Data Workers Podcast: Difference between revisions
From Algolit
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+ | By Algolit | ||
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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. | 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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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. | 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. | ||
+ | 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]] |
Revision as of 15:02, 1 March 2019
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.
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.
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.
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