Allgemein

ISO 29119 - Die Softwaretest-Normen verstehen und anwenden

Know-how zur ISO-Norm 29119 aus erster Hand - Matthias Daigl ist Mitautor der Normenreihe 29119 und Editor von Teil 5 - Leitfaden für alle, die ein modernes Software-Testkonzept erstellen wollen und dabei Wert auf Normen-Konformität legen - mit vielen Hintergrundinformationen sowie ausführlichen Fallstudien aus den unterschiedlichsten Anwendungsbereichen Die ISO/IEC/IEEE ISO 29119 beschreibt bewährte Praktiken für das Software und Systems Engineering – Software Testing. Dieses Buch gibt eine praxisorientierte Einführung und einen fundierten Überblick und zeigt insbesondere die Umsetzung der Anforderungen aus der ISO 29119 an die Testaktivitäten auf. Der Aufbau des Buches spiegelt die Struktur der Normenreihe wider: - Entstehungsgeschichte und Kontext - Inhalte der Normenreihe ISO 29119 - Konzepte und Definitionen (Teil 1) - Testprozesse (Teil 2) - Testdokumentation (Teil 3) - Testverfahren (Teil 4) - Keyword-Driven Testing (Teil 5) - Anwendungsbeispiele Etwas kompakter werden auch die Technical Reports zur Anwendung der Normen im agilen Umfeld (ISO 29119 – Teil 6), beim Testen KI-basierter Systeme (ISO 29119 – Teil 11) und beim Testen biometrischer Systeme (ISO 20119 – Teil 13) behandelt. Das Buch richtet sich in erster Linie an Praktiker, die einen leichteren Einstieg in die Normenreihe und eine Hilfestellung bei der Umsetzung der ISO 29119 in der Praxis suchen. Die 2. Auflage wurde in vielen einzelnen Aspekten aktualisiert. Darüber hinaus wurde ein zusätzliches Projektbeispiel für den neu hinzugekommenen Teil 5 der Norm zu Keyword-Driven Testing aufgenommen.

Azure Adventures with C#

Harness the power of Azure to create cutting-edge applications, services, and infrastructure. This book is a comprehensive guide designed to help experienced C# developers master the fundamentals of Microsoft Azure. Whether you're new to Azure or looking to deepen your cloud expertise, this book provides a solid foundation in Azure basics, and resource organization, and covers some of the most commonly used Azure resources. It also includes C# solutions and bicep scripts to deploy infrastructure on Azure.The book starts with Azure fundamentals covering its structure, resource management, and pricing followed by its resource organization. You will then go through the latest version of Azure Functions and its implementation. Storage account features such as containers and queues are discussed next along with securing your applications with Managed Identity. You will learn how to manage, save, and maintain data in Azure using SQL Server and understand the advanced systems for message delivery. And you will learn about the Virtual Network and how Azure resources can be isolated from other services and from the Internet, if needed.After reading the book, you will be able to build, deploy, and manage Azure as a scalable and robust cloud solution.WHAT YOU WILL LEARN* Create a REST API using Azure Functions* Use messaging services such as Account Storage Queues, Event Grid, or Service Bus* Use trigger in Azure Function for various services in Azure* Secure solutions using Managed Identity and Virtual Networks* Store data in Account Storage and databasesWHO THIS BOOK IS FORC# developers who want to improve their knowledge and be more competitive, DevOps who desire to automate infrastructure deployment for Azure Cloud, architects who want to familiarize themselves with the fundamental services of the Azure platformMICHAŁ ŚWITALIK is a Software Engineer with over eight years of experience. Since the beginning of his journey, he always loved to share his knowledge with others. He shares his knowledge through lectures inside his company, for students, or through blog posts, and by being a mentor for trainees. Michał has diverse knowledge across Azure, SharePoint, and Microsoft 365 environments. He always loves to develop himself and doesn’t avoid hard and complex solutions for his clients.Currently working as a Principal Software Engineer at Volvo, Michał's main responsibilities are to provide secure and stable solutions for his company. He watches other team members to have the same standards in their projects. Additionally, he brings new technologies or ideas and improves ways of working to stay up to date.Michał's Microsoft Certificates: Azure Solutions Architect Expert, Azure Developer AssociateChapter 1: Introduction to Azure.- Chapter 2: Azure Function.- Chapter 3: Application Insights.- Chapter 4: Storage Account.- Chapter 5: Event Grid.- Chapter 6: Service Bus.- Chapter 7: SQL Server.- Chapter 8: Managed Identity.- Chapter 9: Virtual Network.

Angular for Beginners

In this book I will tell you all about Angular in a free discussion format. What is Angular, how can you use it to build a real application and what are all those acronyms you hear every day like pipes, interceptors, lazy loading and so on. You will learn everything in a few minutes. Let us get started. Hello, I am Abdelfattah Ragab, a professional software developer with more than 20 years of experience. I am an expert in Angular, CSS, graphic design and all web related technologies. I have published numerous books on modern CSS layouts and Angular as well as complete business solutions for e-commerce and the like. I hope you enjoy my books. With kind regards.

Was denkt die KI über die KI?

Das Buch "Was denkt die KI über die KI?" besteht aus Antworten der künstlichen Intelligenz (KI) auf menschliche Fragen über die KI. Das Werk ist in sechs Hauptteile gegliedert: 1. Grundlagen: Was bedeutet Denken und was bedeutet KI? Es werden die Grundbegriffe erklärt und die Fähigkeiten von KIs im Vergleich zum menschlichen Denken diskutiert. 2. Anwendungsbereiche der KI: Es wird beleuchtet, wie KI verschiedene Berufsfelder wie Medizin, Architektur, Personalwesen und viele andere revolutionieren könnte. 3. KI und Ethik: Hier werden ethische Fragen und die Verantwortlichkeiten im Umgang mit KI behandelt, einschließlich der Entscheidungsgewalt von KIs in kritischen Situationen. 4. Risiken der KI und deren Regulierung: Dieser Teil widmet sich den Risiken, die mit der Nutzung von KI verbunden sind, und diskutiert mögliche regulatorische Maßnahmen. 5. KI und Fake News: Die Rolle der KI bei der Erzeugung und Verbreitung von Falschinformationen wird untersucht, sowie ihre Fähigkeiten, echte von gefälschten Informationen zu unterscheiden. 6. KI und der Mensch: Abschließend wird die Beziehung zwischen KI und Mensch betrachtet, wie KI unsere Wahrnehmung von Technologie beeinflusst und welche Ängste und Hoffnungen damit verbunden sind. Das Buch schließt mit einem Anhang, der wichtige Begriffe erläutert, die im Zusammenhang mit KI stehen. Es wird betont, dass die menschliche Überprüfung von KI-Aussagen unerlässlich bleibt, insbesondere bei Fragen, die tiefe ethische und kulturelle Verständnisse erfordern. Olivier Hofmann: Olivier Hofmann wurde 1962 in Zürich geboren. Er hat Betriebswirtschaft studiert und befasst sich mit vielen interdisziplinären Fragestellungen.

