To Build Truly Intelligent Machines, Teach Them Cause and Effect

Judea Pearl, a pioneering figure in artificial intelligence, argues that AI has been stuck in a decades-long rut. His prescription for progress? Teach machines to understand the question why.

There's a lot in this post. Judea Pearl is worth taking seriously given his bona fides, but this entire train of thought—using causal reasoning rather than statistical relationships to understand the world—is far outside the norm in the field today. Here's a choice quote from the interview that illustrates just how out of left field it is:

As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial.

I kind of love that.


Want to receive more content like this in your inbox?