Auteur/autrice : noflux (Page 372 of 632)

Ideas need some idle, nonproductive space in which to thrive.

De nombreuses entreprises se mettent aux structures organisationnelles plates, estime Klint Finley pour Wired. A l’exemple de la société de jeux Valve ou de WL Gore, la firme derrière Gore-Tex. GitHub a la même ambition, et elle est d’autant plus symbolique que GitHub fournit un service qui permet justement de collaborer librement sur des projets logiciels. Mais les structures démocratiques plates ne veulent pas dire structures sans jeu de pouvoir, rappelle Finley. La semaine dernière GitHub a suspendu un de ses fondateurs accusé harcèlement. En 1972, Jo Freeman a décrit dans “La tyrannie de l’absence de structure” les premières expériences d’auto-organisation féministes. Le problème avec les organisations non-hiérarchiques est que les structures de pouvoir sont invisibles et donc inexplicables ce qui conduit souvent à des dysfonctionnements et des abus, estimait déjà Freeman. Fred Turner décrit les mêmes problèmes quand il évoque les communautés hippies qui ont voulu éviter la division traditionnelle du travail et qui ont fini par envoyer les femme faire la cuisine, le nettoyage et l’éducation des enfants. Les communautés régies par des structures plus explicites finissent par pouvoir être plus progressives, les responsabilités pouvant être réparties de manière plus égales

Le Parlement Européen a adopté à une très forte majorité une version amendée du rapport Castillo, qui définit pour la première fois et protège la neutralité du net en Europe.

« I am deaf. » I understood and typed in my destination in South Beach into his Google Maps and returned his phone to him. Away we went.

The claim that causation has been “knocked off its pedestal” is fine if we are making predictions in a stable environment but not if the world is changing (as with Flu Trends) or if we ourselves hope to change it.

Four years after the original Nature paper was published, Nature News had sad tidings to convey: the latest flu outbreak had claimed an unexpected victim: Google Flu Trends. After reliably providing a swift and accurate account of flu outbreaks for several winters, the theory-free, data-rich model had lost its nose for where flu was going. Google’s model pointed to a severe outbreak but when the slow-and-steady data from the CDC arrived, they showed that Google’s estimates of the spread of flu-like illnesses were overstated by almost a factor of two. The problem was that Google did not know – could not begin to know – what linked the search terms with the spread of flu. Google’s engineers weren’t trying to figure out what caused what. They were merely finding statistical patterns in the data. They cared about ­correlation rather than causation. This is common in big data analysis. Figuring out what causes what is hard (impossible, some say). Figuring out what is correlated with what is much cheaper and easier. That is why, according to Viktor Mayer-Schönberger and Kenneth Cukier’s book, Big Data, “causality won’t be discarded, but it is being knocked off its pedestal as the primary fountain of meaning”. But a theory-free analysis of mere correlations is inevitably fragile. If you have no idea what is behind a correlation, you have no idea what might cause that correlation to break down. One explanation of the Flu Trends failure is that the news was full of scary stories about flu in December 2012 and that these stories provoked internet searches by people who were healthy. Another possible explanation is that Google’s own search algorithm moved the goalposts when it began automatically suggesting diagnoses when people entered medical symptoms.