Modeling and Optimization of Signals Using Machine Learning Techniques

EXPLORE THE POWER OF MACHINE LEARNING TO REVOLUTIONIZE SIGNAL PROCESSING AND OPTIMIZATION WITH CUTTING-EDGE TECHNIQUES AND PRACTICAL INSIGHTS IN THIS OUTSTANDING NEW VOLUME FROM SCRIVENER PUBLISHING.Modeling and Optimization of Signals using Machine Learning Techniques is designed for researchers from academia, industries, and R&D organizations worldwide who are passionate about advancing machine learning methods, signal processing theory, data mining, artificial intelligence, and optimization. This book addresses the role of machine learning in transforming vast signal databases from sensor networks, internet services, and communication systems into actionable decision systems. It explores the development of computational solutions and novel models to handle complex real-world signals such as speech, music, biomedical data, and multimedia. Through comprehensive coverage of cutting-edge techniques, this book equips readers with the tools to automate signal processing and analysis, ultimately enhancing the retrieval of valuable information from extensive data storage systems. By providing both theoretical insights and practical guidance, the book serves as a comprehensive resource for researchers, engineers, and practitioners aiming to harness the power of machine learning in signal processing. Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture. CHANDRA SINGH is an assistant professor in the Department of Electronics and Communication Engineering at Sahyadri College of Engineering and Management, Mangalore, India, and is pursuing a PhD from VTU Belagavi, India. He has four patents, he has published over 25 papers in scientific journals, and he is the editor of seven books. RATHISHCHANDRA R. GATTI, PHD, is an associate professor at Jawaharlal Nehru University, Delhi, India. With over 20 years of industrial, research, and teaching experience under his belt, he also has four patents, has published over 40 papers in scientific journals, and is the editor of seven research books and one journal. K.V.S.S.S.S.SAIRAM, PHD, is a professor and Head of the Electronics and Communication Engineering Department at the NMAM Institute of Technology, Nitte, India. He has 25 years of experience in teaching and research and has published over 50 papers in international journals and conferences. He is also a reviewer for several journals. MANJUNATHA BADIGER, PHD, is an assistant professor at Sahyadri College of Engineering and Management, Adyar, Mangalore, Karnataka, India. He has over 12 years of experience in academics, research, and administration. He earned his PhD in machine learning in 2024 at Visvesvaraya Technological University. NAVEEN KUMAR S., MTECH, is an assistant professor at the Sahyadri College of Engineering and Management. Previously he was an assistant professor at JSS Academy of Technical Education, Noida, India. He obtained his MTech in automotive electronics from Sri Jayachamarajendra College of Engineering, Mysore, India. VARUN SAXENA, PHD, received his PhD in electromagnetic ion traps from IIT Delhi, New Delhi, in 2018. He is currently an assistant professor at the School of Engineering, Jawaharlal Nehru University, New Delhi. Preface xix1 LAND USE AND LAND COVER MAPPING OF REMOTELY SENSED DATA USING FUZZY SET THEORY-RELATED ALGORITHM 1Adithya Kumar and Shivakumar B.R.1.1 Introduction 21.2 Image Classification 51.3 Unsupervised Classification 71.4 Supervised Classification 81.5 Overview of Fuzzy Sets 91.6 Methodology 111.7 Results and Discussion 161.8 Conclusion 212 ROLE OF AI IN MORTALITY PREDICTION IN INTENSIVE CARE UNIT PATIENTS 23Prabhudutta Ray, Sachin Sharma, Raj Rawal and Dharmesh Shah2.1 Introduction 242.2 Background 242.3 Objectives 252.4 Machine Learning and Mortality Prediction 262.5 Discussions 342.6 Conclusion 342.7 Future Work 352.8 Acknowledgments 352.9 Funding 352.10 Competing Interest 353 A SURVEY ON MALWARE DETECTION USING MACHINE LEARNING 41Devika S. P., Pooja M. R. and Arpitha M. S.3.1 Background 413.2 Introduction 423.3 Literature Survey 443.4 Discussion 533.5 Conclusion 534 EEG DATA ANALYSIS FOR IQ TEST USING MACHINE LEARNING APPROACHES: A SURVEY 55Bhoomika Patel H. C., Ravikumar V. and Pavan Kumar S. P.4.1 Related Work 574.2 Equations 624.3 Classification 644.4 Data Set 654.5 Information Obtained by EEG Signals 694.6 Discussion 704.7 Conclusion 725 MACHINE LEARNING METHODS IN RADIO FREQUENCY AND MICROWAVE DOMAIN 75Shanthi P. and Adish K.5.1 Introduction 765.2 Background on Machine Learning 775.3 ML in RF Circuit Modeling and Synthesis 865.4 Conclusion 936 A SURVEY: EMOTION DETECTION USING FACIAL REORGANIZATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND VIOLA-JONES ALGORITHM 97Vaibhav C. Gandhi, Dwij Kishor Siyal, Shivam Pankajkumar Patel and Arya Vipesh Shah6.1 Introduction 986.2 Review of Literature 996.3 Report on Present Investigation 1016.4 Algorithms 1026.5 Viola-Jones Algorithm 1046.6 Diagram 1056.7 Results and Discussion 1076.8 Limitations and Future Scope 1116.9 Summary and Conclusion 1117 POWER QUALITY EVENTS CLASSIFICATION USING DIGITAL SIGNAL PROCESSING AND MACHINE LEARNING TECHNIQUES 115E. Fantin Irudaya Raj and M. Balaji7.1 Introduction 1167.2 Methodology for the Identification of PQ Events 1177.3 Power Quality Problems Arising in the Modern Power System 1187.4 Digital Signal Processing-Based Feature Extraction of PQ Events 1247.5 Feature Selection and Optimization 1297.6 Machine Learning-Based Classification of PQ Disturbances 1317.7 Summary and Conclusion 1418 HYBRIDIZATION OF ARTIFICIAL NEURAL NETWORK WITH SPOTTED HYENA OPTIMIZATION (SHO) ALGORITHM FOR HEART DISEASE DETECTION 145Shwetha N., Gangadhar N., Mahesh B. Neelagar, Sangeetha N. and Virupaxi Dalal8.1 Introduction 1468.2 Literature Survey 1478.3 Proposed Methodology 1498.4 Artificial Neural Network 1528.5 Software Implementation Requirements 1638.6 Conclusion 1709 THE ROLE OF ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING TO COMBAT THE SOCIO-ECONOMIC IMPACT OF THE GLOBAL COVID-19 PANDEMIC 173Biswa Ranjan Senapati, Sipra Swain and Pabitra Mohan Khilar9.1 Introduction 1749.2 Discussions on the Coronavirus 1759.3 Bad Impacts of the Coronavirus 1809.4 Benefits Due to the Impact of COVID-19 1869.5 Role of Technology to Combat the Global Pandemic COVID-19 1909.6 The Role of Artificial Intelligence, Machine Learning, and Deep Learning in COVID-19 1989.7 Related Studies 2039.8 Conclusion 20310 A REVIEW ON SMART BIN MANAGEMENT SYSTEMS 209Bhoomika Patel H. C., Soundarya B. C. and Pooja M. R.10.1 Introduction 20910.1.1 Internet of Things (IoT) 21010.2 Related Work 21110.3 Challenges, Solution, and Issues 21310.4 Advantages 21611 UNLOCKING MACHINE LEARNING: 10 INNOVATIVE AVENUES TO GRASP COMPLEX CONCEPTS 219K. Vidhyalakshmi and S. Thanga Ramya11.1 Regression 22011.2 Classification 22211.3 Clustering 22711.4 Clustering (k-means) 22711.5 Reduction of Dimensionality 23011.6 The Ensemble Method 23311.7 Transfer of Learning 24011.8 Learning Through Reinforcement 24111.9 Processing of Natural Languages 24211.10 Word Embeddings 24211.11 Conclusion 24312 RECOGNITION ATTENDANCE SYSTEM ENSURING COVID-19 SECURITY 245Praveen Kumar M., Ramya Poojary, Saksha S. Bhandary and Sushmitha M. Kulal12.1 Introduction 24612.2 Literature Survey 24612.3 Software Requirements 24812.4 Hardware Requirements 24912.5 Methodology 25212.6 Building the Database 25312.7 Pi Camera for Extracting Face Features 25512.8 Real-Time Testing on Raspberry Pi 25612.9 Contactless Body Temperature Monitoring 25612.10 Raspberry-Pi Setting Up an SMTP Email 25812.11 Uploading to the Database 25912.12 Updating the Website 26012.13 Report Generation 26012.14 Result 26212.15 Discussion 26712.16 Conclusion 26713 REAL-TIME INDUSTRIAL NOISE CANCELLATION FOR THE EXTRACTION OF HUMAN VOICE 271Vinayprasad M. S., Chandrashekar Murthy B. N. and Yashwanth S. D.13.1 Introduction 27213.2 Literature Survey 27313.3 Methodology 27513.4 Experimental Results 27813.5 Conclusion 28014 MACHINE LEARNING-BASED WATER MONITORING SYSTEM USING IOT 283T. Kesavan, E. Kaliappan, K. Nagendran and M. Murugesan14.1 Introduction 28314.2 Smart Water Monitoring System 28414.3 Sensors and Hardware 28614.4 PowerBI Reports 28814.5 Conclusion 29115 Design and Modelling of an Automated Driving Inspector Powered by Arduino and Raspberry Pi 295Raghunandan K. R., Dilip Kumar K., Krishnaraj Rao N.S. Krishnaprasad Rao and Bhavya K.15.1 Introduction 29615.2 Literature Survey 29615.3 Results 30615.4 Conclusion 30916 KALMAN FILTER-BASED SEIZURE PREDICTION USING CONCATENATED SERIAL-PARALLEL BLOCK TECHNIQUE 313Purnima P. S. and Suresh M.16.1 Introduction 31416.2 Prior Work 31416.3 Proposed Method 31616.4 Serial-Parallel Block Concatenation Approach 31816.5 Algorithm 31916.6 Kalman Filter 32016.7 Results and Discussion 32116.8 Conclusion 32317 CURRENT ADVANCEMENTS IN STEGANOGRAPHY: A REVIEW 327Mallika Garg, Jagpal Singh Ubhi and Ashwani Kumar Aggarwal17.1 Introduction 32817.2 Evaluation Parameters 32917.3 Types of Steganography 33017.4 Traditional Steganographic Techniques 33217.5 CNN-Based Steganographic Techniques 33617.6 GAN-Based Steganographic Techniques 33817.7 Steganalysis 34017.8 Applications 34117.9 Dataset Used for Steganography 34117.10 Conclusion 34418 HUMAN EMOTION RECOGNITION INTELLIGENCE SYSTEM USING MACHINE LEARNING 349Bhakthi P. Alva, Krishma Bopanna N., Prajwal S., Varun A. Naik and Lahari Vaidya18.1 Introduction 35018.2 Literature Review 35018.3 Problem Statement 35218.4 Methodology 35318.5 Results 35518.6 Applications 35518.7 Conclusion 35718.8 Future Work 35719 COMPUTING IN COGNITIVE SCIENCE USING ENSEMBLE LEARNING 361Om Prakash Singh19.1 Introduction 36219.2 Recognition of Human Activities 36319.3 Methodology 36619.4 Applying the Boosting-Based Ensemble Learning 36919.5 Human Activity Features Computability 37319.6 Conclusion 378References 378About the Editors 383Index 385

