As growing numbers of internet-connected sensors are built into cars, planes, trains and buildings, businesses are amassing vast amounts of data.
Tapping into that data to extract useful information is a challenge that's starting to be met using the pattern-matching abilities of machine learning (ML) -- a subset of the field of artificial intelligence (AI).
Firms are increasingly feeding data collected by Internet of Things (IoT) sensors -- situated everywhere from farmers' fields to train tracks -- into machine-learning models and using the resulting information to improve their business processes, products and services.
One of the most visible pioneers is Siemens, whose Internet of Trains project has enabled it to move from simply selling trains and infrastructure to offering a guarantee its trains will arrive on time.
Under the Internet of Trains project, Siemens has embedded sensors in trains and tracks in select locations in Spain, Russia and Thailand, and then used the data to train machine-learning models to spot tell-tale signs that tracks or trains may be failing. Having granular insights into which parts of the rail network are most likely to fail, and when, has allowed repairs to be targeted where they are most needed -- a process called 'predictive maintenance'. That, in turn, has allowed Siemens to start selling what it calls 'outcome as a service' -- a guarantee that trains will arrive on time close to 100 percent of the time.
One of the earliest firms to pair IoT sensor data with machine learning models was thyssenkrupp, which runs 1.1 million elevators worldwide and has been feeding data collected by internet-connected sensors throughout its elevators into trained machine-learning models for several years.
These models provide real-time updates on the status of elevators and predict which are likely to fail and when, allowing thyssenkrupp to target maintenance where it's needed, reducing elevator outages and saving money on unnecessary servicing. Similarly, Rolls-Royce collects more than 70 trillion data points from its engines, feeding that data into machine-learning systems that predict when maintenance is required.
The application of machine learning to Industrial Internet of Things (IIoT) data is not all about predictive maintenance. For agricultural equipment maker John Deere, the computer vision made possible by deep learning is allowing it to experiment with herbicide sprayers whose built-in cameras can distinguish between weeds and plants. The aim is to apply insight to each stage of the farming process, eventually producing planters and harvesting equipment that can adjust how they operate on the fly in order to maximize crop yields. Â
In a recent report, IDC analysts Andrea Minonne, Marta Muñoz, Andrea Siviero say that applying artificial intelligence -- the wider field of study that encompasses machine learning -- to IoT data is already delivering proven benefits for firms.
"Given the huge amount of data IoT connected devices collect and analyze, AI finds fertile ground across IoT deployments and use cases, taking analytics level to uncovered insights to help lower operational costs, provide better customer service and support, and create product and service innovation," they say.
According to IDC, the most common use cases for machine learning and IoT data will be predictive maintenance, followed by analysing CCTV surveillance, smart home applications, in-store 'contextualized marketing' and intelligent transportation systems. That said, companies using AI and IoT today are outliers, with many firms neither collecting large amounts of data nor using it to train machine-learning models to extract useful information. "We're definitely still in the very early stages," says Mark Hung, research VP at analyst Gartner.
