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In the Futuristic Laboratory Creative Engineer Works on the Transparent Computer Display. Screen Shows Interactive User Interface with Deep Learning System, Artificial Intelligence Prototype.

A Critical Look at the Myth of “Deep Learning”

“Deep learning” is as misnamed a computational technique as exists.

I’ve been reviewing philosopher and programmer Erik Larson’s The Myth of Artificial Intelligence. See my earlier posts, herehere, and here.

“Deep learning” is as misnamed a computational technique as exists. The actual technique refers to multi-layered neural networks, and, true enough, those multi-layers can do a lot of significant computational work. But the phrase “deep learning” suggests that the machine is doing something profound and beyond the capacity of humans. That’s far from the case. The Wikipedia article on deep learning is instructive in this regard. Consider the following image used there to illustrate deep learning:

Note the rendition of the elephant at the top and compare it with the image of the elephant as we experience it at the bottom. The image at the bottom is rich, textured, colorful, and even satisfying. What deep learning extracts, and what is rendered at the top, is paltry, simplistic, black-and-white, and unsatisfying. What’s at the top is what deep learning “understands”  in fact, its “understanding,” whatever we might mean by the term, cannot progress beyond what is rendered at the top level. This is pathetic, and this is what is supposed to lay waste and supersede human intelligence? Really now.

You may also wish to read:

Artificial intelligence understands by not understanding The secret to writing a program for a sympathetic chatbot is surprisingly simple… We needed to encode grammatical patterns so that we could reflect back what the human wrote, whether as a question or statement.

Automated driving and other failures of AI How would autonomous cars manage in an environment where eye contact with other drivers is important? In cossetted and sanitized environments in the U.S., we have no clue of what AI must achieve to truly match what humans can do.


Artificial intelligence: Unseating the inevitability narrative. William Dembski: World-class chess, Go, and Jeopardy-playing programs are impressive, but they prove nothing about whether computers can be made to achieve AGI. In The Myth of Artificial Intelligence, Erik Larson shows that neither science nor philosophy back up the idea of an AI superintelligence taking over.

William A. Dembski

Board of Directors, Discovery Institute
A mathematician and philosopher, Bill Dembski is the author/editor of more than 20 books as well as the writer of peer-reviewed articles spanning mathematics, engineering, philosophy, and theology. A past philosophy professor, he retired in 2014 from active research and teaching in intelligent design (ID) to focus on the connections between freedom, technology, and education — specifically, how education helps to advance human freedom with the aid of technology. Bill Dembski is presently an entrepreneur who builds educational software and websites. He lives in Iowa.

A Critical Look at the Myth of “Deep Learning”