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How to Destroy Surveillance Capitalism: Seize the Means of Computation
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CFE Series – Taming Big Tech: Exploring the Alternatives with author and journalist Cory Doctorow in conversation with U of T prof Andrew Clement. May 19 at 4 pm. Free.
Cory Doctorow is an award-winning author, journalist, and blogger who has worked for the Electronic Frontier Foundation, is a MIT Media Lab Research Affiliate, and is a Visiting Professor of Computer Science at Open University. Join Cory in conversation with Andrew Clement, Professor Emeritus in University of Toronto’s Faculty of Information where
Photo by Paula Mariel Salischiker
Last summer, the pandemic was in its first wave and the nation was in chaos. A lack of federal leadership left each state to figure out how to interpret the science, and many states punted public health decisions to counties or cities or even smaller units, like universities.
Leaders, left to their own, often winged it, letting wishful thinking trump prudence in the drive to find ways to “reopen safely.” The novelty of the virus and the chaos of the response opened a space for all kinds of experts to weigh in on the best way to balance conflicting imperatives and evidence.
Photo by Paula Mariel Salischiker
If you learned your economics from Heinlein novels or the University of Chicago, you probably think that “free market” describes an economic system that is free from government interference – where all consensual transactions between two or more parties are permissible.
But if you went to the source, Adam Smith’s
Wealth of Nations, you’ll have found a very different definition of a free market: Smith’s concern wasn’t freedom from
governments, it was freedom from
rentiers.
A rentier is someone who derives their income from “economic rents”: revenues derived from merely owning something. With a factory, you have workers who contribute labor, you have investors who build and maintain the physical plant, and you have the landlord, who siphons off some of the revenues derived from this activity because of his title to the dirt underneath the factory.
Photo by Paula Mariel Salischiker
In “Full Employment“, my July 2020 column, I wrote, “I am an AI skeptic. I am baffled by anyone who isn’t. I don’t see any path from continuous improvements to the (admittedly impressive) ‘machine learning’ field that leads to a general AI any more than I can see a path from continuous improvements in horse-breeding that leads to an internal combustion engine.”
Today, I’d like to expand on that. Let’s talk about what machine learning is: it’s a statistical inference tool. That means that it analyzes training data to uncover correlations between different phenomena. Your phone observes that every time you type “hey,” you usually follow it with “darling” and it learns to autosuggest this the next time you type “hey.” It’s not sorcery, it’s “magic” – in the sense of being a parlor trick, something that seems baffling until you learn the underlying method, whereupon it becomes banal.