- Why Should I Trust You?: Explaining the Predictions of Any Classifier (PDF) — LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. Torkington’s Second Law: there’s no problem with machine learning that more machine learning can’t fix.
- How Etsy Formats Currency — I’m saving this one because it chafes every time I do it, and I do it wrong every time.
- Magic Leap in Wired — massive story by Kevin Kelly on the glories of Magic Leap, which The Verge noted still left a lot of open questions, such as “what the hell IS Magic Leap’s technology” and “why does everyone who works for Magic Leap sound like they’re on acid when they talk about the technology?” Everyone who wants their pixel-free glorious VR to be true is crossing fingers hoping it’s not another Theranos. The bit that stuck from the Wired piece was People remember VR experiences not as a memory of something they saw but as something that happened to them.
- Curing Our Slack Addiction — an interesting counterpoint to the “in the future everyone will be on 15,000 Slacks” Slack-maximalist view. For AgileBits, it distracted, facilitated, and rewarded distracting behaviour, ultimately becoming a drain rather than an accelerant.
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