Marketing with AI For Dummies

STAY AHEAD IN THE MARKETING GAME BY HARNESSING THE POWER OF ARTIFICIAL INTELLIGENCEMarketing with AI For Dummies is your introduction to the revolution that’s occurring in the marketing industry, thanks to artificial intelligence tools that can create text, images, audio, video, websites, and beyond. This book captures the insight of leading marketing executive Shiv Singh on how AI will change marketing, helping new and experienced marketers tackle AI marketing plans, content, creative assets, and localized campaigns. You’ll also learn to manage SEO and customer personalization with powerful new technologies.* Peek at the inner workings of AI marketing tools to see how you can best leverage their capabilities* Identify customers, create content, customize outreach, and personalize customer experience with AI* Consider how your team, department, or organization can be retooled to thrive in an AI-enabled world* Learn from valuable case studies that show how large organizations are using AI in their campaignsThis easy-to-understand Dummies guide is perfect for marketers at all levels, as well as those who only wear a marketing hat occasionally. Whatever your professional background, Marketing with AI For Dummies will usher you into the future of marketing.SHIV SINGH is a future-focused busi­­ness executive who has developed and executed cutting-edge marketing strategies, tools, and techniques for some of the world’s largest brands. He is also the trailblazing author of Social Media Marketing For Dummies and Savvy, Navigating Fake Companies, Leaders & News. Along the way, he has served as VP and Global Social Media Lead for Razorfish, Head of Digital for PepsiCo Beverages, SVP Innovation Go-to-Market for Visa, and most recently, as the Chief Marketing and Customer Experience Officer for LendingTree, where he managed a media budget of $650 million and led a team of 150 marketers. INTRODUCTION 1About This Book 1Foolish Assumptions 3Icons Used in This Book 3Beyond the Book 4Where to Go from Here 4PART 1: GETTING STARTED WITH MARKETING WITH AI 5CHAPTER 1: A BRIEF HISTORY OF AI 7Early Technological Advances 7Alan Turing and Machine Intelligence 8The Dartmouth Conference of 1956 10Machine Learning and Expert Systems Emerge 12An AI Winter Sets In 14The Stanford Cart: From the ’60s to the ’80s 15More AI Developments in the 1980s 16Rapid Advancements of AI in the 1990s and Beyond 17CHAPTER 2: EXPLORING AI BUSINESS USE CASES 23Automating Customer Service 24Enhancing Product and Technology with AI 28Accelerating Research and Development 32Giving Marketing an AI Boost 36Optimizing Sales with AI 39Adding AI to Legal Activities 41CHAPTER 3: LAUNCHING INTO THE AI MARKETING ERA 45Ready or Not: AI Is Your New Marketing Copilot 46Watching AI Upend the Corporate World 48Taking Foundational Steps Toward AI Marketing 49Adopting a Strategic Framework for Entering the AI Era 54PART 2: EXPLORING FUNDAMENTAL AI STRUCTURES AND CONCEPTS 61CHAPTER 4: COLLECTING, ORGANIZING, AND TRANSFORMING DATA 63Defining Data in the Context of AI 64Choosing Data Collection Methods for Marketing with AI 67Putting Your Marketing Data in Its Place 69Understanding Data via Manual and Automated Systems 71Preparing the Data for Use by AI Algorithms and Models 72CHAPTER 5: MAKING CONNECTIONS: MACHINE LEARNING AND NEURAL NETWORKS 77Examining the Process of Machine Learning 78Understanding Neural Networks 79Supervised and Unsupervised Learning 81Exploring Reinforcement Learning 83Mastering Sequences and Time Series 85Developing Vision and Image Processing in AI 88Tools for Machine Learning and Neural Networks 91CHAPTER 6: ADDING NATURAL LANGUAGE PROCESSING AND SENTIMENT ANALYSIS 93Demystifying the Backbone of NLP 94Elevating NLP with Machine Learning 96Examining Transformers and Attention Mechanisms 98Unpacking Sentiment Analysis 99Catching the feeling 100Understanding language nuances 100Integrating social media analytics 101Challenges for NLP and Sentiment Analysis 101Engaging Best Practices for Using NLP and Sentiment Analysis 102CHAPTER 7: COLLABORATING VIA PREDICTIONS, PROCEDURES, SYSTEMS, AND FILTERING 105Understanding Predictive Analytics 106Putting AI Procedures into Practice 111The AI System Development Lifecycle 112Understanding Filtering in AI 114CHAPTER 8: GETTING COMFORTABLE WITH GENERATIVE AI 119Changing the Game with Generative AI 120Getting to Know GPT Models 125Creating New Text, Images, and Video 126Introducing Major Consumer-Facing Generative AI Models 128Addressing the Challenges of Using Generative AI Models 129PART 3: USING AI TO KNOW CUSTOMERS BETTER 133CHAPTER 9: SEGMENTATION AND PERSONA DEVELOPMENT 135Exploring Behavioral Segmentation Elements 136Sourcing the Right Customer Data 137Seeing How AI Performs Segmentation 139Refining, Validating, and Enhancing Segmentation Models 140Aligning Persona Development 142Leveraging AI Personas for All Business Efforts 144Employing Synthetic Customer Panels 147CHAPTER 10: LEAD SCORING, LTV, AND DYNAMIC PRICING 151Working Together: Three Core Concepts 152Scoring Leads with the Help of AI 154Calculating Lifetime Value to Affect Lead Scoring 159Turning Lead Scoring and LTV Insights into Dynamic Pricing 162CHAPTER 11: CHURN MODELING AND MEASUREMENT WITH AI 165Getting the Scoop on Churn Modeling 166Ramping Up Your Measurement Operations 173Checking Out Tools for Churn Modeling and Measurement Operations 180PART 4: TRANSFORMING BRAND CONTENT AND CAMPAIGN DEVELOPMENT 181CHAPTER 12: USING AI FOR IDEATION AND PLANNING 183Engaging AI to Ideate on Behalf of Human Beings 184Deciding whether AI Hallucinations Are a Feature or a Bug 187Following Practical Steps for Idea Generation with AI 190Deciding on AI Ideation Tools to Use 193CHAPTER 13: PERFECTING PROMPTS FOR CONVERSATIONAL INTERFACES 197Reviewing Use Cases for Conversational Interfaces 198Writing Strong Prompts to Guide AI Responses 201Good and Bad Marketing Prompt Design Examples 205Refining and Iterating Strong Prompts 206Fighting AI Bias in Prompt Writing 207Using Prompt Design Apps 209CHAPTER 14: DEVELOPING CREATIVE ASSETS 211Trying Out an AI-Generated Where’s Waldo? Illustration 212Exploring an Approach for Creating Visual Assets with AI 214Enhancing Existing Creative Assets 219Fine-Tuning Creativity with AI Tools and Techniques 222Choosing AI Tools for Creating Visual Assets 227CHAPTER 15: SEARCH ENGINE OPTIMIZATION (SEO) IN THE AI ERA 229Describing Search Generative Experiences (SGEs) 230Strategies for SEO Success in the AI Era 232Enhancing the User Experience with AI 235Maximizing Your SEO Efforts 236Knowing the AI Tools to Use with SEO 242CHAPTER 16: PERFORMING A/B TESTING WITH AI 245Examining the Fundamentals of A/B Testing 246Surveying A/B Testing Extensions 249Gathering AI Tools for A/B Testing 253CHAPTER 17: FINE-TUNING CONTENT WITH LOCALIZATION AND TRANSLATION 255Exploiting AI for Localization and Translation 256Adopting Core Strategies for Localization 262Examining Real-Time Localization and Translation Solutions 265PART 5: TARGETING GROWTH MARKETING AND CUSTOMER FOCUS WITH AI 269CHAPTER 18: APPLYING AI TO PERFORMANCE MARKETING 271Examining Google Performance Max 272Exploring Meta Advantage+ Campaigns 276Inspecting Amazon Ads 281Taking Stock of TikTok Advertising 285AI Tools for Performance Marketing 287CHAPTER 19: E-MAIL AND SMS MARKETING WITH AI 289Tracking E-mail and SMS Marketing 290Adding the Power of AI to E-mail and SMS Marketing 292AI-Powered E-mail and SMS Marketing Tools 297CHAPTER 20: DIVING INTO PERSONALIZED MARKETING 299Adapting Marketing to Meet Consumer Personalization Preferences 300Examining Personalization Concepts 302Unlocking the Deeper Value of Personalization with Generative AI 305Making Personalization Operational with AI 307AI Tools to Help with Personalization 316CHAPTER 21: LEADING YOUR BUSINESS IN THE AI ERA 319Following Steps for Integrating AI into Your Business 320Building AI Capability within Marketing 323Integrating Marketing with the Rest of the Enterprise 328Organizing for the Future 332CHAPTER 22: ADDRESSING ETHICAL, LEGAL, AND PRIVACY CONCERNS WITH AI 335Operating Principles for Ethical AI 336Using All Data Responsibly 340Fighting Bias in Data and Results 344Protecting Copyright and Intellectual Property 346Facing the Deepfake Problem 347Saving Human Beings from Artificial Intelligence 351PART 6: THE PART OF TENS 355CHAPTER 23: TENS PITFALLS TO AVOID WHEN MARKETING WITH AI 357Ignoring Qualitative Insights 357Depending Solely on Generated Personas 358Relying Only on AI for Creative Briefs 358Bypassing Human Creativity 359Losing Your Brand Voice 359Neglecting Emerging Media Channels 360Over-Optimizing for Short-Term Goals 360Creeping Customers Out 361Ignoring the Value of the Human Touch 362Relying Solely on AI for ROI Analysis 362CHAPTER 24: TEN FUTURE AI DEVELOPMENTS TO WATCH FOR 365Quantum Computing–Aided AI 365Autonomous Creative Campaigns 366Cognitive AI Systems for Deep Insights 367AI-Driven Virtual Reality Experiences 367Neural Interface for Marketing Insights 368AI-Curated Personal Digital Realities 368Synthetic Media for Dynamic Content 369Predictive World Modeling 370AI as a Customer Behavior Simulator 370Molecular-Level Product Customization 371Index 373

Atlas der KI (3. Auflg.)

