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Generation Zombie: The new essential guide to why screens and devices are harming our children and what we can do about it
by Dr Charlotte Armitage'It is never too late to make changes and put things right. If we recognise the problem of device use, we can regain control of our families and allow our children to grow up as they should.' Dr Charlotte ArmitageMany theories that form the bedrock of good parenting were created decades before devices even existed - they don't consider the significant impact on a child's psychological and physical development. The landscape of the early years has changed so quickly, yet parents and caregivers do not have access to the evidence-based, practical advice they need to manage this invasion of screens. They can see their children become addicted to devices, but don't know what to do. Generation Zombie will fill this knowledge gap.Through her clinical work as a psychotherapist, work with schools and as a duty of care psychologist, Dr Charlotte Armitage has witnessed the terrifying impact of screen time on her clients and their families. However, unlike other addictions, the harmful consequences of devices are not widely recognised. With the vital insights in her book - including practical tips and engaging case studies - her mission is hopeful and empowering; to enable parents to make a positive change. Away from devices, you will be amazed how quickly you notice children's behaviour change.
Generation Zombie: The new essential guide to why screens and devices are harming our children and what we can do about it
by Dr Charlotte Armitage'It is never too late to make changes and put things right. If we recognise the problem of device use, we can regain control of our families and allow our children to grow up as they should.' Dr Charlotte ArmitageMany theories that form the bedrock of good parenting were created decades before devices even existed - they don't consider the significant impact on a child's psychological and physical development. The landscape of the early years has changed so quickly, yet parents and caregivers do not have access to the evidence-based, practical advice they need to manage this invasion of screens. They can see their children become addicted to devices, but don't know what to do. Generation Zombie will fill this knowledge gap.Through her clinical work as a psychotherapist, work with schools and as a duty of care psychologist, Dr Charlotte Armitage has witnessed the terrifying impact of screen time on her clients and their families. However, unlike other addictions, the harmful consequences of devices are not widely recognised. With the vital insights in her book - including practical tips and engaging case studies - her mission is hopeful and empowering; to enable parents to make a positive change. Away from devices, you will be amazed how quickly you notice children's behaviour change.
Generative Adversarial Learning: Architectures and Applications (Intelligent Systems Reference Library #217)
by Vasile Palade Roozbeh Razavi-Far Ariel Ruiz-Garcia Juergen SchmidhuberThis book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.
Generative Adversarial Networks and Deep Learning: Theory and Applications
by Roshani RautThis book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc. Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning. The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum
Generative Adversarial Networks Cookbook
by Josh KalinThis book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.
Generative Adversarial Networks for Image Generation
by Xudong Mao Qing LiGenerative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook’s AI research director) as “the most interesting idea in the last 10 years in ML.” GANs’ potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable – poignant even. In 2018, Christie’s sold a portrait that had been generated by a GAN for $432,000. Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the details of GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision.
Generative Adversarial Networks in Practice
by Mehdi GhayoumiThis book is an all-inclusive resource that provides a solid foundation on Generative Adversarial Networks (GAN) methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key Features: • Guides you through the complex world of GANs, demystifying their intricacies • Accompanies your learning journey with real-world examples and practical applications • Navigates the theory behind GANs, presenting it in an accessible and comprehensive way • Simplifies the implementation of GANs using popular deep learning platforms • Introduces various GAN architectures, giving readers a broad view of their applications • Nurture your knowledge of AI with our comprehensive yet accessible content • Practice your skills with numerous case studies and coding examples • Reviews advanced GANs, such as DCGAN, cGAN, and CycleGAN, with clear explanations and practical examples • Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs • Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject • Takes you through GAN applications across different data types, highlighting their versatility • Inspires the reader to explore beyond this book, fostering an environment conducive to independent learning and research • Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge • Empowers you with the skills and knowledge needed to confidently use GANs in your projects Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.
Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras
by Kailash AhirwarThis book is intended for data scientists, machine learning developers, deep learning practitioners and AI enthusiasts who want a project guide to test their knowledge and expertise in building real-world GANs models. These full-fledged projects will help you master machine learning, and neural network principles. Basic understanding of machine learning and deep learning concepts will be handy. Hands-on experience in Tensorflow or Keras will be a plus point
Generative AI: Phishing and Cybersecurity Metrics (Cyber Shorts)
by Ravindra DasThe cybersecurity landscape is changing, for sure. For example, one of the oldest threat variants is that of phishing. It evolved in the early 1990s, but even today it is still being used as a primary threat variant and has now become much more sophisticated, covert, and stealthy in nature. For example, it can be used to launch ransomware, social engineering, and extortion attacks.The advent of Generative AI is making this much worse. For example, a cyberattacker can now use something like ChatGPT to craft the content for phishing emails that are so convincing that it is almost impossible to tell the difference between what is real and what is fake. This is also clearly evident in the use of deepfakes, where fake images of real people are replicated to create videos to lure unsuspecting victims to a fake website.But Generative AI can also be used for the good to combat Phishing Attacks. This is the topic of this book. In this, we cover the following: A review of phishing A review of AI, Neural Networks, and Machine Learning A review of Natural Language Processing, Generative AI, and the Digital Person A proposed solution as to how Generative AI can combat phishing attacks as they relate to Privileged Access accounts
Generative AI: Navigating the Course to the Artificial General Intelligence Future
by Martin MusiolAn engaging and essential discussion of generative artificial intelligence In Generative AI: Navigating the Course to the Artificial General Intelligence Future, celebrated author Martin Musiol—founder and CEO of generativeAI.net and GenAI Lead for Europe at Infosys—delivers an incisive and one-of-a-kind discussion of the current capabilities, future potential, and inner workings of generative artificial intelligence. In the book, you'll explore the short but eventful history of generative artificial intelligence, what it's achieved so far, and how it's likely to evolve in the future. You'll also get a peek at how emerging technologies are converging to create exciting new possibilities in the GenAI space. Musiol analyzes complex and foundational topics in generative AI, breaking them down into straightforward and easy-to-understand pieces. You'll also find: Bold predictions about the future emergence of Artificial General Intelligence via the merging of current AI models Fascinating explorations of the ethical implications of AI, its potential downsides, and the possible rewards Insightful commentary on Autonomous AI Agents and how AI assistants will become integral to daily life in professional and private contexts Perfect for anyone interested in the intersection of ethics, technology, business, and society—and for entrepreneurs looking to take advantage of this tech revolution—Generative AI offers an intuitive, comprehensive discussion of this fascinating new technology.
Generative AI: How ChatGPT and Other AI Tools Will Revolutionize Business
by Tom TaulliThis book will show how generative technology works and the drivers. It will also look at the applications – showing what various startupsand large companies are doing in the space. There will also be a look at the challenges and risk factors.During the past decade, companies have spent billions on AI. But the focus has been on applying the technology to predictions – which is known as analytical AI. It can mean that you receive TikTok videos that you cannot resist. Or analytical AI can fend against spam or fraud or forecast when a package will be delivered. While such things are beneficial, there is much more to AI. The next megatrend will be leveraging the technology to be creative. For example, you could take a book and an AI model will turn it into a movie – at very little cost. This is all part of generative AI. It’s still in the nascent stages but it is progressing quickly. Generative AI can already create engaging blog posts, social media messages, beautiful artwork and compelling videos.The potential for this technology is enormous. It will be useful for many categories like sales, marketing, legal, product design, code generation, and even pharmaceutical creation.What You Will LearnThe importance of understanding generative AIThe fundamentals of the technology, like the foundation and diffusion modelsHow generative AI apps workHow generative AI will impact various categories like the law, marketing/sales, gaming, product development, and code generation.The risks, downsides and challenges.Who This Book is ForProfessionals that do not have a technical background. Rather, the audience will be mostly those in Corporate America (such as managers) as well as people in tech startups, who will need an understanding of generative AI to evaluate the solutions.
