Search results for "Python for Generative AI"
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Creative Prototyping with Generative AI
Reimagine different generative AI as useful creative prototyping tools that can be used to augment your own creative process and projects. Gain a deeper understanding of how generative AI can elevate your creative future. You will acquire a comprehensive understanding of how AI works, uncover tools that can enhance your AI interactions, learn how to extract maximum potential from AI-produced content, and experiment with methods for assessing, refining, and boosting the content to transform your creative projects. You'll also explore how creative professionals from varied disciplines are employing generative AI in their workflows to produce distinctive contributions to the world. Each chapter provides examples of how designers and other creative individuals can utilize these technological wonders, adopting various prototyping techniques to fast-track and optimize design processes and workflows. Creators from all disciplines can tap into the vast capabilities and benefits of generative AI, enabling them to rapidly experiment and prototype their ideas.You Will Learn:Understand how generative AI can support your own creative processLearn tools to get the most out of text-text, text-image, and text-video generative AIAugment your design practices using generative AIDraw inspiration from AI generated content to create unique creative workImprove and streamline creatives processes and workflowsWho This Book Is ForDigital media professionals who want to access off-the shelf creative tools to improve and accelerate their creativity and workflow.Designers and engineers who are looking at novel ways to improve their prototyping and testing processes.Students who want to use AI to rapidly generate ideas to support them in prototyping assignments.Instructors interested in pointing their students to a variety of accessible AI resources to manage their own creativity.
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Combating Misinformation in the Age of Generative AI
The rapid advancement of Generative AI (GenAI) is revolutionizing and transforming a wide range of fields, with a notable impact on digital media and information dissemination. Digital platforms, including social media networks, have already grappled with the widespread proliferation of fake news at an increasing intensity are now facing an even bigger threat from machine-generated and machine-boosted fake news. With the increasing growth and sophistication of GenAI models for malicious ends, fake news is spreading rapidly, finding a fertile ground in digital media for largely unchecked dissemination of incorrect and misleading information. The scalability and diversity of current and emerging GenAI techniques present both opportunities and challenges in the field of fake news. On one hand, the ability to create highly realistic and persuasive content in large quantities with minimal effort, in real-time, makes GenAI a potent tool for spreading fake news and misinformation. On the other hand, those same capabilities can be harnessed to employ GenAI models to detect fake news and therefore hinder the spread. In this context, this book examines how GenAI techniques are contributing to combat fake news in digital media. A particular major challenge in combating fake news with GenAI is the explainability of the models: why a specific piece of news is determined to be fake or genuine. Tracing the origins of a specific piece of generated content—whether it stems from accurate or misleading data—requires extracting, interpreting, and representing in a legible manner vast parameter spaces and training data sources. This complexity of GenAI explainability makes it difficult to hold creators of fake news accountable and to ensure transparency in the content creation process. In this book, the authors focus on the positive potential of Generative AI and provide an overview of how it can be leveraged to combat fake news across diverse digital media platforms.The book is structured around the following key topics:• An overview of Generative AI and its relationship with fake news• How Generative AI can be utilized to fight the spread of fake news• Ensuring that the news we encounter on digital media platforms are not misleading or false• Methods for verifying the authenticity of news sources and tracing the origins of information to ensure its credibility• Practical insights into technological advancements and solutions related to the detection and analysis of fake news.Finally, this book serves as a platform for the community to share innovative research on the use of Generative AI in detecting and mitigating the impact of false information across digital platforms. Readers will explore how Generative AI can revolutionize fact-checking, automate the identification of misleading content, and provide adaptive solutions to prevent the spread of fake news.
