For many tasks, it is difficult to know what features should be extracted.
One solution to this problem is to use machine learning to discover not only the mapping from representation to output but also the representation itself.
This approach is known as representation learning.Learned representations often result in much better performance than can be obtained with hand-designed representations.
They also allow AI systems to rapidly adapt to new tasks, with minimal human intervention. A representation learning algorithm can discover a good set of features for a simple task in minutes, or a complex task in hours to months.
Manually designing features for a complex task requires a great deal of human time and effort; it can take decades for an entire community of researchers.