Artificial Intelligence

Artificial Intelligence (AI) is one of the most important digital topics and has been arousing great interest in science, business and the media in recent years. Artificial Intelligence has long since become an everyday technology: We can have entire essays written with the ChatGPT dialogue system, the first self-driving cars are already on the roads and logistics companies have autonomously flying drones in use.

At Fraunhofer IAIS, we research and develop intelligent solutions for decades and evaluate their opportunities and challenges for science, industry and society. Our scientists with their profound expertise are highly demanded by companies to put Artificial Intelligence into practice. We are part of a strong cooperation network with regional and international partners such as universities, start-ups, companies, or other Fraunhofer institutes.

Based on our research findings and experience, we develop AI solutions that support and advance industry, finance, logistics and transport, healthcare, trade, public administration, media and many more. We work hand in hand with our customers and adapt AI solutions to their individual requirements.


© Quardia Inc. -

Our basis: Trustworthy AI

We want to strengthen people's trust in Artificial Intelligence. To establish trust, an AI application must be verifiably designed to function securely and reliably, as well as to ensure data sovereignty. We follow this principle when developing any new AI solution.

We help organizations to identify AI risks, assess them, and secure their AI systems. We are committed to the development of testable standards and norms as well as AI certification »made in Germany« - together with a strong partner network.


Our focus: Hybrid AI

When AI-supported or even autonomous machines are used  on factory floors, in hospitals and in the home, they must – like humans – be able not only to act on the basis of pre-trained models build on large amounts of data, but also to simultaneously incorporate observation and experience, learn on this basis and understand the context.  

Our AI research focuses on "hybrid AI solutions": This is where we combine world or expert knowledge with data-based approaches that use machine learning methods to analyze statistical correlations. In this way, the human ability to understand meaning from context can be replicated. In addition, the approach of a hybrid AI is often more comprehensible for humans and therefore particularly suitable for human-machine interaction.


Our future vision: AI on quantum computers

Quantum computers have the potential to process information faster and handle more complex problems than classical digital computers. Even previously almost unsolvable applications of Artificial Intelligence and Machine Learning can thus be realized by quantum computers. Simulations and the solution of optimization problems are particularly promising areas of application. Simulations can be used, for example, to predict the characteristics of molecules and materials. Optimization problems arise in logistics, for example, when it comes to determining the optimal flow of traffic in order to relieve road congestion.

At Fraunhofer IAIS, we have been researching the potential of
quantum computing for Machine Learning for several years.


Current research projects

Discover selected research projects of Fraunhofer IAIS around Artificial Intelligence: An overview of all AI projects of the Fraunhofer-Gesellschaft can be found on the AI project map of the Fraunhofer Big Data and Artificial Intelligence Alliance.


Creating trust in Artificial Intelligence

AI certification

Within the framework of a strategic cooperation, experts from the German Federal Office for Information Security (BSI) and Fraunhofer IAIS are developing test methods for AI systems. The goal of the cooperation is to establish technical product and process testing of AI systems in the industry and to push the development of an AI certification »made in Germany«.



Voice assistants »made in Germany«


The project aims to create a German voice assistance platform based on Artificial Intelligence that enables a new quality in human-machine communication while complying with European standards on data sovereignty and security.

SPEAKER is funded by the German Federal Ministry for Economic Affairs and Energy BMWi. Fraunhofer IIS and IAIS are leading the project.


Speech technologies via GAIA-X


With OpenGPT-X, a large AI language model for Europe is being created under the leadership of the Fraunhofer Institutes IAIS and IIS. This will result in intelligent speech applications that will be available to companies across Europe via the decentralized cloud solution GAIA-X. This enables companies to exploit the innovation potential of language technologies while remaining digitally independent.


Artificial Intelligence for Europe


AI4EU promotes the exchange of AI expertise, knowledge, and tools in Europe. The project aims to increase public and private investment in AI technologies, facilitate access to data and development tools, and educate and train future experts from all sectors of the economy. It also provides ethical guidelines for AI development in the coming decades.


