Machine Learning and AI: A Critical Introduction

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. You don’t need any prior knowledge of programming.

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 the broader history 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, and resource allocation systems may mean for how society works.

Dr Anne Alexander is Director of Learning at Cambridge Digital Humanities and co-author of Ghosts, Robots, Automatic Writing: an AI-Level Study Guide.

Our CDH Methods workshops have limited places and prioritise students and staff at the University of Cambridge. However, if space is available, we welcome all participants interested in learning about and applying digital methods and tools in their research. This session may be particularly relevant 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

If you have specific accessibility needs for this event please get in touch. We will do our best to accommodate any requests. Please note this is an in-person only workshop

Info: 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

Info:
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.

Info: 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.

Know what’s Happening Next — before everyone else does.

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Host or Publisher Cambridge Digital Humanities