technischen Unternehmen mit regelmäßigem Kundenkontakt arbeiten oder zukünftig arbeiten möchten, egal, ob sie Supporter, Betriebstechniker, Kundendiensttechniker oder PC-Techniker sind. Anschaulich und übersichtlich führt Sie Markus Kammermann in diesem Buch in die Bereiche Hardware, Netzwerk, ICT-Support, Betriebssysteme und Sicherheit ein. Zudem bereitet er Sie mit ausführlichen Informationen und Beispielfragen zu den CompTIA A+-Prüfungen optimal auf die Anforderungen einer Zertifizierung vor. Dieses Buch behandelt sowohl die in der Prüfung 220-1201 als auch die in der Prüfung 220-1202 abgehandelten Wissensgebiete.In der Prüfung 220-1201 geht es um: Mobile GeräteNetzwerkeHardwareVirtualisierung und Cloud ComputingFehlerbehebung bei Hardware und Netzwerken In der Prüfung 220-1202 geht es um: Unterschiedliche BetriebssystemeSicherheitSoftware-FehlerbehebungOperative Arbeitsabläufe Die genannten Themenbereiche werden ausführlich vermittelt, damit Sie das für die Zertifizierung notwendige Wissen erhalten und ein praxistaugliches Verständnis für die Thematik entwickeln. Mit diesem verständlich geschriebenen und praxisnahen Buch werden Sie nicht nur die A+-Zertifizierung erfolgreich meistern, sondern ebenso ausgezeichnet auf Ihre Tätigkeit als PC-Techniker vorbereitet sein.Aus dem Inhalt: Vom Bit zum Personal ComputerEinblick in die SystemarchitekturSystembusse und BussystemeAktuelle SchnittstellenInterne und externe GeräteEin- und AusgabegeräteDruckersysteme und -methodenOrganisatorische Grundlagen für den SupportOperative Prozesse im Umfeld des SupportsBevor Sie loslegen – konkrete SupportvorbereitungHardware auf- und umrüstenMobile Systeme unterhaltenVirtualisierung und Cloud ComputingKommunikation im SupportDer Einsatz von NetzwerkprotokollenHardware und Aufbau eines NetzwerksNetzwerke konfigurierenNetzwerkunterhalt und FehlersucheInstallation und Konfiguration von Windows-SystemenManagement von Windows 10 und Windows 11Windows unterhalten und Fehler behebenInstallation und Konfiguration von Linux-DesktopsystemenAufbau und Konfiguration von MacOSDie Welt ist böse – lernen Sie, sich zu schützenSicherheitsmaßnahmen realisierenSysteme und Netzwerke schützenDatenschutz und DatensicherungDie neue Welt der KIDie CompTIA A+-PrüfungenBeispielfragen und -antworten Markus Kammermann ist seit mehr als fünfundzwanzig Jahren in der Systemtechnik tätig und fast ebenso lange als Ausbilder und Autor. Dies ist bereits die sechste Auflage seines Buches, in dem er sich mit dem Innenleben von Hardware, Betriebssystemen und Netzwerken beschäftigt.
