Although recent advances in the capabilities of machine learning (ML) systems are impressive, they are not equally suitable for all tasks… .We identify eight key criteria that help distinguish successful ML tasks from tasks where ML is less likely to be successful.
- Learning a function that maps well-defined inputs to well-defined outputs.
- Large (digital) data sets exist or can be created containing input-output pairs.
- The task provides clear feedback with clearly definable goals and metrics.
- No long chains of logic or reasoning that depend on diverse background knowledge or common sense.
- No need for detailed explanation of how the decision was made
- A tolerance for error and no need for provably correct or optimal solutions
- The phenomenon or function being learned should not change rapidly over time
- No specialized dexterity, physical skills, or mobility required