abc3d

Francesco D'Orazio's blog on immersive media, information design and data things. @abc3d on Twitter

[abc3d-blog]

"Fears of mass manipulation by new media are as old as mass media themselves. Almost every expansion of media or new media technology provoked paranoia about the contagious emotions of ‘the masses.'”

[mostlysignssomeportents]

Photographs from the archives of the Stasi, East Germany's legendary, paranoid secret police

Canadian Adrian Fish is one of the few photographers who’ve been permitted to take and publish photos from the archives of the Stasi, the legendarily invasive secret police of the former East Germany, who employed one snitch for every 60 people at their peak.

Fish’s series, Deutsche Demokratische Republik: The Stasi Archives, documents the incredible and banal scope of these archives, with “ 69 miles of shelved documents, 1.8 million images, and 30,300 video and audio recordings housed in 13 offices throughout Germany.”

https://boingboing.net/2017/05/11/deutsche-deutsche-revolution.html

[abc3d-blog]

#Bigdataaesthetics

[mostlysignssomeportents]

Analyzing all known Metal lyrics with natural language processing

Iain (“an ex-physicist currently working as a data scientist”) scraped Dark Lyrics and built a dataset of lyrics to 222,623 songs by 7,364 metal bands, then used traditional natural language processing techniques to analyze them.

Iain’s post is a good tour through the natural language processor’s toolkit – Bag of Words Bayesian filtering, log-likelihood ratio, term frequency -Inverse document frequency, cosine distance, etc. The output of the analysis is sometimes fun and interesting, but the value here is mostly as a good primer on how the different techniques work and when you might use them.

Most Metal Words

Rank Word Metalness 1 burn 3.81 2 cries 3.63 3 veins 3.59 4 eternity 3.56 5 breathe 3.54 6 beast 3.54 7 gonna 3.53 8 demons 3.53 9 ashes 3.51 10 soul 3.40 11 sorrow 3.40 12 sword 3.38 13 goodbye 3.28 14 dreams 3.28 15 gods 3.24 16 pray 3.22 17 reign 3.15 18 tear 3.12 19 flames 3.12 20 scream 3.11

Least Metal Words

Rank Word Metalness 1 particularly -6.47 2 indicated -6.32 3 secretary -6.29 4 committee -6.16 5 university -6.09 6 relatively -6.08 7 noted -5.85 8 approximately -5.75 9 chairman -5.69 10 employees -5.67 11 attorney -5.66 12 membership -5.64 13 administrative -5.61 14 considerable -5.60 15 academic -5.51 16 literary -5.49 17 agencies -5.48 18 measurements -5.47 19 fiscal -5.45 20 residential -5.45

https://boingboing.net/2016/07/03/analyzing-all-known-metal-lyri.html

[mostlysignssomeportents]

Emojibot uses deep learning to synthesize expressive new nonverbal communications

Dango is a personal assistant that feeds its users’ messages into a deep-learning neural net to discover new expressive possibilities for emojis, GIFs and stickers, and then suggests never-seen combinations of graphic elements to your text messages that add striking nuances to them.

The model began life without any explicit, human-generated labels for emoji. By using a recurrent neural network, it was able to make inferences about graphic meanings and combine them in fascinating ways that its creators never anticipated.

http://boingboing.net/2016/06/13/emojibot-uses-deep-learning-to.html

[pornosophical]

No. he said. “no,” he said. “no,” i said. “i know,” she said. “thank you,” she said. “come with me,” she said. “talk to me,” she said. “don’t worry about it,” she said. it made me want to cry. no one had seen him since. it made me feel uneasy. no one had seen him. the thought made me smile. the pain was unbearable. the crowd was silent. the man called out. the old man said. the man asked. he was silent for a long moment. he was silent for a moment. it was quiet for a moment. it was dark and cold. there was a pause. it was my turn. there is no one else in the world. there is no one else in sight. they were the only ones who mattered. they were the only ones left. he had to be with me. she had to be with him. i had to do this. i wanted to kill him. i started to cry. i turned to him.

After reading thousands of romance books, Google’s AI is writing eerie post-modern poetry

Google had a problem. Their AI engine spoke with grammatical precision and factual accuracy, but its diction remained terse and limp. They wanted it to be more conversational, so they made it read 2,865 romance novels. Now Google has a poet.

(via pornosophical)

@steinbecks

(via kesja)

@wolvensnothere

Perfect. Utterly perfect.

(via technoccult)

[polishpriests]

Polish cardinal blessing a supercomputer cluster. 2013.