Digital patient model


One click away from the right prevention, diagnosis, and therapy: this is the vision of the lead project "MED²ICIN". The goal is to develop a digital patient model to revolutionize the healthcare industry. Integrating digital innovations into the entire treatment chain not only improves patient care, but also makes targeted and effective treatment more efficient and thus more cost-effective.


Shaping the hospital of tomorrow


The constantly growing amount of health data makes it possible to develop intelligent and personalized applications for early health detection, diagnostics, treatment and aftercare. AI-based systems in particular hold enormous potential, which SmartHospital.NRW aims to leverage and make usable for hospitals in North Rhine-Westphalia.


How autonomous driving becomes safer


In autonomous vehicles, tasks such as environment recognition are performed by an AI. Such AI function modules based on Machine Learning are developing into a key technology. For use in road traffic, the reliability and safety of the AI and thus of all road users must be verifiable. The project created an industry consensus on the safeguarding of AI function modules.


Smart farming for Europe


Interoperability of agricultural machinery, sensors and data analysis services: Under the leadership of Fraunhofer IAIS, 30 partners from eight European countries are working in the EU-funded project »Agricultural Interoperability Analysis System (ATLAS)« to develop a platform that can be used to shape the future of agriculture in a way that is resource-friendly and guarantees data sovereignty for the farmer.


Word sense representation via NRL

Neurosymbolic Representation Learning

Our novel method takes existing deep learning structures and traditional AI approaches to represent knowledge in the vector space. Neurosymbolic representation learning (NRL) unifies vector embeddings as geometrical shapes, which inherit the explainability and reliability from symbolic structures. We successfully pushed the F1 score above 90% in word-sense ambiguation tasks.

Application areas for Artificial Intelligence in the economy

© Patrick Daxenbichler -

Language and text

Artificial Intelligence understands spoken or written content: Our AI solutions evaluate legal or medical texts, for example, and make their content accessible. Our software solutions for automated document and text analysis cover the entire process – from deciphering a digitalized paper document with Optical Character Recognition (OCR) to evaluating it in context.
Our dialog systems, which comply with European data protection standards, can be used, for example, in cars, in industrial applications, in emergency medicine, or in finance and controlling to make important information available immediately.

Image and video

Artificial Intelligence recognizes the content of images and analyzes audiovisual media: For example, the processing and analysis of images and video facilitates the searchability of large media archives by providing metadata. Also, various mining services are searching through the media contributions. Other AI solutions support, for example, industrial quality control through automatic damage detection– as on components of cars or other workpieces.

Industry data

Artificial Intelligence evaluates data generated in production plants and industrial processes. The evaluation of corresponding sensor data and its processing is essential for Industry 4.0. With our AI solutions, customers design production processes intelligently or optimize test planning in research and development.

Education and training

Our highly renowned data science trainings provide the perfect introduction to the topic of Artificial Intelligence. We also offer specific advanced training on topics such as Deep Learning, Trustworthy AI and many more. And at the end of many trainings, participants can receive a certificate.


© Song_about_summer -
  • In computer science, an algorithm is an exact computational specification for solving a task. A learning algorithm (or self-learning algorithm) is an algorithm that receives sample data (learning data or training data) and computes a model for the seen data that can be applied to new data samples.

  • Data Science is an interdisciplinary scientific field that deals with methods, processes, and algorithms for extracting insights from structured and unstructured data. The professional field of data scientists requires knowledge of mathematics, business administration, computer science, and statistics. Data scientists identify and analyze available data resources, determine needs, and develop concepts to use the data profitably.

    Fraunhofer IAIS offers data scientist trainings.

  • Foundation models or large language models (LLM) are large language models with billions of parameters that are trained on huge data sets. They rely on "self-supervised learning", i.e. learning that does not require annotations. Examples of such models are ChatGPT, GPT-4 or BARD, and they produce high-profile results and broad social discussions.

