HomeArtificial IntelligenceIoT And AI: A Co-Evolution

IoT And AI: A Co-Evolution

The Internet of Things and artificial intelligence are among the dominant innovation trends of recent years. Due to the rapid technological development, both have reached a certain degree of maturity and have led to the first practical applications, but are still struggling with various challenges and shortcomings. Although both trends are discussed at length, the close connection between these technologies is rarely considered. The interactions could be decisive for their success.

Internet Of Things: The Arduous Journey From Vision To Reality

The core idea behind the Internet of Things is that very small and interconnected computers become part of objects and the environment. Ultimately, the computer as a device will disappear.

Conversely, one could also say that computers are becoming ubiquitous as integral components in the human environment (ubiquitous computing). This vision, which dates back to the 1990s, is becoming increasingly real. The increasing computing power is one of the drivers of this development. Improved energy efficiency and ubiquitous connectivity also help. After all, today, we have a new generation of highly integrated and multimodal sensors, big data systems and powerful cloud services.

But there are still many problems to be solved. Security problems, missing standards or lack of compatibility due to proprietary protocols are all part of this. In addition, the limited overall benefit of the currently mostly isolated solutions is viewed critically. Some people are even talking about the “Internet of Shitty Things”.

A functioning and meaningful network of things is enormously complex: It not only consists of many different components but above all, it is organized in a decentralized manner. It also requires a variety of new forms of interaction with people.

Conventional user interfaces – such as touchscreens – are often no longer a viable option. On the one hand, this is simply due to the space required and the comparatively high power consumption of such components. On the other hand, more natural forms of interaction are the declared goal. For this, IoT solutions must act intelligently, among other things, by understanding the context. This expectation is also reflected in the fact that the word “smart” appears in practically all IoT applications: at the network level, for example, we speak of smart grids, smart cities, smart mobility, smart homes or smart manufacturing, and at the device level of smart speakers, smart wearables and smartphones.

Artificial Intelligence: The First Summer

The current hype is not the first in the almost seventy-year history of artificial intelligence. In the past, there have always been short-term successes in laboratory situations, which were initially taken for breakthroughs, but then turned out to be bitter disappointments in practical application. This repeatedly lied to the extensive cessation of research activities – the so-called AI winters. The situation is different this time: the massive advances in so-called neural networks have led to practical solutions for the first time on a broad scale. Especially in image recognition (computer vision), speech processing (speech recognition) and text understanding (natural language understanding), these technologies have even arrived in the living room – for example, in the form of smart speakers. So today, everyone can not only speak with the Amazon Echo Look. Therefore, he can even see his counterpart and give fashion tips based on the current outfit.

This technology can be used universally and is used successfully, among other things, for data mining, learning games (e.g. Google’s AlphaGo), and improving or creating images. However, this is not about artificial “intelligence” in the true sense but a small sub-area of ​​AI – namely, machine learning (ML).

New architectures for neural networks, so-called Deep Neural Networks (DNNs), combined with the computing power and storage capacity that are now available, have led to a paradigm shift: Persistent, complex and high-dimensional problems that computer science, despite decades of effort, have so far not been able to solve satisfactorily in terms of algorithms got a grip (e.g. speech recognition) can now suddenly be solved. This is done by training a system with examples and thus learning connections independently. As a result, this system can then deliver the correct results even for unknown inputs. But precisely in this training lies one of the challenges of the new technologies – they require vast amounts of high-quality training data.

AI Needs IoT

One of the central requirements for machine learning is the availability of digital data. It is not for nothing, for example, that image recognition was one of the first successful areas of application: there are now countless categorized or text-annotated digital images – e.g. from social networks or search engines. Translations are also among the pioneers of machine learning. 

The successful use of AI in other areas such as autonomous driving also depends on the amount of data available – on the one hand for training the models, but on the other hand also for controlling the vehicle in real-time. This requires various sensors that can deliver such data in the required quantity, speed, quality and resolution. IoT networks are thus among the most important enablers for using artificial intelligence in physical space.

IoT Needs AI

Even a functioning Internet of Things is difficult to imagine without a certain degree of “artificial intelligence” or would ultimately only enable disappointingly simple and rigid – because programmed – use cases. However, processing large amounts of data, recognizing relationships and patterns, and implementing complex controls are the main disciplines of machine learning. To make Internet of Things smarter, these functionalities can be applied at different levels of the system:

Processing Of Sensor Data

Modern, highly integrated multiple sensors, as well as cameras and microphones, supply a lot of raw data, from which the decisive information can ideally already be filtered or pre-processed in the corresponding IoT device (e.g. distance sensors, which already smooth out measurement errors or surveillance cameras, which no longer capture an image but structured data about deliver detected objects). This makes the use of narrow-band connections more efficient and prevents latencies – i.e. delays that occur when large amounts of data have to be transmitted to a central instance. These instances then still have to process this data, make decisions and give control commands in the opposite direction – this quickly adds up to tough times due to the real-time nature of many IoT systems. The more intelligent the components of a network, the more robust and faster it can function and the more possible applications there are.

Control Of The Overall System

Since a significant added value of IoT is the cooperation of its components, data must be merged, and patterns and connections recognized. The individual members only provide information about a small aspect of the world. Therefore, a context must be created in which incoming data can be interpreted and actions can be triggered based on it.

New User Interfaces

As computers are miniaturized and integrated into everyday objects, conventional user interfaces already mentioned are no longer adequate solutions. Instead, the direct interaction of the user with the device comes to the fore, for example, through approaching, changing position or location, gestures or facial expressions or even through language-based interfaces. The latter, in particular, have great potential because they are universal and enable complex but natural interaction through dialogue – even over a certain distance. In addition, conversational interfaces can also be integrated relatively easily into smaller devices.

The Internet of Things will only be successful if fundamental problems such as interoperability and security are solved. Above all, however, it must ultimately actually be “smart”. The current developments in AI provide new possibilities and tools for this. In particular, machine learning technology is ideally suited to recognizing relationships and patterns in large amounts of data and already provides mature technologies for IoT-compatible user interfaces. Conversely, AI is about to make the decisive leap from purely virtual to real, physical use, which IoT systems make possible. This co-evolution of both technologies will most likely lead to an acceleration of development in both areas.

Also Read: Artificial Intelligence Aims To Make Agriculture More Ecological

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