additional resources#

On this page, you will find links to additional resources/materials relevant to the material covered in the workshop.

books#

In preparing the materials for this workshop, I have used the following books - they are not required reading, but if you have access through a library or the means to pick up a (used) copy, they can help fill in some of the details.

  • Burkov, A. (2019) The Hundred-page Machine Learning Book. Andriy Burkov. ISBN 978-1-9995-7950-0 [Website]

  • Müller, A. C. and S. Guido (2017). Introduction to Machine Learning with Python. O’Reilly. ISBN 978-1-4493-6941-5 [Publisher Link]

  • Shane, J. (2019) You look like a thing and I love you. Wildfire. ISBN 978-1-4722-6901-0 [Author’s Website]

articles and blog posts#

The following articles and blog posts have helped to shape some of the thinking of this workshop, especially the session on ethics.

  • Battersby, M. “Wiley set to earn $44m from AI rights deals, confirms ‘no opt-out’ for authors” [The Bookseller]

  • Burke, G. and H. Schellmann. “Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said” [Associated Press]

  • Clifton, M. “Black teenagers twice as likely to be falsely accused of using AI tools in homework” [Semafor]

  • Cole, S. “Largest datasets powering AI images removed after discovery of child sexual abuse material” [404 Media]

  • Cole, S. “Massive AI dataset back online after being ‘cleaned’ of child sexual abuse material” [404 Media]

  • Cox, J. “LinkedIn is training AI on user data before updating its terms of service” [404 Media]

  • Lee, D. “For The Love Of God, Don’t Let AI Choose Your Mushrooms” [The Takeout]

  • Lee, T. B. and S. Trott. “A jargon-free explanation of how AI large language models work” [Ars Technica]

  • Liang, W. et al. “Monitoring AI-modified content at scale: a case study on the impact of ChatGPT on AI conference peer reviews” [arXiv]

  • Long, K. “Language is a poor heuristic for intelligence” [Nine Lives]

  • Merchant, B. “AI is revitalizing the fossil fuels industry, and big tech has nothing to say for itself” [Blood in the Machine]

  • Muldoon, J. et al. “Opinion: What’s behind the AI boom? Exploited humans” [SiliconValley.com]

  • Perrigo, B. “OpenAI used Kenyan workers on less than $2 per hour to make ChatGPT less toxic” [Time]

  • Saul, J. et al. “AI is already wreaking havoc on global power systems” [Bloomberg]

  • Williams, D. P. “Bias Optimizers” [American Scientist]

lectures, videos, and podcasts#

  • Bender, E. M. and A. Hanna “Mystery AI Hype Theater 3000” [DAIR Institute]

  • Williams, D. P. “On Bullshit Engines: The Socioethical and Epistemic Status of GPTs and other AI” [YouTube]

web resources#

  • Machine Learning Crash Course (Google). Google’s machine learning crash course is another excellent resource you can use to learn more about the basics of machine learning. It uses keras rather than scikit-learn, but the topics and content are otherwise similar.