- Junot Díaz, speaking at Yale (via beaucadeau)
This is truth. Ask me how I know.
Google no longer understands how its “deep learning” decision-making computer systems have made themselves so good at recognizing things in photos.
This means the internet giant may need fewer experts in future as it can instead rely on its semi-autonomous, semi-smart machines to solve problems all on their own.
The claims were made at the Machine Learning Conference in San Francisco on Friday by Google software engineer Quoc V. Le in a talk in which he outlined some of the ways the content-slurper is putting “deep learning” systems to work.
"Deep learning" involves large clusters of computers ingesting and automatically classifying data, such as pictures. Google uses the technology for services like Android voice-controlled search, image recognition, and Google translate, among others. […]
What stunned Quoc V. Le is that the machine has learned to pick out features in things like paper shredders that people can’t easily spot – you’ve seen one shredder, you’ve seen them all, practically. But not so for Google’s monster.
Learning “how to engineer features to recognize that that’s a shredder – that’s very complicated,” he explained. “I spent a lot of thoughts on it and couldn’t do it.” […]
This means that for some things, Google researchers can no longer explain exactly how the system has learned to spot certain objects, because the programming appears to think independently from its creators, and its complex cognitive processes are inscrutable. This “thinking” is within an extremely narrow remit, but it is demonstrably effective and independently verifiable."
Jon Rafman uses the intricate tableaux of Rockstar Games’ Max Payne 3 as cinematic source material for his new machinima work, A Man Digging (2013). In this meandering and Robbe-Grillet inflected narrative, Rafman ruminates on the simulated sunbeams glinting through favela windows within the game, a melancholy sunrise in a deserted subway car, a heavy fog over a slate grey harbor. He can only do so, however, after killing every character—whether enemy or bystander—in the scene. In this way, Rafman makes visible the tension between the game as object of contemplation and the game as a continuous stream of connected events.
The amazing new work of Jon Rafman: http://dismagazine.com/dystopia/49510/a-man-digging/
The interstitial life is just that because it exists in the tears of the social fabric. The promises; that if one works hard and gets a college degree, a job will follow, that immigrants need simply perspicacity and localization in order to Make It, are clearly false. The ones who had the bad luck to believe in them are the ones now scraping to make ends meet, hoping to make it through another means. To try to transform the bad bet they’ve made once through another roll of the dice at low odds. Stuck, as Sarah Kendzior writes, in a post-employment economy.
It’s not hell, but it is economic limbo. Working as a freelancer, working to maintain a foothold in a new country is not working within the laws of society. The new normal, the post-employment ‘disruptor’s economy’ is to be between societies, to be between methods of availment. A refugee is not permitted to use the language of the old land and is considered uppity to use the rights of the new. An at-will employee cannot use the language of his employer nor the language of the unemployed. There is instead this right-less, law-less middle ground. Or, if one is truly unfortunate, a land overflowing with laws: an American does not have the right to whistleblow; a whistleblower doesn’t have the right of refuge. Sheremetyevo becomes the only middle ground in a dark and claustrophobic venn diagram.