JOHN W. SHERRY
Scale suffuses the work we do and, recently, has us considering an aspect of scale best suited to those with ethnographic training. We've been asked to help with scaling up one of the latest blockbusters in high tech – deep learning. Advances in deep learning have enabled technology to be programmed to not only see who we are by using facial ID systems and hear what we say by using natural language systems; machines are now even programmed to recognize what we do with vision-based activity recognition. However, machines often define the objects of their gaze at the wrong scale. Rather than “look for” people or objects, with deep learning, machines typically look for patterns at the smallest scale possible. In multiple projects, we've found that insights from anthropology are needed to inform both the scale and uses of these systems.
Keywords Deep Learning, Human Scale, Ethnographic Insights
Article citation: 2020 EPIC Proceedings...
This paper reports on the use and perceptions of deployed A.I. and recognition social-material assemblages in China and the USA. A kaleidoscope of “boutique” instantiations is presented to show how meanings are emerging around A.I. and recognition. A model is presented to highlight that not all recognitions are the same. We conclude by noting A.I. and recognition systems challenge current practices for the EPIC community and the field of anthropology....