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Artificial intelligence (AI) can be used to make operational processes more efficient, improve products or services and develop new business models. A current white paper from Plattform Lernende Systeme explores the question of how AI can be used successfully to create added value for companies, employees and customers. Using practical examples from various industries, the authors show how AI projects can address economic and social goals in equal measure and what role interdisciplinary collaboration plays in this.

Technological advances in machine learning and robotics are opening up more and more new possibilities: Robots can increasingly be used in social environments and take on tasks that were previously difficult to realise due to their complexity. In interaction with humans - through simple demonstration, improvement and feedback, robots learn to perform tasks in changing environments. In this interview, Jürgen Beyerer explains the potential this opens up for future areas of application and how technology can be used safely and confidently in social environments. He is Professor of Computer Science at the Karlsruhe Institute of Technology (KIT) and Director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. He is co-head of the working group Learning Robotics Systems in Platform Lernende Systeme.

In the coming years, Artificial Intelligence (AI) will change business processes in many areas and influence our everyday working lives. Its use can have a positive impact on the quality of products and services as well as on employees if automated processes create new scope for higher-value activities in everyday working life. In order for value creation through AI to be successful in its entirety, the perspective of the users must be taken into account in the implementation of AI in addition to the economic factors. Irene Bertschek explains the opportunities and challenges associated with this in an interview. She is head of the ‘Digital Economy’ research department at the ZEW - Leibniz Institute for European Economic Research in Mannheim and Professor of ‘Economics of Digitalisation’ at Justus Liebig University Giessen as well as a member of the ‘Innovation, Business Models and Processes working group of Plattform Lernende Systeme.

Artificial Intelligence (AI) can ease the burden on the legal system and fundamentally contribute to fairer and more transparent judgements and proceedings. On the other hand, there are currently still qualitative weaknesses of AI systems in legal applications as well as ethical and legal concerns. A current white paper from Plattform Lernende Systeme provides an overview of possible applications of AI systems in the context of judicial decisions and addresses design options for the successful use of AI in law firms and courtrooms.

In the super election year 2024, deepfakes and co. are attracting a lot of attention. The concern is that fake images, films and audio created with the help of generative Artificial Intelligence (AI) could influence individual voting decisions. Their actual influence on the outcome of elections has not yet been proven. Nevertheless, experts from Plattform Lernende Systeme are calling for AI to be prevented from influencing political processes and opinion-forming. This is because the mere attempt to disinformation undermines trust in democratic institutions. A current white paper highlights the significance of generative AI for elections and democracy using concrete and possible examples. It recommends, for example, proof of origin for AI-generated content and the strengthening of AI-related media skills.

The shortage of skilled labour is already being felt in many industries and professions. The situation will become even more acute when the baby boomers retire. Artificial Intelligence (AI) can counteract the shortage of skilled labour. With the help of technology, employees can be relieved and hidden reserves can be mobilised for the labour market. In this interview, Christoph M. Schmidt explains exactly how this can be achieved and what challenges are associated with the use of AI in companies and administration. He is President of the RWI – Leibniz Institute for Economic Research and a member of the Future of Work and Human-Machine Interaction working group of Plattform Lernende Systeme.

The latest successes in the development of generative AI are based on "more and more" centrally processed data, upscaled neural networks and computing capacities. This is accompanied by data protection issues, costs and increasing resource consumption. Research and industry are therefore currently pursuing a different approach in parallel: the decentralisation of AI architectures along the lines of edge computing - known as edge AI. The aim is not to process and analyse data for AI systems in the cloud, but rather as close as possible to where it is generated - i.e. close to the user.

Artificial Intelligence (AI) is a key technology for the future viability of our economy and society. However, AI systems also harbour risks. With the AI Act, the European Union (EU) has now adopted the world's first transnational set of regulations for the safe and trustworthy use of AI. In its new issue of the AI Compact series, Plattform Lernende Systeme explains in a concise and clear manner what exactly is set out in the AI regulation, what opportunities and challenges are associated with the law and how the regulations are now being implemented.