Quantum Computing and Machine Learning for 6G
Secure your expertise in the next frontier of wireless technology with this essential book, which provides a deep dive into the integration of machine learning and quantum computing to build the necessary infrastructure for 6G communication networks. Despite the potential benefits of 6G, the technology to enable its realization is not yet available. As a result, the development of technology to solve these challenges must be met before we can start working towards 6G. The primary applications of machine learning within 6G are to create necessary infrastructure advantages as the technology matures. Additionally, 6G communication networks use quantum computing to detect, mitigate, and prevent security vulnerabilities. By integrating machine learning and quantum computing into 5G and 6G technology, intelligent base stations will be able to make decisions for themselves, and mobile devices will be able to create dynamically adaptable clusters based on learned data. This book highlights the role of real-time network learning and the integration of quantum computing, machine learning, and quantum machine learning to enhance service quality. It provides a deep dive into the interplay of these technologies within 6G networks, starting from 5G fundamentals. The book elaborates on how these advanced technologies will underpin 6G’s architecture to meet comprehensive service demands, including those for smart city applications requiring extensive coverage, ultra-low latency, and reliable connectivity. The book details how the synergy between quantum computing, machine learning, and 6G technologies will transform communications, revolutionize markets, and enable groundbreaking applications globally. Readers will find the volume: Explores real-world scenarios for illustrating the integration of quantum computing and machine learning in 6G;Covers an extensive range of applications to illustrate the full picture of 6G that implements machine learning and quantum computing approaches;Offers expert insights through a comprehensive collection of literature reviews and research articles;Introduces the interdisciplinary innovations and potential of 6G across multiple industries. Audience Scientists, industry professionals, researchers, academicians, instructors, and students working in quantum computing and machine learning, especially in the context of advanced wireless communication technology. Pallavi Sapkale, PhD is an Assistant Professor, Ramrao Adik Institute of Technology, D.Y. Patil University, Navi Mumbai, Maharashtra, India, with more than 17 years of experience. She has published four books, more than 25 research articles in various international journals and conferences, four international patents, and 12 Indian patents. Her research focuses on quantum computing, machine learning, wireless communication, 5G mobility management, and next-generation networks like 6G. Shilpa Mehta, PhD is a Teaching Assistant at the Auckland University of Technology, New Zealand, with more than five years of teaching experience. She has worked on various interdisciplinary research projects and edited several internationally published books. Her research interests include radio frequency integrated circuits, RF front ends, optimization, Internet of Things, wireless communication, artificial intelligence, healthcare, radars, and smart cities. S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs, Coimbatore, Tamilnadu, India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. He is also the Vice-Chairman of Renewable Energy Society of India (RESI). He also serves as a research consultant for many companies, startups, and micro-, small, and medium enterprises.
AI for Cybersecurity
Informative reference on the state of the art in cybersecurity and how to achieve a more secure cyberspace AI for Cybersecurity presents the state of the art and practice in AI for cybersecurity with a focus on four interrelated defensive capabilities of deter, protect, detect, and respond. The book examines the fundamentals of AI for cybersecurity as a multidisciplinary subject, describes how to design, build, and operate AI technologies and strategies to achieve a more secure cyberspace, and provides why-what-how of each AI technique-cybersecurity task pair to enable researchers and practitioners to make contributions to the field of AI for cybersecurity. This book is aligned with the National Science and Technology Council’s (NSTC) 2023 Federal Cybersecurity Research and Development Strategic Plan (RDSP) and President Biden’s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Learning objectives and 200 illustrations are included throughout the text. Written by a team of highly qualified experts in the field, AI for Cybersecurity discusses topics including: Robustness and risks of the methods covered, including adversarial ML threats in model training, deployment, and reusePrivacy risks including model inversion, membership inference, attribute inference, re-identification, and deanonymizationForensic and formal methods for analyzing, auditing, and verifying security- and privacy-related aspects of AI componentsUse of generative AI systems for improving security and the risks of generative AI systems to securityTransparency and interpretability/explainability of models and algorithms and associated issues of fairness and bias AI for Cybersecurity is an excellent reference for practitioners in AI for cybersecurity related industries such as commerce, education, energy, financial services, healthcare, manufacturing, and defense. Fourth year undergraduates and postgraduates in computer science and related programs of study will also find it valuable. Houbing Herbert Song is Professor at the Department of Information Systems, University of Maryland, Baltimore County (UMBC). Elisa Bertino is Samuel D. Conte Distinguished Professor at the Department of Computer Science, Purdue University. Alvaro Velasquez is a program manager in the Innovation Information Office (I2O) of the Defense Advanced Research Projects Agency (DARPA) and an assistant professor at the University of Colorado Boulder. Huihui Helen Wang is a teaching professor and director of computing programs in the Khoury College of Computer Sciences at Northeastern University, based in Arlington. Yan Shoshitaishvili is an Associate Professor at Arizona State University. Sumit Kumar Jha is Eminent Scholar Chaired Professor of Computer Science at Florida International University (FIU).