Wir tendieren dazu, künstliche Intelligenz als eine wundersame und körperlose Form der maschinellen Klugheit zu betrachten. Von der preisgekrönten Wissenschaftlerin Kate Crawford lernen wir hingegen, dass KI in Wahrheit weder künstlich noch intelligent ist, sondern in ihrer materiellen Wirklichkeit auf Ressourcenausbeutung und Machtkonzentration hinausläuft.Crawford nimmt uns mit auf eine faszinierende Reise zu Lithiumminen und Klickfabriken, zu automatisierten Arbeitsplätzen und riesigen Datenarchiven, zu AI-Trainingscamps und zum algorithmischen Kriegsführungsteam des Pentagon. Auf diese Weise zeichnet sie einen Atlas der künstlichen Intelligenz, der die verschiedenen Bereiche ihrer konkreten Realität kartiert, um unser kritisches Auge zu schulen. Gestützt auf ein Jahrzehnt originärer Forschung zeigt Crawford, dass KI in erster Linie eine Technologie der Extraktion ist – der Abschöpfung von Mineralien, billiger Arbeitskraft und einer unermesslichen Anzahl von Daten. Das planetare Netzwerk der KI schädigt unsere Umwelt massiv, vertieft soziale Ungleichheiten und bedroht demokratische Prinzipien. Crawfords Buch liefert uns einen dringlichen Bericht, was auf dem Spiel steht, wenn große Unternehmen und staatliche Institutionen KI nutzen, um die Welt umzugestalten.Leseprobe (PDF-Link)Über die Autorin:Kate Crawford ist eine international führende Wissenschaftlerin für Künstliche Intelligenz und ihre Auswirkungen. Sie ist Professorin an der University of Southern California in Los Angeles, Forschungsleiterin bei Microsoft Research in New York und war die erste Gastprofessorin für KI und Justiz an der École Normale Supérieure in Paris. Ihr Buch «Atlas of AI» wurde mehrfach ausgezeichnet, in mehr als zehn Sprachen übersetzt und vom New Scientist und der Financial Times zu einem der Bücher des Jahres gekürt. Das Time Magazine hat sie in die TIME100-Liste als eine der einflussreichsten Personen im Bereich KI aufgenommen.

Datenmanagement und Datenanalyse

Derzeit wird kaum noch ernsthaft bestritten, dass die Daten eines Unternehmens ein wichtiges Wirtschaftsgut darstellen und in erheblicher Weise zum Erfolg beitragen können. Allerdings gilt es, nicht nur den Wert der Daten zu erkennen, sondern diese auch in den zugehörigen Geschäftsprozessen gewinnbringend einzusetzen. Als zwingende Voraussetzung erweist sich dabei, eine organisatorische und technische Basis zu etablieren, die nachhaltig darauf ausgerichtet ist, die fachlichen Ausgabenstellungen bestmöglich zu unterstützen. Unter dem Oberbegriff Datenmanagement werden dazu diejenigen Führungs- und Durchführungstätigkeiten diskutiert, die einen tragfähigen Rahmen für den Umgang mit Daten in der Organisation aufspannen. Daneben erweisen sich vor allem die Verfahren zur Auswertung und Analyse der verfügbaren Datenbestände als leistungsfähige Instrumente, um langfristige Wettbewerbsvorteile zu erlangen. Vor diesem Hintergrund widmet sich das vorliegende Buch den stetig an Bedeutung gewinnenden Themenfeldern Datenmanagement und Datenanalyse, denen insbesondere im Rahmen der digitalen Transformation eine große Bedeutung zukommt.PROF. DR. PETER GLUCHOWSKI leitet den Lehrstuhl für Wirtschaftsinformatik, insbesondere Systementwicklung und Anwendungssysteme, an der Technischen Universität in Chemnitz und konzentriert sich dort mit seinen Forschungsaktivitäten auf das Themengebiet Business Intelligence. Er beschäftigt sich seit rund 20 Jahren mit Fragestellungen, die den praktischen Aufbau dispositiver bzw. analytischer Systeme zur Entscheidungsunterstützung betreffen. Seine Erfahrungen aus unterschiedlichsten Praxisprojekten sind in zahlreichen Veröffentlichungen zu diesem Themenkreis dokumentiert.Einführung.- Abgrenzung und Definition: Daten, Information, Wissen.- Datenwert.- Datenarten.- Datenarchitekturen.- Big Data.- Verarbeitung der Daten (Data Processing).- Speicherung von Daten.-Datenübertragung.- Management von Daten in Unternehmen.- Datenstrategie.- Data Governance.- Datenmanagement.- Datenanalyse.- Begriffliche Einordnung.- Anwendungsdomänen von Analytics.- Methoden der Data Analytics.- Vorgehensmodelle für Data-Analytics-Projekte.

Der dynamikrobuste Strategieprozess

Strategiearbeit braucht ein Upgrade. Strategie muss omnipräsent werden. Kürzere Zyklen lassen die Strategie schneller an das reale Marktgeschehen anpassen. Zudem lässt sie sich im Tagesgeschäft deutlich besser verankern. Mit dem dynamikrobusten Strategieprozess liefert Roman P. Büchler einen zukunftsfähigen Ansatz für unsichere Zeiten.ROMAN P. BÜCHLER ist Gründer, Inhaber und Geschäftsführer der FORAN GmbH in St. Gallen in der Schweiz. Nach vielen Jahren erfolgreicher Tätigkeit als Verwaltungsrat, CEO und Unternehmensberater mit den Tätigkeits-Schwerpunkten Strategieentwicklung und Digitale Transformation entschloss er sich, seinen Überzeugungen zu folgen. Er gründete die FORAN, um Führungskräfte und Organisationen zu begleiten und sie auf einer höherenWirkungsebene zu unterstützen. Der Experte für Leadership, Digitale Transformation und Organisationsberatung hat als Autor bereits zahlreiche Fachartikel publiziert und zwei Bücher zu den Themen strategisches IT-Management und Leadership herausgegeben.Warum Strategiearbeit ein Upgrade braucht.- Dynamikrobust – Strategie für unsichere Zeiten.- Der moderne Strategieprozess -Das grosse Zielbild- Agile Ansätze in derStrategiearbeit-Die omnipräsente Strategie-Der Strategieprozess und seine Elemente (Backlog, Planung, Sprint, Review, Retrospektive, Strategieprozess-Owner und Strategie-Team).- Die Strategie der Zukunft.- Die Wirkung und was Sie gewinnen.

Künstliche Intelligenz im Supply Chain Management - Potenziale und Grenzen der KI

Komplexität, Vernetzung und Digitalisierung prägen heute die moderne Geschäftswelt. Die Notwendigkeit für Unternehmen, ihre Liefernetzwerke künftig resilienter gegen externe Einflüsse aufzustellen, ist bereits heute deutlich erkennbar. Veränderungen durch Klimawandel, Pandemien oder Versorgungsengpässe erschweren die Handhabung. KI-Technologien können dabei eine wertvolle Unterstützung leisten. Das vorliegende Buch soll ein grundlegendes Verständnis über die KI-Technologien vermitteln und insbesondere den Fokus auf die Konzeptentwicklung im SCM legen.DR.-ING. ALEXANDER GOUDZ ist wissenschaftlicher Mitarbeiter am Lehrstuhl Transportsysteme und –logistik an der Universität Duisburg-Essen. Seine Tätigkeitsschwerpunkte sind u.a. Anwendungen der Blockchain-Technologie und Künstlicher Intelligenz in der Logistik.SIBEL ERDOGAN studierte Wirtschaftsingenieurwesen an der Universität Duisburg-Essen. Es ist ihr ein besonderes Anliegen, KI den Menschen zugänglich zu machen..- Einleitung..- Grundlagen der KI.- Digitalisierung der Supply Chain..- Anwendungsgebiete und aktuelle Forschung zur künstlichen Intelligenz im SCM..- Fazit.

Microsoft Lists Essentials

Unlock the full potential of Microsoft Lists, an indispensable tool within the Microsoft 365 suite for tracking and organizing information. This book is your comprehensive guide, from the fundamentals to the advanced capabilities of Microsoft Lists, and will help you improve productivity and foster collaboration. Whether you’re making a basic to-do list or managing a complex inventory system, Lists is built for a broad spectrum of users, from beginners to professionals, catering to diverse enterprise and consumer use cases.The book provides thorough coverage of a wide array of topics, from the history of the product and initial setup to the entire set of features it offers. Insights are presented on using templates, optimizing columns, managing your lists, mastering the views, JSON features, and the various sharing and collaboration tools to utilize the product for maximum productivity. The book includes the latest features, such as commenting, Lists forms, real-time presence, and an in-depth look at the mobile experience.This guidebook is designed to transform your approach to information tracking and management. It offers practical examples, scenarios, and step-by-step instructions, complemented by rich visuals. The book is ideal for enterprises seeking to boost team productivity and also for individuals who wish to manage information with friends and family.WHAT YOU WILL LEARN* Optimize information management and manage lists like a pro, with list templates, custom columns, views, and JSON customization* Boost collaboration through sharing and commenting, and by employing Microsoft's security and privacy features* Maintain productivity on the go by navigating and managing lists on mobile devices, keeping your information accessible anywhere* Enhance productivity by connecting Microsoft Lists with other products in the Microsoft 365 ecosystemWHO THIS BOOK IS FORBusiness professionals. IT administrators, and individuals keen on leveraging Microsoft Lists within the Microsoft 365 ecosystemNAVEED AHMED is based in Toronto, Canada. He is a distinguished user experience designer with a career of over a decade, working at the intersection of technology and human-centric design. During his tenure with industry leaders, such as Microsoft, Adobe, and Red Hat, Naveed has made significant contributions to the design excellence of products that have reached millions of users worldwide. Between 2018 and 2023, Naveed played a critical role within the OneDrive & SharePoint team at Microsoft. As a Senior Design Manager, he was pivotal in shaping the UX Design for Microsoft Lists. His efforts were key to modernizing the product and enhancing the user experience across numerous web features, and with the launch of its mobile apps.Chapter 1: Introduction to Microsoft Lists.- Chapter 2: Getting Started with Microsoft Lists.- Chapter 3: Basics of List Creation.- Chapter 4: Columns and Column Formatting.- Chapter 5: Working with Lists.- Chapter 6: Mastering List Views.- Chapter 7: Collaboration in Lists.- Chapter 8: Lists on the go: Mobile & tablet apps.- Chapter 9: Advanced Productivity with Lists.- Chapter 10: Appendix 1: Supported CSS Styles in JSON Formatting.