In the run-up to democratic elections, experts have increasingly warned against attempts to influence voting decisions with targeted misinformation in recent years. While disinformation is not a new phenomenon in political debates and conflict situations, the use of artificial intelligence (AI) can exacerbate the threat. This puts pressure on the free formation of opinion as a pillar of democracy.

Artificial Intelligence can accelerate the development of medicines. Dagmar Krefting (member of the Health Care, Medical Technology, Care working group) explains exactly how the use of AI can improve drug research and what needs to be done to ensure that the population can benefit from cost-effective medicines.

Large AI language models that generate text or program codes can increase the competitiveness and innovative strength of the German economy enormously. However, the most advanced generative AI models currently come from the USA and China - and mostly do not meet the ethical and legal requirements discussed in Europe. A current white paper from Plattform Lernende Systeme uses practical examples to illustrate the opportunities and challenges of language models and analyzes the conditions under which companies can leverage the potential of the technology with confidence and legal certainty. The experts recommend the creation of an open, commercially usable data set in German that complies with European values and rules and supports the development of language models in Germany.

Artificial Intelligence (AI) can make an important contribution to a sustainable economy and society - be it through optimized business processes or products that promise personalized healthcare, safer road traffic or better educational opportunities. At present, however, companies are still reluctant to use AI. The frequently cited reason is that the data protection hurdles appear to be too high. A current white paper from Plattform Lernende Systeme shows technical approaches that ensure privacy and data protection in the development and use of AI systems. The authors call for legal recognition of the procedures in order to strengthen legal certainty for companies using AI.

Whether as an assistance system in the doctor's office, in a rollator that prevents falls, or as software for evaluating X-ray images - Artificial Intelligence (AI) can improve healthcare. However, if medical AI systems are faulty, people can be harmed. In this interview, Dr. Abtin Rad explains the risks associated with the use of AI in medicine, how these can be controlled, and what contribution the EU's AI Regulation can make to safe AI applications. He is Global Director Functional Safety, Software and Digitization at TÜV SÜD and a member of the "Health Care, Medical Technology, Care" working group of Plattform Lernende Systeme.

Artificial Intelligence (AI) supports employees in the workplace. AI technologies can enable people with disabilities to perform new activities and allow them to participate in working life in a more self-determined and equal manner. However, the opportunities offered by the technology can only be exploited if the right framework conditions are in place in the world of work: These include (digitally) accessible workplaces, a corporate culture that promotes diversity, and consideration of people with impairments as early as the research and development stage. A current white paper from Plattform Lernende Systeme illustrates the potential of AI for an inclusive working world using concrete practical examples and shows ways in which AI-supported participation can succeed.

Large language models such as ChatGPT or PaLM 2 promise powerful AI solutions for business and society. However, their results are not always comprehensible, often flawed, or fraught with bias. Experts from Plattform Lernende Systeme believe that research in Germany is in a good position to tackle the technical challenges of AI technology, develop it in line with European values and thus exploit its potential for Germany and Europe. A current white paper from the platform identifies the most urgent research needs and options for action.

Not only in factories, but also in classic office jobs or social professions, AI-based chatbots and assistance systems are increasingly providing support at work. Eva Bittner, professor of socio-technical system design at Universität Hamburg and member of the AI expert network Plattform Lernende Systeme, and her team are investigating how this affects work processes and employees. She explains why the topic fascinates her, how humans and AI systems cooperate optimally and what challenges exist on the occasion of the nationwide Girls' Day.

Artificial Intelligence (AI) has made great strides in recent years. Today, AI systems can already be found on social media platforms, in search engines and as recommendation providers in online retail - even in sensitive areas such as medicine or in autonomous driving cars, the self-learning software is involved in decisions. AI can support us in our everyday lives, but if it is maliciously manipulated, it can cause great harm. Prof. Dr. Ahmad-Reza Sadeghi explains the IT security challenges associated with the use of AI and how AI systems and the underlying data can be protected against attacks. He is head of the System Security Lab at the Technical University of Darmstadt and a member of Plattform Lernende Systeme.

