korzacsol

Ruminations…

“Science fiction has predicted everything from the internet to mobile phones. Could it help us create concrete-free cities of the future?”

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portrait of a “visage recognition”

“That is the state of the art today—that you need tons of data to teach a machine. […] State of the art changes with research.”

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my evolution to meta data

Crackle, snap, pop. One of the most common applications of machine learning today is in recommendation systems. Netflix and YouTube use it to push you new shows and videos; Google and Facebook use it to rank the content in your search results and newsfeed. While the systems offer a great deal of convenience, they also cause two undesirable side effects. You may have heard them before: filter bubbles and echo chambers.

In a new paper, researchers at DeepMind analyzed how different recommendation algorithms can speed up or slow down both phenomena, which they refer to in academic-speak as “degenerate feedback loops.” (In other words, the higher the degeneracy, the stronger the filter bubble or echo chamber effect.)

They ran a simulation for five different algorithms, which use different principles to select content to push to the user. They found that the more the algorithm prioritized accurately predicting exactly what the user wanted, the more it sped up the system’s degeneracy. Therefore, the best way to combat the formation of filter bubbles and echo chambers is to design algorithms with a greater emphasis on the random exploration of new content. Growing the overall set of information from which the recommendations are drawn can have a combative effect as well. It’s worth noting a likely trade-off, however—that users will find their recommendation systems less accurate.

The researchers caveat that their analysis is limited because it is based on a fully theoretical simulation that didn’t involve real user input. More work must be done to better understand how user dynamics might change the behavior of these systems.

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my learning algorithm

MY PLEASURE IS TO CREATE DIFFERENT THINGS NOT THE SAME\

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Bien évidemment, c’est cette deuxième figure de la bourgeoisie qui finira par l’emporter sur l’autre: Elon Musk ou Mark Zuckerberg sont, et ce jusque dans leurs jeans et leurs tee-shirts, aux antipodes de Monsieur Prudhomme. C’est d’ailleurs de ce bourgeois révolutionnaire dont parle Marx que les bohèmes barbus de Verlaine finiront par se rapprocher, ce qui constitue une des principales clefs de compréhension de l’histoire de l’art moderne. L’artiste est de gauche, l’acheteur de droite, la réconciliation des deux s’effectuant dans le concept d’innovation: c’est Pompidou qui fait entrer Picasso de son vivant au Louvre et Chirac qui offre l’Ircam à Boulez. Ce à quoi nous avions assisté avec les «gilets jaunes», du moins avant que les marges brunes ou rouges du mouvement ne sombrent dans la violence et la haine, c’est à la révolte de la France profonde contre cette bourgeoisie «révolutionnaire» qui bouleverse le monde par ses innovations incessantes aussi incompréhensibles que menaçantes pour ceux qui n’appartiennent pas à ce qu’on appelle l’élite.

L’artiste est de gauche, l’acheteur de droite, la réconciliation des deux s’effectuant dans le concept d’innovation.

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different…

A SUIVRE…