Artificial intelligence and cognitive computing have only recently become a reality for the mass market due to a combination of two things–increased, low-cost computing power and an explosion of data. If you’ve been keeping up with the trends over the past five years, it’s not hard to guess where much of that data is coming from. Embedded sensors and connected devices are creating vast amounts of data; much of which isn’t currently being put to good use. That’s because most companies and individuals haven’t yet started to take advantage of technologies like IBM Watson that can ingest that data, understand it, and act up on it. Once they do, the implications are profound.
An example that’s often used by experts relates to buildings. Think about large structures in parts of the world that experience temperature extremes. That could include shopping malls in Canada, or casinos right here in the Las Vegas desert. These buildings consume huge amounts of energy in order to maintain comfortable environments for occupants. But the HVAC systems in these buildings are often cycling on and off based on simple programmable thermostats, which keep spaces at a constant temperature whether they’re occupied or not. Applying a cognitive system to these buildings could lead to huge savings in energy costs.
One way to start would be to add simple motion sensors to rooms and public spaces. If a cognitive system determines that a space is unoccupied due to lack of motion for a defined period, it can let the ambient temperature go up (or down) within a set range until motion is detected again. But that’s just a start. Next, combine the sensor information with historical foot traffic patterns and calendars. If the cognitive system in a mall understands that tomorrow is a holiday and that traffic normally increases because people have the day off, system can anticipate an increased number of people and direct HVAC, lighting, and other systems to over-ride existing programs.
Looking into the future, traffic and gridlock could become a thing of the past due to a combination of sensors, self-driving cars, and AI. Imaging getting into a car, telling it where you want to go, and sitting back to watch a show. A centralized AI system uses data from sensors embedded in the road and other infrastructure and determine the best route to take. The system can re-route traffic in the event of a road blockage, anticipate peak travel times and plan routes for each car to prevent traffic from even starting to build up, and always ensure the quickest travel time from point A to point B–all while helping reduce the number of collisions by orders of magnitude.
These are only a few examples of what’s possible, and I’m excited to see what the next 10 years bring as all of that IoT data finally starts to help us build better lives. How do you see cognitive systems and the IoT combining? What are the opportunities? What are the challenges?
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