ChatGPT is currently moving the public. The text bot is one of the so-called big language models that are celebrated as a breakthrough in AI research. Do big language models promise real progress or are they just hype? How can voice assistants be used - and what conditions do we need to create in Europe so that business and society benefit from them? Volker Tresp answers these questions in an interview. He is a professor at Ludwig Maximilian University in Munich with a research focus on machine learning in information networks and co-leader of the "Technological Enablers and Data Science" working group of Plattform Lernende Systeme.

What is the state of Artificial Intelligence (AI) research in Germany? To what extent is the transfer from science to industry successful? Where are skills in AI being taught and how widespread are they among employees? The new AI monitoring of Plattform Lernende Systeme provides key figures on this - and thus shows the status quo as well as development potentials in research and transfer of AI in Germany.

Artificial Intelligence (AI) can support sustainable agriculture: The AI-supported field robot from the University of Hohenheim can distinguish crops from weeds using image recognition and weeds the latter independently in the field - without the use of pesticides. At this year's Digital Summit, Plattform Lernende Systeme presented the agricultural robot to German Chancellor Olaf Scholz as well as Federal Minister of the Interior Nancy Faeser and Federal Minister of Transport Volker Wissing.

Low-emission mobility, climate-friendly energy supply, clean industrial production or optimized circular economy - Artificial Intelligence (AI) can contribute to climate protection in many ways. At the same time, the development and deployment of AI systems is often itself accompanied by high resource consumption. Where the technology can best unleash its potential and what needs to be done to keep the ecological footprint of AI applications low is explained in an interview by Oliver Zielinski, head of the Competence Center AI for Environment and Sustainability (DFKI4planet) and member of Plattform Lernende Systeme.

Artificial Intelligence (AI) supports people in medicine, mobility and everyday work. The basis for AI systems is training with data – often also personal information. The method of distributed Machine Learning (ML) can improve data protection in the development of AI applications because the data used is not bundled centrally, but remains on the end devices and thus with the users. However, this can also create new points of attack for cybercriminals. A concise overview of the potential and risks of distributed Machine Learning is provided in the first issue of AI at a Glance, a new publication series of Plattform Lernende Systeme.

Artificial Intelligence (AI) can help companies to operate in an ecologically compatible, socially just and successful manner. On the other hand, the use of AI systems can also run counter to operational sustainability goals, for example due to the high resource consumption of the technology itself. With its new publication, Plattform Lernende Systeme provides companies with strategic recommendations and numerous practical examples that show how the sustainable use of AI in the company can succeed. The experts advise strategically linking innovation and sustainability management in the company. This requires a comprehensive data infrastructure and digital skills.

AI systems can help find tumors, control insulin pumps or assist during operations. But on their way from research to healthcare, AI innovations in Germany have to overcome many hurdles. Karsten Hiltawsky explains the challenges facing manufacturers of medical AI products and what needs to change so that people benefit more quickly from the innovations. He is Head of Corporate Technology and Innovation at Dräger and head of the Health Care, Medical Technology, Care working group of Plattform Lernende Systeme.

Artificial Intelligence (AI) is changing everyday work in offices and factories. It is not only in industrial production that intelligent robots support people with heavy tasks. In the near future, AI-based assistance software will also be able to take over monotonous routine tasks and employ people at their desks. At the same time, its use poses challenges for all those involved - for example, in the handling of personal data or the humane design of working conditions. A fictitious application scenario from Plattform Lernende Systeme shows how employees benefit from a so-called information butler and how its introduction in the company succeeds.

Artificial Intelligence (AI) is already making our lives and work easier today - as a driving assistance system in cars, as a control element in production or as a recommendation system for online shopping. For the future, self-learning algorithms are considered a key technology for whose development and application specialists are urgently sought. Many universities have already launched corresponding courses of study. On the occasion of the nationwide Girls' Day, Ute Schmid explains why girls and women in particular should concern themselves with Artificial Intelligence and what role models there are. She is Professor of Cognitive Systems at the University of Bamberg and a member of Plattform Lernende Systeme.

