My latest novel is Attack Surface, a sequel to my bestselling Little Brother books. @washingtonpost called it "a political cyberthriller, vigorous, bold and savvy about the limits of revolution and resistance."
I have a (free) new book out! "How to Destroy Surveillance Capitalism" is an anti-monopolist critique of Big Tech that connects the rise of conspiratorial thinking to the rise of tech monopolies and proposes a way to deal with both:
My ebooks and audiobooks (from @torbooks, @HoZ_Books, @mcsweeneys, and others) are for sale all over the net, but I sell 'em too, and when you buy 'em from me, I earn twice as much and you get books with no DRM and no license "agreements."
My first picture book is out! It's called Poesy the Monster Slayer and it's an epic tale of bedtime-refusal, toy-hacking and monster-hunting, illustrated by Matt Rockefeller. It's the monster book I dreamt of reading to my own daughter.
If you prefer a newsletter, subscribe to the plura-list, which is also ad- and tracker-free, and is utterly unadorned save a single daily emoji. Today's is "🧛🏼♀️". Suggestions solicited for future emojis!
Super Smash Bros. Melee is a 20-year-old Nintendo game with a huge cult following; it's considered one of the best fighting games of all time. Nintendo abandoned it years ago, but the fans have kept it alive.
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Fans used Dolphin (an emulation environment that can simulate the Gamecube and Wii) and mods from Slippi that let users play head-to-head over the internet. This combo has enabled many gamers to turn pro, winning esports contracts.
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All that was true before the pandemic. Now, with the world in lockdown, SSBM tournaments have only grown in popularity. @TheBigHouseSSB was about to host one of the largest of these tournaments when Nintendo shut them down with a copyright threat.
"Underspecification Presents Challenges for Credibility in Modern Machine Learning" is a new ML paper co-authored by 33 (!) Google researchers. It's been called a "wrecking ball" for our understanding of problems in machine learning.
There's been a lot of work on the problems of inadequate, low-quality, biased or poorly labeled training date in machine learning classifiers ("garbage in, garbage out"), but that's not what these researchers are documenting.
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They're focused on "underspecification," a well-known statistical phenomenon that has not been at the center of machine learning analysis (until now).
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