Serendipity is the occurrence and development of events by chance in a happy or beneficial way. That is what happened to me few days ago. After meeting with an extremely kind Uber driver and reminiscing on the 1994 world cup final between Brazil and Italy, by a series of fortunate coincidences, I then almost physically bumped into a colleague of mine when I entered a Starbucks in Foster City, California. He introduced me to a very interesting lady, Sudha Jamthe, a noted author of several books on IoT, who is now focusing her attention on the driverless world. She kindly invited me to the IoT Summit in San Francisco on the 12th April and following below are a few keywords from this event that illustrate the wide spread of IoT and Machine Learning within very different applications.
— KM Labs Europe (@KMLabsEU) April 12, 2018
- Mosquitoes: low cost, easy maintenance and connected IoT devices have enabled us to easily collect and quickly elaborate huge amounts of data. Mohammed Shokoohi-Yekta, from Stanford University, presented different use cases that demonstrate the pervasiveness of these systems. In particular, he described how one of their use cases for monitoring and recognising insects is finding interesting application in a project dedicated to disease prevention, that is developed in collaboration with Microsoft.
- Laundry: why are we speaking about laundry at a conference on IoT and machine learning? In his story, Gal Rozov described a quite long series of alternate events that have led him to become the CEO of Foldimate, a start-up that patented, and is now looking to commercialise the first laundry-folding machine for domestic use. Somewhat resembling an office printer, the machine will generate plenty of data about its functioning processes, with which one could adopt predictive monitoring approaches similar to those we have applied within our predictive analytics for MFPs.
- Radiation: After the mosquitoes and the laundry, another IoT device was presented by Daniel Valentino, from Landauer. They are developing a Real-time Dosimetry Service that is capable to monitor the radiation that radiologists and other workers are exposed to, in order to substitute current methodologies that are now becoming obsolete. The objective of more accurate observation of the operators could lead to the creation of new procedures that aim to reduce their overall exposure to potentially harmful levels of radiation.
- Dating: ‘Er Siri! Who should I date tonight?’ This is the provocative (and a little bit dystopic) question presented by Greg Ceccarelli, Head of Analytics at The League. As one of the latest dating apps to arrive to the market, Greg described how machine learning is changing the way that dating systems work. By reducing the time spent in identifying matches, the number of potential contacts increase and then the possibility of exploiting more detailed profiling will lead toward what he called ‘Dating 4.0’…