ML optimized for running on a low-end device
Europa room, 3rd floor
14th November, 11:00-12:00
Flare is a unique approach to home security. It protects user's home all by itself, recognizes and identifies friends from foes and adapts to home specifics - such as noisy environments, random schedules, various visitors. Using embedded machine learning algorithms, Flare can understand what is happening in the world around it, and predict dangerous events occurring at home. We are analysing both audio (dog barking, glass breaking, steps, speech, alarm, etc.) and video (humans, dogs, cats, faces, fire, etc.) in a parallel manner, to classify events, and learn patterns from them in time. All this intelligence working on a small embedded device, is what makes Flare unique, powerful and challenging to build. Top technical limitations and challenges, together with various solutions, usability concerns in IoT, state-of-the-art of the industry will be presented and discussed.
George is a full-stack developer, with a great passion for new technologies, hardware and machine learning. During the years, he was working in different positions, such as web developer, mobile developer, team leader, branch manager and lately entrepreneur. He enjoys getting the best learnings from all the positions he has been through and the people he met. Currently, George is part of BuddyGuard, a home security product that disrupts the smart home market through special features powered by artificial intelligence (face recognition, voice analysis, pet detection, etc.). At BuddyGuard, he is managing the technical side, which involves many challenges on data security, distributed cloud systems scalability, mobile phones integrations, machine learning algorithms and hardware electronics integrations.