Deep Learning Is Not Just Inadequate for Solving AGI, It Is Useless

Some of Us Know Exactly Why: DL Can’t Generalize

Abstract

AI experts should not get offended when AGI researchers complain about the inadequacies of deep learning. Nobody is really out to get rid of DL. While it is true that the advent of AGI will render DL obsolete in some fields, we believe that it will probably continue to be useful for many automation tasks even after AGI is solved. But, in order to make progress toward solving AGI, researchers must point out that DL is not just inadequate for solving AGI, it is useless. And some of us know exactly why it is useless.

Note: AGI = Artificial General Intelligence.

No Generalization, No AGI

The biggest problem with DL is its inherent inability to effectively generalize. Without generalization, edge cases are an insurmountable problem, something that the autonomous vehicle industry found out the hard way after wasting more than $100 billion by betting on DL.

A deep neural network cannot perceive this bicycle unless it has been previously trained to recognize it.

Generalization is the ability of an intelligent system to perceive any object or pattern without recognizing it. An Amazon Indian, for example, can instantly perceive a bicycle even if he has never seen one before. He can instantly see its 3D…