Artificial Intelligence (AI) can improve medicine and care. Commercially viable business models help to bring AI innovations to the breadth of the healthcare system. However, the financing and approval of AI applications poses major challenges for medical technology companies, many of which are medium-sized. A recent white paper from Plattform Lernende Systeme discusses new business models and innovation processes that take into account the specifics of AI. In order for patients to benefit from the opportunities offered by this key technology, new instruments for cost coverage and advice centers for small and medium-sized enterprises are needed, it says.

Artificial Intelligence is a helpful tool that can make people's work and everyday lives easier in a variety of ways. At the same time, AI systems, which are often highly networked and partly embedded in other systems, can also be used for illegal purposes. Protecting them from misuse is an important task. Using the example of digital image processing, Detlef Houdeau (Infineon AG and member of the IT Security, Privacy, Legal and Ethical Framework working group) outlines how AI algorithms help professional and amateur photographers to take better pictures - but can also be used with criminal intent to forge travel documents.

Artificial Intelligence (AI) can make an important contribution to a sustainable orientation of the economy and society. The use of the technology opens up new business models for companies that support the success of the energy transition, low-emission mobility or environmentally friendly agriculture, for example. However, the energy consumption of the AI systems themselves is often high. A current white paper by Plattform Lernende Systeme shows how sustainable business models can be created with the help of AI and identifies design options that can reduce the ecological footprint of AI systems. In addition to technological measures, the experts recommend a broad exchange of data between companies, research and public authorities.

The EU Commission wants to regulate by law what Artificial Intelligence may and may not do. In its draft AI law, the Commission has defined a catalogue of criteria that will be used in future to assess the risks, the so-called criticality, of an AI system. Depending on the level of risk, an AI system can be banned, must meet certain requirements before it is introduced to the market. Armin Grunwald, professor of philosophy of technology at the Karlsruhe Institute of Technology (KIT) and head of the Office of Technology Assessment at the German Bundestag (TAB), explains why the assessment of risks in Artificial Intelligence is particularly difficult.

Companies generally expect that the use of AI will place new demands on employees - and want to qualify skilled workers and managers accordingly. Above all, technical skills in dealing with AI systems are not yet sufficiently available in the companies. These are the results of a recent online survey of companies conducted by the Fraunhofer Institute for Industrial Engineering IAO and the University of Augsburg for Plattform Lernende Systeme. The authors of the study warn against underestimating the need for further training of the low-skilled. Employees across all job profiles need to be empowered to work with AI systems. Besides technical skills, it is also about social skills.

Artificial Intelligence is a powerful tool that can be used to improve processes and develop new products and services. Many things are technically possible. But with what goal do we want to use AI as a society? A broad socio-political discourse on this is necessary to create framework conditions within which technical applications can be successfully developed and implemented. Jessica Heesen explains in an interview how this can succeed. The philosopher and media ethicist heads the research focus on media ethics and information technology at the International Centre for Ethics in the Sciences and Humanities at the University of Tübingen as well as the working group "IT Security, Privacy, Legal and Ethical Framework" of Plattform Lernende Systeme.

Artificial Intelligence (AI) is considered a key technology and will change our everyday lives and work. Nevertheless, many citizens have only a vague idea of what is meant by it. The new web offering www.ki-konkret.de from Plattform Lernende Systeme provides a sound introduction to the topic - and explains in a clear way how AI works, where it is already being used today and in the near future, and what challenges it poses for society.

We also encounter Artificial Intelligence (AI) in democratic elections. AI-controlled social bots in social media or deepfakes aim to influence the election decision. On the other hand, AI systems can provide people with individual voting recommendations and assist in detecting fake news. Which AI applications can be used in elections and where their potential and challenges lie is examined by Plattform Lernende Systeme in a current white paper.

