Many of the fantasies of science fiction have become commonplace and ordinary. The cell phones in Star Trek, Asimov's positronic brain, the seashell radios of Fahrenheit 451 - these are things that are now everyday parts of us.
Rather than seeing technology as a utopia, a dystopia or a fantasy, we have turned it into a tool. Rather than saying that we have reached the ultimate technology and enough is enough, these developments have shown us that many more things are possible. Arthur C. Clark's law that "a sufficiently advanced technology is indistinguishable from magic" is now pushing us towards even more fascinating things.
One of these fascinating things is Artificial Intelligence. - Artificial intelligence that we watch developing with "Machine Learning" like the children of humanity rather than the monsters we fear like Frankenstein and Terminator.
In the accumulation and transfer of knowledge to support this development, Industry 4.0 allows us to keep the records of digital history after the records of oral and written history of humanity.
Machine learning enables predictions to be made based on large amounts of data. This branch of artificial intelligence is based on pattern recognition and is capable of acquiring knowledge independently of experience.
Today, large data centers and enormous storage capacities make possible what for years were believed to be distant concepts. Two branches of AI - machine learning and deep learning - use the possibilities of this massive data to optimize processes, find new solutions and gain new insights.
Every organization, from small and medium-sized companies to large international corporations, collects data that they can use. With software, this data is combined and evaluated to make predictions. Machine learning recognizes features and relationships and uses algorithms to derive generalizations from them.
With the help of properly analyzed customer, registration and sensor data, new solutions can be found and processes can be made more efficient. In addition to masses of data, this requires an IT infrastructure tuned to AI processes and machine learning workloads. The precise tasks of machine learning systems are clearly defined: recognizing patterns and drawing conclusions from them. The findings can then be used.
In a smart factory, production processes are interconnected - machines, interfaces and components communicate with each other. Large amounts of data can be collected to optimize the production process. Big data supports process optimization, for example using image analysis and image recognition.
In production plants, intelligent systems identify objects on conveyor belts and can sort them automatically. Such systems are also used in quality control: They recognize product defects, such as whether the product is the wrong color.
Companies today use machine learning in maintenance and support services. Thanks to sensors, artificial intelligence helps capture the energy consumption of individual machines, analyzes maintenance cycles and then optimizes them in the following phase. Operating data shows when a part needs to be replaced or when a defect is likely to occur. As the amount of data increases, the system gets better at optimizing itself and making more accurate predictions.