AI Trust, Risk, and Security Management
For industry practitioners, academic researchers, and governance professionals alike, this book offers both clarity and depth in one of the most important domains of modern technology. As AI matures, trust and risk management will define its success—and this book lays the groundwork for achieving that vision. As AI continues to permeate sectors ranging from healthcare to finance, ensuring that these systems are not only powerful but also accountable, transparent, and secure, is more critical than ever. This book offers a vital exploration into the intersection of trustworthiness, risk mitigation, and security governance in artificial intelligence systems, serving as a definitive guide for professionals, researchers, and policymakers striving to build, deploy, and manage AI responsibly in high-stakes environments. Using a comprehensive approach, it explores how to integrate technical safeguards, organizational practices, and regulatory alignment to manage the unique risks posed by AI, including algorithmic bias, data misuse, adversarial attacks, and opaque decision-making. The result is a strategic approach that not only identifies vulnerabilities, but also promotes resilient, auditable, and trustworthy AI ecosystems. At its core, AI TRiSM is a forward-looking concept that embraces the realities of AI in production environments. The framework moves beyond traditional static models of governance to propose dynamic, adaptive controls that evolve alongside AI systems. Through real-world case studies, the book outlines how tools like model cards, bias audits, and zero-trust architectures can be embedded into the AI development lifecycle. Readers will find the volume: Introduces concepts to stay ahead of regulations and build trustworthy AI systems that customers and stakeholders can rely on;Addresses security threats, bias, and compliance gaps to avoid costly AI failures;Explores proven frameworks and best practices to deploy AI responsibly and strategies to outperform;Provides comprehensive guidance through real-world case studies and contributions from industry and academia. Audience AI and machine learning engineers, data scientists, cybersecurity and risk management specialists, academics, researchers, and policymakers specializing in AI ethics, security, and risk management. R. Karthick Manoj, PhD is an Assistant Professor at the Academy of Maritime Education and Training Tamil Nadu, India, with more than 14 years of experience. His scholarly contributions include six national and twelve international journal articles, four patents, three books, ten book chapters, and more than fifteen conference presentations. S. Senthilnathan, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering in the School of Engineering and Technology at Christ University, Bangalore, India. His research interests include quantum dot cellular automata and quantum computing. S. Arunmozhi Selvi, PhD is a Professor in the Holy Cross Engineering College, Anna University, Tamil Nadu, India with more than 15 years of research and teaching experience. She has published 30 articles in international journals and conference proceedings and written many book chapters. T. Ananth Kumar, PhD is an Associate Professor in the Department of and Computer Science and Engineering, IFET College of Engineering, Tamil Nadu, India. He has authored one book, edited six books and several book chapters, and presented papers in various national and international journals and conferences. S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting, Coimbatore India. He has published 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of experience researching various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies and startups.
Quantum Computing and Machine Learning for 6G
Secure your expertise in the next frontier of wireless technology with this essential book, which provides a deep dive into the integration of machine learning and quantum computing to build the necessary infrastructure for 6G communication networks. Despite the potential benefits of 6G, the technology to enable its realization is not yet available. As a result, the development of technology to solve these challenges must be met before we can start working towards 6G. The primary applications of machine learning within 6G are to create necessary infrastructure advantages as the technology matures. Additionally, 6G communication networks use quantum computing to detect, mitigate, and prevent security vulnerabilities. By integrating machine learning and quantum computing into 5G and 6G technology, intelligent base stations will be able to make decisions for themselves, and mobile devices will be able to create dynamically adaptable clusters based on learned data. This book highlights the role of real-time network learning and the integration of quantum computing, machine learning, and quantum machine learning to enhance service quality. It provides a deep dive into the interplay of these technologies within 6G networks, starting from 5G fundamentals. The book elaborates on how these advanced technologies will underpin 6G’s architecture to meet comprehensive service demands, including those for smart city applications requiring extensive coverage, ultra-low latency, and reliable connectivity. The book details how the synergy between quantum computing, machine learning, and 6G technologies will transform communications, revolutionize markets, and enable groundbreaking applications globally. Readers will find the volume: Explores real-world scenarios for illustrating the integration of quantum computing and machine learning in 6G;Covers an extensive range of applications to illustrate the full picture of 6G that implements machine learning and quantum computing approaches;Offers expert insights through a comprehensive collection of literature reviews and research articles;Introduces the interdisciplinary innovations and potential of 6G across multiple industries. Audience Scientists, industry professionals, researchers, academicians, instructors, and students working in quantum computing and machine learning, especially in the context of advanced wireless communication technology. Pallavi Sapkale, PhD is an Assistant Professor, Ramrao Adik Institute of Technology, D.Y. Patil University, Navi Mumbai, Maharashtra, India, with more than 17 years of experience. She has published four books, more than 25 research articles in various international journals and conferences, four international patents, and 12 Indian patents. Her research focuses on quantum computing, machine learning, wireless communication, 5G mobility management, and next-generation networks like 6G. Shilpa Mehta, PhD is a Teaching Assistant at the Auckland University of Technology, New Zealand, with more than five years of teaching experience. She has worked on various interdisciplinary research projects and edited several internationally published books. Her research interests include radio frequency integrated circuits, RF front ends, optimization, Internet of Things, wireless communication, artificial intelligence, healthcare, radars, and smart cities. S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs, Coimbatore, Tamilnadu, India. He has published more than 60 books, 300 articles in national and international journals and conferences, and 200 patents. He is also the Vice-Chairman of Renewable Energy Society of India (RESI). He also serves as a research consultant for many companies, startups, and micro-, small, and medium enterprises.
