Researchers at Technion have developed a platform able to accelerate the learning process of AI systems a 1000 fold
Prof. Shahar Kvatinsky and doctoral student Tzofnat Greenberg-Toledo, together with students Roee Mazor and Ameer Haj-Ali of Technion’s Andrew and Erna Viterbi Faculty of Electrical Engineering recently published their research in the IEEE Transactions on Circuits and Systems journal, published by the Institute of Electrical and Electronics Engineers (IEEE).
Prof. Shahar Kvatinsky and his research team
In recent years, there has been major progress in the world of artificial intelligence, mainly due to models of deep neural networks (DNNs); sets of algorithms inspired by the human brain and designed to recognize patterns. Inspired by human learning methods, these DNNs have had unprecedented success in dealing with complex tasks such as autonomous driving, natural language processing, image recognition and the development of innovative medical treatments which is achieved through the machine’s self-learning from a vast pool of examples often represented by images. This technology is developing rapidly in academic research groups and leading companies such as Facebook and Google are utilizing it for their specific needs.