The dramatic evolvement in the functional capabilities of IoT devices, and the fact that data generated by devices is incomparably larger than that generated by humans, are two particularly important factors contributing to the fast-paced innovation in various industries. Similarly, advancement in Deep Learning research is expanding its applications beyond pure data analysis to device actuation and control in the physical world. However, in order for algorithms to be able to efficiently learn real-time control of real world devices, a combination of advancement in both Deep Learning and computing is essential. That is the concept of Edge-Heavy-Computing where by bringing intelligence close to the network edge devices, the overall system makes it possible for those devices to efficiently learn in a distributed and collaborative manner, while resolving the data communication bottleneck often faced in IoT applications. In this talk, I will introduce some of the work we have been doing at PFN, highlight some results, and also give examples of how new computing boosts the value brought by Deep Learning.
Toru Nishikawa is the president and CEO of Preferred Networks, Inc., a Tokyo-based startup company specialized in applying the latest artificial intelligence technologies to emerging problems in the area of Internet of Things (IoT). He was one of the world finalists of the ACM ICPC (International Collegiate Programming Contest) while he was a graduate student of University of Tokyo. In 2006, he, together with his collegemates and his fellow contenders at ICPC, founded Preferred Infrastructure, Inc., a precursor company. In 2014, Nishikawa founded Preferred Networks with the aim of expanding their businesses into the realization of Deep Intelligence – a future IoT in which all devices, as well as the network itself, are equipped with machine intelligence. He is an ambitious entrepreneur, programmer, and father of a child.