Computers, however, find causal reasoning hard. Machine-learning models excel at spotting correlations but are hard pressed to explain why one event should follow another. That’s a problem, because without a sense of cause and effect, predictions can be wildly off. Why shouldn’t a football reverse in flight?
This is a particular concern with AI-powered diagnosis. Diseases are often correlated with multiple symptoms. For example, people with type 2 diabetes are often overweight and have shortness of breath. But the shortness of breath is not caused by the diabetes, and treating a patient with insulin will not help with that symptom.
The AI community is realizing how important causal reasoning could be for machine learning and are scrambling to find ways to bolt it on.
Researchers have tried various ways to help computers predict what might happen next. Existing approaches train a machine-learning model frame by frame to spot patterns in