Big Data & Machine Learning

AI in the job of protecting homes

ImpactHub - Atlantic room

18th November, 11:30-12:00

Protecting your home is not longer only the job of the police or a good dog. In the new social era we are living, we need smarter solutions, that can recognize and identify easily our friends, from intruders or unwanted presence. Such solutions should adapt to the home specifics, such as homes with noisy environments, random schedules, large variety of sounds, variety in the types of visual visitors, in order to avoid false alarms and keep your home secure. We are analyzing both audio (dog barking, glass breaking, steps, speech, alarm, etc.) and video analysis (humans, dogs, cats, faces, fire, etc.) in a parallel manner, to predict and classify events happening at home, and learn patterns from them in time. All machine learning algorithms are running on a small hardware device, which implies high optimization challenges for the performance of the algorithms using the low computation resources available. We are proud to be among the first ones to have such a large deployment of machine learning on a small custom-made IoT hardware device.

George Platon


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.