Artificial Intelligence with Internet of Things

Hello readers, in this blog we are going to talk about the actual applications of combining Internet of Things (IoT) and Artificial Intelligence (AI).

Both IoT and AI fall into the general pit of buzzword-vagueness. While IoT defines the objects that are able to connect and transfer data via the Internet, AI is the functionality necessary to operate the connected “things” online and interpret the volume of data streamed in from these devices. Both these technologies are the fulcrum of smart lives that promoters have been promising us for some time now. From smart homes to smart cities, IoT and AI will play an important role in the evolution of the human race.

Combining AI and IoT

To understand and experience the benefits of both AI and IoT, we need to combine them not just at the device level but also at the server level.


For instance, AI with Machine Learning (ML) can leverage and self learn from the historical data (thanks to Big Data) to help us predict future actions in advance, such as order alternatives in marketing or failure of equipment at the workshop.

Three Working Examples of AI-based IoT

The example applications discussed below are all in use today and are only representative examples of a broader trajectory of applications.

Automated vacuum cleaners

iRobot Roomba is the pioneer in the field of the automatic vacuum cleaner. Founded by MIT roboticist, iRobot was first commercially launched in 2002. The puck-shaped vacuum robot is the industry standard in its class. It maps and memories a home layout, adapt to all kinds of surfaces or new items, clean a room efficiently through the most effective patterns, and docks itself to recharge the batteries. iRobot is the perfect example of artificial intelligence “embodied” in a robot.

Smart thermostat solutions

Although we are yet to experience the revolutionizing impact of ‘smart home’, there are companies striving to deliver a similar experience. The nest is the leader in this regard. The company has developed a smart thermostat solution as an IoT device with a clean digital interface, which is a welcome change from the clunkier physical dial. Smartphone integration enables users to check and control temperature from anywhere. The device “learns” the regular temperature settings and adapts to the user’s work schedule by turning down energy use.

Self-driving vehicles

In an ideal smart world, cars are ‘things’ that leverage powerful AI to learn not just from their experience but also from each other. Self-driving cars employ ML to predict the behavior of cars and pedestrians in various circumstances.

This is exactly how Tesla’s autonomous vehicle technology really. According to Tesla’s CEO Elon Musk, “Tesla’s fleet of self-driving cars operate as a network. Thus, when one car learns something, they all learn it.”

Unlocking the Potential of IoT with AI

AI is slowly but surely beginning to play a prominent role in the development and deployment of IoT applications. The influence of AI is such that a majority of vendors of IoT platform software is now offering integrated AI capabilities such as ML-based analytics.

AI can help wring insights from data, while ML helps identify patterns and irregularities in the data that devices generate. In comparison to business intelligence tools that wait for numeric thresholds to cross, ML can help make operational predictions 20 times faster and better accuracy. Additionally, AI technologies such as speech recognition and computer vision can help gain insights from data that requires human intervention. The confluence of AI with IoT enables companies to minimize downtime, improve operating efficiency, and enhance risk management.

Increasing Operational Efficiency

AI-powered IoT can improve operational efficiency. ML not only predicts equipment failure but also operating conditions and identify parameters that can be adjusted readily for ideal outcomes, by crunching the constant streams of data to detect patterns that are not easy for humans to pick.

ML is also good at finding counterintuitive insights. For instance, an ML tool being used by a shipping fleet operator identified that cleaning the hulls of the ships more often — an expensive and downtime-causing process — actually helped increase business profitability. The finding was against the shipping industry belief of keeping the hulls smooth via frequent cleaning improves fuel efficiency sufficiently that it outweighs the increased cleaning costs.

Minimizing Downtime

Many industries lose a lot of money due to unplanned downtime caused by equipment breakdown. ML ensures predictive maintenance of machinery through analytics, forecasting equipment failure. Industries can, thus, schedule maintenance and mitigate the damaging economics related to unplanned downtime. It is done by identifying patterns in the endless streams of data from the machines.

Enhancing Risk Management

Another aspect of IoT and AI pairing is that it helps organizations predict a variety of risks and automate rapid response to better manage financial loss, cyber threats, and other business risks. Some such applications are already in use, from detecting fraudulent behavior at bank ATMs to identifying hazardous conditions for factory workers to predicting driver insurance premiums based on driving patterns.

Enabling New and Improved Products and Services

The confluence of IoT and AI can also result in new products and services. This is possible because of enhancement in technologies such as NLP that are getting better at letting people speak with machines and eliminating the need for a human operator. Drones and robots powered by AI are enabling the monitoring and inspection of places that were inaccessible for humans.

Closing Lines

It is predicted by International Data Corp. that by 2019 all IoT efforts will be supported by AI and data from the deployments will have “limited value” without AI. Therefore, you will find a majority of IoT implementation in the future that are powered by AI, from security and access devices to emotional analysis and facial recognition.

Let us know your thoughts on the topic in the comments below.

Until next time!

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