Statistical Learning Tools for Electricity Load Forecasting

Introduction.- Part I: A Toolbox of Models.- Additive Modelling of Electricity Demand with mgcv.- Probabilistic GAMs: Beyond Mean Modelling.- Functional Time Series.- Random Forests.- Aggregation of Experts.- Mixed Effects Models for Electricity Load Forecasting.- Part II: Case Studies: Models in Action on Specific Applications.- Disaggregated Forecasting of the Total Consumption.- Aggregation of Multi-Scale Experts.- Short-Term Load Forecasting using Fine-Grained Data.- Functional State Space Models.- Forecasting Daily Peak Demand using GAMs.- Forecasting During the Lockdown Period.

Programmierung in Python

Wer heute das Programmieren lernen möchte, kommt nicht daran vorbei, sich mit einer der beliebtesten Programmiersprachen für Einsteiger und Profis zu befassen: Python. Mit Python hat ihr Erfinder, Guido van Rossum, einen Nerv der Zeit getroffen, denn das Konzept dahinter bietet viele Ansätze, Lösungen und Vorgehensweise für Probleme, die andere Sprachen in dieser Form nicht integrieren. Dieses Buch ist ein idealer Einstieg in die Programmierung mit Python. Ausführlich erläutert der Autor die elementaren Grundlagen, die nötig sind, um mit dieser Sprache Programme zu erstellen und zu pflegen. Daneben zeigt er, wie sich die OOP (objektorientierte Programmierung) mit Python realisieren lässt und wie man Module und API-Schnittstellen integriert. Diverse weiterführende Themen wie die Erstellung grafischer Oberflächen oder der Zugriff auf Dateien und Datenbanken runden das Fachbuch ab. Einfache Beispiele veranschaulichen die grundsätzliche Anwendung der verschiedenenTechniken und machen das Buch dadurch zu einer unbedingten Empfehlung für Einsteiger und Praktiker, die die Programmierung mit Python lernen wollen. Der Inhalt * Grundlagen * Tools * Grundlegende Konzepte und die Syntax * Anweisungen * Datentypen, Variablen und Literale * Ausdrücke, Operatoren und Operanden * Funktionen * Sequenzielle Datenstrukturen * Objektorientierte Programmierung in Python * Exceptionhandling * String-Verarbeitung * Datei-, Datenträger- und Datenbankzugriffe * Umgang mit Datum und Zeit * Grafische Oberflächen (GUI) mit Python Die Zielgruppen * Praktiker, die Python lernen wollen * Einsteiger, die mit Python das Programmieren lernen wollen * Auszubildende zum Fachinformatiker Der Autor Ralph Steyer ist Diplom-Mathematiker und hat sich 1996 nach fünf Jahren als Programmierer bei einer Versicherung als Freiberufler im Bereich EDV-Schulung, Programmierung und Beratung selbstständig gemacht. Spezialgebiete sind die Internetprogrammierung und OOP. Er unterrichtet in Firmen und Fachhochschulen und veröffentlicht regelmäßig Zeitungsartikel, Videotrainings und Fachbücher. Wer heute das Programmieren lernen möchte, kommt nicht daran vorbei, sich mit einer der beliebtesten Programmiersprachen für Einsteiger und Profis zu befassen: Python. Mit Python hat ihr Erfinder, Guido van Rossum, einen Nerv der Zeit getroffen, denn das Konzept dahinter bietet viele Ansätze, Lösungen und Vorgehensweise für Probleme, die andere Sprachen so nicht integrieren.  Das Potential dieser einfachen und übersichtlichen Programmiersprache haben auch viele Universitäten erkannt, die mittlerweile in den Anfängerkursen der Informatik-bezogenen Studiengänge Python statt Java als Einsteigersprache lehren. Der klare Programmierstil legt darüber hinaus eine hervorragende Grundlage für das spätere Erlernen weiterer Sprachen. Denn Python unterstützt nicht nur die objektorientierte und aspektorientierte, sondern auch die strukturierte und funktionale Programmierung. So wird der Programmierer nicht zu einem einzigen Programmierstil gezwungen, sondern kannflexibel das am besten geeignete Paradigma für die jeweilige Aufgabe wählen. Der universelle Zugang, der es möglich macht, die Erfahrungen aus anderen Programmierkonzepten mehr oder weniger direkt weiter zu nutzen, ist ein weiterer Grund für den Erfolg von Python.  Dieses Buch ist ein idealer Einstieg in die Programmierung mit Python. Ausführlich erläutert der Autor die elementaren Grundlagen, die nötig sind, um mit dieser Sprache Programme zu erstellen und zu pflegen. Daneben zeigt er, wie sich die OOP mit Python realisieren lässt und wie man Module und API-Schnittstellen integriert. Diverse weiterführende Themen wie die Erstellung grafischer Oberflächen oder der Zugriff auf Dateien und Datenbanken runden das Fachbuch ab. Einfache Beispiele veranschaulichen die grundsätzliche Anwendung der verschiedenen Techniken und machen das Buch dadurch zu einer unbedingten Empfehlung für Einsteiger und Praktiker, die die Programmierung mit Python lernen wollen. Einleitung und Grundlagen – Bevor es richtig losgeht.- Erste Beispiele – Der Sprung ins kalte Wasser.-Built-in-Functions – Modularisierung durch Unterprogramme.- Grundlegende Begriffe – Kommentare, SheBang und Strukturanalysen.- Anweisungen – Dem Computer Befehle geben.- Datentypen, Variablen und Literale – Die Art der Information.- Ausdrücke, Operatoren und Operanden – Die Verarbeitung von Daten.- Kontrollstrukturen – Die Steuerung des Programmflusses.- Funktionen in Python – Modularisierung mit „Unterprogrammen“.- Sequenzielle Datenstrukturen – Mehrere Informationen gemeinsam verwalten.- Objektorientierte Programmierung in Python –  Klassen, Objekte, Eigenschaften und Methoden.- Exceptionhandling – Ausnahmsweise.- String-Verarbeitung in Python – Programmierte Textverarbeitung.- Datei-, Datenträger- und Datenbankzugriffe – Dauerhafte Daten.- Umgang mit Datum und Zeit – Terminsachen.- Grafische Oberflächen (GUI) mit Python – tkinter & Co als GUI-Framework. Ralph Steyer ist Diplom-Mathematiker und hat sich 1996 nach fünf Jahren als Programmierer bei einer Versicherung als Freiberufler im Bereich EDV-Schulung, Programmierung und Beratung selbstständig gemacht. Spezialgebiete sind die Internetprogrammierung und OOP. Er unterrichtet in Firmen und Fachhochschulen und veröffentlicht regelmäßig Zeitungsartikel, Videotraining und Fachbücher.

Mastering Java Persistence

Mastering Big Data and Hadoop

Mastering Deep Learning with Keras

Mastering Jenkins

GitLab Guidebook

Effektive Softwarearchitekturen

- Aktueller Überblick und methodische Einführung - Direkt umsetzbare Tipps für praktizierende Softwarearchitekten - Ideal zur Vorbereitung auf die Zertifizierung zum «Certified Professional for Software Architecture™» (Foundation Level) des iSAQB - Praxisnahe Darstellung von Architekturstilen und -mustern, ausführliche technische Konzepte, NoSQL-Datenbanken sowie aktualisierte und erweiterte Beispielarchitekturen - Neu in der 7. Auflage: Evolution und Verbesserung bestehender Systeme - Detaillierte Beispiele zum Einsatz von arc42 - Extra: E-Book inside Softwarearchitekten müssen komplexe fachliche und technische Anforderungen an IT-Systeme umsetzen, und sie müssen diese Systeme durch nachvollziehbare Strukturen flexibel und erweiterbar gestalten. Dieser Praxisleitfaden zeigt Ihnen, wie Sie Softwarearchitekturen effektiv und systematisch entwickeln können. Gernot Starke unterstützt Sie mit praktischen Tipps, Architekturmustern und seinen Erfahrungen. Sie finden Antworten auf zentrale Fragen: - Welche Aufgaben haben Softwarearchitekten? - Wie gehe ich beim Entwurf vor? - Wie kommuniziere und dokumentiere ich Softwarearchitekturen? - Wie helfen Architekturstile und -muster? - Wie bewerte ich Softwarearchitekturen? - Wie behandle ich Persistenz, grafische Benutzeroberflächen, Geschäftsregeln, Integration, Verteilung, Sicherheit, Fehlerbehandlung, Business-Process-Management, Microservices und sonstige technische Konzepte? - Was müssen Softwarearchitekten über NoSQL, Domain-Driven-Design und arc42 wissen? - Wie verbessere ich bestehende Systeme? AUS DEM INHALT // Vorgehen bei der Architekturentwicklung // Architekturmuster und -stile // Technische Konzepte // SOA und Enterprise-IT-Architektur // Architekturbewertung // Dokumentation von Architekturen // Modellierung für Softwarearchitekten // Werkzeuge für Softwarearchitekten // Beispiele realer Softwarearchitekturen // iSAQB Curriculum

