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11/30/2020
Artificial intelligence, machine learning and the IoT
Max Burkhalter
Artificial intelligence has been powered up by supercomputing to the point that AIs can now build other AIs. Machine Learning, the ability to input more and more data and extrapolate more and more accurate findings, has also become augmented to the point that predictions can be made about future events with eerie accuracy.
Both of these, however, rely on data to function. How can you amass the granular-level, micro-segmented data these processes require to return actionable analysis? Data can come from many sources, including website tracking cookies, social media pixels or freely given information as part of a signup process.
IoT devices and AI intersect
Real-time data can also be sources from devices with sensors that transmit data to the Internet of Things. These IoT-connected devices make it easy to track all types of data streams in real time, delivering massive amounts of minute data that can be used to track patterns.
For example, your health can be monitored by a fitness tracker like Biotricity, which tracks and analyzes signs of heart health. Devices like these can recognize the signs of a pending heart attack thanks to collated and anonymized user data across hundreds of thousands of users and years of data collection.
The patterns can be further extrapolated by ML and AIs that have been developed to understand trends and sound an alarm in case vitals stray too far from preset parameters. An alert can instruct the device wearer to take a break, ingest medication or call for emergency services.
Other uses for IoT and AI analysis
On a commercial level, IoT is invaluable in the manufacturing, warehousing and logistics industries. A tire that is about to give out can be identified and replaced in advance of a blowout on a busy highway thanks to IoT sensors and connectivity to the IoT. A machine on the packing line that needs service can be pulled out before a malfunction occurs.
In the workplace, IoT and AI converge to make employees safer, more comfortable and more productive. Facial recognition can track when an employee enters the building at the beginning of their shift, and begin the process of setting up their workstation and turning on their equipment before they even reach their desk. Lighting and temperature can be set to the most preferred settings for the group in the building at any given time. Morale and productivity can be boosted for happier workers and a smoothly operating workplace.
In the financial sector, encouraging insurance customers to use IoT devices can help adjust their risk category. Drivers who obey speed limits, wear seatbelts, keep tires properly pressurized and so forth are at a lower risk for accident and can be rewarded with a lower premium.
Driving habits analyzed by machine learning and risks calculated by an AI can help customize individual rates and improve customer satisfaction. According to the National Association of Insurance Commissioners, the cost of claims could be cut by as much as 30% with IoT adoption.
Strong network infrastructure is key for organizations and individuals seeking to take advantage of everything the IoT has to offer. High connectivity tools like industrial-grade Ethernet converters, reliable console servers, and data management systems can help enterprise-employed IT admins focus on building strong frameworks for IoT integration, and deliver a support system that bolsters your adoption of IoT devices. Contact us to learn more.