Nobody knows what will happen in the future, but some guesses are a lot better than others. A kicked football will not reverse in midair and return to the kicker’s foot. A half-eaten cheeseburger will not become whole again. A broken arm will not heal overnight.
By drawing on a fundamental description of cause and effect found in Einstein’s theory of special relativity, researchers from Imperial College London have come up with a way to help AIs make better guesses too.
The world progresses step by step, every instant emerging from those that precede it. We can make good guesses about what happens next because we have strong intuitions about cause and effect, honed by observing how the world works from the moment we are born and processing those observations with brains hardwired by millions of years of evolution.
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.
“It’s very cool to see ideas from fundamental physics being borrowed to do this,” says Ciaran Lee, a researcher who works on causal inference at Spotify and University College London. “A grasp of causality is really important if you want to take actions or decisions in the real world,” he says. It goes to the heart of how things come to be the way they are: “If you ever want to ask the question ‘Why?’ then you need to understand cause and effect.”
The original article can be found here.
In the field of causal inference, Professor Judea Pearl is a pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.” In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea also contributes to Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.