This episode analyzes the research conducted by Andrew Kyle Lampinen, Stephanie C. Y. Chan, and Katherine Hermann at Google DeepMind, as presented in their paper titled "Learned feature representations are biased by complexity, learning order, position, and more." The discussion delves into how machine learning models develop internal feature representations and the various biases introduced by factors such as feature complexity, the sequence in which features are learned, and their prevalence within datasets. By examining different deep learning architectures, including MLPs, ResNets, and Transformers, the episode explores how these biases impact model interpretability and the alignment of machine learning systems with cognitive processes. The study highlights the implications for both the design of more robust and interpretable models and the understanding of representational biases in biological brains.

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For more information on content and research relating to this episode please see: https://openreview.net/pdf?id=aY2nsgE97a