Listen: Difference between revisions
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− | '''listen here | + | '''You can listen here to the stories that were recorded for the [[Data_Workers_Podcast|podcast]] of the exhibition [[Data_Workers|Data Workers]], Mundaneum, Mons, 2019''' |
− | [https:// | + | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/I%20-%2001.%20Why%20contextual%20stories%3f.mp3 '''Why contextual stories?'''] |
− | [https:// | + | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/I%20-%2002.%20We%20create%20'algoliterary'%20works.mp3 We create 'algoliterary' works] |
− | [https:// | + | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/I%20-%2003.%20What%20is%20literature%3f.mp3 What is literature?] |
− | [https:// | + | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/I%20-%2004.%20An%20important%20difference.mp3 An important difference] |
− | [https:// | + | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/II%20-%2000.%20WRITERS.mp3 '''Writers'''] |
− | [https:// | + | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/II%20-%2001.%20Programmers%20are%20writing%20data%20workers%20into%20being.mp3 Programmers are writing data workers into being] |
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/II%20-%2002.%20Cortana%20speaks.mp3 Cortana speaks] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/II%20-%2003.%20Open%20source%20learning.mp3 Open source learning] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/II%20-%2004.%20Natural%20language%20for%20artificial%20intelligence.mp3 Natural language for artificial intelligence] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/III%20-%2000.%20ORACLES.mp3 '''Oracles'''] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/III%20-%2001.%20Racial%20AdSense.mp3 Racial AdSense] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/III%20-%2002.%20What%20is%20a%20good%20employee%3f.mp3 What is a good employee?] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/III%20-%2003.%20Quantifying%20100%20years%20of%20gender%20and%20ethnic%20stereotypes.mp3 Quantifying 100 years of gender and ethnic stereotypes] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/III%20-%2004.%20Wikimedia's%20ORES%20service.mp3 Wikimedia's ORES service.mp3] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/III%20-%2005.%20Tay.mp3 Tay] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/IV%20-%2000.%20CLEANERS.mp3 '''Cleaners'''] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/IV%20-%2001.%20Project%20Gutenberg%20and%20Distributed%20Proofreaders.mp3 Project Gutenberg and Distributed Proofreaders] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/IV%20-%2002.%20An%20algoliterary%20version%20of%20the%20Maintenance%20Manifesto.mp3 An algoliterary version of the Maintenance Manifesto] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/IV%20-%2003.%20A%20bot%20panic%20at%20Amazon%20Mechanical%20Turk.mp3 A bot panic at Amazon Mechanical Turk] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/V%20-%2000.%20INFORMANTS.mp3 '''Informants'''] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/V%20-%2001.%20Dataset%20as%20representation.mp3 Dataset as representation] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/V%20-%2002.%20Labeling%20for%20an%20oracle%20that%20detects%20vandalism%20on%20Wikipedia.mp3 Labeling for an oracle that detects vandalism on Wikipedia] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/V%20-%2003.%20How%20to%20make%20your%20dataset%20known.mp3 How to make your dataset known] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/V%20-%2004.%20The%20ouroboros%20of%20machine%20learning.mp3 The ouroboros of machine learning] | ||
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+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VI%20-%2000.%20READERS.mp3 '''Readers'''] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VI%20-%2001.%20Character%20n-gram%20for%20authorship%20recognition.mp3 Character n-gram for authorship recognition] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VI%20-%2002.%20A%20history%20of%20n-grams.mp3 A history of n-grams] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VI%20-%2003.%20God%20in%20Google%20Books.mp3 God in Google Books] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VI%20-%2004.%20Grammatical%20features%20taken%20from%20Twitter%20influence%20the%20stock%20market.mp3 Grammatical features taken from Twitter influence the stock market] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VI%20-%2005.%20Bag%20of%20words.mp3 Bag of words] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VII%20-%2000.%20LEARNERS.mp3 '''Learners'''] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VII%20-%2001.%20Naive%20Bayes%20&%20Viagra.mp3 Naive Bayes & Viagra] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VII%20-%2002.%20Naive%20Bayes%20&%20Enigma.mp3 Naive Bayes & Enigma] | ||
+ | |||
+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VII%20-%2003.%20A%20story%20about%20sweet%20peas.mp3 A story about sweet peas] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VII%20-%2004.%20Perceptron.mp3 Perceptron] | ||
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+ | [https://sound.constantvzw.org/Algolit/Data_workers_podcast_EN/VII%20-%2005.%20BERT.mp3 '''Bert'''] | ||
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+ | <br> | ||
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Latest revision as of 16:10, 3 June 2019
You can listen here to the stories that were recorded for the podcast of the exhibition Data Workers, Mundaneum, Mons, 2019
We create 'algoliterary' works
Programmers are writing data workers into being
Natural language for artificial intelligence
Quantifying 100 years of gender and ethnic stereotypes
Project Gutenberg and Distributed Proofreaders
An algoliterary version of the Maintenance Manifesto
A bot panic at Amazon Mechanical Turk
Labeling for an oracle that detects vandalism on Wikipedia
How to make your dataset known
The ouroboros of machine learning
Character n-gram for authorship recognition
Grammatical features taken from Twitter influence the stock market