Sehr schöne Infografik zur Visualisierung der Unterschiede zwischen Data Science, Machine Learning und Artificial Intelligence
- Data science produces insights
- Machine learning produces predictions
- Artificial intelligence produces actions
These definitions are overly simplistic, David acknowledges, and not without their flaws: “A fortune teller makes predictions, but we’d never say that they’re doing machine learning!”. However, I feel its a great first attempt at demystification. Particularly, the applied example with which David continues make matters more clear:
Suppose we were building a self-driving car, and were working on the specific problem of stopping at stop signs. We would need skills drawn from all three of these fields.
Machine learning: The car has to recognize a stop sign using its cameras. We construct a dataset of millions of photos of streetside objects, and train an algorithm to predict which have stop signs in them.
Artificial intelligence: Once our car can recognize stop signs, it needs to decide when to take the action of applying the brakes. It’s dangerous to apply them too early or too late, and we need it to handle varying road conditions (for example, to recognize on a slippery road that it’s not slowing down quickly enough), which is a problem of control theory.
Data science: In street tests, we find that the car’s performance isn’t good enough, with some false negatives in which it drives right by a stop sign. After analyzing the street test data, we gain the insight that the rate of false negatives depends on the time of day: it’s more likely to miss a stop sign before sunrise or after sunset. We realize that most of our training data included only objects in full daylight, so we construct a better dataset including nighttime images and go back to the machine learning step.