David shares how aligning model explanations with human reasoning improves performance without sacrificing accuracy. The debate between post hoc explainability and changing training methods is discussed, highlighting the trade-offs in performance. Delving into low-level representations and weights, David explores the intricate workings of models and the parallel to neuroscience in understanding behavior.
