|4 Dec 2023||13:00–17:00||Milstein Seminar Room, University Library|
This in-person workshop will provide an accessible, non-technical introduction to Machine Learning systems, aimed primarily at graduate students and researchers in the humanities, arts and social sciences. No prior knowledge of programming is required.
We will focus on the technical, ethical and societal implications of embedding Machine Learning systems for classifying and generating texts and images into the world of work, with a particular emphasis on the impact of Large Language Models such as ChatGPT. We will explore these text generation systems in the context of longer histories of AI, including the ‘deep learning revolution’ in image-based Machine Learning systems which laid the foundations for popular text-to-image generation models such as StableDiffusion.
Participants will have the chance to both learn more about how AI works and also discuss what the embedding of such systems into labour processes, management structures, resource allocation systems may mean for how society works.
Target audience: CDH Methods sessions are open to the University of Cambridge staff and graduate students who want to learn and apply digital methods and use digital tools in their research, these sessions may be of particular interest to:
- PhD students in the Arts, Humanities and Social Sciences
- Early Career Researchers in the Arts, Humanities and Social Sciences
- Other Cambridge students and staff welcome
About the convenors: Anne Alexander is Director of Learning at Cambridge Digital Humanities and co-author of Ghosts, Robots, Automatic Writing: an AI-Level Study Guide.
Emily Sandford is a postdoctoral researcher in the astrophysics group of the Cavendish Laboratory, research fellow at Caius, and member of the Cambridge University and College Union Executive Committee. In previous research she used the machinery of large language models to model planetary systems.
Jarrah O’Neill is a PhD candidate in the Department of Sociology and a member of the Cambridge University and College Union Executive Committee.
|Session 1: Technical fundamentals of Machine Learning systems||
The first session will introduce basic principles in text and image-based ML, system architectures, the challenges of dimension reduction, classification and generalisation and discuss sources of bias and problems of interpretation
|Session 2: TypeCast - experimental approaches to exploring bias in text and image generators.||
This hands-on session will demonstrate some of the ways in which text and image generation models reproduce bias and stereotypes associated with national identities, gender and social class.
|Session 3: Generative AI in the workplace||
We will begin with a workshop based on a real scenario and ask participants to imagine the problems that the use of these kinds of technology might create. We will then examine some other examples and their consequences for workers, both individually and collectively.