Robots have long been a reality in large factories today. In a few years, supported by Artificial Intelligence (AI), they will also be able to assist employees in assembly in small companies. The prerequisite is that collaboration with the learning machines is safe and that people retain motivating, self-determined activities. In a fictitious application scenario, Plattform Lernende Systeme takes a look at the future of industrial work. The interactive graphic illustrates how collaboration with AI-based robot systems will change and what needs to be done to ensure that employees benefit from the self-learning tools.

Artificial Intelligence (AI) is increasingly changing the way we live. AI systems are also being used in democratic elections. In the run-up to the 2021 Bundestag elections, Plattform Lernende Systeme organised a virtual background discussion in cooperation with the Science Press Conference on 24 June to discuss the opportunities, but also the risks, of AI in relation to elections.

Self-driving cars on the roads are only conceivable in a few years. Intermodal testing and learning is still underway. The trust that drivers place in AI-supported assistance systems is crucial for the acceptance of automated vehicles. In the application scenario 'On the Way to Intelligent Mobility' developed by Plattform Lernende Systeme, the interaction between the protagonist Carla and her automated vehicle works well. Dr Corina Apachiţe explains in an interview how trust is created and what challenges still exist. She heads the Artificial Intelligence department at Continental Automotive and is responsible for the overall technical strategy and operational implementation.

There is a need to catch up in the transfer of AI knowledge to marketable AI-based applications. In a recent white paper, experts from Plattform Lernende Systeme show how the exchange of research results and application knowledge between science and companies can succeed and identify design options. They recommend promoting the training and recruitment of AI talent, cooperation between educational institutions and industry, and suitable framework conditions in companies and universities.

More than four million people in Germany are currently dependent on nursing care, and their number will increase significantly in the coming years as a result of demographic developments. Some care tasks could be taken over by assistance robots in the future. Elisabeth André explains which tasks they are suitable for, to what extent their use is to the benefit of professionals and those in need of care, and what needs to be taken into account when developing care robots. She is Chair of Human-Centered Multimedia at the Interdisciplinary Institute of Informatics of the University of Augsburg. At Plattform Lernende Systeme, Elisabeth André heads the working group "Future of Work and Human-Machine Interaction"

Artificial Intelligence (AI) can make our everyday lives easier, traffic safer and healthcare better. But realizing this potential requires trustworthy AI systems that people are happy to use. Certification of AI can increase trust in the technology. Jörn Müller-Quade explains what specifics need to be considered when certifying AI and why not all AI systems need to undergo testing. The professor researches cryptography and learning systems at the Karlsruhe Institute of Technology. He is also head of the "IT Security" working group of Plattform Lernende Systeme.

Self-learning robots and AI systems can provide support in space, in the deep sea or in the event of disasters - by exploring unexplored terrain or carrying out work in dangerous regions. To do this, the technical systems must act as autonomously as possible, but at the same time always remain under the ultimate control of humans. Jürgen Beyerer explains in this interview how these requirements can be implemented and where there is still a need for research. He is a professor of computer science at the Karlsruhe Institute of Technology (KIT) and director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. At Plattform Lernende Systeme, he heads the "Hostile-to-life Environments” working group.

Artificial Intelligence is on everyone's lips, and Machine Learning is an important driver of current technological development. But how do intelligent computer systems learn to solve tasks independently? An eight-part video tutorial series from Plattform Lernende Systeme conveys this in a clear way. Using catchy examples and supported by modern graphics, three students explain the basics as well as applications of Machine Learning in the approximately five-minute clips.

Artificial Intelligence (AI) is already used in many industries. To exploit its economic and social potential, it is essential that people trust AI systems and the processes and decisions associated with them. Certification of AI systems can help to increase trust in the technology. It is important to choose the right level of certification in order to maintain the innovative strength of Germany as a business location. In a current white paper, Plattform Lernende Systeme shows when and according to which criteria AI systems should be certified and how an effective testing infrastructure can be designed.