Generative AI and Cyberbullying
by Ravindra DasEver since the COVID-19 pandemic occurred in 2020, the world has transformed itself greatly. For example, not only is the near-99% remote workforce now a reality, but businesses today are taking incident response and disaster recovery much more seriously these days as well. But another area that has blossomed in the last couple of years has been that of Generative AI. It is actually a subfield of artificial intelligence, which has been around since the 1950s.But Gen AI (as it is also called) has been fueled by the technology of ChatGPT, which has been developed and created by OpenAI. Given the GPT4 algorithms Gen AI is powered by, an end user can merely type in, or even speak into the platform a query, and an output that is specific to that query will be automatically generated. The answer (or "output") can be given as a text, video, image, or even an audio file.The scalability and diversity of Gen AI has allowed it to be used in a myriad of industries and applications. But although it has been primarily designed to serve the greater good, it can also be used for very nefarious purposes, such as online harassment and Cyberbullying.In this particular book, we actually take the good side of Gen AI and provide an overview as to how it can be used to help combat Cyberbullying. This book is broken down into the following topics: What Cyberbullying is all about How Gen AI can be used to combat Cyberbullying An overview into Gen AI Advanced topics into Gen AI Conclusions
Generative AI and Digital Forensics
by Ravindra DasIn today’s world, cybersecurity attacks and security breaches are becoming the norm. Unfortunately, we are not immune to it, and any individual and entity is at dire risk. The best and only thing that we can do is to mitigate the risks as much as much as possible so that they do not happen at all. But even when a security breach does indeed happen, the immediate reaction is to contain it so that it does not penetrate further into the information technology/network infrastructure. From there, mission-critical processes need to be restored, until the business can resume a normal state of operations, like it was before the security breach.But another key step here is to investigate how and why the security breach happened in the first place. The best way to do this is through what is known as “digital forensics”. This is where specially trained digital forensics investigators collect and comb through every piece of evidence to determine this. Eventually, the goal is then to use this evidence in a court of law so the cyberattacker can be made to answer for their crime and eventually be brought to justice.However, the area of digital forensics is a large one, and many topics around it can be covered. Also, generative AI is being used to not only help in the analysis of the evidence but also to help automate the digital forensics software packages that are available today. Therefore, in this book, the following is covered: Examples of security breaches and overview of digital forensics. How digital forensics can be used to investigate the loss or theft of data. An introduction to the SQL Server Database. A review of the SQL Injection Attack. How generative AI can be used in digital forensics.
Generative AI Application Integration Patterns: Integrate large language models into your applications
by Juan Pablo Bustos Luis Lopez SoriaUnleash the transformative potential of GenAI with this comprehensive guide that serves as an indispensable roadmap for integrating large language models into real-world applications. Gain invaluable insights into identifying compelling use cases, leveraging state-of-the-art models effectively, deploying these models into your applications at scale, and navigating ethical considerations.Key FeaturesGet familiar with the most important tools and concepts used in real scenarios to design GenAI appsInteract with GenAI models to tailor model behavior to minimize hallucinationsGet acquainted with a variety of strategies and an easy to follow 4 step frameworks for integrating GenAI into applicationsBook DescriptionExplore the transformative potential of GenAI in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI. With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns. We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought. Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.What you will learnConcepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAGFramework for integrating AI: entry points, prompt pre-processing, inference, post-processing, and presentationPatterns for batch and real-time integrationCode samples for metadata extraction, summarization, intent classification, question-answering with RAG, and moreEthical use: bias mitigation, data privacy, and monitoringDeployment and hosting options for GenAI modelsWho this book is forThis book is not an introduction to AI/ML or Python. It offers practical guides for designing, building, and deploying GenAI applications in production. While all readers are welcome, those who benefit most include: Developer engineers with foundational tech knowledge Software architects seeking best practices and design patterns Professionals using ML for data science, research, etc., who want a deeper understanding of Generative AI Technical product managers with a software development background This concise focus ensures practical, actionable insights for experienced professionals
Generative AI Apps with LangChain and Python: A Project-Based Approach to Building Real-World LLM Apps
by Rabi JayFuture-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level. Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries. Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape. What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI. Structure effective prompts. Master LangChain concepts, such as chains, models, memory, and agents. Apply embeddings effectively for search, content comparison, and understanding similarity. Setup and integrate Pinecone vector database for indexing, structuring data, and search. Build Q & A applications for multiple doc formats. Develop multi-step AI workflow apps using LangChain agents. Who This Book Is For Python programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
Generative AI: Current Trends and Applications (Studies in Computational Intelligence #1177)
by Khalid Raza Naeem Ahmad Deepak SinghThis comprehensive volume focuses on the latest advancements in Generative AI, including state-of-the-art techniques and models that are pushing the boundaries of what is possible. It covers recent developments in areas such as Generative AI models, transfer learning and Natural Language Processing (NLP) highlighting their potential to revolutionize content generation and creative applications including OpenAI, LangChain, NLTK and their practical implementations across diverse domains. The volume provides insights into emerging research areas, novel architectures, and innovative approaches in Generative AI, giving searchers a glimpse into the exciting future of the field. The aim is to offer readers a deep understanding of Generative AI and how it can be harnessed to tackle complex real-world challenges.
