Texas A&M Univerisity

Center for Geospatial
Sciences, Applications
and Technology

Resources & Further Reading

Books and eBooks

  • Generative Deep Learning” by David Foster (2019) – A comprehensive introduction to generative modeling techniques like GANs, VAEs, and autoregressive models.
  • Deep Learning with Python” by Francois Chollet (2017) – While not solely focused on generative AI, this book covers the fundamentals of deep learning that underpin modern generative models.
  • Generative Adversarial Networks Cookbook” by Eric Baptista and Masashi Ono (2021) – A hands-on guide with code examples for building different types of GANs.
  • Deep Learning for Coders with fastai and PyTorch” by Jeremy Howard and Sylvain Gugger (2020) – Covers the fastai library which has great support for generative models like GANs.
  • Deep Learning for Natural Language Processing” by Bharath Ramsundar et al. (2021) – Explains core techniques like transformers used in large language models.
  • The Precipice” by Toby Ord (2020) – An influential book analyzing existential risks including potential risks from advanced AI systems like generative models.
  • You Look Like a Thing and I Love You” by Janelle Shane (2019) – A lighthearted and accessible look at the creative potential and quirks of generative AI systems.
  • Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville – Although not exclusively about generative AI, this book provides a comprehensive introduction to deep learning, including a detailed chapter on generative models that is essential for understanding the fundamentals of technologies like GANs (Generative Adversarial Networks).
  • GANs in Action: Deep learning with Generative Adversarial Networks” by Jakub Langr and Vladimir Bok – This book focuses specifically on GANs, offering insights into the architecture and applications of these powerful generative models. It includes practical examples and code snippets, making it accessible for programmers and data scientists.
  • Architectural Intelligence: How Designers and Architects Created the Digital Landscape” by Molly Wright Steenson – This book delves into the intersection of architecture, design, and AI, providing a historical perspective that enriches the understanding of generative design in AI.
  • Artificial Intelligence: A Guide for Thinking Humans” by Melanie Mitchell – While broader in scope, this book addresses key concepts in AI, including generative models, and discusses the implications of AI from an ethical and practical perspective.
  • Impromptu: Amplifying Our Humanity Through AI” by Reid HoffmanThis book, written with the help of GPT-4, provides an insider’s perspective on generative AI from a former OpenAI board member.
  • The Creative Mind: Myths and Mechanisms, 2nd Edition” by Margaret A. BodenThis classic book explores the nature of human creativity and how it relates to the development of artificial intelligence, including generative AI.
  • The Alignment Problem” by Brian Christian While not specifically about generative AI, this book discusses the broader ethical and safety challenges that arise when AI systems don’t behave as expected, which is highly relevant to the field.
  • Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play” by David FosterThis hands-on guide delves into the practical applications of generative AI in creative domains, making it a great introduction to the field.

Blogs and Online Magazines

  • Towards Data Science – A Medium publication that features content on data science and AI, including topics on generative AI techniques and applications.
  • The Gradient – Focuses on cutting-edge AI and deep learning research and trends, often covering the latest advancements in generative models.
  • AI Weirdness – Janelle Shane’s blog explores the quirky and humorous side of AI, often experimenting with generative models to create amusing and unexpected results.
  • DeepMind Blog – Offers insights into the research projects and breakthroughs from one of the leading AI research organizations.
  • OpenAI Research News – A key source for the latest research, findings, and updates directly from OpenAI, known for their pioneering work in generative models like GPT-3.
  • Synced – Provides AI industry news and analysis, with comprehensive coverage of new research developments in generative AI.
  • Distill – Publishes clear and understandable explanations of machine learning concepts and findings, including generative AI technologies.
  • Analytics Vidhya – Features articles, tutorials, and case studies on various AI topics, including practical guides on implementing generative AI models.
  • MarkTechPostA California-based website that covers the latest developments in machine learning, AI, and deep learning, including research and industry news.
  • There’s An AI For That One of the world’s largest AI aggregators, providing information on over 300,000 AI tools for various tasks.
  • The BAIR Blog (Berkeley Artificial Intelligence Research)- A blog from the University of California, Berkeley that provides accessible research findings, perspectives, and updates on AI.
  • Machine Learning Mastery – A blog written by Jason Brownlee, PhD, that offers tutorials, guides, and advice for developers working on machine learning projects.
  • Bernard Marr A blog by a successful social media influencer in the business and technology space, covering topics like the history of generative AI, music AI, and the future of the digital revolution.
  • AWS Machine Learning Blog A frequently updated blog from Amazon Web Services that provides information and resources on the latest AI services and developments.OpenAI BlogThe blog from the creators of ChatGPT, which covers the latest news and developments in the world of AI.
  • The AI Alignment Forum – In-depth technical discussions and analysis on AI safety and ethics, including generative models.
  • Lil’Log – Blog by Lilian Weng covering the latest AI research, with a focus on generative models.
  • Anthropic AI Research Blog – Technical deep dives into the latest generative AI models from Anthropic.
  • Ars Technica AI/Machine Learning Section – Excellent reporting and analysis on the latest generative AI breakthroughs.
  • MIT Technology Review AI Section – Insightful articles on the capabilities and implications of generative AI.

Documentaries and Educational Videos

There are several great documentaries and educational video series that can help provide an understanding of generative AI. These resources range from short engaging videos to entire university course lectures. They cover the technical underpinnings as well as the broader context around the development and use of generative AI models.

Acknowledgement & Disclaimer

Gratitude is extended to Mateo (MrMundial) Alexander, BAAS for his diligent efforts in collecting all the information pertain to Generative AI in Urban Planning . His meticulous work and dedication are highly appreciated and have significantly contributed to this endeavor.

The Website is designed and developed by Yaohao Chen, supported and maintained by Shoujia Li. Visual artworks of a computer algorithm or artificial intelligence are included in the website, which do not contain sufficient human authorship to support a copyright claim.