Shmuel Ur received his Ph.D. in Algorithms Optimization and Combinatorics in 1994 in Carnegie Mellon University under Michael Trick and Nobel Prize winner Herbert Simon. After a 16 years long experience in the IBM research lab in Haifa, and numerous professional publications, granted patents and patent applications, he joined the Intellectual Ventures group and is now a full-time independent inventor and consultant. His interests focus on software and hardware engineering, in areas such as guiding the visually impaired, augmented reality, gaming, privacy and social networks. Dr. Ur will be a Speaker at the IOT360 Summit on the Internet of Things, taking place in Rome on October 27-29, 2015. In this interview, he talks to us about the mechanisms ruling the cooperation between industry and academia in the IoT field.
In what ways do you expect the IOT360 Summit will bring contributions to the field of the IoT?
The field of the IoT is emerging. There are many players, the industry (start-ups and large companies), the academy, standards, and governments. They are all going to be in IOT360 and the communication is very important.
The Internet of Things may be connected with the problem of every “thing” getting its own IP address. Is it possible to run into the problem of lacking of IP addresses?
It is possible but will not be a major hurdle. We may need to add more digits to the IP address.
Investments in the IoT are large and benefits may be huge, what is the future of the connection between research and industry in the field?
Industry in general tries to make use of advances done in the academia and research. Researchers have the idea, write the code, the IP, usually prototype. Industry understands what it means to get it to the field. This includes many issues that generally the academy does not concern itself with, such as driving change in the behavior of people, marketing, and of course profits. The technology also needs to scale in a way that is usually not done in the lab. Working together, one can take ideas and prototypes from the lab to be deployed worldwide. This requires skills and resources that exist only in the industry but also the algorithmic knowhow and understanding that usually is to be found in research.