The Handbook of Data Science and AI

- A comprehensive overview of the various fields of application of data science and artificial intelligence. - Case studies from practice to make the described concepts tangible. - Practical examples to help you carry out simple data analysis projects. - BONUS in print edition: E-Book inside Data Science, Big Data, Artificial Intelligence and Generative AI are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Using exercises and real-world examples, it will show you how to apply data science methods, build data platforms, and deploy data- and ML-driven projects to production. It will help you understand - and explain to various stakeholders - how to generate value from such endeavors. Along the way, it will bring essential data science concepts to life, including statistics, mathematics, and machine learning fundamentals, and explore crucial topics like critical thinking, legal and ethical considerations, and building high-performing data teams. Readers of all levels of data familiarity - from aspiring data scientists to expert engineers to data leaders - will ultimately learn: how can an organization become more data-driven, what challenges might it face, and how can they as individuals help make that journey a success. The team of authors consists of data professionals from business and academia, including data scientists, engineers, business leaders and legal experts. All are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and machine learning, and raising awareness of the opportunities and potential risks of these technologies. WHAT‘S INSIDE // - Critical Thinking and Data Culture: How evidence driven decision making is the base for effective AI. - Machine Learning Fundamentals: Foundations of mathematics, statistics, and ML algorithms and architectures - Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, for real world applications. - Foundation Models and Generative AI: Understand the strengths and challenges of generative models for text, images, video, and more. - ML and AI in Production: Turning experimentation into a working data science product. - Presenting your Results: Essential presentation techniques for data scientists.

Cybersecurity in Context

“A masterful guide to the interplay between cybersecurity and its societal, economic, and political impacts, equipping students with the critical thinking needed to navigate and influence security for our digital world.”—JOSIAH DYKSTRA, Trail of Bits “A comprehensive, multidisciplinary introduction to the technology and policy of cybersecurity. Start here if you are looking for an entry point to cyber.”—BRUCE SCHNEIER, author of A Hacker’s Mind: How the Powerful Bend Society’s Rules, and How to Bend Them BackTHE FIRST-EVER INTRODUCTION TO THE FULL RANGE OF CYBERSECURITY CHALLENGESCybersecurity is crucial for preserving freedom in a connected world. Securing customer and business data, preventing election interference and the spread of disinformation, and understanding the vulnerabilities of key infrastructural systems are just a few of the areas in which cybersecurity professionals are indispensable. This textbook provides a comprehensive, student-oriented introduction to this capacious, interdisciplinary subject. Cybersecurity in Context covers both the policy and practical dimensions of the field. Beginning with an introduction to cybersecurity and its major challenges, it proceeds to discuss the key technologies which have brought cybersecurity to the fore, its theoretical and methodological frameworks and the legal and enforcement dimensions of the subject. The result is a cutting-edge guide to all key aspects of one of this century’s most important fields. Cybersecurity in Context is ideal for students in introductory cybersecurity classes, and for IT professionals looking to ground themselves in this essential field. CHRIS JAY HOOFNAGLE is Professor of Law in Residence at the University of California, Berkeley, where he has taught since 2006. He has published extensively on cybersecurity law and related subjects, and is a practicing attorney with venture law firm Gunderson Dettmer, as well as an advisor to multiple defense and intelligence technology companies. GOLDEN G. RICHARD III is Professor of Computer Science and Director of the Cyber Center at Louisiana State University. He is a Fellow of the American Academy of Forensic Sciences with over thirty years of experience in teaching cybersecurity and related topics in computer science. His primary areas of expertise are in memory forensics, digital forensics, malware analysis, reverse engineering, and systems programming. About the Authors xiiiPreface xvAcknowledgments xixAbout the Companion Website xxiIntroduction xxiiiI WHAT IS CYBERSECURITY?1 WHAT IS CYBERSECURITY? 31.1 What Is the Cyber in Cybersecurity? 51.1.1 Cyberspace’s Places and the Problem of Internet Sovereignty 81.2 What Is the Security in Cybersecurity? The “CIA” Triad 121.2.1 The Internet’s Threat Model 151.2.2 Computer Security Versus “Cybersecurity” 191.2.3 Security, Innovation, “Hacking” 231.2.4 Security from a Private Sector Perspective 241.2.5 Building on the CIA Triad 261.2.6 Cybersecurity Definitions 271.3 Encryption Is Critical in Cybersecurity 281.3.1 Modern Cryptosystems 291.3.2 Hashing 331.4 Cyberpower: How Insecurity Empowers and Undermines Nations 371.5 Is Disinformation a Cybersecurity Concern? 401.5.1 From Information Scarcity to Glut 411.5.2 The Power of Influence Campaigns on the Internet 431.5.3 Libicki’s Disinformation Framework 461.5.4 The US Approach: Free Speech First 481.5.5 Election Interference 501.5.6 Is There Really Reason to Be Concerned? 531.6 International Views 551.7 Conclusion: A Broad Approach 572 TECHNOLOGY BASICS AND ATTRIBUTION 592.1 Technology Basics 602.1.1 Fundamentals 602.1.2 Reliance Is a Fundamental Element of Computing and the Internet 662.1.3 Internet Layers 682.1.4 Cybersecurity Depends on Generations of Legacy Technologies 772.1.5 “Controlling” the Internet 842.1.6 Why Not Start Over? 852.2 Attribution 862.2.1 Types of Attribution 912.2.2 Attribution Process 922.2.3 Don’t Be Surprised: Common Dynamics in Attribution 1032.2.4 The Future of Attribution 1062.3 Conclusion: An End to Anonymity? 108II CYBERSECURITY’S CONTOURS3 ECONOMICS AND THE HUMAN FACTOR 1113.1 Economics of Cybersecurity 1123.1.1 Asymmetry and the Attack/Defense Balance 1163.1.2 Incentive “Tussles” 1183.2 The People Shaping Internet Technology and Policy 1203.2.1 Tragedies of the Un- managed Commons 1243.3 The Human Factor— The Psychology of Security 1273.3.1 Attackers as Behavioral Economists 1273.3.2 Institutions as Rational Choice Economists 1303.3.3 User Sophistication 1343.3.4 The Role of Emotion and the Body 1363.3.5 Security as Afterthought 1383.3.6 RCT: The User View 1383.4 Conclusion 1404 THE MILITARY AND INTELLIGENCE COMMUNITIES 1414.1 Why Cybersecurity Is Center Stage 1444.2 Are Cyberattacks War? 1484.2.1 Cyber War Will Not Take Place 1484.2.2 Cyber War Is Coming 1534.2.3 The Law of War 1554.2.4 Cyber Realpolitik 1624.3 Computers and the Future of Conflict 1654.3.1 The Changing Nature of Conflict 1664.4 Cybersecurity and the Intelligence Community 1764.4.1 The Intelligence Community 1784.4.2 The Power of the Platform 1874.4.3 The Vulnerabilities Equities Process 1894.4.4 Cyber Soldiers and/or Cyber Spies? 1934.5 Conclusion 1955 CYBERSECURITY THEORY 1975.1 Deterrence Theory 1985.1.1 Deterrence Theory Contours 1995.1.2 Deterring with Entanglement and Norms 2075.1.3 Cyber “Power” 2095.1.4 The Deterrence Theory Critique 2135.2 Security Studies: Anarchy, Security Dilemma, and Escalation 2155.2.1 Anarchy 2155.2.2 The Security Dilemma 2165.2.3 Escalation and the Security Dilemma 2185.2.4 Securitization: Nissenbaum Revisited 2225.2.5 The Problem of Referent Object 2235.2.6 Nissenbaum’s Alternative Vision: Cyberattacks Are Just Crimes 2245.2.7 A Response to Nissenbaum: Strategic Risks Do Exist 2255.3 Economic Theory: The Tragedy of the Cybersecurity Commons 2265.3.1 The Free Problem 2275.4 The Public Health Approach 2305.5 Gerasimov and “Hybrid War:” Information Domain Revisited 2335.5.1 The US Reaction 2355.6 Barlowism as Theory 2375.6.1 Technology Utopianism: The Internet as Democratizing 2375.6.2 Utopia as No Place, But as Organic 2425.6.3 High Modernism and Authoritarian High Modernism 2435.7 Conclusion 246III CYBERSECURITY LAW AND POLICY6 CONSUMER PROTECTION LAW 2496.1 Federal Trade Commission Cybersecurity 2506.1.1 FTC’s Legal Authority 2526.1.2 Unfairness 2546.1.3 Deception 2576.1.4 The Zoom Case— Complaint 2586.1.5 The Zoom Case— Settlement 2626.2 FTC Adjacent Cybersecurity 2676.2.1 The Attorneys General 2676.2.2 Self- regulation 2686.2.3 Product Recalls 2706.3 The Limits of the Consumer Protection Approach 2716.3.1 Two Litigation Moats: Standing and Economic Loss 2726.3.2 The Devil in the Beltway 2756.4 Conclusion 2797 CRIMINAL LAW 2817.1 Computer Crime Basics 2827.2 Computer Crime Incentive Contours 2837.3 The Political/Economic Cyber Enforcement Strategy 2877.4 Cybercrime’s Technical Dependencies 2917.5 The Major Substantive Computer Crime Laws 2937.5.1 Identity Theft 2947.5.2 The Computer Fraud and Abuse Act (CFAA) 2977.5.3 Other Computer Crime Relevant Statutes 3097.5.4 Digital Abuse 3117.6 High- Level Investigative Procedure 3127.6.1 Investigative Dynamics 3127.6.2 Investigative Process 3177.6.3 Obtaining the Data 3177.6.4 Stored Communications, Metadata, Identity, and “Other” 3187.7 Live Monitoring 3247.7.1 International Requests and the CLOUD Act 3267.7.2 National Security Access Options 3297.8 Conclusion 3328 CRITICAL INFRASTRUCTURE 3338.1 What Is “Critical Infrastructure” 3368.2 Political Challenges in Securing Critical Infrastructure 3418.3 Cyber Incident Reporting for Critical Infrastructure Act of 2022 3438.4 Technical Dynamics 3458.4.1 What Does CI Designation Mean 3458.5 NIST Cybersecurity Framework 3468.5.1 NIST Broken Down 3468.5.2 Electricity and Cybersecurity 3488.6 Alternative Approaches to the NIST Cybersecurity Framework 3518.6.1 Assessments and Audits— They’re Different 3528.6.2 Requirements- based Standards 3528.6.3 Process- Based and Controls- Based Standards 3548.6.4 Privacy != Security 3568.6.5 Standards Critiques 3578.7 The Other CISA— Cybersecurity Information Sharing Act of 2015 3588.7.1 Information- sharing Theory 3588.7.2 Information- Sharing Practice 3608.7.3 Provisions of CISA (the Act) 3628.8 Conclusion 3659 INTELLECTUAL PROPERTY RIGHTS 3679.1 IPR Problems: Context 3689.1.1 IP Threats 3699.1.2 Apt1 3719 2 Protection of Trade Secrets 3739.2.1 Reasonable Measures for Protecting Trade Secrets 3749.2.2 Rights Under the DTSA 3759.2.3 The Electronic Espionage Act (EEA) 3789.3 Copyright and Cybersecurity 3799.3.1 The DMCA and Critical Lessons for Software Testing 3859.4 Online Abuse and IP Remedies 3859.4.1 Public Law Remedies for Abuse 3879.4.2 Private Law Remedies for Abuse 3929 5 Conclusion 39210 THE PRIVATE SECTOR 39310.1 There Will Be Blood: Risk and Business Operations 39410.2 The Politics of Sovereignty 39710.2.1 Homo Economicus Meets North Korea 40010.2.2 Technological Sovereignty 40210.2.3 Committee on Foreign Investment in the United States 40410.2.4 Data Localization 40510.2.5 Export Control 40610.3 The APT Problem 40710.4 The Security Breach Problem 41110.4.1 Trigger Information 41310.4.2 What Is an Incident? What Is a Breach? 41410.4.3 Notification Regimes 41510.4.4 Does Security Breach Notification Work? 42010.5 Hacking Back: CISA (The Statute) Revisited 42110.6 The Special Case of Financial Services 42510.6.1 Gramm Leach Bliley Act (GLBA) 42510.7 Publicly Traded Companies and Cybersecurity 43010.7.1 Material Risks and Incidents 43110.7.2 SEC Enforcement 43210.7.3 The Board of Directors 43410.8 Cybersecurity Insurance 43710.8.1 Insurer Challenges 43810.8.2 Buying Insurance 43910.9 Conclusion 440IV CYBERSECURITY AND THE FUTURE11 CYBERSECURITY TUSSLES 44311.1 A Public Policy Analysis Method 44411.2 Software Liability: Should Developers Be Legally Liable for Security Mistakes? 44611.3 Technical Computer Security Versus Cybersecurity Revisited 44911.3.1 The Criminal Law Alternative 45011.3.2 The Consumer Law Approach 45111.3.3 The Industrial Policy Approach 45111.4 Encryption and Exceptional Access 45311.5 Disinformation Revisited 45711.5.1 Racist Speech and Cybersecurity 46011.5.2 What Expectations About Disinformation Are Reasonable? 46111.6 Conclusion 46112 CYBERSECURITY FUTURES 46312.1 Scenarios Methods 46412.2 Even More Sophisticated Cyberattacks 46512.3 Quantum Computing 46612.4 Automaticity and Autonomy: Artificial Intelligence and Machine Learning 46712.5 The Data Trade and Security 47012.6 The Sovereign Internet 47112.7 Outer Space Cyber 47312.8 Classification Declassed 47512.9 Attribution Perfected or Not 47612.10 Conclusion 476V FURTHER READING AND INDEXFurther Reading 481Index 495

