  This paper revisits the principle of uniform convergence in statistical
learning, discusses how it acts as the foundation behind machine learning, and
attempts to gain a better understanding of the essential problem that current
deep learning algorithms are solving. Using computer vision as an example
domain in machine learning, the discussion shows that recent research trends in
leveraging increasingly large-scale data to perform pre-training for
representation learning are largely to reduce the discrepancy between a
practically tractable empirical loss and its ultimately desired but intractable
expected loss. Furthermore, this paper suggests a few future research
directions, predicts the continued increase of data, and argues that more
fundamental research is needed on robustness, interpretability, and reasoning
capabilities of machine learning by incorporating structure and knowledge.