[warrenellis-deactivated20200730]

Excerpt from End of Summer – a film by Jóhann Jóhannsson Music: Jóhann Jóhannsson, with Hildur Guðnadóttir and Robert Aiki Aubrey Lowe Coming December 2015 on Sonic Pieces End Of Summer - Part 3 (Excerpt) Jóhann Jóhannsson

[vintagemarlene]

keep young and beautiful if you want to be loved: 1930s radium make-up products (www.retronaut.com)

[brucesterling]

*Glow in the darkness

[pohnnyworld]

The last thing you want to see in the sky is an error message, especially now that science is 85 percent sure that The Matrix is real. If we’re all living in a computer simulation, we at least want to know that shit isn’t running on freaking Windows 8.

That is, however, an untouched photo of a real sky and a real tree — if you had been standing in that spot, this is what you’d have seen. The photo was taken in Odessa, Ukraine when a digital billboard malfunctioned, projecting a Windows error into the fog and temporarily convincing who knows how many passing motorists that somebody was going to have to go out into space and reboot God.

[brucesterling]

*Wow, the Russian-held Odessa area of the Ukraine is an excellent spot for that cosmic error message 

[abc3d-blog]

Installing this must have been loads of fun #cerythwynevans #neonforms (at White Cube)

[abc3d-blog]

Perfect morning #minecraft #vinyl #ghostlyinternational (at Dehavilland Studios)

[abc3d-blog]

Birth of Music Visualization (Apr, 1924) http://blog.modernmechanix.com/birth-of-music-visualization/

[abc3d-blog]

Me, the Flat Irons and my 4 months old cabin neighbour Sky #bigboulder #boulder (at Chautauqua Trail)

[abc3d-blog]

Jamon baby light my fire (at Plaza de la Alfalfa)

[abc3d-blog]

"There is something distinctly precapitalist in the inclination of the King and his poets to purify the commerce of words, to render the language fluent, and to regenerate an urban circulation hindered by the accumulation of waste. This compulsive purification makes most sense when understood not as a step forward in history, but as a regression that paralleled the Renaissance's return to the values of antiquity in other spheres. "Humanism", in fact, could be define by its penchant for waste, that is, human waste." #adayinbed #flu #historyofshit (at Dehavilland Studios)

[mostlysignssomeportents]

Imaginary ISIS attack on Louisiana and the twitterbots who loved it

Gilad Lotan has spotted some pretty sophisticated fake-news generation, possibly from Russia, and possibly related to my weird, larval twitterbots, aimed at convincing you that ISIS had blown up a Louisiana chemical factory.

On September 11, 2014, Lotan, a data scientist, started researching a massive, coordinated, and failed hoax to create panic over an imaginary ISIS attack on a chemical plant in Centerville, Louisiana. The hoax included Twitter, Facebook and Wikipedia identities (some apparently human piloted, others clearly automated) that had painstakingly established themselves over more than a month. Also included: fake news stories, an imaginary media outlet called “Louisiana News,” and some fascinating hashtag trickery whereby a generic hashtag was built up in Russian Twitter by one set of bots, then, once trending, was handed over to a different set of English-language bots that used it to promote the hoax.

More interesting is the fact that the hoax failed. Lotan shows that Facebook’s Edgerank proved to be resistant to gaming using the process employed by the hoax’s creator(s); that Twitter clusters can be trumped by real news sources; and that Wikipedia’s vigilance was adequate to catching fakesters who create hoax pages.

Lotan has some important thoughts on the future of fake news, hoaxes and political manipulation. One important takeaway from Lotan’s analysis is that, despite the energy and technical sophistication of the attack, the hoaxer(s) made some dumb mistakes, like not giving their fake Wikipedian a richer, longer edit history; and not changing the sent-by string on their twitterbots (all the hoax tweets were sent by an app called “mass post” or “mass post2.”

Finally, I’m fascinated to see that the bot-tweets were sufaced into real Twitter by long-standing, still-extant, apparently human piloted accounts from Russian Twitter. Are @GelmutKol, @Kiborian and @Galtaca sleeper agents who carry on normal Twitter discourse for years at a time, but every now and again promote botnoise into real Twitter? Are they regular users whose compromised PCs (or stolen passwords) are used to push out messages every now and again? Or are they spectacularly subtle bots themselves, computationally intensive members of the botherd who pass among humans?

Did this stuff kick off in Russia? Or was it a false flag from non-Russians using Russian Twitter to point attention overseas? Or a Russian firm working for hire on behalf of foreigners? Why try to create gwot-panics on Sept 10? It’s head-spinningly futurismic.

Read the rest…

[abc3d-blog]

Seeing circles, sines and signals: explaining things using interactive visualizations http://jackschaedler.github.io/circles-sines-signals/index.html