Machine learning works well on simple tasks with lots of data, but struggles when concepts are complex or data is scarce. It often fails when it encounters new types of data it hasn't seen before.
Machine learning tends to work well when you're trying to learn a simple concept, such as something that you could do with less than a second of mental thought, and when there's lots of data available. Machine learning tends to work poorly when you're trying to learn a complex concept from small amounts of data. A second underappreciated weakness of AI is that it tends to do poorly when it's asked to perform on new types of data that's different than the data it has seen in your data set. Let me explain with an example.
