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The Limitations of Machine Learning in Categorizing Data
The speaker raises two points: the question of whether machines without human input can learn categories, and the context-dependent nature of image interpretation. They share an example of a computer vision system identifying a horse while a sheep farmer sees sheep and a meteorologist sees a cloud. The speaker argues that images have multiple meanings depending on human use. However, self-supervised image representation algorithms seem to extract a universal representation. The speaker also acknowledges the complexity of categories and the challenges in efficient parameter space utilization. Overall, they express some pessimism about relying solely on self-supervised learning.