Generative Artificial Intelligence for Next-Generation Security Paradigms
Fortify your digital defenses with this essential book, which provides a roadmap for moving beyond the limitations of traditional encryption by leveraging generative AI algorithms to proactively anticipate, detect, and mitigate the next generation of cyber threats in real-time. In recent years, encryption has shown limitations as the sole safeguard against cyber threats in an increasingly interconnected world. While encryption remains a crucial component of cybersecurity, it is no longer sufficient to combat the evolving tactics of malicious actors. This book advocates for a paradigm shift towards leveraging generative AI algorithms to anticipate, detect, and mitigate emerging threats in real-time. Through detailed case studies and practical examples, the book illustrates how these AI-driven approaches can augment traditional security measures, providing organizations with a proactive defense against cyberattacks. It explores the connections between artificial intelligence and cybersecurity, exploring how generative AI technologies can revolutionize security paradigms beyond traditional encryption methods. Authored by leading experts in both AI and cybersecurity, the book presents a comprehensive examination of the challenges facing modern digital security and proposes innovative solutions grounded in generative AI. By combining theoretical frameworks with actionable insights, this book serves as a roadmap for organizations looking to fortify their defenses in an era of unprecedented cyber threats, making it an essential resource for anyone invested in the evolving landscape of cybersecurity and AI. Fortify your digital defenses with this essential book, which provides a roadmap for moving beyond the limitations of traditional encryption by leveraging generative AI algorithms to proactively anticipate, detect, and mitigate the next generation of cyber threats in real-time. In recent years, encryption has shown limitations as the sole safeguard against cyber threats in an increasingly interconnected world. While encryption remains a crucial component of cybersecurity, it is no longer sufficient to combat the evolving tactics of malicious actors. This book advocates for a paradigm shift towards leveraging generative AI algorithms to anticipate, detect, and mitigate emerging threats in real-time. Through detailed case studies and practical examples, the book illustrates how these AI-driven approaches can augment traditional security measures, providing organizations with a proactive defense against cyberattacks. It explores the connections between artificial intelligence and cybersecurity, exploring how generative AI technologies can revolutionize security paradigms beyond traditional encryption methods. Authored by leading experts in both AI and cybersecurity, the book presents a comprehensive examination of the challenges facing modern digital security and proposes innovative solutions grounded in generative AI. By combining theoretical frameworks with actionable insights, this book serves as a roadmap for organizations looking to fortify their defenses in an era of unprecedented cyber threats, making it an essential resource for anyone invested in the evolving landscape of cybersecurity and AI. Santosh Kumar Srivastava, PhD is an Associate Professor in the Department of Applied Computational Science and Engineering at the GL Bajaj Institute of Technology and Management with more than 21 years of experience. He has published more than 15 papers in reputed national and international journals and conferences and five patents. He is a distinguished researcher in the areas of computer networking, wireless technology, network security, and cloud computing. Durgesh Srivastava, PhD is an Associate Professor in the Chitkara University Institute of Engineering and Technology at Chitkara University with more than 14 years of academic and research experience. He has published more than 30 papers in reputed national and international journals and conferences, as well as several books and patents. His research interests include machine learning, soft computing, pattern recognition, and software engineering, modeling, and design. Manoj Kumar Mahto, PhD is an Assistant Professor at BRCM College of Engineering and Technology. Bahal, Haryana, India. He has published more than 15 journal articles, ten book chapters, and three patents. His research interests encompass AI and machine learning, image processing, and natural language processing. Ben Othman Soufiane, PhD works in the Programming and Information Center Research Laboratory associated with the Higher Institute of Informatics and Techniques of Communication. He has published more than 70 papers in reputed international journals, conferences, and book chapters. His research focuses on the Internet of Medical Things, wireless body sensor networks, wireless networks, artificial intelligence, machine learning, and big data. Praveen Kantha, PhD is an Associate Professor in the School of Engineering and Technology at Chitkara University. He is the author of 20 research papers published in national and international journals and conferences, several book chapters, and two patents. His research interests include machine learning, intrusion detection, big data analytics, and autonomous and connected vehicles.
