The age of autonomics

The role of computer scientists could change as autonomous systems gain in importance. These systems require a high level of intelligence and should be able to operate independently and make decisions to overcome challenges, such as in the energy transition or industrial manufacturing. A new discipline could be "autonomics", which combines various technical areas and could provide an important basis for ensuring quality in autonomous systems.

Podcast Episode: The age of autonomics

In this episode, I talk to Prof. Peter Liggesmeyer about the future of software quality and the development of autonomous systems. We discuss whether computer science is sufficient for the challenges ahead or whether new skills and disciplines will be needed. Prof. Liggesmeyer brings exciting perspectives, including the term "autonomics" - a discipline that deals with the design of autonomous systems. He explains how these systems will play an increasingly important role in areas such as Industry 4.0 and the energy transition.

"I am convinced that we will see the establishment of a new discipline in the next few years. I would like to call it autonomics, in reference to computer science." - Peter Liggesmeyer

Prof. Dr.-Ing. habil. Peter Liggesmeyer is the director of the Fraunhofer Institute for Experimental Software Engineering IESE in Kaiserslautern and holds the Chair of Software Engineering at the Department of Computer Science at the Rhineland-Palatinate University of Technology Kaiserslautern-Landau. From 2014 to 2017, he was President of the German Informatics Society (GI e.V.). He is the scientific spokesperson for the “Industry 4.0 Research Advisory Board”. In 2022, he was elected to the Board of Directors of the “Gesellschaft Deutscher Naturforscher und Ärzte GDNÄ” as a representative of mathematics/computer science. The “Feldafinger Kreis” accepted him as a member in 2022. In 2023, he was awarded the “German Prize for Software Quality” for his research work.

Highlights der Episode

• The discipline of autonomics will be crucial for the future of software development.
• Autonomous systems require a combination of computer science, electrical engineering and mechanical engineering.
• Mass-customized products are revolutionizing manufacturing and reducing costs.
• The quality of autonomous systems must be ensured through continuous testing during operation.
• Trust in autonomous systems is created through transparent and reproducible test processes.

Computer science and autonomics

Introduction

Autonomous systems are a central part of future technology, already being applied in many areas of the modern world. In this article we will cover the following topics:

• The importance of autonomous systems in today's society.
• The difference between autonomous systems and conventional technologies.
• What you will learn from this article.

Autonomous systems have the potential to transform various industries and create new opportunities for innovation. Let's dive into the world of autonomous systems and find out what makes this future technology so exciting.

The role of autonomics as an interdisciplinary discipline

Autonomics is a promising discipline that combines different fields of expertise to drive the development of autonomous systems. Computer science, electrical engineering and mechanical engineering are particularly important for success:

Computer science

Computer scientists play a key role in the programming and software development of autonomous systems. They ensure the smooth functionality and interaction of the systems.

Electrical engineering

Electrical engineers are essential for the hardware components of autonomous systems. They ensure that the technological basis functions optimally and meets the requirements.

Mechanical engineering

Mechanical engineers contribute their expertise to the design of physical structures of autonomous systems. These must be precisely planned and realized to ensure efficiency and safety.

An interdisciplinary education is essential for prospective experts in the field of autonomics. By understanding and collaborating between these disciplines, innovative solutions can be developed to meet the requirements of the modern world.

Core competencies and challenges in the development of autonomous systems

The development of autonomous systems requires an in-depth understanding of various technical principles. Computer scientists in the field of autonomous systems require knowledge of hardware fundamentals in particular in order to safely design the interfaces between software and the physical environment. A sound knowledge of microelectronics is helpful, but not essential, as the discipline focuses on integration and application.

Mathematical Modeling

Mathematical modeling plays a central role in the realization of autonomous systems. It makes it possible to develop complex regulation and control algorithms that can precisely predict and adapt the behavior of the systems. Complex mathematical methods are often part of research, while practical applications tend to rely on comprehensible models.

Challenges posed by complexity

Challenges arise due to the high complexity of autonomous systems, which comprise different levels of hardware and software. Safe interaction with the environment requires continuous adjustments and optimizations - both during development and during operation. AI and test automation play a decisive role here, as they help to implement these adaptations efficiently.

Security vs. availability

Another aspect is the balance between safety and availability. Autonomous systems must function reliably, even if they also have to react flexibly to new situations. These requirements lead to an area of tension in which careful compromises must be found to ensure functionality, efficiency and safety.

To successfully master such challenges, agility could provide a suitable answer. Agility provides a framework for creatively mastering the challenges of the future and promoting both personal and team-oriented development.

Responsibility for technology adoption

In addition, it is important to take responsibility for technology adoption and use it as a tool to address societal challenges. This is further explained in the article "Who's the boss here?".

Opportunities and risks of autonomous systems: finding the right balance

Autonomous systems open up a wide range of potential that will significantly shape the age of autonomy.

Opportunities

• Increased efficiency in industry: Autonomous control and optimization of production processes allows resources to be used more efficiently and costs to be reduced. The ability to independently monitor and adapt complex processes leads to higher productivity and less downtime.
• Improved traffic safety: Autonomous vehicles can minimize accidents through precise sensor technology and fast reaction times. Continuous analysis of the environment makes it possible to detect hazards at an early stage and react accordingly.

