The conversation explores how machine learning can model categories in relation to human perception, emphasizing their fluidity rather than fixed definitions. By acknowledging the biases present in training data, this approach allows for a nuanced understanding of concepts that evolve over time, aligning with the goals of humanists. This inversion of a common critique of machine learning reveals its potential as a valuable tool in historical analysis.