Generative AI, Cybersecurity, and Ethics
by Mohammad Rubyet Islam“Generative AI, Cybersecurity, and Ethics’ is an essential guide for students, providing clear explanations and practical insights into the integration of generative AI in cybersecurity. This book is a valuable resource for anyone looking to build a strong foundation in these interconnected fields.” —Dr. Peter Sandborn, Professor, Department of Mechanical Engineering, University of Maryland, College Park “Unchecked cyber-warfare made exponentially more disruptive by Generative AI is nightmare fuel for this and future generations. Dr. Islam plumbs the depth of Generative AI and ethics through the lens of a technology practitioner and recognized AI academician, energized by the moral conscience of an ethical man and a caring humanitarian. This book is a timely primer and required reading for all those concerned about accountability and establishing guardrails for the rapidly developing field of AI.” —David Pere, (Retired Colonel, United States Marine Corps) CEO & President, Blue Force Cyber Inc. Equips readers with the skills and insights necessary to succeed in the rapidly evolving landscape of Generative AI and cyber threats Generative AI (GenAI) is driving unprecedented advances in threat detection, risk analysis, and response strategies. However, GenAI technologies such as ChatGPT and advanced deepfake creation also pose unique challenges. As GenAI continues to evolve, governments and private organizations around the world need to implement ethical and regulatory policies tailored to AI and cybersecurity. Generative AI, Cybersecurity, and Ethics provides concise yet thorough insights into the dual role artificial intelligence plays in both enabling and safeguarding against cyber threats. Presented in an engaging and approachable style, this timely book explores critical aspects of the intersection of AI and cybersecurity while emphasizing responsible development and application. Reader-friendly chapters explain the principles, advancements, and challenges of specific domains within AI, such as machine learning (ML), deep learning (DL), generative AI, data privacy and protection, the need for ethical and responsible human oversight in AI systems, and more. Incorporating numerous real-world examples and case studies that connect theoretical concepts with practical applications, Generative AI, Cybersecurity, and Ethics: Explains the various types of cybersecurity and describes how GenAI concepts are implemented to safeguard data and systems Highlights the ethical challenges encountered in cybersecurity and the importance of human intervention and judgment in GenAI Describes key aspects of human-centric AI design, including purpose limitation, impact assessment, societal and cultural sensitivity, and interdisciplinary research Covers the financial, legal, and regulatory implications of maintaining robust security measures Discusses the future trajectory of GenAI and emerging challenges such as data privacy, consent, and accountability Blending theoretical explanations, practical illustrations, and industry perspectives, Generative AI, Cybersecurity, and Ethics is a must-read guide for professionals and policymakers, advanced undergraduate and graduate students, and AI enthusiasts interested in the subject.
Generative AI Engineering: Build apps with transformer and diffusion-based large and foundational models
by Konrad BanachewiczApply creativity and engineering to create apps with transformer and diffusion based large and foundational models Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn with practical examples, code snippets, and use cases from a variety of generative AI applicationsConquer the core concepts and techniques of generative AI engineeringGet to grips with productionizing operational pipelines with generative AIBook DescriptionGenerative AI Engineering is a hands-on guide to utilizing generative AI for creating advanced AI based applications. It’s designed for both beginners and experienced practitioners who want to learn how to develop generative AI models-based applications and use them to solve real-world problems. As you progress through the chapters, you’ll cover all the core concepts and techniques of generative AI engineering, including transformer and diffusion-based models. You'll also learn from practical examples and carefully chosen code snippets that will help you understand how to apply these concepts to real-world problems. You will learn the generative AI services and platforms offered by major cloud providers, GCP, AWS, and Microsoft Azure. With its comprehensive coverage and practical examples, this book will provide you with the skills and knowledge you need to build cutting-edge AI applications that can create new content, generate realistic images and videos, and solve complex problems in a wide range of industries.What you will learnGet to grips with the fundamentals of generative AI and its applicationsFamiliarize yourself with different types of generative models and when to use themTrain and Finetune generative models using PyTorchEvaluate the performance of your models and fine-tune them for optimal resultsFind best practices for deploying and scaling generative AI models in production environmentsWho this book is forWhether you are a software engineer, data scientist, or AI enthusiast, Generative AI Engineering is an essential resource that will help you master the art of building generative AI models. To get started, you’ll need to have basic knowledge of Python coding, including Jupyter Notebooks, setup cloud or local infrastructure using Docker containers for sample code, and deep learning and PyTorch.
