Digitalisation – from AI to Industry4.0

Digitalisation is changing materials science. Modern digital technologies let researchers collect and analyse data on materials in different environments. Artificial intelligence is a key driver of this change. In combination with technologies such as machine learning, deep learning and big data, it is not only changing the way we live and work, but also how we communicate with each other and how companies organise their processes.  

Artificial intelligence: theory and development of computer systems

Artificial intelligence is the development of computer systems that can do things that people used to do. This includes seeing, thinking, speaking and understanding different languages. AI research has grown a lot in the last few decades. Machines can now do things that needed human help before. AI can even outperform humans. AI can now learn, adapt and make decisions without instructions. At present (2024), artificial intelligence cannot yet control creative processes or answer complex questions, such as ethical issues. For such questions, AI lacks the self-awareness to see itself as a human being.  

Machine learning: the ability to adapt without explicit instructions

Machine learning (ML) is a sub-area of artificial intelligence. It enables computers to learn from data and improve themselves as a result. In contrast to traditional software programming, in which the computer works precisely according to fixed instructions, in machine learning it learns independently by analysing data. Computers generally use algorithms and statistical models to answer questions. In machine learning, these methods are then continuously refined and adapted during the learning process in order to identify patterns and make predictions or decisions based on the learnt data. Today, machine learning is used in a wide range of applications, from financial analysis to personalised advertising and medical diagnoses. The ability of systems to continuously improve based on experience and data is a crucial factor in the further development of AI technology.  

Deep learning: a revolutionary method of machine learning

Deep learning is a special approach within machine learning. It is based on artificial neural networks. These networks are composed of multiple layers, each extracting increasingly complex features from the input data. Deep learning can process large and complex amounts of data and recognise patterns, in a way that is similar to the way the human brain processes the world. This technology is particularly effective for tasks such as image and speech recognition, as well as for processing text and other unstructured data. This technology is the basis for many modern AI applications, from self-driving cars to voice assistants.  

Generative AI

Generative AI is a special application of deep learning that independently creates content such as text, images or code from input. It recognises patterns in data and generates output without the need for explicit instructions. To do this, it is trained with large data sets and uses a combination of supervised and unsupervised learning methods.  

Big data: analysing huge data sets

Big data refers to the processing and analysis of large sets of data that cannot be processed using traditional methods due to their size and complexity. Even humans cannot analyse large amounts of data without the help of machines. Modern technologies and powerful algorithms can be used to gain valuable insights from these huge amounts of data. For example, large amounts of medical data help to develop new drugs and treatment methods.  

Ontology: structured knowledge representation

Ontology is a traditional term from philosophy and stands for the study of being. In computer science, attempts are made to represent the real world as data records in order to create a digital being. This is why the term was taken over and adapted. In computer science and data management, an ontology refers to a structured and normally machine-readable representation of concepts and their relationships to one another. The aim is to create a digital world view that not only describes objects and processes, but also considers the interactions and relationships between objects. These descriptions are used to formalise knowledge in a specific area and enable the exchange of information. An ontology defines which concepts exist and how they are linked to each other. The ontology is often used in artificial intelligence to provide systems with structured access to knowledge. It plays an important role in semantic web technology. This aims to enable machines to better interpret data and link it together in a meaningful way. Ontology is the basis for digital twins.  

Automation: efficiency via intelligent systems

Automation describes the transfer of tasks to machines and computer systems so that they can carry out, control and regulate processes independently. Industry uses automation to increase efficiency and precision in production. The implementation of robots, machines and software solutions makes production processes faster, more cost-effective and requires less human intervention. One example of automation is production lines. Here, robots take over certain work steps that were previously carried out manually. This development goes hand in hand with the introduction of intelligent systems that are able to react to changes in the production environment and adapt their actions accordingly.  

Industry 4.0: The digital transformation of production

Industry 4.0 refers to the fourth industrial revolution. It is characterised by the digitalisation and networking of machines, processes and systems in production. The intelligent networking of production facilities with modern information technology enables more efficient, flexible and personalised production. In Industry 4.0, production processes are optimised by the Internet of Things (IoT), artificial intelligence and big data. Machines can communicate with each other in order to control each other and react to changes in the production environment. This leads to greater efficiency, better quality and lower costs in production. The effects of Industry 4.0 are far-reaching and affect not only production, but also the world of work, logistics and the entire economic system.  

Summary

The development of artificial intelligence, machine learning and deep learning is changing the way we interact with computers and machines. The use of big data, automation and ontologies is creating new opportunities to optimise processes and make systems more intelligent. In industry, this digital transformation is leading to increasing automation and networking, which forms the basis for Industry 4.0 and digitalisation. This digital transformation is making materials science more efficient, innovative and sustainable, leading to advances in a wide range of fields, including aerospace, medical technology and sustainable energy production.   Further information:

• J. Kimmig, S. Zechel, and U. S. Schubert, „Digital Transformation in Materials Science: A Paradigm Change in Material’s Development,“ Adv Mater, vol. 33, no. 8, p. e2004940, Feb 2021