Nonlinear Dispersive Waves

Preface.- BOUNDARY VALUE PROBLEMS RELATED TO THE MUSKAT PROBLEM.- SOME FLOW CHARACTERISTICS OF STOKES WAVES VIA COMPLEX ANALYSIS.- RECOVERY OF TRAVELING WATER WAVES WITH SMOOTH VORTICITY FROM THE HORIZONTAL VELOCITY ON A LINE OF SYMMETRY FOR VARIOUS WAVE REGIMES.- NUMERICAL COMPUTATION OF STEADY ROTATIONAL WAVES AND RECOVERY OF THE SURFACE PROFILE FROM BOTTOM PRESSURE MEASUREMENTS.- Hamiltonian models for the propagation of long gravity waves, higher-order KdV-type equations and integrability.- An introduction to the Zakharov equation for modelling deep water waves.- Rotating convection and flows with horizontal kinetic energy backscatter.- Flexural-gravity waves under ice plates and related flows.- Nonlinear water waves and wave-current interactions at arbitrary latitude.- HOLLOW VORTICES AS NONLINEAR WAVES.- SPHERICAL COORDINATES FOR ARCTIC OCEAN FLOWS.

Mathematical Models Using Artificial Intelligence for Surveillance Systems

THIS BOOK GIVES COMPREHENSIVE INSIGHTS INTO THE APPLICATION OF AI, MACHINE LEARNING, AND DEEP LEARNING IN DEVELOPING EFFICIENT AND OPTIMAL SURVEILLANCE SYSTEMS FOR BOTH INDOOR AND OUTDOOR ENVIRONMENTS, ADDRESSING THE EVOLVING SECURITY CHALLENGES IN PUBLIC AND PRIVATE SPACES.Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing. The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information. PADMESH TRIPATHI, PHD, completed his Ph.D. from Sharda University, Greater Noida, UP, India. Currently, Dr Tripathi is working as Professor of Mathematics in Department of AIDS at Delhi Technical Campus, Greater Noida, UP, India. He has more than 23 years of teaching experience, published 22 papers/book chapters in reputed journals/publishers and 4 Indian innovation patents. His research areas include Data Science, Machine Learning, Inverse Problems, Optimization, Signal/Image Processing, etc. Dr Tripathi has been listed in lifetime achievement by Marquis Who's Who and received the best academician of 2021 award from SEMS Foundation, Noida, India. Dr Tripathi has been associated with several reputed publishers like IGI Global, Wiley-Scrivener, Taylor & Francis, Elsevier, Springer, Inderscience, etc. in various roles like author, reviewer, editor, guest editor, etc. Dr Tripathi received grants from prestigious institutes like Cambridge University, UK; University of California at Los Angeles, USA; INRIA, Sophia Antipolis, France; University of Eastern Finland, Kuopio, Finland; RICAM, Linz, Austria, etc and visited these places.MRITUNJAY RAI, PHD, has completed his Ph.D. in Thermal imaging applications in the department of Electrical Engineering from IIT-ISM Dhanbad, Master of Engineering (with distinction) in Instrumentation and Control from Birla Institute of Technology-Mesra, Ranchi, and B.Tech in ECE from Shri Ramswaroop Memorial College of Engineering and Management, Lucknow. Currently, Dr. Rai is working as Assistant Professor in Shri Ramswaroop Memorial University, Barabanki, U.P., India. Dr. Rai has more than 12 years of working experience in research as well as academics. In addition, he has guided several UG and PG projects. He has published many research articles in reputed journals published by Springer, Elsevier, IEEE, Inderscience, and MECS. He has contributed many chapters to books published by Intech Open Access, CRC, IGI Global, and Elsevier. He is an editor of books (edited) published by reputed publishers Wiley, AAP, NOVA & IGI, He is an active reviewer and has reviewed many research papers in journals and at international and national conferences. His areas of interest lie in image processing, speech processing, artificial intelligence, machine learning, deep learning, Intelligent Traffic Monitoring System, the Internet of Things (IoT), and robotics and automation.NITENDRA KUMAR, PHD, an accomplished scholar with a PhD in Mathematics from Sharda University and a master’s degree in mathematics and Statistics from Dr. Ram Manohar Lohia Avadh University, boasts over a decade of expertise as an Assistant Professor at Amity Business School, Amity University, Noida. His diverse research interests encompass Wavelets and its Variants, Data Mining, Inverse Problems, Epidemic Modelling, Fractional Derivatives Business Analytics, and Statistical Methods, reflecting a profound commitment to advancing knowledge across multiple domains. Dr. Kumar's prolific contributions to academia are evidenced by his extensive publication record, comprising over 30 research papers in esteemed journals, 16 book chapters, and 12 authored books on engineering mathematics, computation, and Business Analytics and related topics. Notably, his scholarly impact extends beyond traditional research avenues, as evidenced by his involvement in patenting two innovative solutions. Beyond his individual achievements, Dr. Kumar actively engages with the academic community, serving as editor for two edited books and as Guest Editor for reputable journals like the Journal of Information and Optimization Sciences, Journal of Statistical and Management Sciences, and Environment and Social Psychology. His editorial roles underscore his dedication to fostering intellectual discourse and shaping the trajectory of scholarly inquiry. Dr Nitendra Kumar epitomizes academic excellence, blending profound expertise with a steadfast commitment to advancing mathematical knowledge and its interdisciplinary applications.SANTOSH KUMAR, PHD, is Assistant Professor in the Department of Mathematics, Sharda School of Basic Sciences and Research, Sharda University, Greater Noida, India. He obtained his Ph.D. degree from Aligarh Muslim University Aligarh, in 2016. He is actively involved in the research areas, namely nonlinear partial differential equations, diffusion models, wavelet transform, mathematical modeling, image processing, etc. He has taught undergraduate subjects such as linear algebra, differential equations, complex analysis, advanced calculus, and probability and statistics. He has taught real analysis, topology, functional analysis, partial differential equations, and many more at the post-graduation level. Besides attending, presenting scientific papers, delivering invited talks, and chairing sessions at national/international conferences and seminars, he has organized several workshops and conferences as an organizing secretary. He has published many research papers in reputed national and international journals and book chapters published in an edited book published by international publishers. He is also reviewer of many reputed journals.Preface xv1 Elevating Surveillance Integrity-Mathematical Insights into Background Subtraction in Image Processing 1S. Priyadharsini2 Machine Learning and Artificial Intelligence in the Detection of Moving Objects Using Image Processing 19K. Janagi, Devarajan Balaji, P. Renuka and S. Bhuvaneswari3 Machine Learning and Imaging-Based Vehicle Classification for Traffic Monitoring Systems 51Parthiban K. and Eshan Ratnesh Srivastava4 AI-Based Surveillance Systems for Effective Attendance Management: Challenges and Opportunities 69Pallavi Sharda Garg, Samarth Sharma, Archana Singh and Nitendra Kumar5 Enhancing Surveillance Systems through Mathematical Models and Artificial Intelligence: An Image Processing Approach 91Tarun Kumar Vashishth, Vikas Sharma, Bhupendra Kumar, Kewal Krishan Sharma, Sachin Chaudhary and Rajneesh Panwar6 A Study on Object Detection Using Artificial Intelligence and Image Processing—Based Methods 121Vidushi Nain, Hari Shankar Shyam, Nitendra Kumar, Padmesh Tripathi and Mritunjay Rai7 Application of Fuzzy Approximation Method in Pattern Recognition Using Deep Learning Neural Networks and Artificial Intelligence for Surveillance 149M. Geethalakshmi, Sriram V. and Vakkalagadda Drishti Rao8 A Deep Learning System for Deep Surveillance 169Aman Anand, Rajendra Kumar, Nikita Verma, Akash Bhasney and Namita Sharma9 Study of Traditional, Artificial Intelligence and Machine Learning Based Approaches for Moving Object Detection 187Apoorv Joshi, Amrita, Rohan Sahai Mathur, Nitendra Kumar and Padmesh Tripathi10 Arduino-Based Robotic Arm for Farm Security in Rural Areas 215Canute Sherwin, Shahid D. P., N. R. Hritish, Sujan Kumar S. N., Nikhil R. and K. Raju11 Graph Neural Network and Imaging Based Vehicle Classification for Traffic Monitoring System 241Shivam Sinha, Nilesh kumar Singh and Lidia Ghosh12 A Novel Zone Segmentation (ZS) Method for Dynamic Obstacle Detection and Flawless Trajectory Navigation of Mobile Robot 271Rapti Chaudhuri, Jashaswimalya Acharjee and Suman Deb13 Artificial Intelligence in Indoor or Outdoor Surveillance Systems: A Systematic View, Principles, Challenges and Applications 293Varun Gupta, Tushar Bansal, Vinay Kumar Yadav and Dhrubajyoti BhowmikReferences 330Index 335