Artificial Intelligence (AI) is the next stage of digitisation and promises great benefits for the economy and society. The basis for successful AI applications is data. In order to exploit the potential of Artificial Intelligence, comprehensive data management is needed to make the available data accessible in the first place. A current whitepaper of Plattform Lernende Systeme shows the importance of the interdisciplinary research branch Data Science as a key discipline for science as well as economy and names options for action. The experts suggest that knowledge of data management should also be taught in courses of study that are not related to IT and in schools and recommend an independent data infrastructure.

Medical assistance systems, software for fraud detection or the predictive maintenance of industrial plants promise great benefits. Co-Author Kai-Uwe Sattler, Professor of Databases and Information Systems at the TU Ilmenau, explains why it is important to have the "right" data, what is meant by data engineering and what skills data science experts need. He is a member of the working group "Technological Pioneers and Data Science" of the Plattform Lernende Systeme and co-author of the white paper "From Data to AI".

Artificial Intelligence (AI) can improve health care and support nurses and doctors in diagnoses and therapy decisions. Patient representatives see the use of AI-based assistance systems in medicine and nursing predominantly as an opportunity. This is the result of a qualitative survey, which Plattform Lernende Systeme presents in a current conference report. The majority of those questioned hope for more personalised treatment and a more comprehensive and faster diagnosis. The patient representatives name the misuse of their data and incorrect or discriminatory decisions made on the basis of AI systems as risks.

Artificial Intelligence promises many beneficial applications. The extent to which these are actually realised depends largely on whether people have confidence in the technology. What challenges arise for the responsible development and application of AI systems? According to which criteria should they be implemented? And what can companies do to apply AI without discrimination? Answers to these questions can be found in a guideline that was developed by the IT Security, Privacy, Law and Ethics working group of Plattform Lernende Systeme under the direction of Jessica Heesen.

The corona crisis accelerates digitization. Home offices, video conferences and learning platforms are part of our so-called "new normality". However, the challenges of digitization can also be viewed as if under a magnifying glass in the pandemic. How do we deal with the risks for data protection and IT security, for example? What impact will the pandemic have on our use of technology? And will we make greater use of Artificial Intelligence in our everyday lives? Experts from Plattform Lernende Systeme and acatech discussed these questions with citizens at the Political Academy Tutzing from September 9 to 10.

Applications of Artificial Intelligence (AI) are finding their way into more and more areas of life. At the same time, many people are still not quite clear about what is behind the technology. How can the functioning, applications, opportunities and challenges of Artificial Intelligence (AI) be successfully communicated to a broad public? This question was the focus of a web-talk to which the Bavarian Research Institute for Digital Transformation (bidt) also invited representatives of the Plattform Lernende Systeme on July 17, 2020.

From intelligent failure forecasts for production lines to data-based monitoring in greenhouses and AI-controlled hand prostheses - Plattform Lernende Systeme uses a total of 13 practical examples from SMEs and industry to illustrate in its current report how new business models can be developed from data. The study shows: Collaboration and the sharing of data, technologies and skills in businessnetworks will be essential for companies in the future if they are to remain competitive in a digitalised economy.

In the age of digitalisation, data are considered treasures, the value of which many companies are not yet aware of. Through Artificial Intelligence and Machine Learning, they can be made usable - even for small and medium-sized companies. Olga Mordvinova, founder and managing director of the AI start-up incontext.technology GmbH, explains how intelligent data analysis can support them in improving existing processes or developing new business models.

The use of Artificial Intelligence (AI) in the world of work fundamentally changes the division of tasks between man and machine. Learning AI systems can perform increasingly complex tasks independently and work hand in hand with the employees. A current whitepaper shows how the division of labour between man and AI can be shaped in the interest of the employees.

Artificial Intelligence has long since arrived in the everyday lives of many people. But what is AI anyway, what developments and applications can be expected and what questions does this raise for society? On the occasion of the nationwide Digital Day on June 19, the Plattform Lernende Systeme, acatech and the Bayerische Landeszentrale für politische Bildungsarbeit invited to a virtual advanced training for teachers on the topic of AI.

