Pluralistic: 18 Sep 2020

Today's links

In Search Of A Flat Earth (permalink)

"In Search Of A Flat Earth" is Dan Olson's 1:16m documentary about the true nature of conspiratorial thinking, AKA "Why do some people think the Earth is flat?" (and also that Qanon is real, etc).

This is a subject I'm really interested in. I think we grossly overemphasize the role that algorithms play, and largely ignore the role that corruption and other real-world "conspiracies" play in making other conspiracy theories seem plausible.

But I don't think that algorithms are irrelevant, nor do I think that everything can be explained by the trauma of living through real-world conspiracies – like the ones that led to wage stagnation, deindustrialization, and skyrocketing health and housing costs.

And this is where Olson comes in: he does a fantastic and illuminating (and often entertaining) job of showing how conspiracism is multifactorial, and the role that fascism plays in conspiratorial thinking and its drive to reduce complex problems to simple, sinister cabals.

And he points out something that I hadn't entirely internalized about Qanon: it's a literal death cult. The core tenets of its adherents include the idea that their enemies should be mass-murdered. Seen in that light, it's no wonder than Q stuff is so creepy.

76 minutes' worth of video is a big ask, and to be honest, I listened to (rather than watched) most of this (it still worked). Despite the length, I'd recommend it. Olson's made a real contribution to the discourse around conspiracism and the digital world.

A cryptographic mystery solved (permalink)

Andrey Bezrukov and Elena Vavilova were Russian spies who operated in the USA for 20 years (this is the basis for "The Americans"); they were caught in 2010. "Compromised," is the new memoir by Peter Strzok, the FBI agent who had their case.

As Matt Blaze writes, a throwaway detail in the book resolves a longstanding cryptographic mystery: that of a Cuban "numbers station" that operated for years, including a decade where it behaved very erratically (by numbers station standards).

Some background. Numbers stations – ratio stations in which people (or synthesized voices) read out strings of random numbers – are a means of messages for use with "one-time pads," a cryptographic tool that is, in theory, unbreakable.

One-time pads are collections of random numbers used to encipher messages through simple operations: adding each byte of your message to the next number on the pad. If the pad is truly random, secret and never reused, the code can't be broken.

If your spies are sent abroad with a thick one-time pad, then you can simply broadcast your messages over the entire region in which they operate, and they can use their pads to decipher the messages, while your adversaries just get random numbers.

Numbers stations, like the powerful shortwave transmitter in Bauta, Cuba, were used to communicate with Soviet (and, later, Russian) spies in the US in this way.

Though one-time pad messages can't be deciphered, it's still possible to leak information using numbers stations. If a radio station ceases operation every time a spy travels, then your adversary can match the station's operating schedule with suspects' itineraries.

To prevent this "traffic analysis" attack, the station broadcasted dummy traffic (random numbers that weren't encoded messages) every single day, even if the spies were not listening that day.

However, for mysterious reasons – still not understood – the dummy traffic never contained the number nine ("nueve"). That made it easy to tell the real numbers station traffic from the dummy traffic, and from there, it was possible to derive the spies' travel schedules.

Even with this glaring error, it was a decade before the FBI made their their move. That was a whole decade in which the Cuban numbers station was making this weird, stupid blunder.

One-time pads are incredibly powerful, but they're also super-awkward and unforgiving. An error as simple as pad re-use can blow them up, as happened with the notorious Venona affair:

As Blaze writes, "OTPs have long been a favorite of hucksters selling supposedly 'unbreakable' crypto. Remember this story next time someone tries to sell you their super-secure one-time-pad crypto. If actual Russian spies can't use it securely, chances are neither can you."

Blaze was one of the researchers who followed – and recorded! – the Cuban numbers station, and noted the mysterious and telling absence of "nueve" in some of the traffic. He's posted a recording of the station to his site:

Youtube's war on algorithmic radicalization (permalink)

Writing for Wired, Clive Thompson gets a first-of-its-kind behind-the-scenes look at Youtube's algorithm development team, in order to document the company's attempt to reduce the service's role in spreading and reinforcing conspiracy theories.

Thompson traces the origin of the crisis to the company's drive for more "engagement" that led them to tune their recommendation system to identify and propose more specialized, esoteric versions of the video you'd just watched.

The idea was to lead you down a rabbit hole of ever-more-specific versions of your interests, helping you discover niches you never knew existed.

This dynamic in recommendation systems has gotten a lot of attention lately, and most of it is negative, but let's pause for a moment and talk about what this means for non-conspiratorial beliefs.

Say you happen upon a woodworking video, maybe due to a friend's post on social media. You watch a few of them and you find yourself interested in the subject and tuning in more often.

The recommendation system presents an array of possible next-views, but tilted away from general-interest woodworking videos, instead offering you a menu of specialized woodworking styles, like Japanese woodworking.