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Data Lakehouse Revolution
We are racing toward a new kind of AI--faster, smarter, and more connected than ever. At the heart of it is the Data Lakehouse, and Databricks is the engine powering the transformation. Whether you're a data scientist training models, an engineer scaling pipelines, or an architect modernizing your stack, this book gives you what you need to stay ahead. Inside, you'll understand how to unlock the full potential of Machine Learning and Generative AI (GenAI) using Databricks--no fluff, just real tools, real strategies, and real results. From MLFlow and AutoML to Unity Catalog, Retrieval Augment Generation (RAG), and Vector Search, you'll get a complete blueprint for building intelligent systems that actually work in production. With step-by-step labs, industry case studies, and expert tips from someone who's lived through the entire evolution of enterprise AI, this book is your guide to mastering what's next. If you're serious regarding building AI that matters, this is where your journey begins. What You'll Learn Build full-stack ML and GenAI solutions on Databricks Train and track models with MLFlow, AutoML, and tuning strategies Secure and govern data with Unity Catalog Apply explainable, ethical AI techniques Deploy and monitor ML models in real-world pipelines Use RAG and vector search to power GenAI applications Gain confidence with hands-on labs and real enterprise use cases Who This Book Is For Azure administrators, data architects, and data engineers
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Generative Ai In Clinical Practice: A Physician's Guide To Transforming Medicine
Generative AI in Clinical Practice: A Physician's Guide to Transforming Medicine, is a practical guide for clinicians who want to understand and apply generative AI in everyday care. Designed for readers without a background in computer science, the book explains how large language models and related tools are being integrated into clinical workflows, with clarity, realism, and clinical relevance.You'll explore how generative AI is already supporting tasks like ambient documentation, diagnostic reasoning, patient education, and care coordination. Each chapter combines clinical vignettes, implementation strategies, and curated examples from frontline practice to show what works, and what to watch out for.The book also addresses critical issues of ethics, bias, regulation, and physician oversight. Rather than offering abstract predictions, it provides grounded, actionable guidance for safely using AI at the point of care.Whether you are a physician, nurse, or healthcare executive, this book will help you make informed decisions about AI adoption. It will also give you the language and insight needed to lead conversations about these emerging tools within your organization. Clear-eyed and practical, this guide is written to help clinicians gain confidence, not just in the technology, but in how to use it wisely.
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GenAI Security
This book is an essential guide for anyone looking to build secure and resilient generative AI applications. It provides actionable insights into identifying vulnerabilities and preventing attacks.Authored by security expert Paul Zenker, the book explores the critical aspects of securing GenAI systems across their lifecycle - design, development, and operation. Through numerous practical examples, detailed illustrations of threat scenarios, and application architectures, readers will gain a comprehensive understanding of potential risks. Step-by-step instructions cover techniques such as prompt injections, jailbreaks, and other attack vectors, equipping readers with the skills to anticipate and mitigate threats effectively.This resource is ideal for developers, security professionals, AI researchers, and organizations working with generative AI technologies. This book offers the tools and knowledge needed to create systems that can withstand sophisticated attacks in today’s high-stakes digital landscape.
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Optimizing Generative AI Workloads for Sustainability
This comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible. As advances in Generative AI such as large language models accelerate, optimizing these resource-intensive workloads for efficiency and alignment with human values grows increasingly urgent.The book starts with the concept of Generative AI and its wide-ranging applications, while also delving into the environmental impact of AI workloads and the growing importance of adopting sustainable AI practices. It then delves into the fundamentals of efficient AI workload management, providing insights into understanding AI workload characteristics, measuring performance, and identifying bottlenecks and inefficiencies. Hardware optimization strategies are explored in detail, covering the selection of energy-efficient hardware, leveraging specialized AI accelerators, and optimizing hardware utilization and scheduling for sustainable operations. You are also guided through software optimization techniques tailored for Generative AI, including efficient model architecture, compression, and quantization methods, and optimization of software libraries and frameworks. Data management and preprocessing strategies are also addressed, emphasizing efficient data storage, cleaning, preprocessing, and augmentation techniques to enhance sustainability throughout the data life cycle. The book further explores model training and inference optimization, cloud and edge computing strategies for Generative AI, energy-efficient deployment and scaling techniques, and sustainable AI life cycle management practices, and concludes with real-world case studies and best practicesBy the end of this book, you will take away a toolkit of impactful steps you can implement to minimize the environmental harms and ethical risks of Generative AI. For organizations deploying any type of generative model at scale, this essential guide provides a blueprint for developing responsible AI systems that benefit society. What You Will LearnUnderstand how Generative AI can be more energy-efficient through improvements such as model compression, efficient architecture, hardware optimization, and carbon footprint trackingKnow the techniques to minimize data usage, including evaluation, filtering, synthesis, few-shot learning, and monitoring data demands over timeUnderstand spanning efficiency, data minimization, and alignment for comprehensive responsibilityKnow the methods for detecting, understanding, and mitigating algorithmic biases, ensuring diversity in data collection, and monitoring model fairness Who This book Is ForProfessionals seeking to adopt responsible and sustainable practices in their Generative AI work; leaders and practitioners who need actionable strategies and recommendations that can be implemented directly in real-world systems and organizational workflows; ML engineers and data scientists building and deploying Generative AI systems in industry set
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Generative AI and Cyberbullying
Ever since the COVID-19 pandemic occurred in 2020, the world has transformed itself greatly ever since them. 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 that 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 be used in a wide 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, provide an overview as to how it can be used to help combat Cyberbullying. This book is broken down into the following topics:Ø What Cyberbullying Is All AboutØ How Technology (Gen AI) Can Be Used To Combat CyberbullyingØ An Overview Into Gen AIØ Advanced Topics Into Gen AIØ Conclusions
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