Our minds aren’t equipped to handle AI

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AI is junk food for the mind: easy, tempting and ultimately very bad for you.

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Norbert Wiener, godfather of cybernetics, once said, “The thought of every age is reflected in its technique.” For the past century, our thought has been reflected in our computers, including by those in the AI industry. Google’s Demis Hassabis calls the brain “a biological approximation to a Turing machine.” Elon Musk puts it more bluntly, declaring that “people should just think of the brain as a biological computer.” (Musk’s brain often worries me.)

But humans are more complex than a straightforward comparison to computers gives us credit for — and far more than most of the AI industry seems to appreciate. And as their products push ever deeper into culture, this helps explain why they’re making such a mess. AI, it turns out, is something like a cognitive version of a hot dog: It appeals to us in the moment, but undermines the health and sustainability of cognitive systems we’ve evolved over several millennia.

To understand the problem, we’ll need a little neuroscience and the entirety of human evolutionary history.

The idea that brains are computers traces back to Alan Turing. It posits thought as a three-step process, functionally an algorithm: Our minds take in information from the world (input), manipulate it in some fashion (computation), and then enact behavior (output). Perception leads to cognition, which in turn leads to action. In slightly more elaborate terms, it looks something like this chart:

In many ways, this model has been productive. Imagining our brains as computers, technologists have pushed actual computing from simple adding machines to artificial neural networks and generative AI, seeking ever more detailed mirrors of our own minds.

But this mirror’s image, while not exactly wrong, is warped and limited. John von Neumann, a seminal figure in developing computers and computer science, doubted that the computational model could possibly capture the “exceptional complexity of the human nervous system.” That is, our nervous systems evolved to help us navigate the large ecological system that we call “the world.” And we act upon the world to exercise control over it (as best we can).

The computational approach looks at the end product of our minds and tries to “reverse engineer” how they function. But instead of working backward, we might instead examine the long arc of evolutionary history to build our model from the ground up. This is the approach favored by Paul Cisek, a neuroscientist at the University of Montreal, who’s patiently developed a biological model of brain and nervous system development spanning millions of years.