Every few days I post some links to things I enjoyed that don’t neatly fit into the topics I usually cover on this blog. Use it to fill your reading queue with interesting stuff.

• The Most Ridiculous and Weird Tech Gadgets From the Last 25 Years. “The Hushme was a ‘voice mask’ intended to let you make phone calls without bothering anyone.”
• Between-time by Mandy Brown. “We live in a world full of distractions but short on breaks. The time between activities is consumed by other activities—the scrolling, swiping, tapping of managing a never-ending stream of notifications, of things coming at us that need doing. All that stuff means moments of absolutely nothing—of a gap, of an interval, of a beautiful absence—are themselves absent, missing, abolished.”
• Movie Trailers Keep Tweaking Well-Known Songs. The Tactic Is Working. (NYT gift article link) “As a composer, Rosen is at the forefront of the trailerization movement: He’s in demand for his ability to rework existing songs to maximize their impact in trailers for films and TV shows.”
• All Human Systems are Enormous Trash Fires. “Once you recognize that all human systems are enormous trash fires, you stop trying to figure out how to switch to a system that isn’t an enormous trash fire, since they don’t exist. […] Eventually you even start to appreciate the beauty of it. How impressive it is that we manage to get anything done at all, given how completely trash everything is, and how on fire it is all the time.”
• This is a beautifully-written piece about standup comedy but also about so much more. I don’t want to spoil it, except to say it starts like this: “Perhaps due to lockdown and the interruptions to normal service, but more likely due to autumnal intimations and a long dormant weakness for sentimentality, I now cherish the belief that the only flavour for which a grown-up should cultivate a taste is the bittersweet.”
• KC Green reflects on creating the “This is Fine” meme. “When a work gets as big as this has, is it still yours?”
• Eightify: AI Youtube Summary with GPT. A Chrome plugin that promises: “Instant AI summaries for Youtube videos using GPT. Summarize video into 8 key ideas.”
• Can Doom Run It? An Adding Machine in Doom. “I demonstrate that it is possible to run any bounded computation in Doom, minus constraints on level size.”

Every few days I post some links to things I enjoyed that don’t neatly fit into the topics I usually cover on this blog. Use it to fill your reading queue with interesting stuff!

• The Astronomy Photographer of the Year 2022 Winners are out.
• Did the Music Business Just Kill the Vinyl Revival? “On an aggregate level, consumers are simply not buying music. They prefer to stream it for pennies rather than purchase it for dollars.”
• How Do Big Tech Giants Make Their Billions? I know infographics are so 2000s, but this comparison data is super interesting.
• This week’s useful app → Readow provides book recommendations powered by AI.
• This week’s WTF Link → The lights have been on at a Massachusetts school for over a year because no one can turn them off. “The lighting system was installed at Minnechaug Regional High School when it was built over a decade ago and was intended to save money and energy. But ever since the software that runs it failed on Aug. 24, 2021, the lights in the Springfield suburbs school have been on continuously, costing taxpayers a small fortune.”
• This week’s Gen X link → Remembering horse_ebooks in the age of GPT3. “it’s this fear of the uncanny which i think drove the negative response to the discovery that horse_ebooks was actually no longer a bot at all. more than the disgust at feeling like you’d been played in service of a viral marketing campaign, the deeper sense that a future is coming where it won’t be possible to reliably tell the difference between bot activity and human activity lay underneath that negative reaction. and ten years later - that future is here.”

Ryan Bigge makes some very good points in his post about better personalized recommendations through transparency and content design:

Data-driven companies know something that the user doesn’t. Yet the language used to convince people to act on recommendations lacks variety and explanatory power.

Algorithms aren’t neutral — or as Ryan puts it:

Every facet of machine learning is fueled by human judgement, so it must be multi-disciplinary.

Users are getting more skeptical about where these magical recommendations for what to watch, listen to, and buy come from. To establish and build trust, companies have to get better at explaining exactly why they’re recommending a specific product or action.

A lot of words are being written about AI and machine learning these days, so it’s sometimes hard to know what to pay attention to. M.C. Elish and danah boyd’s Don’t Believe Every AI You See is one of those essays that I would consider essential reading on the topic. On the ethics of artificial intelligence:

When we consider the ethical dimensions of AI deployments, in nearly every instance the imagined capacity of a technology does not match up with current reality. As a result, public conversations about ethics and AI often focus on hypothetical extremes, like whether or not an AI system might kill someone, rather than current ethical dilemmas that need to be faced here and now. The real questions of AI ethics sit in the mundane rather than the spectacular. They emerge at the intersections between a technology and the social context of everyday life, including how small decisions in the design and implementation of AI can create ripple effects with unintended consequences.

And on the supposed “neutrality” of machines:

[There is] a prevailing rhetoric around AI and machine learning, which presents artificial intelligence as the apex of efficiency, insight, and disinterested analysis. And yet, AI is not, and will not be, perfect. To think of it as such obscures the fact that AI technologies are the products of particular decisions made by people within complex organizations. AI technologies are never neutral and always encode specific social values.

As Kevin Kelly also pointed out years ago in his book What Technology Wants, technology is never neutral. It possesses the collective values of its creators. And that’s where things so often go wrong. A great resource on this topic is Sara Wachter-Boettcher’s book Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech.