Generative AI for Academics
by Mark CarriganThis is your indispensable guide to navigating the rise of generative AI as an academic. It thoughtfully explores rapidly evolving AI capabilities reshaping higher education, examining challenges and ethical dilemmas across the sector. It provides useful strategies for using generative AI in your scholarly work while upholding professional standards. This practical guidance addresses four core areas of academic work: Thinking: How to use generative AI to augment individual and collaborative scholarly thinking that can assist in developing novel ideas and advancing impactful projects Collaborating: Explore how generative AI can be used as a research assistant, coordinating teams and enhancing scholarly cooperation Communicating: Cautioning against over-reliance, examine how generative AI can relieve communication burdens while maintaining professionalism and etiquette Engaging: thoughtful and practical frameworks are offered for using these developments to support online engagement without sacrificing scholarly principles Mark Carrigan is a digital sociologist, author and Lecturer in Education at the University of Manchester.
Generative AI for Academics
by Mark CarriganThis is your indispensable guide to navigating the rise of generative AI as an academic. It thoughtfully explores rapidly evolving AI capabilities reshaping higher education, examining challenges and ethical dilemmas across the sector. It provides useful strategies for using generative AI in your scholarly work while upholding professional standards. This practical guidance addresses four core areas of academic work: Thinking: How to use generative AI to augment individual and collaborative scholarly thinking that can assist in developing novel ideas and advancing impactful projects Collaborating: Explore how generative AI can be used as a research assistant, coordinating teams and enhancing scholarly cooperation Communicating: Cautioning against over-reliance, examine how generative AI can relieve communication burdens while maintaining professionalism and etiquette Engaging: thoughtful and practical frameworks are offered for using these developments to support online engagement without sacrificing scholarly principles Mark Carrigan is a digital sociologist, author and Lecturer in Education at the University of Manchester.
Generative AI For Dummies
by Pam BakerGenerate a personal assistant with generative AI Generative AI tools capable of creating text, images, and even ideas seemingly out of thin air have exploded in popularity and sophistication. This valuable technology can assist in authoring short and long-form content, producing audio and video, serving as a research assistant, and tons of other professional and personal tasks. Generative AI For Dummies is your roadmap to using the world of artificial intelligence to enhance your personal and professional lives. You'll learn how to identify the best platforms for your needs and write the prompts that coax out the content you want. Written by the best-selling author of ChatGPT For Dummies, this book is the ideal place to start when you're ready to fully dive into the world of generative AI. Discover the best generative AI tools and learn how to use them for writing, designing, and beyond Write strong AI prompts so you can generate valuable output and save time Create AI-generated audio, video, and imagery Incorporate AI into your everyday tasks for enhanced productivity This book offers an easy-to-follow overview of the capabilities of generative AI and how to incorporate them into any job. It's perfect for anyone who wants to add AI know-how into their work.
Generative AI for Effective Software Development
by Pekka Abrahamsson Anh Nguyen-Duc Foutse KhomhThis book provides a comprehensive, empirically grounded exploration of how Generative AI is reshaping the landscape of software development. It emphasizes the empirical evaluation of Generative AI tools in real-world scenarios, offering insights into their practical efficacy, limitations, and impact. By presenting case studies, surveys, and interviews from various software development contexts, the book offers a global perspective on the integration of Generative AI, highlighting how these advanced tools are adapted to and influence diverse cultural, organizational, and technological environments. This book is structured to provide a comprehensive understanding of Generative AI and its transformative impact on the field of software engineering. The book is divided into five parts, each focusing on different aspects of Generative AI in software development. As an introduction, Part 1 presents the fundamentals of Generative AI adoption. Part 2 is a collection of empirical studies and delves into the practical aspects of integrating Generative AI tools in software engineering, with a focus on patterns, methodologies, and comparative analyses. Next, Part 3 presents case studies that showcase the application and impact of Generative AI in various software development contexts. Part 4 then examines how Generative AI is reshaping software engineering processes, from collaboration and workflow to management and agile development. Finally, Part 5 looks towards the future, exploring emerging trends, future directions, and the role of education in the context of Generative AI. The book offers diverse perspectives as it compiles research and experiences from various countries and software development environments. It also offers non-technical discussions about Generative AI in management, teamwork, business and education. This way, it is intended for both researchers in software engineering and for professionals in industry who want to learn about the impactof Generative AI on software development.
