Generative AI: Innovations and Applications Across Industries

This book explores Generative AI, covering its transformative applications and innovative technologies. Each chapter meticulously examines Generative AI's impact across diverse domains, including creative fields, healthcare, education, entertainment, finance, and more.

The book begins by establishing the foundation for comprehending Generative AI, presenting fundamental concepts, tracing its historical progression, and analyzing key technologies like deep neural networks (DNNs), variational autoencoders (VAEs), and generative adversarial networks (GANs) in Chapter 1. In Chapter 2 we explore Generative AI's role in revolutionizing creative domains like writing, visual arts, and music composition. Chapter 3 delves into its impact on business and marketing, reshaping strategies and operations through AI-generated content and personalized experiences. In Chapter 4, we investigate Generative AI's influence on healthcare, accelerating drug discovery, personalized medicine, and medical imaging. Education is spotlighted in Chapter 5, showcasing Generative AI's contributions to personalized learning and interactive educational materials. Chapter 6 examines its impact on entertainment and media, while Chapters 7 through 11 delve into legal, financial, industrial, and environmental applications, addressing ethical considerations and future trends. Finally, Chapter 12 concludes with case studies and expert insights, offering readers a comprehensive understanding of Generative AI's potential and societal implications. Join us on this enlightening journey through the diverse landscapes of Generative AI, where innovation knows no bounds and the future is limited only by our imagination.

In the end, readers will gain a comprehensive understanding of the transformative potential and societal implications of Generative AI.

What you will learn:

• What are the foundational concepts and technological advancements behind Generative AI.
• What are large language models
• Understand Generative AI's role in reshaping business landscapes.
• What is the future of Generative AI and its potential impact.

Who this book is for:

AI Professionals, machine learning engineers and software professionals seeking a comprehensive understanding of Generative AI across various business domains.

Brijesh Kumar Karna is a Lead AI Engineer at Ford’s Advanced AI Research team, where he builds scalable, production-grade AI systems for enterprise use cases. Earlier, he worked at UnitedHealth Group as a Lead AI/ML Engineer, where he designed a Policy-as-Code platform to automate claim adjudication. Before that, at Ascendion, he led Generative AI initiatives focused on large language models, AI governance, bias detection, privacy-aware systems, and enterprise LLMOps platforms. At Jio Platforms, he was part of the Edge AI team and contributed to building Jio’s first in-house large language model. He holds an M.Tech in Artificial Intelligence from IIT Jodhpur and a Computer Science degree from Siksha ‘O’ Anusandhan University. His work spans Generative and Agentic AI, AI safety, model optimization, and real-world AI deployment

Suryabhan Singh is currently serving as a Senior Software Engineer-III at Carelon Global Solutions India LLP in Bangalore. With a background in research and development (R&D), he has honed his skills in developing optimized AI models for automating diverse business operations across industries. His expertise extends to AI model optimization techniques and tools, with a focus on both generic and fine-tuned Large Language Models (LLMs). Holding a master’s degree from Amrita Vishwa Vidyapeetham in Coimbatore, he boasts over four years of IT industry experience combined with three years of research experience. Suryabhan's contributions to various IEEE and international conferences highlight his dedication to advancing emerging technologies such as AI model optimization, model compression, streaming analytics, and generative AI.

Dr. Pethuru Raj currently serves as the chief architect at the Edge AI division of Reliance Jio Platforms Ltd. (JPL) in Bangalore. With a rich professional background, he has previously held positions at IBM Global Cloud Centre of Excellence (CoE), Wipro consulting services (WCS), and Robert Bosch Corporate Research (CR). Boasting over 23 years of IT industry experience and nine years of research experience, Dr. Raj completed his CSIR-sponsored PhD at Anna University, Chennai, followed by a UGC-sponsored postdoctoral research stint at the Department of Computer Science and Automation, Indian Institute of Science (IISc), Bangalore. Additionally, he held two international research fellowships (JSPS and JST) in leading Japanese universities, where he worked as a research scientist for 3.5 years. Dr. Raj's current focus lies in emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI) Model Optimization Techniques, Large Language Models (LLMs), Edge AI, Blockchain, Digital Twins, Cloud-native and Edge computing, Green and Generative AI, Reliability and Platform Engineering, and 5G/6G technologies.

Akshitta R Chaudhary is a seasoned computing professional currently enrolled in a Master's program in Data Science at the University of Aberdeen. With 5 years of extensive experience at Reliance Jio Platforms Limited, she has honed a versatile skill set encompassing functional software testing, AI, machine learning, and cloud-native technologies. Beginning as a Graduate Engineer Trainee, Akshitta specialized in reverse engineering, while her tenure in the AI and ML research team enriched her understanding of intricate concepts such as deep learning and natural language processing. Transitioning to a role as a Site Reliability Engineer, she excelled in cloud-native technologies like Docker, Kubernetes, and CICD, presenting innovative ideas to her peers. Akshitta also co-authored the book "Cloud-Native Computing: How to Design, Develop and Secure Micro-services," where she applied her expertise to event-driven microservice applications and implemented Kafka clusters with a focus on security.