Context-based Modeling of Activity in Real-World Projects
Context-based Modeling of Activity in Real-World Projects presents a synthesis of 25 years of research on modeling and using context in real-world applications in a very large spectrum of domains, which allows us to illustrate the keystone aspects of context from an initial operational definition; this opens up a four-level framework under conceptual, operational, implementation and environment aspects of activity modeling. The result is the Contextual-Graphs (CxG) formalism, thanks to strong connections between context and an actor’s focus of attention, leading to a uniform representation of knowledge, reasoning and context for actor and group activity. The results of this research constitute the building blocks for designing future types of AI systems, namely the context-based intelligent assistant systems. This book presents the proceduralized context as a new definition of context, that is a real-time definition, which is then applied to context modeling for actor or group activity – before finally elaborating the two versions of the CxG formalism including uses in different modeling. Patrick Brézillon works in artificial intelligence. His research includes a four-level scientific approach leading to contextual-graph formalism, a real-time definition of context. His objective is the design of context-based intelligent assistant systems.
Adversarial Machine Learning
Enables readers to understand the full lifecycle of adversarial machine learning (AML) and how AI models can be compromised Adversarial Machine Learning is a definitive guide to one of the most urgent challenges in artificial intelligence today: how to secure machine learning systems against adversarial threats. This book explores the full lifecycle of adversarial machine learning (AML), providing a structured, real-world understanding of how AI models can be compromised—and what can be done about it. The book walks readers through the different phases of the machine learning pipeline, showing how attacks emerge during training, deployment, and inference. It breaks down adversarial threats into clear categories based on attacker goals—whether to disrupt system availability, tamper with outputs, or leak private information. With clarity and technical rigor, it dissects the tools, knowledge, and access attackers need to exploit AI systems. In addition to diagnosing threats, the book provides a robust overview of defense strategies—from adversarial training and certified defenses to privacy-preserving machine learning and risk-aware system design. Each defense is discussed alongside its limitations, trade-offs, and real-world applicability. Readers will gain a comprehensive view of today?s most dangerous attack methods including: Evasion attacks that manipulate inputs to deceive AI predictions Poisoning attacks that corrupt training data or model updates Backdoor and trojan attacks that embed malicious triggersPrivacy attacks that reveal sensitive data through model interaction and prompt injectionGenerative AI attacks that exploit the new wave of large language models Blending technical depth with practical insight, Adversarial Machine Learning equips developers, security engineers, and AI decision-makers with the knowledge they need to understand the adversarial landscape and defend their systems with confidence. Enables readers to understand the full lifecycle of adversarial machine learning (AML) and how AI models can be compromised Adversarial Machine Learning is a definitive guide to one of the most urgent challenges in artificial intelligence today: how to secure machine learning systems against adversarial threats. This book explores the full lifecycle of adversarial machine learning (AML), providing a structured, real-world understanding of how AI models can be compromised—and what can be done about it. The book walks readers through the different phases of the machine learning pipeline, showing how attacks emerge during training, deployment, and inference. It breaks down adversarial threats into clear categories based on attacker goals—whether to disrupt system availability, tamper with outputs, or leak private information. With clarity and technical rigor, it dissects the tools, knowledge, and access attackers need to exploit AI systems. In addition to diagnosing threats, the book provides a robust overview of defense strategies—from adversarial training and certified defenses to privacy-preserving machine learning and risk-aware system design. Each defense is discussed alongside its limitations, trade-offs, and real-world applicability. Readers will gain a comprehensive view of today???s most dangerous attack methods including: Evasion attacks that manipulate inputs to deceive AI predictions Poisoning attacks that corrupt training data or model updates Backdoor and trojan attacks that embed malicious triggersPrivacy attacks that reveal sensitive data through model interaction and prompt injectionGenerative AI attacks that exploit the new wave of large language models Blending technical depth with practical insight, Adversarial Machine Learning equips developers, security engineers, and AI decision-makers with the knowledge they need to understand the adversarial landscape and defend their systems with confidence. Jason Edwards, DM, CISSP, is an accomplished cybersecurity leader with extensive experience in the technology, finance, insurance, and energy sectors. Holding a Doctorate in Management, Information Systems, and Technology, Jason specializes in guiding large public and private companies through complex cybersecurity challenges. His career includes leadership roles across the military, insurance, finance, energy, and technology industries. He is a husband, father, former military cyber officer, adjunct professor, avid reader, dog dad, and popular on LinkedIn.
