Semantics

Automated Learning covers a large area starting with the automation of feature detection and selection for given datasets as well as model search and optimization, continuing with their automated evaluation, and ending with the adaptive adjustment of models through training data and system feedback.

Sequence-based Learning concerns itself with the temporal and causal relationships found in data in applications such as language processing, event processing, biosequence analysis, or multimedia files. Observed events are used to determine the system’s current status, and to predict future conditions. This is possible both in cases where only the sequence in which the events occurred is known, and when they are labelled with exact time stamps.

Learning from Experience refers to methods whereby a system is able to optimize itself by interacting with its environment and evaluating the feedback it receives, or dynamically adjusting to changing environmental conditions. Examples include automatic generation of models for evaluation and optimization of business processes, transport flows, or control systems for robots in industrial production.

Data-centric AI (DCAI) offers a new perspective on AI modeling that shifts the focus from model building to the curation of high-quality annotated training datasets, because in many AI projects, that is where the leverage for model performance lies. DCAI offers methods such as model-based annotation error detection, design of consistent multi-rater annotation systems for efficient data annotation, use of weak and semi-supervised learning methods to exploit unannotated data, and human-in-the-loop approaches to improve models and data.

To ensure safe and appropriate adoption of artificial intelligence in fields such as medical decision-making and quality control in manufacturing, it is crucial that the machine learning model is comprehensible to its users. An essential factor in building transparency and trust is to understand the rationale behind the model's decision making and its predictions. The ADA Lovelace Center is conducting research on methods to create comprehensible and trustworthy AI systems in the competence pillar of Trustworthy AI, contributing to human-centered AI for users in business, academia, and society.

Process-aware Learning is the link between process mining, the data-based analysis and modeling of processes, and machine learning. The focus is on predicting process flows, process metrics, and process anomalies. This is made possible by extracting process knowledge from event logs and transferring it into explainable prediction models. In this way, influencing factors can be identified and predictive process improvement options can be defined.

Mathematical optimization plays a crucial role in model-based decision support, providing planning solutions in areas as diverse as logistics, energy systems, mobility, finance, and building infrastructure, to name but a few examples. The Center is expanding its already extensive expertise in a number of promising areas, in particular real-time planning and control.

Tiny Machine Learning (TinyML) brings AI even to microcontrollers. It enables low-latency inference on edge devices that typically have only a few milliwatts of power consumption. To achieve this, Fraunhofer IIS is conducting research on multi-objective optimization for efficient design space exploration and advanced compression techniques. Furthermore, hierarchical and informed machine learning, efficient model architectures and genetic AI pipeline composition are explored in our research. We enable the intelligent products of our partners.

Hardware-aware Machine Learning (HW-aware ML) focuses on algorithms, methods and tools to design, train and deploy HW-specific ML models. This includes a wide range of techniques to increase energy efficiency and robustness against HW faults, e.g. robust training for quantized DNN models using Quantization- and Fault-aware Training, and optimized mapping and deployment to specialized (e.g. neuromorphic) hardware. At Fraunhofer IIS, we complement this with extensive research in the field of Spiking Neural Network training, optimization, and deployment.