Issue link: https://www.e-digitaleditions.com/i/1081695
32 I ICT TODAY feeding them, the result will be an avalanche of computing happening at the edge. The sensor, itself, is only applying the model. The logic to do something interesting with that model and data still needs to be programmed and housed somewhere onsite. One interesting application has cropped up recently. Since the advent and massive adoption of IP cameras throughout intelligent buildings, a few smart data scientists saw they could do even more. There is now software that exists that can in real time identify whether someone is holding a real pistol or a water gun automatically without the need for humans to inspect it. These types of systems are starting to spring up in high schools to thwart any potential active shooters. This is only possible due to recent machine learning advances that are only possible because of the sheer amount of data that these cameras are collecting. Smart building indeed! The linear journey from big data to machine learning to the edge has surprised many in ICT, IT, and operational technology (OT), even though in hindsight it is only logical. Another big area for applying machine learning in building management systems is with the notably complex HVAC systems. Good engineers can tune these systems by hand using the data they have available, but there is no way they can continuously tune to adapt to real-time data that streams in from thousands of given inputs. Calculating the thermal load in a room can be done using an equation with quite a few parameters. Machine learning in this case is finding the optimal values for each of these variables in an automated fashion. The decision from those values can be tested and learned over time thanks to the copious amount MACHINE LEARNING MAGIC The entire field of inference includes a wide range of activity that ten years ago would have been viewed as magical. Automated image classification looking for defects in wind turbines, acoustic detection that can "'hear" manufacturing lines operate in ways that would make dogs blush, not to mention maritime's need for detecting, tracking and classifying marine vessels. Currently, there are systems smart enough to understand what type of vessel is out there in the world's waters simply by "listening." Predictive maintenance is already in place in industries, such as oil and gas, to predict workover, rod change, and cleaning operations leading to drastically reduced downtime and preventing catastrophic repair costs. Of course, seismic interpretation can be enhanced here as well for salt classification. Parted rods or leaking tubing can leave pumps not pumping for days or weeks. A new technology is what some people are calling "super sensors." Super sensor technology is when regular sensors cross-breed with machine learning to detect complex events. As the more granular these super sensors get and the larger the supply chain FIGURE 2: Schools are using cameras paired with machine learning to identify and flag individuals carrying weapons on school grounds. With hundreds of cameras per school, this would be impossible without edge computing.