Data Workers Podcast: Difference between revisions
From Algolit
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By Algolit | By Algolit | ||
| − | + | [https://www.algolit.net/index.php/Listen Listen to the stories] | |
| − | + | [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 | + | 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. |
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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. | ||
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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. | ||
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Latest revision as of 17:38, 4 June 2019
By Algolit
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