Publications

Artificial Intelligence (AI) has recently made breakthroughs in very different areas of application. This progress has been made possible by advances in algorithms, particularly in the case of Machine Learning (ML) and Deep Learning (DL), coupled with the availability of very large volumes of data (Big Data) and advances in fast, parallel computing (Kersting, Tresp 2019). Applications that just a few years ago seemed to be far out of reach are now already – or are soon to become – part of our daily lives.

A safer, more flexible and more economical way to get from A to B by road, rail or water – Artificial Intelligence (AI) can play an important part in achieving this vision. Indeed, AI-based assistance systems help make transport systems more intelligent and futureproof. This is made possible by the interplay of sensors, cameras and intelligent infrastructures and platforms that capture, manage and share traffic data. Increasingly sophisticated Machine Learning (ML) processes are being used to process captured data and derive operations to be executed either by people or by the systems themselves.

Whether in prevention, early diagnosis or selecting the ideal treatment, Artificial Intelligence (AI) and Machine Learning (ML) could soon be playing a big part in ensuring that people receive better and more personalised medical care. There is a whole variety of ways that self-learning systems could be put to use in the medical practices and hospitals of the future. For instance, doctors could use AI systems across the board to help them evaluate imaging procedures, thereby obtaining more accurate diagnoses. By using networked data, self-learning systems could derive proposals for suitable preventive approaches or treatments, thus helping medics and patients make important decisions.

Whether exploring terrain that is difficult to access or carrying out rescue operations, the use of Artificial Intelligence (AI) promises to provide helpful solutions in hostile-to-life environments. Mobile robots and assistance systems that adapt to changed situations without needing to be re-programmed can effectively support human beings with activities in hazardous environments. These activities could include fires, disaster control and deep-sea maintenance work, for example. Self-learning systems like these reduce risks for the personnel involved, speed up responses in time-critical situations and plug capability shortfalls in situations where humans are not yet able to respond adequately. In doing so, they deliver valuable social benefits. At the same time,
AI-based systems offer huge potential for research and the economy. Thanks to their ability to learn, they make operations in hazardous or poorly accessible environments much more cost-effective than manned missions – or even possible in the first place. There are, however, still a number of technical challenges that need to be met when it comes to deploying self-learning systems in hostile-to-life environments. These include ensuring long-term autonomy and autonomous learning in unknown environments. Another important area involves shaping the interaction and cooperation between autonomous robots or assistance systems and people.

Artificial Intelligence (AI) is already being used far more widely today than we might initially expect, and the associated potential for discrimination is not always obvious. Although people themselves are guilty of unjustifiable discrimination at times, they perceive the decisions taken by computer programs and software solutions to often be factual, objective and neutral. However, in reality, AI-based systems sometimes make decisions that are problematic, discriminatory or that draw distinctions without good reason. Many software systems explicitly or implicitly comprise a set of social rules for controlling behaviour, whether in the form of regulations, transactions and coordination, or access and usage rights. First and foremost, they are an effective technical means of putting systems of rules into practice. Consequently, self-learning systems have the potential to not just adopt pre-existing discrimination, but even to enhance it.

Artificial Intelligence (AI) is a catalyst for the processes of digital transformation that are sweeping through society and the world of work. AI technologies not only impact on the requirements that people have to meet and the skills they need to have, but also on the types of activity they undertake, the places they work and the way their work is organised, both within individual companies and across the entire labour market. Furthermore, Artificial Intelligence is changing the relationship between people and technology and making new types of human-machine collaboration possible. Shaping these profound processes of change means identifying ways to achieve a future (working) world that utilises Artificial Intelligence, striking a balance between safe workplaces and trained staff, and creating work that is rewarding and centred on people.

We are living in the golden age of Artificial Intelligence1 (AI). Continuing progress in algorithm development, particularly in Machine Learning2 (ML) and Deep Learning (DL), combined with the availability of huge data sets and advances in rapid, parallel computing have helped deliver breakthroughs in diverse fields of use. Applications that just a few years ago seemed to be plucked from the realms of science fiction are now already – or are soon to become – part of our daily lives. Knowledge of unimaginable breadth and astonishing depth is becoming accessible at the click of a mouse, voice-controlled assistance systems are helping us in many aspects of life, image recognition systems have achieved near-human performance levels, autonomous vehicles are increasingly becoming a reality, business models are changing rapidly and personalised medicine is supporting optimum and personalised treatment.

Technologies based on Artificial Intelligence (AI) are increasingly permeating all spheres of life. The ways in which they can improve the security of IT systems, together with the security of AI systems themselves, are essential for enabling citizens, businesses, politics and public authorities to reap the benefits of advancing digitalisation.

Intelligent virtual assistants that can answer straightforward questions or carry out simple instructions are already to be found in smartphones, tablets and millions of households. As yet, however, these systems are not capable of mastering the complex daily work involved in journalism, administration or consulting, for example, as they still cannot process huge volumes of information, handle different subjects, sources and means of communication or contact a variety of different people. In a few years, self-learning assistance systems that are intuitive to use will support knowledge workers in their everyday tasks.

Manufacturing companies already use a range of robotic systems – from gripping arms to lightweight robots – that automate factory processes and make the jobs of the people working on the assembly line easier. Each of these machines takes on strictly defined repetitive production steps, requiring complicated modifications whenever any changes are made, and they are therefore primarily suited for mass production from a financial perspective. They can only be used with the support of comprehensive expert knowledge. In the future, humans will direct robots in factories and, depending on current demand, teach them new skills during the production process. These robotic tools with learning capability will work safely hand-in-hand with the people on the assembly line.

There are already more than 1,300 offshore wind turbines dotted along Germany’s coastline, with more currently being built. Their foundations reach down to 50 metres below the water’s surface. This presents a challenge during inspections, servicing and repairs, making work on underwater structures elaborate, expensive and dangerous for the divers involved. Within a few years, self-learning robotic assistance systems could help humans to inspect, service and repair offshore turbines and other underwater infrastructures. These autonomous underwater vehicles (AUVs) deploy Artificial Intelligence methods.

In just a matter of years, self-learning systems will be able to provide rescue workers with effective support during operations in life threatening environments. Whether remote-controlled or autonomous, these systems can undertake a whole range of tasks – from risk prevention and defence measures to repairing damage and providing emergency aid.

Cancer is the second leading cause of death in Germany after cardiovascular diseases. A tumour in the lung is one of the most common types of cancer, with only around one in five patients surviving the first five years after diagnosis. In the future, doctors will be able to draw upon Artificial Intelligence (AI) to combat lung cancer – from the initial screening and diagnosis to treatments and follow-up care. For patients, this will open up new, personalised and highly effective treatment options that will significantly improve their prognosis.