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Data Workers Podcast: Difference between revisions

From Algolit

 
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By Algolit
 
By Algolit
  
During the monthly meetings of Algolit, we study manuals and experiment with machine learning tools for text processing. And we also share many, many stories. With this podcast we hope to recreate some of that atmosphere.
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[https://www.algolit.net/index.php/Listen Listen to the stories]
  
For outsiders, algorithms only become visible in the media when they achieve an outstanding performance, like the Alpha Go. Or when they break down in fantastically terrifying ways. Humans working in the field though, create their own culture on and offline. They share the best stories and experiences during live meetings, research conferences and yearly competitions like Kaggle. These stories that contextualize the tools and practises can be funny, sad, shocking, interesting.
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[https://gitlab.constantvzw.org/algolit/mundaneum/tree/master/exhibition/1-Writers/data%20workers%20podcast Sources on gitlab]
  
A lot of them are experiential learning cases. The implementations of algorithms in society generate new conditions of labour, storage, exchange, behaviour, copy and paste. In that sense, the contextual stories capture a momentum in a larger antropo-machinical story that is being written at full speed and by many voices.  
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During our monthly Algolit meetings, we study manuals and experiment with machine learning tools for text processing. And we also share many, many stories. With this podcast we hope to recreate some of that atmosphere.  
  
Voices: David Stampfli, Cristina Cochior, An Mertens, Gijs de Heij, Karin Ulmer, Guillaume Slizewicz
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For outsiders, algorithms only become visible in the media when they achieve an outstanding performance, like Alpha Go, or when they break down in fantastically terrifying ways. Humans working in the field though, create their own culture on and offline. They share the best stories and experiences during live meetings, research conferences and annual competitions like Kaggle. These stories that contextualize the tools and practises can be funny, sad, shocking, interesting.
  
Editing: Javier Lloret
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A lot of them are experiential learning cases. The implementations of algorithms in society generate new conditions of labour, storage, exchange, behaviour, copy and paste. In that sense, the contextual stories capture a momentum in a larger anthropo-machinic story that is being written at full speed and by many voices. The stories are also published in the publication of Data Workers.
  
Recording: David Stampfli
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Texts: Cristina Cochior, An Mertens
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'''Voices''': David Stampfli, Cristina Cochior, An Mertens, Gijs de Heij, Karin Ulmer, Guillaume Slizewicz
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'''Editing''': Javier Lloret
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'''Recording''': David Stampfli
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'''Texts''': Cristina Cochior, An Mertens
  
 
[[Category:Data_Workers]][[Category:Data_Workers_EN]]
 
[[Category:Data_Workers]][[Category:Data_Workers_EN]]

Latest revision as of 17:38, 4 June 2019

By Algolit

Listen to the stories

Sources on gitlab

During our monthly Algolit meetings, we study manuals and experiment with machine learning tools for text processing. And we also share many, many stories. With this podcast we hope to recreate some of that atmosphere.

For outsiders, algorithms only become visible in the media when they achieve an outstanding performance, like Alpha Go, or when they break down in fantastically terrifying ways. Humans working in the field though, create their own culture on and offline. They share the best stories and experiences during live meetings, research conferences and annual competitions like Kaggle. These stories that contextualize the tools and practises can be funny, sad, shocking, interesting.

A lot of them are experiential learning cases. The implementations of algorithms in society generate new conditions of labour, storage, exchange, behaviour, copy and paste. In that sense, the contextual stories capture a momentum in a larger anthropo-machinic story that is being written at full speed and by many voices. The stories are also published in the publication of Data Workers.


Voices: David Stampfli, Cristina Cochior, An Mertens, Gijs de Heij, Karin Ulmer, Guillaume Slizewicz

Editing: Javier Lloret

Recording: David Stampfli

Texts: Cristina Cochior, An Mertens