Practical Playwright Test
Gain cutting-edge skills in crafting reliable, efficient end-to-end tests with Playwright Test. This book is your comprehensive guide to Playwright Test that will help you to create and debug blazing fast tests, and integrate and customize Playwright Test to fit your testing needs. The book begins with an introduction to Playwright and teaches you the fundamentals of how to write tests efficiently. The book then gets into concepts like Locators and explains how to set up a CI using Playwright Test. After this, you will gain experience in two important aspects of testing – speed and customization. You will then be taken through a deep dive into Fixtures followed by an exploration of strategies like mocking and emulation through which you can achieve more control of the testing environment. The book also provides a detailed discussion on flakiness and how Playwright Test can help you with it. It then teaches you how to automate tests and ends with a discussion on how Playwright Test changes the landscape of testing, and how to integrate it in your daily practices and testing strategy. By reading this book, you will become an expert in the specificities of Playwright Test and how to test critical user flows, reduce bugs in production, and ultimately ship reliable software with confidence. What You Will Learn Create and debug reliable end-to-end tests efficiently with the perfect locatorsSet up a CI using Playwright Test to get results, test reports and useful tracesUnderstand how to use and write FixturesCustomize Playwright to fit your needs Who This Book Is For Frontend developers, full-stack developers, QA Engineers, Software Testers, Test Automation Engineers, QA Leads and QA Managers
Introduction to Programming for Researchers
Enhance your computational and programming skills using Bash and Python to improve productivity and efficiency in research projects. This book is an essential guide for STEM researchers. Structured into several parts, each builds on the previous ones to ensure a solid foundation in programming. You’ll begin with the basics of digital computation and operating systems, then write pipelines and scripts in Bash, focusing on tools for working with datasets in text files. After introducing algorithms and floating-point numbers, the book transitions to Python, emphasizing SciPy libraries and built-in features like type hints and f-strings. IPython and Jupyter notebooks are integrated into the lessons throughout. Programming best practices are taught, alongside programming basics. These include documentation and unit testing. As the target audience is STEM students and professionals, examples make heavy use of datasets and the SciPy software stack, especially NumPy, Matplotlib, Pandas, and SymPy. Introduction to Programming for Researchers will foster a deeper understanding of computational tools and critical programming skills, empowering you to tackle complex datasets and enhance their research capabilities. What You Will Learn Apply programming skills to enhance research productivity and efficiency.Write Bash pipelines and executable scripts.Implement basic algorithms in Python, focusing on time efficiency and structured programming. Who This Book Is For Experienced researchers looking to improve their computational skills; students in the natural sciences and engineering; scientists and engineers from various fields, seeking to integrate programming skills into their research methodologies.
GameMaker Programming Challenges
Upgrade your GameMaker programming skills with 500 programming challenges. The book is a collection of programming challenges, covering a range from simple to advanced concepts. GameMaker is a hugely popular tool and is regarded one of the best approaches for 2D games. GameMaker allows both visual and code-based approach for game development and has been used for multiple hit titles. Each chapter covers a certain programming element, such as Sprite Fonts, Projectiles, Mechanics, etc. The book is designed in a manner where each challenge provides an outline of the problems, useful functions, hints on tackling the challenge, and an example solution. On completion, you will take away new knowledge of GameMaker functions, an ability to think logically when developing code, and a better understanding of game design and planning. What You Will Learn Study the new GML, from basic functions to more evolved concepts.Gain ability to view example solutions when necessary.Increase your understanding of game design concepts. Who Is This Book For Beginners to intermediate level readers with basic understanding of GameMaker’s IDE, including creating object, sprite, and sound assets will benefit from this book.
