How Artificial Intelligence Affects Our Living Conditions

Why Artificial Intelligence Will Help The Internet Of Things To Break Through

The Internet of Things (IoT) is one of the trends that has been ascribed great potential for several years, but has not yet caught on in the broad masses. It feels like there are more texts about the networked refrigerator, which knows when the milk has expired or used up, than people who actually used it. In Gartner’s “Hype Cycle”, too , the IoT is listed as a trend that is associated with high expectations, but is currently still mainly rated as a trigger for innovations.

In order to correctly assess the potential of the IoT, however, the IoT should – in stark contrast to how it is done in the “hype cycle” – be viewed in close connection with the development in the field of artificial intelligence (AI). Because only an intelligent evaluation and control of data processes through the use of AI will help the networking of things to break through. Since the cost of AI has been falling significantly for a short time and application maturity has increased at the same time, the interaction between AI and IoT has become increasingly likely.

Data forms the interface between IoT and AI

The networked things deliver data on a previously unknown scale. This data plays an important role in two ways. On the one hand, the collection of data serves to make certain things more intelligent than they were before in their non-networked state. For example, the dash button from Amazon, which can detect via a sensor whether it is pressed and then translates this information into an order and forwards it via the Internet.

In one form or another, all IoT devices perform real-time analysisof this kind and perceive their surroundings. On the other hand, data and data analysis from the perspective of the companies that manufacture connected products play a central role in the success of the IoT.

By analyzing usage data, companies need to learn how their products are actually being used and how they can be improved. For example, what happens if a child presses the dash button 23 times in a row while playing? In contrast to real-time analyzes, this is also referred to as post-event analyzes . Through the use of deep learning or machine learning patterns can be recognized and analyzed that help to better understand the use of IoT products and to better control the associated processes.

AI makes networked things intelligent

Many networked things, such as a smart vacuum cleaner robot, function as self-sufficient units, but which are integrated into a more comprehensive ecosystem such as a smart home via the cloud can be integrated. Only when these units are able to learn from their daily work and to draw intelligent conclusions do they really become smart things. One speaks here of adaptive intelligence. In the case of the vacuum cleaner, for example, this could mean that he gradually gets to know the apartment and at a certain point can be sent to the kitchen to clean it. At the same time, the security of IoT devices increases through the integration of AI. Because monitoring and pattern recognition can better distinguish between normal use and external attack.

The IoT needs AI

The IoT is primarily about the collection, evaluation and use of data from everyday objects and things. The better the data can be evaluated and translated into meaningful use, the more likely companies and end users will offer or accept IoT devices. AI offers the opportunity to analyze the data generated in the IoT environment better than ever before. Machine learning algorithms in particular can be used to better integrate IoT solutions into given contexts, because on the one hand this improves real-time analyzes, but also because recurring patterns can be derived in post-event processing.

Also a recent PwC studyended with the conclusion: “The IoT needs smart machines. So there is a need for AI. ”Conversely, this also means: only if the IoT and AI are consistently thought together can intelligent solutions be developed that generate real, meaningful added value from the data .