Shaping the future with human and artificial intelligence

Artificial intelligence (AI) plays a crucial role in transforming the economy and society. Both are undergoing fundamental structural change, as AI is already taking on numerous tasks that would otherwise be impossible – or at least not achievable at this speed. AI enables technical systems to plan processes, make predictions, and interact with humans and with each other.

The use of AI not only creates opportunities but also presents ongoing challenges. Where does artificial intelligence obtain its knowledge? What data is used to train it, and what happens to the information you provide to an AI system? To ensure control over data, it is essential to develop independent AI solutions from Germany and Europe – the key term here is “digital sovereignty.”

At Fraunhofer IIS, researchers have been developing AI solutions for efficient applications across a wide range of domains and industries for many years.

They follow two complementary approaches: On the one hand, they develop specialized, task-oriented AI that enables tailored and highly efficient solutions. On the other, scientists are advancing the development of broadly applicable foundation models and base methods optimized for specific application areas.

In our magazine series, you can learn how artificial intelligence is researched and applied at Fraunhofer IIS. We showcase current projects and highlight ways you can collaborate with the institute.

Which technology trends are shaping the future of large language models (LLMs) – and what makes Europe’s role unique? In this interview, Jan Plogsties, Head of the Generative AI Department at Fraunhofer IIS, explains what the institute is currently researching and where those developments are heading.

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Anyone exploring technological advances in artificial intelligence (AI) will inevitably encounter spiking neural networks (SNNs) — the next step toward energy‑efficient real‑time AI. The difference from conventional neural networks is striking: while standard artificial neurons continuously output values, SNN neurons fire only when critical thresholds are exceeded, sending electrical impulses (spikes) through the network. This event‑driven mode of operation saves both energy and computation time, making it a compelling option for certain use cases. Yet few companies are willing to discard their painstakingly trained deep neural networks (DNNs) and start from scratch. The pressing question, therefore, is: How can the proven knowledge embedded in DNNs be transferred into SNNs? This is precisely where current research comes in.

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Allinga harnesses advanced speech recognition and synthesis to enable natural communication between humans and machines, adapted to the working environment and sector. This allows customized language solutions to be used in industry while conforming to data protection regulations.

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AI language models have fundamentally transformed the way people interact and work with artificial intelligence (AI). Yet for many European companies, existing solutions raise concerns regarding data protection and information security. In collaboration with a broad network of academic and industry partners, Fraunhofer IIS has developed Teuken-7B—an efficiently trained European language model that is available as open source and fully compliant with EU data protection regulations.

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A problem with many AI networks is that they’re good — really good — but sometimes a little big and overly precise. That means a lot of energy, memory, and computing time are spent on complex calculations, even though simpler models could solve the task at hand more efficiently. But how do you turn an overly complicated neural network into a compact artificial intelligence (AI) that also runs reliably on small devices, for example, in a headset for sleep monitoring? This is where neural architecture search comes into play.

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AI algorithms that generate text, images, or videos based solely on prompts – i.e., input in human language – will play a major role in business, research, and society in the future. Known as generative algorithms, these broaden the options for making use of artificial intelligence. And Fraunhofer IIS is at the forefront of their development.

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Synchrotrons support extremely rapid 4D X-ray imaging in the millisecond range. This makes it possible to study rapid processes, such as the decomposition of foams or the formation of pores, as shown here in this schematic image.

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Clear sound, even when you are surrounded by noisy traffic or your colleague is hammering on their keyboard? A team at Fraunhofer IIS developed a low-complexity real-time AI-based noise reduction technology. What makes it unique is that it is device independent.

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Everyone’s talking about ChatGPT. The AI-powered chatbot from US company OpenAI answers questions and produces texts – and is currently available to everyone for free. Volker Bruns, Group Manager Medical Image Analysis in the Digital Health Systems department, “talked” to the bot about digital pathology. He found the results impressive, but they also caused him to wonder: What are the implications of robots like ChatGPT for scientific work? He talked to us about this in an interview.

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The European Association for Artificial Intelligence (EurAI) is an umbrella organization for scientific associations devoted to work in the field of artificial intelligence. Every year, about 3 percent of the members of the EurAI member societies are named EurAI Fellows. One of this year’s four new Fellows is Dr. Ute Schmid, Professor of Applied Computer Science / Cognitive Systems at the University of Bamberg and head of the Group on Comprehensible AI (CAI; formerly Explainable AI) at Fraunhofer IIS. In this interview, she explains what this honor means to her and her research, and looks back on her research findings of the past few years.

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Dr. Marco Breiling (51) wants to assist in shaping the future of Germany. The electrical engineer from Erlangen helps get successful high-tech products made in Germany. Inspired by the energy efficiency of a bee’s brain, his team develops clever microchips that process very large data volumes in an energy-saving manner. This achievement impressed the jury of the pilot innovation competition “Energy-Efficient AI System” organized by the German Federal Ministry of Education and Research (BMBF). Here, Marco Breiling gives fascinating insights into his field of research.

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The SEC-Learn (Sensor Edge Cloud for Federated Learning) project, involving eleven Fraunhofer Institutes, promises a major technological leap in the field of neuromorphic hardware: for the first time, a chip is being developed to accelerate spiking neural networks (SNN) in conjunction with what is known as federated learning. This would offer practical benefits for companies and individuals.

