The article discusses how early machine learning used human knowledge to process data before training models, leading to potential errors. With the development of deep learning, machines can now learn features directly, improving accuracy. Meta-learning aims to give machines human-like learning abilities, evolving from basic memory to active learning. This shift is crucial for overcoming current limitations in machine learning algorithms.