Generative AI For Executives: A Strategic Roadmap for Your Organization
by Ahmed Bouzid Paolo Narciso Weiye MaIn the fast-evolving digital landscape, understanding the potential of generative AI is a strategic advantage. This book can serve as an easy to read introduction to the topic of the transformative power of AI in content creation, customer engagement, and operational efficiency. By deciphering complex AI concepts into practical insights, we empower decision-makers to envision innovative strategies, foster cross-industry collaborations, and navigate ethical considerations. The book will help executives and business decision makers to harness the immense potential of generative AI responsibly, ensuring data integrity and compliance while fostering a competitive edge. The book is focused on (1) Explaining in jargon-free language what Generative AI, and AI in general, (2) What problems they solve, and (3) What technologies make them possible.What You Will LearnHow generative AI models are built, how they generate new data or content, and the underlying algorithms powering these processesVarious practical applications of generative AI in business contextsThe challenges that could arise during the integration of generative AI into business processesWho This Book is ForThis book is meant to be bought and read by busy executives and business leaders
Generative AI for Web Development: Building Web Applications Powered by OpenAI APIs and Next.js
by Tom Auger Emma SaroyanExplore the world of Generative AI and understand why it matters. This book is divided into two parts, introducing tools such as ChatGPT, DALL-E, and will show you how to use them to build AI-powered web apps. The first part of the book describes Generative AI and covers the essential models and APIs from OpenAI. Legal, ethical, and security considerations are discussed to help you decide whether it is an appropriate tool for your projects. You’ll then review ChatGPT and see how to use it effectively for generating code. This is followed by a review of best practices, and tips and techniques for getting around the limitations of ChatGPT and other OpenAI APIs. The second part of the book provides practical guide to building a series of web apps with Next.js that showcase how to use the OpenAI APIs. For example, you’ll learn how to build a Story/Poetry generator, a language learning app, and a blog site with a custom Chatbot widget. The code for the web apps is generated using ChatGPT. When done with this book, you’ll have a clear understanding of Generative AI and be well on your way to building web applications powered by OpenAI APIs and Next.js. What You Will Learn Assess the legal, ethical, and security concerns with using Generative AI in web applications Review the latest APIs provided by OpenAI for generating text and image Use ChatGPT to generate code for web projects, as well as tips and tricks to working around the limitations. Who This Book Is For Experienced web developers and software engineers who know their way around HTML, CSS, and JavaScript, but have limited or no experience using Generative AI to build web applications.
Generative AI in Action (In Action)
by Amit BahreeGenerative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action!Generative AI in Action is the comprehensive and concrete guide to generative AI you&’ve been searching for. It introduces both AI&’s fundamental principles and its practical applications in an enterprise context—from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action you will find: • A practical overview of of generative AI applications • Architectural patterns, integration guidance, and best practices for generative AI • The latest techniques like RAG, prompt engineering, and multi-modality • The challenges and risks of generative AI like hallucinations and jailbreaks • How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You&’ll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you&’ll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You&’ll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside • Best practices for deploying Generative AI apps • Production-quality RAG • Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1 1 Introduction to generative AI 2 Introduction to large language models 3 Working through an API: Generating text 4 From pixels to pictures: Generating images 5 What else can AI generate? Part 2 6 Guide to prompt engineering 7 Retrieval-augmented generation: The secret weapon 8 Chatting with your data 9 Tailoring models with model adaptation and fine-tuning Part 3 10 Application architecture for generative AI apps 11 Scaling up: Best practices for production deployment 12 Evaluations and benchmarks 13 Guide to ethical GenAI: Principles, practices, and pitfalls A The book&’s GitHub repository B Responsible AI tools