Navigating the Limitations of Deep Learning

This chapter examines the constraints of deep learning models, emphasizing their role as pattern recognition tools without true understanding of causality. The speakers advocate for augmented intelligence to bolster human decision-making and call for new methodologies to improve model interpretability. The discussion also highlights the complexities of measuring interpretability, underlining the need for robust evaluation strategies that align machine learning processes with human reasoning.