September 13, 2016

"In an AI-saturated future, it might just be a selling point that a new game is so realistic that even a computer will dig it."

motherboard: Thanks to the modern gaming industry, we can now spend our evenings wandering around photorealistic game worlds, like the post-apocalyptic Boston of Fallout 4 or Grand Theft Auto V’s Los Santos, instead of doing things like “seeing people” and “engaging in human interaction of any kind.” by Jordan Pearson

'Games these days are so realistic, in fact, that artificial intelligence researchers are using them to teach computers how to recognize objects in real life. Not only that, but commercial video games could kick artificial intelligence research into high gear by dramatically lessening the time and money required to train AI.

'“If you go back to the original Doom, the walls all look exactly the same and it’s very easy to predict what a wall looks like, given that data,” said Mark Schmidt, a computer science professor at the University of British Columbia (UBC). “But if you go into the real world, where every wall looks different, it might not work anymore.”

'Schmidt works with machine learning, a technique that allows computers to “train” on a large set of labelled data—photographs of streets, for example—so that when let loose in the real world, they can recognize, or “predict,” what they’re looking at. Schmidt and Alireza Shafaei, a PhD student at UBC, recently studied Grand Theft Auto V and found that self-learning software trained on images from the game performed just as well, and in some cases even better, than software trained on real photos from publicly available datasets.

“Video game graphics have actually gotten good enough that you can train on raw data and have it be almost as good as real-world data,” Schmidt continued. Of course, video games aren’t advanced enough to be indistinguishable from reality, and so real images are still preferred. But you can cull so many labelled images from games that their sheer number makes up for the lack of detail in individual images.'

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