Künstliche Intelligenz selber programmieren für Dummies Junior (2. Auflg.)

WAS KÖNNEN INTELLIGENTE COMPUTER – UND WAS NICHT?Finde heraus, wie Künstliche Intelligenz funktioniert. Dieses Buch hilft dir dabei. Kapitel für Kapitel beantwortet es folgende Fragen: Was ist Künstliche Intelligenz? Wie denken Computer? Wie lernen Computer? Wie kannst du mit Computern sprechen? Wie generieren Computer Texte und Bilder? Spielen Computer auch? Und können Computer fühlen? Grundlegende Methoden und Anwendungsbereiche von Künstlicher Intelligenz werden anhand von anschaulichen Beispielen erklärt. Beispielprogramme in Python zeigen, wie KI-Methoden konkret umgesetzt werden können und es werden Anregungen für eigene Experimente gegeben. DU LERNST* GEMEINSAMKEITEN UND UNTERSCHIEDE ZWISCHEN MENSCHLICHEM DENKEN UND KI##SINGLE_LINE##* WIE PROGRAMME FUNKTIO-NIEREN, DIE LERNEN KÖNNEN##SINGLE_LINE##* WIE DU EINEN EIGENEN CHATBOT PROGRAMMIEREN KANNST##SINGLE_LINE##* WIE DU EINEM COMPUTER BEIBRINGEN KANNST, GEGEN DICH TIC-TAC-TOE ZU SPIELEN##SINGLE_LINE####SINGLE_LINE## UTE SCHMID, KATHARINA WEITZ UND MICHAEL SIEBERS haben alle drei Informatik und Psychologie studiert. Sie forschen in den Themenbereichen Künstliche Intelligenz und maschinelles Lernen und geben ihr Wissen in Vorträgen und Workshops an Kinder und Jugendliche weiter. Widmung 7EINFÜHRUNG 9Über Künstliche Intelligenz 9Über dieses Buch 11Über dich 11Über die Symbole, die wir in diesem Buch verwenden 12KAPITEL 1: DENKEN 13Wie denken eigentlich Menschen? 13Netze ohne Spinnen – dafür mit Knoten und Kanten 15Schlussfolgerndes Denken mit semantischen Netzen 17Wissensfragen 17Schlussfolgerungsfragen 18Komplizierte Schlussfolgerungsfragen 18Baue dein eigenes semantisches Netz 21Was heißt eigentlich »Denken«? 22Deduktives Denken 22Abduktives Denken 23Induktives Denken 24Denken mit Wahrscheinlichkeiten 25KAPITEL 2: LERNEN 27Warum Lernen so wichtig ist 27Wie lernen wir Menschen? 28Wie kann ein Computer lernen? 28Geschenke, Katzen und andere Konzepte 30Lernen mit Perzeptron 31Testen des Perzeptrons 35Schwierigere Paketprobleme 36Vom Perzeptron zum neuronalen Netz 37Vom neuronalen Netz zum tiefen Lernen 39Auswendiglernen vermeiden 41Lernen aus ganz wenigen Beispielen 42Lernen mit Bäumen 42Lernen und Vorurteile 48Und die Profis? 49Lösung: Welche Pakete enthalten ein Geschenk? 50KAPITEL 3: SPRECHEN UND SCHREIBEN 51Natürliche und künstliche Sprachen 51Sprachverarbeitung mit Künstlicher Intelligenz 52Muster suchen und erkennen 52Porzellankisten sind nicht immer Porzellankisten 52Computer, die Sprache verstehen – von SHRDLU, WATSON und ELIZA 53Hallo LILI 55Familiengespräche 55Schreiben statt sprechen 56Mensch oder Computer? 56Die Chatbots kommen 57Der Chatbot, der alle zum Staunen bringt 57Ein Blick hinter die Kulissen 58Sehr überzeugend – bei völliger Ahnungslosigkeit! 59Wo bleibt der Link zu ChatGPT? 60KAPITEL 4: BILDER GENERIEREN 61Ein Prompt, aber prompt! 61So malst du mit Generativer KI 62Tipps und Tricks für bessere Ergebnisse 65SDXL – kein Buchstabensalat, sondern eine KI, die Bilder generiert 66KAPITEL 5: SPIELEN 69Roboterfußball – Toooor 69Schlangen und ärgerliche Vögel 70Brett vorm Kopf? Nicht bei Brettspielen! 72Tic-Tac-Toe 73Die Regeln 73Tic-Tac-Toe mit einem Computer spielen 73Gute Spieler, schlechte Spieler 74Warum kann man nicht alle Züge ausprobieren? 76Schieben und rutschen 78KAPITEL 6: FÜHLEN 81Über die Emotionen 81Computer, die einen ärgern 82Kreise und Dreiecke mit Absichten 83Ein emotionaler Staubsauger? 84Erklären, was die Künstliche Intelligenz sieht 85KAPITEL 7: WAS DU JETZT ÜBER KI WEIẞT 87Wie unterscheiden sich KI-Systeme von Standard-Software? 88KI ist nicht immer korrekt, aber trotzdem nützlich 90Was unterscheidet menschliche und künstliche Intelligenz? 90Geschichte der KI 92KI vor der KI 92Von Informatik- und KI-Pionieren 94Wo steht KI jetzt? 95Ein Blick in die Glaskugel 96KAPITEL 8: KI SELBER PROGRAMMIEREN MIT PYTHON 97Schnelleinstieg Python 98Der Python-Editor IDLE 98Einfache Datentypen und Variablen 100Listen und Tupel 102Bedingte Anweisungen 104Schleifen 106Funktionen 109Module 110Klassen 112Denken 113Netze ohne Spinnen – dafür mit Knoten und Kanten 113Semantische Netze in Python 115Darf’s ein bisschen komplizierter werden? 119Lernen 120Pakete wahrnehmen 121Das Perzeptron lernt aus Fehlern 122Testen des Perzeptrons 124Schwierigere Paketprobleme 126Entscheidungsbäume in Python 128Testen des Entscheidungsbaums 131Sprechen und Schreiben 132Familiengespräche 134Schreiben statt Sprechen 135LILI spricht 139Spielen 140Tic-Tac-Toe in Python 140Der Minimax-Algorithmus 142Wer gewinnt? 145Zum Wiederfinden 147Über die Autoren 151Danksagung 153Was du jetzt denkst 155