Question: Explain why machine learning does not have a widely accepted formal measure of task difficulty. Then describe how network depth is used as a rough indicator of difficulty, including what shallow and deep models suggest.

Sample answer: Machine learning still lacks a standard formal definition of how difficult a task is. As a result, people often use an informal proxy based on how much sequential computation seems necessary. If a task can be represented with relatively few processing steps, it is treated as easier and may be handled by a shallow neural network. If the task appears to require many successive transformations, it is treated as harder and may call for a deeper network with more layers.

Key points:

• There is no broadly accepted formal definition of task difficulty.
• Difficulty is often estimated by the number of computation steps needed.
• Easier tasks tend to align with shallower neural networks.
• Harder tasks tend to align with deeper neural networks.

Rubric: The response should state that there is no formal measure, explain that fewer computation steps indicate an easier task and a shallow network, and explain that more computation steps indicate a harder task and a deeper network.