Yesterday's column by John Naughton in the Observer revisited Nathan Myhrvold's 1997 prediction that when Moore's Law runs out -- that is, when processors stop doubling in speed every 18 months through an unbroken string of fundamental breakthroughs -- that programmers would have to return to the old disciplines of writing incredibly efficient code whose main consideration was the limits of the computer that runs on it. Read the rest

Machine learning systems are notorious for cheating, and there's a whole menagerie of ways that these systems achieve their notional goals while subverting their own purpose, with names like "model stealing, rewarding hacking and poisoning attacks." Read the rest

Columbia Journalism School's Tow Center has published the results of a longrunning investigation into a network of shadowy "local news" sites whose bulk content is composed of algorithmically generated stories about subjects like local rates of gas tax, or which bridges are most structurally sound. Read the rest

The Chinese Supreme People’s Court has just released a report on a "mobile court" pilot program that's been running since March to manage procedures in civil legal disputes through the Wechat social media platform, through which litigants are prompted by an AI chatbot "judge" (with a judicial avatar) to state their cases; the evidence is entered into the blockchain. Read the rest

A team of researchers from Microsoft and Harvard's Berkman Center have published a taxonomy of "Failure Modes in Machine Learning," broken down into "Intentionally-Motivated Failures" and "Unintended Failures." Read the rest

For ten years, activists and theorists have been developing a critique of "algorithms" (which have undergone numerous renamings over the same time, e.g. "filter bubbles"), with the early critiques focusing on the way that these can misfire with dreadful (or sometimes humorous) consequences, from discrimination in which employment and financial ads get served to the "dark patterns" that "maximized engagement" with services that occupied your attention but didn't bring you pleasure. Read the rest

Cards Against Humanity asked Spencer Kelly to teach a computer to write mean, funny joke-cards for a new, AI-based expansion pack to the game; Kelly trained the popular GPT-2 generative language model (previously) on existing cards, and now the company is livestreaming a 16-hour competition between its AI and its human joke-writers, with a voting system to up/downvote the resulting jokes (at the end of the day, these votes will be "tallied up and thrown in the garbage"). You can choose to buy the resulting packs, and if the human team outsells the robots, it will receive a $5,000 bonus. If they fail, they will all be fired. Read the rest

In TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents, a group of Boston University researchers demonstrate an attack on machine learning systems trained with "reinforcement learning" in which ML systems derive solutions to complex problems by iteratively trying multiple solutions. Read the rest

Princeton computer scientist Arvind Narayanan (previously) has posted slides and notes from a recent MIT talk on "How to recognize AI snake oil" in which he divides AI applications into three (nonexhaustive) categories and rates how difficult they are, and thus whether you should believe vendors who claim that their machine learning models can perform as advertised. Read the rest

Back in 2016, Naomi Kritzer won the Hugo award for her brilliant, endearing story Cat Pictures Please, in which an AI with an insatiable craving for cat pictures explains its view on the world and the way that it makes humans' lives better; now Kritzer has adapted the story into her new novel, the equally brilliant, superbly plotted YA thriller Catfishing on CatNet. Read the rest

Mac Pierce created a simple wearable to challenge facial recognition: do a little munging to an image of a face, print it on heat transfer paper, iron it onto see-through mosquito netting, slice, and affix to a billed cap -- deploy it in the presence of facial recognition cameras and you'll be someone else. It's the kind of "adversarial example" countermeasure that fools computers pretty reliably but wouldn't work on a human. (via JWZ) Read the rest

"Affordances" is my new science fiction story for Slate/ASU's Future Tense project; it's a tale exploring my theory of "the shitty technology adoption curve," in which terrible technological ideas are first imposed on poor and powerless people, and then refined and normalized until they are spread over all the rest of us. Read the rest

Gaggle is one of a handful of creepy companies that sell surveillance software to school districts, which monitor every keystroke and click on school networks -- they're the latest evolution in spy-on-kids tech, which started off by promising that they'd stop kids from seeing porn, then promised they could end bullying, and now advertise themselves as a solution for school shootings, under the banner of being a "Safety Management Platform." Read the rest

Hirevue is an "AI" company that companies contract with to screen job applicants: it conducts an hour-long videoconference session with applicants, analyzing their facial expressions, word-choices and other factors (the company does not actually explain what these are, nor have they ever subjected their system to independent scrutiny) and makes recommendations about who should get the job. Read the rest

More bad news for Google's beleaguered spinoff Jigsaw, whose flagship project is "Perspective," a machine-learning system designed to catch and interdict harassment, hate-speech and other undesirable online speech. Read the rest

Jonathan Zittrain (previously) is consistently a source of interesting insights that often arrive years ahead of their wider acceptance in tech, law, ethics and culture (2008's The Future of the Internet (and how to stop it) is surprisingly relevant 11 years later); in a new long essay on Medium (shorter version in the New Yorker), Zittrain examines the perils of the "intellectual debt" that we incur when we allow machine learning systems that make predictions whose rationale we don't understand, because without an underlying theory of those predictions, we can't know their limitations. Read the rest

The Scite project has a corpus of millions of scientific articles that it has analyzed with deep learning tools to determine whether any given paper has been supported or contradicted by subsequent publications; you can check Scite via the website, or install a browser plugin version (Firefox, Chrome). (Thanks, Josh!) Read the rest