The exorbitant environmental impact of machine learning

There is, alas, no such thing as a free lunch. This simple and obvious truth is invariably forgotten whenever irrational exuberance teams up with digital technology in the latest quest to “change the world.”

A case in point was the bitcoin frenzy , where one could apparently become insanely rich by “mining” for the elusive coins.

All you needed was to get a computer to solve a complicated mathematical puzzle and — lo! — you could earn one bitcoin, which at the height of the frenzy was worth US$19,783.06.

All you had to do was buy a mining kit (or three) from Amazon.com, plug it in and become part of the crypto future.

The only problem was that mining became more difficult the closer we got to the maximum number of bitcoins set by the scheme, and so more computing power was required.

Which meant that increasing amounts of electrical power were needed to drive the kit.

Exactly how much is difficult to calculate, but one estimate published in July by the Judge Business School at the University of Cambridge suggested that the global bitcoin network was then consuming more than 7 gigawatts of electricity.

Over a year, that is equal to about 64 terawatt-hours (TWh), which is 8TWh more than Switzerland uses annually.

So each of those magical virtual coins turns out to have a heavy environmental footprint.

At the moment, much of the tech world is caught up in a new bout of irrational exuberance.

This time, it is about machine learning, another one of those magical technologies that “change the world,” in this case by transforming data (often obtained by spying on humans) into — depending on who you are talking to — information, knowledge and/or massive revenues.

As is customary in these frenzies, some inconvenient truths are overlooked.

For example, warnings by leaders in the field such as Ali Rahimi and James Mickens that the technology bears some resemblances to an older specialty called alchemy.

However, that is par for the course: When you have embarked on…

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