    On the one hand, they can be fine-tuned for specific tasks with labelled data. On the other hand, large models can also perform tasks directly by formulating a mission and explaining it with a few examples if necessary. Clear advantages lie in the high flexibility for the application of such models. Disadvantages include, in addition to the enormous computing power that is currently still required for training, the associative mode of operation of the models, which seek plausible formulations and are not oriented towards lexicon-like fact fidelity. On closer examination, inconsistencies and jumbled information sometimes become apparent. Furthermore, it is usually completely unclear how the models arrived at their results. However, transparency and traceability are of elementary importance in cases of critical decisions, such as in the health sector. Despite the already enormous potential of LLMs, there are still important research questions about their trainability, reliability and fairness.

  • Artificial Intelligence is a discipline of computer science, that deals with how a computer can mimic intelligent human behavior. Neither is defined what "intelligent" means, nor which technology is used. One of the foundations of modern Artificial Intelligence is Machine Learning. Subject matter experts distinguish Weak AI and Strong AI.

  • Weak AI implements AI methods to solve narrowly defined tasks. While it can already surpass human capabilities in individual areas, such as image analysis, Weak AI falls far short of reaching the same level for broader context tasks or tasks that require world knowledge. All current AI solutions are examples of Weak AI and have a limited horizon: An Artificial Intelligence that can recognize images cannot necessarily play chess.

  • Strong AI stands for the vision of using AI techniques to emulate human intelligence to its full extent and outside of individual, narrowly defined fields of action. Strong AI has so far only been found in science fiction. Since Artificial Intelligence emerged in the 1950s, there have been predictions that Strong AI would become realizable in a few decades.

  • Hybrid AI combines data-based Machine Learning, world or expert knowledge, and logical reasoning. Knowledge and the respective conclusions are directly introduced into the learning process, for example, in order to emulate the human ability to correctly understand meanings from the context and to make the AI system more robust overall.

  • Only trustworthy AI applications guarantee IT security, control, legal certainty, accountability, and transparency. For this reason, guidelines for the ethical design of Artificial Intelligence are being developed within companies and at the societal and political level. These focus, for example, on the dimensions of ethics and law, fairness, autonomy and control, transparency, reliability, security and privacy.

  • Machine Learning aims to generate "knowledge" from "experience" by having learning algorithms develop a complex model automatically from examples. The model, and thus the acquired knowledge representation, can then be applied to new, potentially unknown data of the same type. Whenever processes are too complicated to describe analytically, but enough example data – such as sensor data, images, or text - is available, Machine Learning comes in handy. The learned models can be used to make predictions or generate recommendations and decisions often surpassing the human level of performance – without any predefined rules or calculation rules.

    Machine Learning is one of the core competencies at Fraunhofer IAIS.

  • Informed Machine Learning describes the approach of making the calculations of an ML model transparent by using world knowledge or well-understood components so that the decisions made by the model can be better understood by humans. This comprehensibility is important, for example, when Artificial Intelligence systems are used in medicine.

    Informed Machine Learning is intended to avoid a so-called black box (into which humans cannot figuratively see) and one of the core competencies at Fraunhofer IAIS.

  • Artificial neural networks are Machine Learning models that are inspired by the brain's natural neural networks. They consist of many layers of interconnected computational units implemented in software, called artificial neurons. Using examples, a learning algorithm modifies the interconnectedness between neurons until the neural network produces good results. The number of neurons, layers, and their interconnections have a significant effect on the model's ability to solve problems.

  • Deep Learning describes the implementation of a Machine Learning process in the form of an artificial neural network with many layers composed of a large number of artificial neurons. The generation of features relevant for learning is done autonomously. Deep Learning is responsible for the successes in speech, text, image, and video processing.

    Crucially, Deep Learning works particularly well when very large amounts of data – Big Data – are available to train neural networks. Thanks to its many years of experience with neurocomputing and Big Data Analytics and Intelligence Solutions, Fraunhofer IAIS is one of the pioneers in Germany in the development of Deep Learning approaches for industry.

  • Quantum computers base their elementary computational steps on quantum mechanical states – called qubits – instead of the binary states (bits) in classical digital computers. Qubits are processed using quantum mechanical principles, which is expected to provide a tremendous speed advantage for some applications. The new computer architectures also offer potential for Machine Learning and thus Artificial Intelligence.

    Learn more about Quantum Computing at Fraunhofer IAIS.