You sample one of these and find it fascinating, so you start watching more of these. The recommendation system clues you in to Japanese nail-free joinery:

And from there, you discover the frankly mesmerizing "Niju-mizu-kumi-tsugi" style of joinery, and you start to seek out more. You have found this narrow, weird, self-reinforcing community.

This community could not exist without the internet and its signature power to locate and connect people with shared, widely dispersed, uncommon interests.

This power isn't just used to push conspiracies and woodworking techniques, either.

It's how people who know that their gender identity doesn't correspond to the gender they were assigned at birth find each other, and acquire a vocabulary for describing their views, and foment change.

It's how people who believe Black Lives Matter find one another, it's how the Green New Deal movement came together.

It's also how people who wanted to cosplay Civil War soldiers in Charlottesville, waving tiki torches, chanting "Jews will not replace us" found each other.

And that is the conundrum of the recommendation engine. Helping people find others who share their views, passions and concerns is not, in and of itself, bad. It is vital. It's the thing that made the internet delightfully weird. It's also what made the world terribly weird.

Thompson takes us inside Youtube's algorithm team as they try to balance three priorities:

I. Increasing their traffic and profits

II. Helping people find others with common interest

III. Stopping conspiracies from spreading

And he traces how they try, with limited success, to manage these competing goals by creating extremely fine-grained rules that define what is banned on the platform.

But naturally, this just gives rise to a new kind of content: stuff that is ALMOST bad enough for blocking, but not quite. The problem is that this stuff is indistinguishable (in all but the narrowest, technical way) from banned content.

So then Youtube has to create a new set of moderation guidelines: "What is so close to prohibited content that it, too, is prohibited?"

Naturally, this is creating a new kind of content: "Stuff that is not close-to-bannable, but is close-to-close-to-bannable."

This dynamic should be familiar to anyone who's watched the moderation policies of Big Tech platforms evolve: what is "hate speech?" "What is 'almost-hate-speech?'" "What is almost-almost-hate-speech?'"

Ultimately, this ends up creating thick binders of pseudo-law that delivers advantages to the worst people: they can study the companies' policies and figure out how to skate right up to the cliff's edge (no matter how it is defined).

And at the same time, they can goad their adversaries – the people they torment – into crossing these fractally complex lines and then nark them out, so that over time, these speech policies preferentially block good speech and leave bad speech untouched.

I am increasingly convinced that the problem isn't that Youtube is unsuited to moderating the video choices of a billion users – it's that no one is suited to this challenge.

Remedies that put moderation choices closer to the user – breaking up monopolies, allowing interoperable recommendation systems – solve the problem of scaling up and covering edge cases by eliminating scale altogether, and letting the edge cases make their own calls.

(Image: Mark Sargent)

The Hardware Lottery (permalink)

Google Brain researcher Sara Hooker's new paper "The Hardware Lottery," proposes that there is a secret, relentless force that bends the course of machine learning: processor architecture.

Hooker observes that computer scientists are curiously indifferent to the constraints and capabilities of processors, and proceed in a vacuum of information about these, designing machine learning algorithms that are sometimes stymied by bottlenecks in processor designs.

While other machine learning techniques thrive and grow to dominate, not merely because they do something useful, but because they do something useful that can be readily accomplished with the hardware that we can currently access.

(This indifference is multidirectional: hardware researchers often ignore systems and algorithm designers, while systems designers ignore hardware and algorithms)

There is a LOT to this: computer science bends towards the accomplishable: things that are cheaper to do in hardware happen more. I think the rise of cryptocurrency, VR, and machine learning is connected to the rise of cheap, massively parallel GPUs:

One of the very best people on this is Herb Sutter, whose classic "Welcome to the Jungle" identifies the ways in which processors are becoming increasingly specialized and combined in a system system ("A Heterogeneous Supercomputer in Every Pocket").

Likewise important is Neil "Fab Lab" Gershenfeld's mind-blowing talk about "nonbinary computing" from Shmoocon 2016:

The idea that available processor architectures exert both subtle and overt selective pressure on research agendas is really important and undertheorized.

What's interesting about Hooker's paper is that she also explores how this dynamic plays out from the hardware and systems side, where they seem to keep getting caught flatfooted by algorithmic advances that require them to retool.

Disciplinary specialization has allowed systems, algorithms and hardware research to make huge advances, but it has also reached a breaking point due to poor coordination.

Meanwhile, generalization in processor and system design has ALSO been tapped dry, necessitating a switch to specialized designs purpose-built for different tasks.

There's something really magic about watching the whole field stumble due to BOTH generalization AND specialization.

This day in history (permalink)

#5yrsago Tell-all free-to-play-game dev's confessions

#5yrsago Poker malware infects your computers and peeks at your cards

Colophon (permalink)

Today's top sources: Kottke (, Bruce Schneier (, Four Short Links (

Currently writing: My next novel, "The Lost Cause," a post-GND novel about truth and reconciliation. Yesterday's progress: 517 words (62625 total).

Currently reading: Gideon the Ninth, Tamsyn Muir

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