This paper undertakes a critical examination of unsupervised learning within the context of Raven's Progressive Matrices (RPMs). We trace the historical trajectory of computational models for RPMs, from early rule-based approaches to modern neural networks, and we focus on the innovative work of Zhuo et al. in introducing semi-supervised learning to RPMs. Our discussion highlights the nuances of unsupervised learning, emphasising the role of noisy labels as a form of guidance, albeit with a trade-off in precision compared to traditional supervised learning. In this paper, we recognise the challenge in formalising the distinction between supervised and unsupervised learning, but we underscore the importance of precision in communication and nomenclature, especially in regards to facilitating knowledge transfer and directing future research. We hope that this contribution enhances the discourse on unsupervised learning and offers valuable insights towards the challenges and opportunities in attaining human-level reasoning capabilities in machine learning and artificial intelligence.