Machine learning algorithms are not like other computer programs. In the usual sort of programming, a human programmer tells the computer exactly what to do. In machine learning, the human programmer merely gives the algorithm the problem to be solved, and through trial-and-error the algorithm has to figure out how to solve it.
This often works really well - machine learning algorithms are widely used for facial recognition, language translation, financial modeling, image recognition, and ad delivery. If you’ve been online today, you’ve probably interacted with a machine learning algorithm.
But it doesn’t always work well. Sometimes the programmer will think the algorithm is doing really well, only to look closer and discover it’s solved an entirely different problem from the one the programmer intended. For example, I looked earlier at an image recognition algorithm that was supposed to recognize sheep but learned to recognize grass instead, and kept labeling empty green fields as containing sheep.
There are so many really interesting examples she has collected here, and show us the power and danger of black boxes. In a lot of ways machine learning is just an extreme case of all software. People tend to write software on an optimistic path, and ship it after it looks like it's doing what they intended. When it doesn't, we call that a bug.
The difference between traditional approaches and machine learning, is debugging machine learning is far harder. You can't just put an extra if condition in, because the logic to get an answer isn't expressed that way. It's expressed in 100,000 weights on a 4 level convolution network. Which means QA is much harder, and Machine Learning is far more likely to surprise you with unexpected wrong answers on edge conditions.