Seen in → No.98
A quick look at a new direction AI pioneer Yoshua Bengio is working on; bringing more contextual and causal understanding to Artificial Intelligence.
Now, Bengio says deep learning needs to be fixed. He believes it won’t realize its full potential, and won’t deliver a true AI revolution, until it can go beyond pattern recognition and learn more about cause and effect. In other words, he says, deep learning needs to start asking why things happen. […]
The algorithm in the paper essentially forms a hypothesis about which variables are causally related, and then tests how changes to different variables fit the theory. The fact that smoking is not only related to cancer but actually causes it, for instance, should still be apparent even if cancer is correlated with other factors, such as hospital visits. […]
Marcus adds that the lesson from human experience is obvious. “When children ask ‘why?’ they are asking about causality,” he says. “When machines start asking why, they will be a lot smarter.”