Ok this is creepy:

Over the past few years, Spotify has been ramping up its data analytic capabilities in a bid to help marketers target consumers with adverts tailored to the mood they’re in. They deduce this from the sort of music you’re listening to, coupled with where and when you’re listening to it, along with third-party data that might be available.

And they’re not alone:

Spotify is far from the only platform helping brands target people according to their emotions; real-time mood-based marketing is a growing trend and one we all ought to be cognizant of. In 2016, eBay launched a mood marketing tool, for example. And last year, Facebook told advertisers that it could identify when teenagers felt “insecure” and “worthless” or needed “a confidence boost”.

Nicholas Carr discusses the importance of using the right terminology when we talk about how companies use our data in his essay I am a data factory (and so are you). On the problems with the “data mining” metaphor:

Data does not lie passively within me, like a seam of ore, waiting to be extracted. Rather, I actively produce data through the actions I take over the course of a day. When I drive or walk from one place to another, I produce locational data. When I buy something, I produce purchase data. When I text with someone, I produce affiliation data. When I read or watch something online, I produce preference data. When I upload a photo, I produce not only behavioral data but data that is itself a product. I am, in other words, much more like a data factory than a data mine. I produce data through my labor — the labor of my mind, the labor of my body.

On extending the “data factory” metaphor to the platform companies:

The platform companies, in turn, act more like factory owners and managers than like the owners of oil wells or copper mines. Beyond control of my data, the companies seek control of my actions, which to them are production processes, in order to optimize the efficiency, quality, and value of my data output (and, on the demand side of the platform, my data consumption). They want to script and regulate the work of my factory — i.e., my life — as Frederick Winslow Taylor sought to script and regulate the labor of factory workers at the turn of the last century. The control wielded by these companies, in other words, is not just that of ownership but also that of command. And they exercise this command through the design of their software, which increasingly forms the medium of everything we all do during our waking hours.

The factory metaphor makes clear what the mining metaphor obscures: We work for the Facebooks and Googles of the world, and the work we do is increasingly indistinguishable from the lives we lead. The questions we need to grapple with are political and economic, to be sure. But they are also personal, ethical, and philosophical.

This brings up a point I haven’t given much thought to. It’s not just that platforms use the data we create to further their business interests. It’s that they are also invested in having us create a very specific kind of data. Data that can be as useful as possible to advertisers. That changes our behavior and gives rise to the prevailing wisdom that people are not being authentic on social media.

This bit from Raluca Budiu and Page Laubheimer’s user study of digital assistants like Alexa, Google Assistant, and Siri echoes my thoughts exactly on why I don’t like interacting with them:

Many participants started speaking before formulating the query completely (as you would normally do with a human), and occasionally paused searching for the best word. Such pauses are natural in conversation, but assistants did not interpret them correctly and often rushed to respond. Of course, answers to such incomplete queries were incorrect most of the time, and the overall effect was unpleasant: participants complained that they were interrupted, that the assistant “talked over them”, or that the assistant was “rude.” Some even went as far as to explicitly scold the assistant for it (“Alexa, that’s rude!”).

Yep. When I interact with voice UIs I spend way more time and energy planning the exact sentence construction and pronunciation than when I simply type and swipe and figure it out as I go. And then, if one word is out of place, the whole thing falls apart. It’s just too stressful. I can’t.

This story by Rene Chun about China’s New Frontiers in Dystopian Tech is wild:

Don’t even think about jaywalking in Jinan, the capital of Shandong province. Last year, traffic-management authorities there started using facial recognition to crack down. When a camera mounted above one of 50 of the city’s busiest intersections detects a jaywalker, it snaps several photos and records a video of the violation. The photos appear on an overhead screen so the offender can see that he or she has been busted, then are cross-checked with the images in a regional police database. Within 20 minutes, snippets of the perp’s ID number and home address are displayed on the crosswalk screen. The offender can choose among three options: a 20-yuan fine (about $3), a half-hour course in traffic rules, or 20 minutes spent assisting police in controlling traffic. Police have also been known to post names and photos of jaywalkers on social media.

I can’t help but be reminded of that “biometric advertising” scene in Minority Report:

Two related articles about YouTube caught my eye over the past few days. The first, Zeynep Tufekci’s YouTube, the Great Radicalizer explains how YouTube’s algorithms almost always lead people to conspiracy theory videos:

It seems as if you are never “hard core” enough for YouTube’s recommendation algorithm. It promotes, recommends and disseminates videos in a manner that appears to constantly up the stakes. Given its billion or so users, YouTube may be one of the most powerful radicalizing instruments of the 21st century. […]

What we are witnessing is the computational exploitation of a natural human desire: to look “behind the curtain,” to dig deeper into something that engages us. As we click and click, we are carried along by the exciting sensation of uncovering more secrets and deeper truths. YouTube leads viewers down a rabbit hole of extremism, while Google racks up the ad sales.

This is bad enough, but then there’s James Cook’s article YouTube suggested conspiracy videos to children using its Kids app, in which he explains how not even the YouTube Kids app is immune to this:

YouTube’s app specifically for children is meant to filter out adult content and provide a “world of learning and fun,” but Business Insider found that YouTube Kids featured many conspiracy theory videos which make claims that the world is flat, that the moon landing was faked, and that the planet is ruled by reptile-human hybrids.

I try not to be too quick to call technology evil, but this is definitely not a “all technology is neutral” situation. Product managers and developers have the power to stop this kind of escalation from happening.