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." 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.

Your phone doesn't know how you're feeling -- but you may want it to if that capability came with a few fringe benefits. Affectiva makes emotion-detection software, and CEO Rana el Kaliouby was full of ideas today at Disrupt SF as to how it could be deployed, from gifs to Ubers. "We're obsessed with emotional AI, we wake up thinking about it," said Rana el Kaliouby. "I imagine a future where every device has a little emotion chip and can read your emotions, just like it's touchscreen enabled or GPS enabled." If you aren't creeped out by that, you might see a few of the benefits.

Researchers fed the software this picture of actor J.K. Simmons, among others, to teach it to recognize specific faces. Since 1989, Cops has famously aired footage of suspected criminals, many with their faces blurred out to protect their privacy. Ever since then, blurred or pixelated faces have become standard fare for concealing the identity of individuals who prefer not to be recognized in the media. YouTube got in the game a few years ago, offering a facial blurring tool to help protect protestors against retribution from law enforcement or employers. But machine learning researchers at Cornell Tech and the University of Texas at Austin have developed software that makes it possible for users to recognize a person's concealed face in photographs or videos.

Earlier this year, Andreessen Horowitz investor Chris Dixon noted that how challenging it's become for investors to help groom promising AI startups, given how quickly Facebook, Google, and Amazon are bringing aboard related talent. Dixon noted, for example, that Wit.ai, a Y Combinator startup that built voice-activated interfaces that Facebook bought and which now powers its Messenger platform, was only in Andreessen Horowitz's portfolio for a few months when Facebook bought it. But firm co-founder Marc Andreessen said on stage at Disrupt today that the firm is beginning to see things swing in the opposite direction. "Two years ago, it seemed like four or five companies were hoovering up all the talent . . . I think something like 1,500 people over four years [were involved in] building Alexa," the technology that powers Amazon's voice-controlled home computer Echo.

The researchers managed to defeat three privacy protection technologies. Firstly, they successfully identified faces from YouTube's blur tool, which allows users to automatically blur out objects and faces in their uploaded videos. It could also identify people and numbers from pixelated images created by Photoshop and the JPEG photo format's P3 - designed to be a secure way of hiding information. The algorithm is successful but not complete. It is only able to identify people or things it has previously been shown. However, an attacker, for instance, could use online social media photos to train the computer, which could make the method mainstream and dangerous.

Yi Wang was hearing the same refrain over and over: Why are English classes in China so expensive? The former Google product manager decided to do something about it and started an app called LiuLiShuo, which basically means "speaking fluently" in Mandarin. The app, which claims more than 30 million users, is one of scores of English-learning startups looking to disrupt China's hidebound language schools. To differentiate itself from products started by Internet giants like Baidu and Tencent, LiuLiShuo brings gaming and social media features to the genre. Users win points when they move to the next level and text each other encouragement and tips.

Chatbots are a major trend in tech today. Facebook Messenger has more than 30,000 bots; Kik has 20,000 -- but how many of those are actually useful? Nobody can go through that many bots individually, but it is certain that few early players in the "chatbot rush" have taken off in a major way. Sensay, one contestant at the TechCrunch Disrupt SF Battlefield, is aiming to be that first breakout cross-platform chatbot -- but, unlike most that provide services and information at a convenience via chat, its currency is people. Founders Ariel Jalali (CEO) and Crystal Rose (CMO) told TechCrunch that the premise of the service is to connect people by sharing knowledge, information and experiences.

Sam Kronick has a bunch of rocks arrayed in front of him on a raised desk in his Oakland studio. He's an artist and his plan is to sketch the rocks, but not with pen and paper. He and his artistic partner Tara Shi are going to do a 3D scan of them so that an artificial intelligence program can map their contours, learn to recognize rocks and then start generating its own craggy depictions. The project is deceptively simple: trying to get artificial intelligence to make nature art. Kronick and Shi are using a neural net, a computer program loosely modeled on biological neural systems like the human brain.

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They have the torch network configuration for each network at the end of the paper. I notice they use some linear units, why? Also, why are the networks so different? Is there a good resource on one designs their network? I notice they use some linear units, why?