Coding Clairvoyance

An AI can reliably predict whether or not you’re going to die within a year from a heart attack, only its coders can’t explain how.

The experiment in late 2019 used ECG, age, and gender data on 400,000 patients to challenge robot and human diagnosticians to make their calls, and the AI consistently outperformed their biological counterparts.

“That finding suggests that the model is seeing things that humans probably can’t see, or at least that we just ignore and think are normal,” said one of the researchers quoted in the New Scientist.

AI is not a modern day spinning jenny or, with apologies to Oldsmobile, it isn’t your father’s industrial revolution.

Most models of technology innovation study the creation of machines built first to replace and then augment tasks done by human beings; functionality is purposefully designed to do specific things faster, better, and more cheaply over longer periods of time. This tends to improve the lives of workers and the consumers of their wares, even if it takes a few generations to reveal how and to whom.

The grandchildren of displaced craftsmen tend to benefit in unanticipated ways from the technological innovation that put their forbearers out of work. 

Central to this thesis is the idea that machines are subservient to people.

Granted, it might not have always looked that way, especially to a worker replaced by, say, a mechanized loom, but there were always humans who built, managed, and profited from those machines.

They knew exactly how they functioned and what they would deliver.

AI is different because it can not only learn on its own, but decide what and how it wants to gain those smarts. 

An AI embedded in a robot or car isn’t a machine as much as it’s an ever-evolving capability to make decisions and exert agency. Imagine that spinning jenny deciding it wants to learn how to write comedy (or whatever).

We can’t predict what it will do nor how it will do it. So already, AI have learned not just how to best humans at playing games like chess and go, but how to cheat. It’s not just limited to the biases of its founding code but then riffs and takes it in new, unanticipated directions.

Those medical researchers have shown that it can look at the exact same data set that we see, only see something different, something more insightful and reliably true.

I wonder how much of our technology past tells us about what our technology future will bring?

Maybe somebody should ask an AI to look at the data?

[This essay originally appeared at DaisyDaisy]

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About Jonathan Salem Baskin

Author, Advisor, Agitator