Disrupt SF: Part 1 of 3

 

TechCrunch’s Disrupt SF takes place September 12-14 and a number of new technologies are being introduced for the first time. Elearning! Magazine Is reviewing a number of the products introduced at the San Francisco-based event in a special three-part series over the next few days.

Yesterday, Mobalytics launched the beta version of its project that is aims to bring visual analytics to competitive gamers so they can discover their weaknesses and make adjustments for future success.

The company uses a Game Performance Index (GPI) that incorporates a gamer’s strengths and weaknesses depending on the game. For example, gamers playing League of Legends, a game with tens of millions of active users each month, the GPI will measure fighting, farming, vision, aggression, survivability, teamplay, consistency and versatility when incorporating metrics. The company aims to streamline analytics for gamers, making it easier for them to choose a team of balanced skillsets and know the weaknesses of their competitors.

Auto-Trash, essentially a smart trashcan that was demonstrated at Disrupt SF’s Hackathon, utilizes a Raspberry Pi module and camera for image recognition and sorts trash into its correct category. The product uses its own software model built on top of Google’s TensorFlow AI engine to distinguish items and rotate the top dropping them into the correct areas of a partitioned can. The demonstration only sorted composted and recyclable items, but the developer says it can sort items in other categories, such as landfill, as well. The device uses machine learning to get smarter over time. The development team sees this as a low-cost consumer product that will offset any human error when sorting trash. 

 

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