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Ghost fictions (Guided project) new Mon 26 Oct 2020   14:00 In progress

'Application forms should be returned to CDH Learning (learning@cdh.cam.ac.uk) by Tuesday 13 October 2020. Successful applicants will be notified by 15 October 2020.

This CDH Guided Project series which also includes a Methods Workshop will explore the generation of ‘synthetic’ texts using neural networks.

The release of OpenAI’s GPT-2 and GPT-3 language models in 2019 and 2020 has shown that predictive algorithms trained on very large general datasets can generate ‘synthetic’ texts, perform machine translation tasks, rudimentary reading comprehension, question answering and summarisation automatically without needing large amounts of task-specific training. These ‘ghostwritten’ texts have provoked wide attention in the media.

Researchers have experimented with prompting GPT-3 to write short stories, answer philosophical questions and apparently propose potential medical treatments -although GPT-3 had some difficulty with the question “how many eyes does a horse have?”. The Guardian ‘commissioned’ op-ed from GPT-3.

Through interactive hands-on sessions and demonstrations we will explore synthetic text production and look at how ideas about the distinction between ‘fact’, ‘fiction’ and ‘non-fiction’ are shaping the reception of this emerging technology. Our aim is to stimulate deeper critical engagement with machine learning by humanities researchers and to encourage more public debate about the role of AI in culture and society.

We invite applications from early career researchers and others at the University of Cambridge to join a small project team for four online sessions during the Guided Project phase in Oct-November. Participants will need to commit to joining the live sessions and to set aside at least 3-4 hours work on a small-scale individual project during the course. We are interested in assembling an interdisciplinary group of researchers drawing on insights from across humanities, social science and technology disciplines .Prior knowledge of programming, computer science or Machine Learning is not required.