How IoT and AI go hand in hand

Google, Facebook, Microsoft – all the big players in the digital industry invest currently in Artificial Intelligence (AI) technology. Of course they don’t spend billions just for the fun of it: AI is capable to revolutionize industries. And with the rise of the Industrial Internet of Things (IIoT), AI and IIoT can mutually stimulate each other.

Plugging their business to the Internet of Things, companies hope to improve the efficiency of their production, reduce costs, break ground for completely new business models or all of these together. It’s the sheer mass of data generated by sensors, machine to machine (M2M) communication, or for example social media, that promises to find new insights to their business. To cope with all the information is the job of big data analytics, and to speed up this process corresponding to the increase of IoT data is where Artificial Intelligence and Machine Learning comes into play.

Machine Learning to deal with unknown data

As a part of AI, Machine Learning means to make computers learn autonomously and find insights without programming them on what they should do. Machine Learning is not a new concept, but with the technological progress achieved in recent years, exorbitant growth of CPU power, big data applications, cloud computing, cost-effective sensors and data communication, now is the time to take full advantage of it.

With Machine Learning, manufacturers can offer their customers Predictive Maintenance services for machinery and equipment. If a system is able to recognize recurring patterns independently on the basis of production data, it can apply this knowledge to unknown data. Thus, the system can detect trends but also possible sources of error and calculate the probabilities of default. Automated condition monitoring, optimized energy management, prediction of customer requirements – all this is possible with Machine Learning. Then, the next step is Deep Learning.

Deep Learning to add new value

Deep Learning, as Intel defines it, is a branch of Machine Learning “that uses neural network models to understand large amounts of data. It can accelerate processes like image and speech recognition, and natural language recognition.” In the realm of building automation, for example, photocells, sensors for temperature and motion, detectors for smoke and CO2 are used to make buildings smart. Deep Learning can add new value by analyzing input from video surveillance and voice control systems.

Edge computing for real time reaction

Transferring the data computing process from the cloud to the sensors (edge or fog computing) additionally provides real time analysis. By using Artificial Intelligence, systems can react within milliseconds to unexpected situations – vitally important for use cases like autonomous driving or the collision warning for the connected car, as it is currently tested by Deutsche Telekom at its “digital A9 motorway testbed”. AI is also needed for proactive intervention within remote machine control, when critical incidents occur, or the so-called Tactile Internet, which requires “extremely low latency in combination with high availability, reliability and security”, as the International Telecommunication Union (ITU) defines it.

“Partnership on AI”

So it’s no surprise that all the big players have an eye on Artificial Intelligence. Google, Facebook, Microsoft, Amazon, and IBM just started the “Partnership on AI” to “benefit people and society”, as they say. The intention is “to conduct research, organize discussions, share insights, provide thought leadership, consult with relevant third parties, respond to questions from the public and media, and create educational material that advance the understanding of AI technologies including machine perception, learning, and automated reasoning.” And in the end, as one can assume, to benefit from the spread of Artificial Intelligence themselves.

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