Why IoT needs AI

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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 into this abstract term that conjures images of walking robots doing your household chores while you just sit back and relax.

But what does it really mean, and where did the term actually come from? Artificial intelligence encompasses the new paradigm of machine learning and big data processes that enable you to get predictive insights from a combination of historical amounts of preexisting data processes and 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 data, makes inferences about this data, and ultimately enables recommendations in real time.

Let’s look at some examples from the insurance industry

When I was at Humana, around 2012, one of the projects we worked on was with seniors (65+) living in their own homes. We wanted to understand how to reduce the incidence of falls and predict the likelihood of a need for emergency services. We needed to do this in real time so we could act beforehand, improving the seniors’ health status and saving costs. Armed with pre-existing claims data, we needed to understand the baseline — e.g., the typical activities that occur in the home. Here IoT devices came into play through the use of mobile sensors.

Read the source article at VentureBeat.