GENERATIVE AI AND LARGE LANGUAGE MODELS: OPPORTUNITIES, CHALLENGES, AND APPLICATIONS

CALL FOR CHAPTERS


Generative AI and Large Language Models: Opportunities, Challenges, and Applications

SUBMIT NOW

Generative AI and Large Language Models: Opportunities, Challenges, and Applications

Book Editors

Motivation

The rapid emergence of Generative AI and Large Language Models (LLMs) is a testament to AI's evolution. These technologies are drastically reshaping the boundaries of what's possible, from generating diverse content forms to interpreting colossal data beyond human reach. While LLMs, like GPT variants, have revolutionized natural language processing, advances in computer vision with Diffusion Models, GANs, and Vision Transformers (ViT) have broadened AI applications across sectors including healthcare, education, and finance. Despite the clear benefits, the AI community faces challenges: fine-tuning, risk mitigation, and understanding broader societal implications. Currently, information on these topics remains scattered, lacking a unified reference.
This book aims to consolidate these fragmented insights, offering a comprehensive guide into the exhilarating world of Generative AI and LLMs, meeting the growing demand for a single, authoritative resource.

Objectives of the Book

    In a rapidly evolving digital landscape, the development and application of Generative Artificial Intelligence, particularly Large Language Models (LLMs), stands at the forefront of technological innovation. This book seeks to be an indispensable reference, crafted to cater to academics, industry professionals, and hands-on practitioners alike.
    Our ambition is to bridge a prevailing gap. While there are resources that touch upon various facets of LLMs and Generative AI, few provide a holistic, in-depth view that is both technically rigorous and practically relevant. This book aspires to be a singular, comprehensive guide in the domain by covering foundational theories, shedding light on cutting-edge paradigms like Retrieval-augmented generation (RAG), and offering practical insights from real-world implementations and use cases.
    Moreover, this book is not merely an academic exercise; it recognizes the critical importance of applications. Hence, readers will find extensive explorations into how LLMs are transforming diverse sectors - from healthcare and education to finance and legal systems. By doing so, we aim to equip professionals in these fields with the knowledge and tools they need to harness the potential of Generative AI effectively.
    Emerging trends and innovations do not exist in a vacuum. They bring with them a slew of ethical, social, and technical challenges. In acknowledging this, the book does not shy away from addressing the broader implications of these advancements, ensuring readers are well-informed of both the possibilities and the pitfalls.
    In essence, this book's objective is multifaceted: to serve as a beacon for those navigating the intricate flows of Generative AI, to empower its readers with a balanced and thorough understanding of current trends, and, most importantly, to be the definitive reference in this exciting and ever-evolving field.

Topics

Suggested topics include (but are not limited to) the following methods:

  • Foundations and Evolution of Generative Models.
  • Effect of Generative AI Development on Climate Change.
  • Open Source Large Language Models (LLMs): A New Paradigm.
  • Architectures for LLMs: Design and Functionality.
  • Performance Metrics: Evaluating the Efficiency of LLMs.
  • Reinforcement Learning with Human Feedback: Integrating Human Intuition.
  • Fine-Tuning LLMs for Optimal Performance.
  • Retrieval-augmented generation (RAG).
  • Code auto-generation.
  • Sustainable Large Language Models.
  • Practical Applications and Use Cases Across Industries.
  • Experimental Studies Reports.
  • Business Innovations through Generative AI.
  • LLMs in Healthcare: Diagnosis, Treatment, and Beyond.
  • Modernizing Education with LLMs.
  • Legal Systems: AI's Role in Legal Analysis and Predictions.
  • Financial Forecasting and Risk Management with LLMs.
  • LLMs: Bridging Natural Language and Code Generation.
  • Generative Models in Game Design and Virtual Reality.
  • Ethical Dynamics and Societal Echoes.
  • The Social Footprint of LLMs.
  • Ethical Labyrinths: Navigating Challenges and Controversies.
  • Risk Mitigation: Strategies and Best Practices.

Tentative Chapters

    Chapter 1: Introduction to Generative AI and LLMs

  • Background and Evolution.
  • Why Generative AI and LLMs Matter.

  • Chapter 2: Foundations of Generative Models

  • Theoretical underpinnings.
  • Historical context and progression.

  • Chapter 3: Architectural Deep Dive into LLMs

  • Design principles.
  • Exploring various architectures and their characteristics.

  • Chapter 4: Open Source LLMs: The Power of Collective Intelligence

  • Tracing the journey of open-source philosophy in AI, emphasizing the rise and significance of open-source LLMs.
  • Highlighting the evolution of AI, rapid community-driven innovation, and the transparency fostered by open-source LLMs.
  • Addressing potential risks, vulnerabilities, and ethical considerations unique to open-source LLM frameworks.

  • Chapter 5: Fine-Tuning and Performance Enhancement for LLMs

  • Best practices.
  • Integrating human feedback.
  • Integrating human feedback.

  • Chapter 6: Generative Models in Business and Industry

  • Business use cases and innovation.
  • Logistics, surveillance, and smart city applications.
  • Practical impacts on industries.

  • Chapter 7: AI-driven Solutions in Healthcare, Education, and Legal Systems

  • LLMs in diagnosis and treatment.
  • Modernizing pedagogy.
  • Legal analysis and forecasting.

  • Chapter 8: Ethical and Social Implications of LLMs

  • Navigating challenges and controversies.
  • Societal impact and considerations.

  • Chapter 9: Beyond Text: Bridging Natural Language and Visual Processing

  • Generative AI in computer vision.
  • Domain adaptation and cross-discipline applications.

  • Chapter 10: The Future Landscape of Generative AI and LLMs

  • Predictions and forward-looking analysis.
  • Opportunities and challenges.
  • Conclusion and the road ahead.
    na
    na

Submission Guidelines

  • Authors are invited to submit original manuscripts, in English. For details on how to prepare the manuscripts and style files refer to the instruction page for authors . This page also includes the LATEX templates and further instructions. Authors should submit their original work that must not be submitted or currently under consideration for publication elsewhere.
  • Submissions of the book chapters will be done using the Easychair system, using the following submissions link: EasyChair Submission Link
  • Any questions about this book, please feel free to contact: akoubaa@psu.edu.sa

Important Dates

  • Full-length paper submission: Feb 28, 2024
  • Notification of first decision: April 30, 2024
  • Revised chapter submission: May 30, 2024
  • Final decision: June 31, 2024
  • Camera-ready: July¬†15,¬†2024