Micro Focus Groups

AI Grid is a network of small AI communities. Within these communities, members share their research work, initiate collaborations and forge fruitful connections for the future.

Over 50 communities should emerge over time.

Here you will find our current micro focus groups

Bio-inspired Learning explores computational models and algorithms inspired by biological systems, such as neural networks, evolution, and swarm intelligence. This approach aims to create efficient, adaptable, and intelligent systems by mimicking the problem-solving strategies of nature, advancing fields like AI, robotics, and optimization.

ML: Bio-inspired Learning

This groups tackles challenges in safety, infrastructure, and sustainability. From emergency detection in households to clean water prediction, electric grid optimization, and real-time health monitoring, they combine innovative computational methods with practical applications. Together, they aim to deliver impactful solutions for pressing societal and environmental needs.

ML: Probabilistic Methods

This group focuses on designing AI systems that learn to generate and reconstruct data, uncovering complex patterns and structures. These models are vital for advancements in data compression, anomaly detection, and creative AI applications, driving innovation in fields like image synthesis, language generation, and scientific simulations.

ML: Deep Generative Models & Autoencoders

This group explores the dynamics between humans and robots to design systems that are intuitive, safe, and effective. This research is crucial for advancing robotics in areas like healthcare, industry, and daily life, ensuring robots enhance human capabilities while fostering trust and collaboration.

ROB: Human-Robot Interaction

This group explores how to mitigate biases, ensure fairness, enhance transparency, and safeguard privacy in AI-driven language technologies. This work is vital to building ethical, trustworthy, and inclusive AI systems that positively impact society.

SNLP: Bias, Fairness, Transparency & Privacy

Climate change and its consequenes are one of the most pressing topics of todays society so it is only natural that AI algorithms should play a vital role in the efficient use of our energy.

Energy-efficient AI

Automated Machine Learning can save valuable time and energy when using ML algorithms. By continously adjusting hyperparameters, performance can significantly increase.

ML: Auto ML and Hyperparameter Tuning

Adressing fairness and biases in algorithms is extremely relevant when incorporating technology into our society.

PEAI: Bias, Fairness & Equity

Increasing use of LLMs calls for more fine grained analysis of style and sentiment to ensure that models give an accurate representation of what is intended.

SNLP: Sentiment Analysis and Stylistic Analysis

Developing LLMs for mutliple languages, including low resource languages, is essential to ensure gobal access to new technologies.

SNLP: Machine Translation & Multilinguality/Multimodality

Enabling teamwork between humans and AI surely is the key to success for solving complex problems in the digital age. The fear of automation overtaking jobs is dominating the conversation about AI but what if the promising prospect of human-AI collaboration simply facilitates certain tasks and does not take them away but leave room for others?

HAI: Human-Machine Teams

Without a doubt, the nature of collaboration between humans and computers is one of the most pressing research needs in the field of AI. We need to learn how to integrate new technology into our daily lives to enhance our given competences and make our lives more efficient.

HAI: Human-Computer Interaction

As the world around us is complex, it is important to incorporate multiple modalities into systems created to support humans whether it is in medicine as robotic surgical assistants or other work environments.

ML: Multimodal Learning

Societal impact of AI is one of the most talked about topics in media at the moment due to its grave importance. Focusing on human-centered needs whether in applications in healthcare or governance of sociotechnological systems is essential.

PEAI: Societal Impact of AI

Time series analysis occurs in many different areas, from medical data (e.g. EEG) to mobility and transportation. This micro focus group concentrates on CNN models to analyze their data.

ML: Time-Series / Data Streams

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