Illustrations by Todd Proctor / YouWorkForThem

Follow Backchannel: Facebook | Twitter

We are increasingly relying on machines that derive conclusions from models that they themselves have created, models that are often beyond human comprehension, models that “think” about the world differently than we do.

Models Beyond Understanding

In a series on machine learning, Adam Geitgey explains the basics, from which this new way of “thinking” is emerging:

Two Models of Models

In 1943, the US Army Corps of Engineers set Italian and German prisoners of war to work building the largest scale model in history: 200 acres representing the 41 percent of the United States that drains into the Mississippi River. By 1949 it was being used to run simulations to determine what would happen to cities and towns along the way if water flooded in from this point or that. It’s credited with preventing flooding in Omaha in 1952 that could have caused $65 million in damage.[2] In fact, some claim its simulations are more accurate than the existing digital models.

The Knowability Premise

In the Galileo Museum in Florence, a beautiful armillary from 1593 looms large in its room. It consists of metal and gilded wooden gears nested within gears nested within gears nested within an external layer of circles. Set its outer meridian ring to be “perpendicular to the horizon, and parallel to the actual meridian” and orient it by sighting the sun or a known star, and it will accurately show the position of celestial bodies. This is a model that produces reliable knowledge about where objects show up in the Earth’s skies, but it does so using a model that is thoroughly wrong.

Post-Scarcity Computing

For their first fifty years, computers assumed scarcity. They were famous for it. The minimal information required for a purpose was gathered, and was structured into records that were the same for each instance. That limitation was built into computers’ initial ingestion medium: punch cards. These cards turned information into a spatial array that could be read because the array and its encoding were uniform. That uniformity squeezed out differences, peculiarities, exceptions, and idiosyncrasies…the stuff of life, as beatniks and other malcontents recognized from the start.

Foreswearing Knowledge

Back at the beginning of Western culture’s discovery of knowledge, Plato told us that it’s not enough for a belief to be true because then your uninformed, lucky guess about which horse will win the Preakness would have to count as knowledge. That’s why knowledge in the West has consisted of justifiable true beliefs — opinions we hold for a good reason.

Alien Justification

Somewhere there is a worm more curious than the rest of its breed. It has slowly traveled through the soil tasting every new patch it comes to, always looking for the next new sample, for it believes that the worm’s highest calling is to know its world, and taste is its means of knowledge. Because of this particular worm’s wide experience and its superior powers of categorization and expression, it becomes revered among worms as a sage who can impart wisdom about what the planet Earth really tastes like.


1. More specifically, it models how the human visual cortex processes signals, according to Natalie Wolchover, “A Common Logic to Seeing Cats and Cosmos,” Quanta Magazine, Dec. 4, 2014, seeing-cats-and-cosmos/


Mining the tech world for lively and meaningful tales and analysis.

Thanks to Steven Levy.


David Weinberger

Written by

I mainly write about the effect of tech on our ideas


Mining the tech world for lively and meaningful tales and analysis.