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Bavaria has set its sights on becoming a hotspot for artificial intelligence (AI). To this end, the State Government established the Bavarian Council on AI. It is chaired by Prof. Sami Haddadin, Director of the Munich School of Robotics and Machine Intelligence (MSRM). The body’s 21 members have been drawn from various fields. High-caliber experts from universities, non-university research institutions and industry are pooling their know-how and resources to advance Bavaria’s High-Tech Agenda in the field of AI through intensive cooperation in a dense network.

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Computer architecture inspired by the human brain – to most people, this sounds rather abstract and removed from everyday life. Far from it: a research team from Fraunhofer IIS and Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) has developed neuromorphic hardware capable of detecting early signs of atrial fibrillation and considerably reducing the risk of stroke. In other words, this application has an entirely practical place in everyday life.

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Group manager Dr. Matthias Völker and senior engineer Dr. Markus Stahl-Offergeld both work in the Smart Sensing and Electronics Division at Fraunhofer IIS. They are currently working on “KI-PREDICT”, a project funded by Germany’s Federal Ministry of Education and Research (BMBF) devoted to on-sensor data processing supported by AI functions. For this project, the two experts are focusing on the development of a “sensor interface ASIC” with integrated AI functions. One of the partners in the “KI-PREDICT” project is Rolf Slatter, the managing director of Sensitec GmbH since 2009. Sensitec is a global market leader in high-quality, innovative magnetic sensor solutions. We asked Matthias Völker, Markus Stahl-Offergeld, and Rolf Slatter to tell us about the project and their collaboration.

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Bringing intelligence to devices is an essential prerequisite for autonomous driving. After all, real-life traffic situations demand fast responses and leave no time to transmit sensor data to a cloud for intelligent analysis. In the KI-FLEX project, researchers are developing neuromorphic hardware that allows artificial intelligence (AI) to be integrated directly into a car – in the form of a flexible platform. Project coordinator Michael Rothe from the Fraunhofer Institute for Integrated Circuits IIS offers his insights into the project.

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Analog or digital – two terms that will be quite familiar to most people. But when it comes to artificial intelligence, only the experts know that there’s something “in between.” They call this “in between” the spiking neural network (SNN). One of the main hopes for the SNN approach is that it will resolve how to integrate “intelligence” directly into devices. Cognitive scientist Johannes Leugering explains what’s involved.

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Robotic lawn mowers have only limited “intelligence”: up to now they have needed a boundary cable to prevent them from accidentally buzzing the neighbor's grass as well. Edge AI will make it possible to equip lawn mowers, sensors or other devices with artificial intelligence – without having to send the data gathered for the intelligent task to the cloud. Instead, the “intelligence” will be located in the device itself. But in many cases, this can work only with the help of special, extremely energy-efficient chips; powerful microcontroller chips are rarely suitable because they consume too much energy and are too expensive.

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Professor Ute Schmid has been at the head of the Project Group Explainable AI (EAI) since January 2020. Schmid and her team explore ways to help users understand machine learning.

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In our interview, Professor Ute Schmid, head of the Comprehensible Artificial Intelligence project group and professor at the University of Bamberg, and Dominik Seuss, head of the Intelligent Systems group and head of the Image Analysis and Pattern Recognition business unit, describe in an interview how AI is constantly expanding to include more and more applications and further areas of implementation and explain why humans require transparency and explainability in AI systems.

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Some challenges are best faced shoulder to shoulder – like developing the chips, algorithms, and the hardware needed to integrate artificial intelligence directly into end devices, a paradigm also known as “Edge AI”. Marco Breiling, chief scientist at the Fraunhofer IIS Communication Systems division and Dr. Loreto Mateu, group manager at the Fraunhofer IIS Smart Sensing and Electronics division, talk about why their collaboration on Edge AI is so fruitful, and the benefits of working in an international team.

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How can we leverage our expertise on a European level so as to ensure European sovereignty in AI? Statement from Prof. Dr. Alexander Martin, Fraunhofer IIS director, responsible for the Positioning and Networks as well as the Supply Chain Services research areas

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Whether it’s about automating processes or analyzing large data volumes, smart and self-learning systems are increasingly becoming an important part of business processes. In the past, these smart systems had to be connected to a cloud because that was the only way of providing the level of computing power required to run the mathematical algorithms. Now edge AI, short for edge artificial intelligence, is enabling the next generation of smart systems and transferring the know-how to the devices themselves.

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Artificial intelligence gives technical systems the ability to learn, thus enabling them to apply what they have learned to new situations. Typically, the “intelligence” is not stored in the system itself, but rather in clouds that provide the high computing power required. The common term is therefore cloud artificial intelligence, or cloud AI. But people are also hearing more and more about edge AI.

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How can AI benefit business and industry, and what is Fraunhofer IIS currently researching? The division directors of Fraunhofer IIS highlight the opportunities AI technologies have to offer.

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Head of Department Christian Menden is responsible for developing an AI strategy for the Working Group for Supply Chain Services (SCS).

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Prof. Alexander Martin is head of the Fraunhofer IIS and also in charge of the co-located ADA Lovelace Center. He and his team are looking into ways to extract information from big data using artificial intelligence (AI).

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Dr. Christopher Mutschler is one of the AI experts at Fraunhofer IIS. He researches machine learning and ensures that scientific findings make their way into industry. In this interview, Ilona Hörath reflects on his career path and field of work.

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Voice assistants such as Alexa, Siri and the like are becoming more and more popular, but the systems struggle when it comes to understanding complex contents and context or deriving semantic meaning. Birgit Brüggemeier knows what the problem is: »The machine lacks common sense.« Together with her project team, she is working on the European version of a voice assistant, looking at how these error messages can be communicated to the user without them getting annoyed and ending their interaction with the voice assistant.

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