Cross-Domain Transfer in Humans and Machines
By | Leonidas Doumas (School of Philosophy, Psychology and Language Sciences, Edinburgh, UK)
Date | Thursday 12 March 2020
Time | 13:00
Venue| Senior Common Room, Level 2 (2D17), Priory Road Complex
Recent advances in deep learning have produced models that far outstrip human performance in a number of domains. However, where machine learning approaches still fall far short of human-level performance is in the capacity to transfer knowledge across domains. While a human learner will happily apply knowledge acquired in one domain (e.g., mathematics) to a different domain (e.g., cooking; a vinaigrette is really just a ratio between edible fat and acid), machine learning models still struggle profoundly at such tasks. I will present a case that human intelligence might be (at least partially) usefully characterised by our ability to transfer knowledge widely, and a framework that we have developed for learning representations that support such transfer. The model is compared to a current machine learning approaches.