A Mind for Our Minds

Generative AI changes how we should think about designing machines. How might we design AI to be a mind for our minds?

An abstract image of a mind

Until recently, we rarely needed to consider an intelligence beyond the biological intelligences we know. Digital intelligence was complicated but not inscrutable. Algorithms were logical and intelligible, albeit by experts. Data was intuitive in scale and interpretable, even if it required a statistician to make sense of its meaning.

A computer was a tool—a bicycle for our minds as Steve Jobs famously said in 1980. Steve’s metaphor was based on a study in Scientific American that showed a dramatic increase in movement efficiency when a human was on a bicycle rather than walking alone. Even in the early days of computing, Steve recognized the powerful increase in efficiency that a computer could have for the human mind.

For nearly 40 years, computers have been these efficiency-increasing machines, following the directions of humans, doing exactly—exactly—what a human asked them to. Humans precisely steered their computers to a prescribed outcome. Even if a request was complicated and highly expert, the machine’s output was always reducible to something rule-based, comprehensible, and controllable. Humans changed code and released new versions. Machines did not change their code under their own volition. But now we have artificial intelligence—machines that learn on their own from data whose scale is beyond our comprehension. Machines are no longer controlled solely by humans—they are minds themselves.

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