Kate discusses the challenges in training models to recognize images across different domains. She highlights how self-supervised learning techniques can lead models to prioritize domain similarities over category similarities, resulting in unexpected associations—like a sketch of a giraffe being deemed closer to a sketch of a guitar than to a photo of a giraffe. This insight sheds light on the intricacies of unsupervised training objectives in AI.
