Discrete vs. Continuous

Tim and Alexander discuss the practical implications of discrete versus continuous computation, highlighting the limitations of universal approximation theorems in real-world scenarios. They delve into the challenges of working with finite samples rather than ideal functions in machine learning, shedding light on the complexities hidden within infinite possibilities.