IoT needs Artificial Intelligence

IoT - Internet of Things has been the biggest buzzword in 2016. Yet, one fails to derive value for IoT devices, and IoT has not yet hit the mainstream and continues to be at the periphery - but with a lot of hype surrounding it.

Personally, I have tested several of these IoT devices. Wearable devices such as FitBit, Google Glass, Smart Helmets - but failed to derive value from it. The main reason, I had to work more to make sense of these devices.  In short, I had to work to make IoT work for me!

There are two main problems plaguing IoT.

1. Lack of inbuilt intelligence to derive value out of IoT
2. Power supply for IoT

In this article, I will concentrate on the first problem plaguing IoT.

Essentially, IoT produces raw data and lots of it. The type of data depends on the type of device: Sensor data in cars, heartbeat information from Pace Makers, etc. Collectively, all this sensor data usually falls under BIG DATA category.  The sheer volume of data being created by them will increase to a mind-boggling level. This data holds extremely valuable insight into what's working well or what's not – pointing out conflicts that arise and providing high-value insight into new business risks and opportunities as correlation and associations are made.

The problem is that it takes huge amount of work to find ways to analyze this huge deluge of raw data and build valuable information out of it.

For example, with a health wearable, I can get to know my heartbeat pattern, heart rate, how many steps I walked, how many calories I burnt, how much rest, how much sleep etc. But this information is useless to me unless I can analyze it and develop a plan to change my activities. The wearable IoT does not tell what I need to do to reach my health goals, nor can it handle any anomalies.

For corporates using Big Data Analytics tools (which is expensive), one can get a really valuable insight. But, it takes a large team of experts to develop a big data analytics tools & platform - which then product valuable insight. The organizational leaders, must then understand the insights and ACT on it. All this means - LOTS OF WORK!

The only way to keep up with this IoT-generated data and make sense of it is with machine learning or Artificial Intelligence.

As the rapid expansion of devices and sensors connected to the IoT continues, it will produce a huge volume of data: Big Data which can be used to develop self driving cars, save fuel in Airplanes, improve public health etc. The treasure trove of big data is valuable only when machine intelligence is built on it - which can take autonomous decisions.

While the idea of AI sounds great. There is limitless benefits of AI - which will eliminate the need for humans to intervene in daily mundane tasks.  However, the big problem will be to improve the speed and accuracy of AI.

In an IoT based AI system must be able to regulate the action without errors. If IoT & AI does not live up to its promise, then the consequences should not be disastrous. A minor glitch like home appliances that don't work together as advertised. But a life-threatening   malfunction like the Tesla car crash would force people from embracing IoT & AI.

AI is already in use in many ways, for example Netflix, Amazon, Pandora use it to recommend products/movies/songs that you may like.

Today, there are lots of opportunities in IoT-AI solution space. For example an intelligent insulin pump which can regulate insulin levels based on the person's activities and sugar levels. Similarly, driverless vehicles - trains, planes, cars etc., so that better decisions can be made with out human intervention.