Risks

The safety of autonomous systems is a key challenge. Risks arise in particular from the complexity of the systems and possible malfunctions.

• Risk assessment: Careful assessment of potential hazards is essential in order to avoid undesirable consequences. This includes the consideration of technical errors as well as ethical issues.
• Responsible development: Safety aspects must already be integrated into the development process. Continuous testing, simulations and adaptive mechanisms help to make systems robust in the face of faults.
• Regulatory measures: Legal framework conditions create standards for the approval and operation of autonomous systems. These help to build trust among users and society.

The interplay of opportunities and risks requires a balanced approach that combines innovation with care in order to responsibly exploit the full potential of autonomous technologies.

Mass customization and Industry 4.0: drivers for the future of autonomous production

The fusion of mass customization and Industry 4.0 represents a significant advance in manufacturing. Mass customization combines the advantages of mass production with the ability to customize products. Industry 4.0 technologies enable this combination through intelligent networking and autonomous systems that can respond flexibly to customer requirements.

What is Industry 4.0?

Industry 4.0 is based on a network of cyber-physical systems that exchange and process data in real time. This intelligent interaction makes it possible to dynamically adapt production processes to individual requirements without sacrificing efficiency or quality. Autonomous systems take on tasks such as controlling, monitoring and optimizing production.

Examples of customized products

Examples of customized products that are efficiently manufactured by autonomous systems can be found in various industries:

• Pharmaceutical industry: Production of personalized cancer drugs that are precisely tailored to the patient.
• Automotive industry: Vehicles with individual equipment variants and functions that are flexibly integrated during production.
• Electronics manufacturing: Adaptation of devices to specific customer requirements while using standardized components.

These developments open up new opportunities for companies to manufacture customer-oriented products in large quantities while remaining competitive. Autonomous systems form the backbone of this transformation by mastering complexity and integrating individual customization into the mass production process.

The role of software testing

An important aspect of this transformation is the role of software testing. According to the latest software testing trends, AI and agile approaches are significantly shaping the future of testing. These trends are also visible in the test organization of the next decade, where AI and new models are shaping the industry and will have a greater impact on the distribution of roles between humans and machines.

The road to the smart factory: automation, optimization and quality assurance in real time

Autonomous systems enable a significant cost reduction in automation as they not only automate processes, but also continuously ensure the availability and reliability of the systems. Companies benefit from an intelligent factory in which processes are controlled and optimized autonomously. Automation reduces manual intervention and minimizes sources of error, leading to more efficient production processes.

The challenge of quality assurance

The quality assurance of autonomous systems poses a particular challenge. In contrast to conventional systems, autonomous systems require dynamic monitoring during operation in order to be able to react to changes in real time. This real-time quality management ensures that safety and functional requirements are met even under variable conditions.

Optimization compromises during development

A central aspect is the optimization trade-offs that must be made when developing autonomous systems. For example, increasing safety can come at the expense of availability. Such conflicting goals require intelligent solutions that strike a balance between different system properties. Autonomous factories use advanced algorithms to determine and adapt optimal operating conditions.

Prediction of safe operating conditions

The ability of autonomous systems to provide themselves with a valid image of their own states is crucial for predicting safe operating conditions during adjustments or malfunctions. This ensures continuous system availability alongside high safety standards - a basic prerequisite for the success of smart factories in Industry 4.0 environments.

In this future of testing, it will be necessary to combine soft skills and automation to meet the increasing requirements in an AI-dominated world.

Agile development processes for trustworthy autonomous systems: Continuous certification and user acceptance

Why continuous safety certification is essential for the operation of autonomous systems

Autonomous systems operate independently and must be constantly tested for safety. Continuous certification ensures that the systems meet the required standards. Risks such as data manipulation or failures can be detected and rectified at an early stage.

How agile methods can be used to increase user confidence in these technologies

Agile development enables rapid adaptation to new requirements and changing threats. User needs can be better understood and implemented through regular feedback. Transparent processes and open communication help to build acceptance and trust.

Future-oriented study programs in the field of autonomics: Artificial intelligence and data science as key disciplines

Autonomics degree programs address the increasing demand for specialists who can design and develop complex autonomous systems. The curricula of these innovative training programs are specifically geared towards the needs of the modern job market.

Important components include:

• Artificial Intelligence (AI): Teaching methods for intelligent information processing and decision making in autonomous systems.
• Data Science: Techniques for analyzing and using large amounts of data to provide autonomous systems with relevant information.
• Interdisciplinary content: Combination of computer science, control engineering, electrical engineering and mechanical engineering to promote a comprehensive understanding of system components.

The focus is on training future experts not only in technical fundamentals, but also preparing them for the dynamic challenges of autonomous systems. This produces specialists who are both algorithmically adept and able to think systemically.

The integration of AI and data science into the Autonomics degree course forms the foundation for a future-proof qualification in this growing field of technology.

Conclusion: The future is autonomous - an outlook into the age of autonomics

• The importance of interdisciplinary cooperation for the success of autonomous systems is becoming increasingly clear in times of technological change.
• By combining expertise from different disciplines such as computer science, electrical engineering and mechanical engineering, innovative solutions can be developed.
• In the age of autonomy, the self-sufficiency of systems will become increasingly relevant, as they will be able to act independently and intelligently to accomplish complex tasks.