Why IoT needs AI

At one of my recent talks in New York about AI in the supply chain, one of the key questions that came up was ‘are you talking about robots?’

You see, AI has been romanticized to this abstract term that conjures images of walking robots doing your house chores while you just sit and relax.

But what does it really means and where did the term come from? Artificial intelligence encompasses the new paradigm of machine learning and big data processes that enables you to get predictive insights out of historical amounts of preexisting data processes combined with real-time observations. To get to true AI, you need to train large amounts of data sets (both historical and real-time), achieve some baseline, enable deep learning with incremental information and begin to uncover predictive value.

AI typically works in tandem with the Internet of Things (IOT) which includes devices like wearables and connected home gadgets. Simple put, IoT collects the information but AI is the engine that will power analytics and decision-making from that information.

IoT connects disparate devices such as wearables and can scale to connect a nearly unlimited number of devices, continuously streaming data. AI processes, makes inferences about this data and ultimately enables recommendations in real-time.

Let’s make some examples from the insurance industry

A couple of years ago, when I was at Humana around 2012, one of the projects we worked on was understanding seniors (65+) living in their homes to better reduce the incidence of falls and predict the likely use of emergency services in real-time so we can act beforehand, improve their health status and save costs. Armed with pre-existing claims data, we needed to understand the baseline — e.g., the activities that occur in the home. Here is where IoT devices came to play with mobile sensors.

With permission of the seniors, we installed multiple mobile sensors in the home, particularly areas such as the kitchen, bathrooms, and living room.