Geog-ChatGPT: How Does Geography Look According to Foundation Models? (panel)
Channel: Spatial Data Science Symposium
Date: 09/05/2023

Moderator Krzysztof Janowicz (University of Vienna; University of California, Santa Barbara) led a discussion on the potential, design, evaluation and implications of foundation models, both geo-specific and non-geo-specific. Panelists included Mark Gahegan (Computer Science, University of Auckland), Ni Lao (Google), Xiaofan Liang (City and Regional Planning, Georgia Institute of Technology), Renée Sieber (Department of Geography, McGill University), Seth Spielman (Core Search and Artificial Intelligence Group, Microsoft), and Bo Zhao (Department of Geography, University of Washington).

00:00 Introduction
01:55 Q: Where do you see (non-geo-specific) foundation models having the most significant potential impact on research and practice in geography and geosciences?
07:27 Q: How can we evaluate the quality of GPT model predictions when we can't separate training and testing data because we don't have access to the black box of the data that was used in training?
11:25 Q: Geography has been a human discipline -- natural science from a human perspective. Will foundation models provide an emerging dimension that is different from human and natural perspectives?
20:07 Q: Given that the validity of facts can vary over space and time, how would appropriate spatial and temporal variables be built into geo-foundation models? 
25:09 Q: How do we handle calibration in situations where the ethical standards defined by the country/location where a model is created/maintained are different from those of where it is used?
32:36 Q: What can we learn about geography or society from the biases or problems encoded in foundation models?
36:42 Q: What would geo-foundation models be, how could they be used, and when can we expect them?