Artificial Intelligence (AI) is already used in many industries. In order to exploit its economic and social potential, it is essential to strengthen trust in AI systems and the processes and decisions associated with them. A possible key prerequisite for this is the certification of AI systems. The benefits it promises and the demands it makes in terms of technical implementation, public welfare and maintaining innovative strength are outlined by experts from the Plattform Lernende Systeme in a discussion paper. It provides an overview of existing certification projects in Germany and forms the basis for further discussions.

Thomas Schauf, Senior Expert Public and Regulatory Affairs at Deutsche Telekom AG, together with other members of Plattform Lernende Systeme, has examined data management and IT security when using AI in medicine in a current white paper. In our interview he explains how patient data can be protected and how access to sensitive information can be regulated.

Data Scientists are desperately sought. The Gesellschaft für Informatik e.V. (Society for Computer Science) has defined the skills that need to be taught at colleges and universities, but also through further education courses. (GI) in cooperation with Plattform Lernende Systeme. Co-author Ulf Brefeld from the Leuphana University of Lüneburg explains why the target groups are very different.

The intelligent use and linking of data enables new forms of entrepreneurial cooperation and completely new business models. In the future, products and services can be adapted to the individual wishes and needs of customers. This raises the question for companies: How can they use AI for their business or develop new AI-based business models? Our member Andreas Liebl provides answers to these questions.

Artificial Intelligence (AI) will improve the security of IT systems in the future. In the hands of cybercriminals, on the other hand, it opens up whole new avenues of attack for threatening IT security. The AI systems themselves also have to be protected from manipulation. The “Artificial Intelligence and IT security” white paper published by Plattform Lernende Systeme for Hannover Messe 2019 analyses the dynamic between IT security and AI. The authors, who are drawn from the fields of business, science and public authorities, call on small and medium-sized enterprises and IT specialists in particular to help build up core skills.

To mark the start of “Year of Science 2019 – Artificial Intelligence”, Plattform Lernende Systeme is tracing the development of Artificial Intelligence (AI) since its earliest days. A five-minute explanatory film illustrates the different stages of the technology’s development, milestones in AI applications and the challenges that lie ahead.

Phishing mails, false online requests or fake news - on Safer Internet Day the focus is on security in the network and of IT systems. In an interview, Holger Hanselka, steering committee member of Plattform Lernende Systeme and President of KIT, explains how AI can improve IT security and where it opens up entry points for new threats.

Germany has the potential to become a sustainable hub for innovative Artificial Intelligence where self-learning systems ensure growth and better quality of life. At the Digital Summit organised by the German Federal Ministry for Economic Affairs and Energy, the panel of experts from Plattform Lernende Systeme, demonstrated that achieving this goal calls for industry, SMEs, researchers and politicians to work together.

Plattform Lernende Systeme is bringing the societal debate on Artificial Intelligence to a wider audience. The decision to do so was taken by the Steering Committee of the platform – an initiative of the German Federal Ministry of Education and Research (BMBF) – on 30 October in Berlin.

The international artificial intelligence (AI) race is well under way. With the help of a national AI strategy, the German Federal Government aims to pool the country’s resources and make it the world’s leading AI location.

Artificial Intelligence (AI) is becoming a driving force of digitalisation. Experts from Plattform Lernende Systeme – Germany’s platform for Artificial Intelligence – are at this year’s CEBIT to discuss how the use of AI is changing work, business models and everyday life. Trade fair visitors can also stop by the stand run by the Federal Ministry for Education and Research (BMBF) to try out self-learning systems for themselves.

Federal Research Minister Johanna Wanka opened “Science Year 2018 – Working life of the future” on 19 February at the Federal Ministry of Education and Research (BMBF) in Berlin.

The Plattform Lernende Systeme links experts from industry and science across sectors and disciplines to achieve this aim. The heads of the platform’s seven working groups presented the priority topics of their work programmes at a meeting in Berlin on January 8.

Industrie 4.0 is still shaping current developments in the economy as Germany now faces new tasks for the future: Learning Systems and Artificial Intelligence. The Federal Ministry of Education and Research (BMBF) plans to expand on this topic structurally and promote collaboration between research and industry in particular.