Artificial intelligence (AI) will become an unavoidable choice in manufacturing companies where massive use is made of devices that monitor operations. To evaluate the potential of AI in the management of industrial processes and elucidate the reasons behind the slow adoption of this technology in the manufacturing industry,

According to a panel of experts, the cognitive abilities of AI – the processing of natural language, the detection of objects and faces, etc. – can be easily applied in any industry, including the manufacturing industry. Thanks to machine learning and deep learning, it is possible to analyze the data that the sensors carry during the manufacturing process (backend) and to quickly detect abnormal signals, failures and errors.

This is called predictive-type maintenance, which improves processes, reduces downtime and optimizes equipment performance. Operationally, AI is also being used more and more for visual inspection to detect defects in products. This reveals the emergence of a new area of ​​application for AI, known as “predictive quality”. With the Internet of Things, it is expected that the operational technology infrastructure will be integrated with a company’s IT infrastructure. This integration should have a profound impact on business processes.

In contrast to the strong AI, the weak AI is about mastering concrete application problems of human thinking. Human thinking should be supported here in individual areas. The ability to learn is a key requirement of AI systems and must be an integral part that can not be added later. A second major criterion is the ability of an AI system to deal with uncertainty and probabilistic information. In particular, those applications are of interest, for the solution of which, according to general understanding, a form of “intelligence” seems to be necessary. Ultimately, the weak AI is thus about the simulation of intelligent behavior using mathematics and computer science, it is not about creating awareness or a deeper understanding of intelligence. While the creation of strong AI in their philosophical quest has failed to date, significant progress has been made on the side of weak AI in recent years.

A strong AI system does not have to have much in common with humans. It will probably have a different kind of cognitive architecture and in its stages of development it will also not be comparable to the evolutionary cognitive stages of human thought ( evolution of thought ). Above all, it is not to be assumed that artificial intelligence possesses feelings such as love, hate, fear or joy. However, it can simulate behavior corresponding to such feelings.

In addition to the research results of Nuclear Informatics itself, the results of psychology, neurology and neurosciences, mathematics and logic, communication science, philosophy and linguistics have been incorporated in the research of AI. Conversely, AI research also influenced other areas, especially neuroscience. This can be seen in the training of neuroinformatics, which is assigned to biology-oriented computer science, and computational neuroscience.

In artificial neural networks, there are techniques that have been developed from the mid-20th century, building on neurophysiology.

Thus, AI does not represent a closed field of research. Rather, techniques from different disciplines are used without having to have a connection with each other.

An important conference is the International Joint Conference on Artificial Intelligence (IJCAI), which has been taking place since 1969.

Knowledge-based Systems
Knowledge-based systems model a form of rational intelligence for so-called expert systems. They are able to respond to a users question based on formalized expertise and logical conclusions drawn from it. Exemplary applications can be found in the diagnosis of diseases or the search for and elimination of errors in technical systems.

Examples of knowledge-based systems are Cyc and Watson.

Pattern analysis and pattern recognition
Visual intelligence makes it possible to recognize and analyze images or forms. As application examples here handwriting recognition, identification of persons by face recognition, comparison of the fingerprints or the iris, industrial quality control and manufacturing automation (the latter in combination with findings of robotics) may be mentioned.

By means of linguistic intelligence, for example, it is possible to convert a written text into speech ( speech synthesis ) and vice versa a spoken text to written ( speech recognition ). This automatic language processing can be expanded so that meaning can be attached to the meaning of words and texts through latent semantic analysis ( LSI for short ).

Examples of pattern recognition systems are Google Brain and Microsoft Adam.

Pattern prediction
The pattern prediction is an extension of the pattern recognition. It represents, for example, the basis of Jeff Hawkins hierarchical temporal memory.

Prediction is not just one of the things your brain does. It is the primary function of the neocortex and the foundation of intelligence.

Prediction is not just one of the things your brain does. It is the main function of the neocortex and the foundation of intelligence.

– Jeff Hawkins : On Intelligence
Such systems offer the advantage that z. B. not only a specific object in a single image is detected (pattern recognition), but also can predict from an image series, where the object will be next.

Robotics
The robotics deals with manipulative intelligence. With the help of robots can be about dangerous activities such as the mine search or always the same manipulations, such as. B. may occur during welding or painting, be automated.

The idea is to create systems that can understand the intelligent behaviors of living things. Examples of such robots are ASIMO and Atlas.

Modeling by the force of entropy
Based on the work of the physicist Alexander Wissner-Gross, an intelligent system can be modeled by the entropy force. Here, an intelligent agent tries its surroundings (state X 0, by an action (force field) F to influence) to the greatest possible freedom of action (entropy S ) in a future state X to achieve.

Artificial Life
AI overlaps with the discipline Artificial Life ( Artificial Life, AL) is seen as a parent or as a sub-discipline. AL has to integrate their insights because cognition is a core property of natural life, not just of humans.