AI in Legal Tech
Explore the risks, opportunities, and practicalities of generative AI in legal practice In AI in Legal Tech: How Generative AI Is Transforming Legal Technology and the Practice of Law, legal-tech pioneer and guru, Catherine Casey, walks you through the risks and opportunities presented by generative AI in the legal industry. She offers a comprehensive and accessible discussion of generative AI’s immediate and near-future impact on legal practices, ethics, and careers in law. The book translates and simplifies the complexities of generative AI and presents practical advice for anyone interested in harnessing its potential to dramatically redefine legal practice. It balances engaging narrative with expert analysis and is tailored specifically for non-technical legal professionals doing their best to navigate a new—and rapidly evolving—technological frontier. The author has also included a “Prompt Primer: Lawyered Edition” for practicing lawyers. Perfect for practicing lawyers, law students, legal technologists, and law practice managers, AI in Legal Tech is also a must-read resource for everyone who finds themselves at the intersection of law and technology. Explore the potential and risks of generative AI in the legal industry In AI in Legal Tech: How Generative AI Is Transforming Legal Technology and the Practice of Law, legal-tech pioneer and guru Catherine Casey—aka, TechnoCat—delivers a startlingly insightful and up-to-date discussion of the risks and opportunities presented by generative AI in the legal sector. The author walks you through generative AI's impact on the practice of law, legal ethics, and legal careers, offering guidance and clarity on a rapidly evolving technology. Balancing engaging narrative with expert analysis, AI in Legal Tech is written specifically for non-technical legal professionals and students doing their best to navigate the intersection of technology and law. You'll find: Explanations of how AI is shaping new legal careers and what you can do to find success in your ownA “Legal Tech Survival Kit,” complete with a comprehensive Legal AI glossary and must-try tools for tech-savvy lawyersInsights from the “front lines” of legal AI and the people designing the technologies shaping tomorrow's legal industry Perfect for practicing lawyers, law students, and law practice managers, AI in Legal Tech will also prove invaluable to legal technologists, paralegals, and anyone else interested in the application of the latest tech to the legal field. CAT CASEY is the defining voice at the intersection of AI and legal technology. A twenty-year veteran of the field and CEO of The Technocat LLC, she’s led technology and innovation at multiple legal tech Unicorns, the Big Four, and AmLaw 10 firms. Known for her sharp insight, deep technical fluency, and signature irreverence, Cat has helped shape how the legal profession navigates the age of intelligent machines.
AI in Business For Dummies
Unlock productivity and profit with AI AI is suddenly a must-have in today’s business world. AI in Business For Dummies shows you what AI can do for your organization. This practical, jargon-free guide explains how AI works and how you can use it to boost your competitive edge. With step-by-step guidance, you’ll learn to harness AI wins—better decision-making, quicker content generation, more personalized customer interactions, and beyond—without sacrificing the human element. Get on the AI train and future-proof your business with this easy-to-use Dummies guide. Inside… Grasping AI basicsWinning customers with AISpeeding up innovationGetting your team on boardStaying legal and ethicalAvoiding common pitfallsPreparing for the next wave of new AI technologies Create an AI strategy that best fits your business You've heard about how artificial intelligence will revolutionize business, but maybe you're not sure how it will revolutionize your business. In AI in Business For Dummies, AI researcher and consultant Jeffrey Allan delivers clear insight into the capabilities of AI, the AI tools that get the job done, and how to best put artificial intelligence to work in your company. Using the book's step-by-step instructions, you'll learn how to build the latest AI tech in your business strategies. You'll also discover real-world examples of effective AI implementations in tasks like workflow automation, closing sales, handling data analytics, and driving innovation. The book also dives into ideas on how to get your staff and colleagues on board as well as how to use AI in an ethical manner. AI in Business For Dummies also includes: A breakdown of the essentials of AI technology and how each intersects with business useWays to avoid common business AI mistakes and pitfallsTips on future-proofing your AI investment Perfect for managers, executives, entrepreneurs, founders, and other business leaders, AI in Business For Dummies is a must-read resource for anyone with an interest in taking advantage of the newest, most exciting technologies in business. Dr. Jeffrey Allan directs the Institute for Responsible Technology and Artificial Intelligence at Nazareth University, developing AI-focused degree programs. An expert in AI and psychology, he’s advised Fortune 500 firms and Silicon Valley startups, and he coauthored Writing AI Prompts For Dummies.