Different representation learning objectives recover distinct latent structures from the same psychometric data
The episode discusses a paper demonstrating that AI's interpretation of data is not neutral, but rather dictated by its training goals. Hosts explain that choosing different representation learning objectives—such as contrastive versus classification—yield distinct views of the data's underlying geometry and structure. This suggests that complex psychometric data requires a multi-objective approach.