Our three pillars: Machine Learning, Logical Reasoning, Computational Perception
The LIT AI Lab headed by Prof. Sepp Hochreiter was founded as a permanent research center of the Linz Institute of Technology (LIT). In the unique environment offered by the Johannes Kepler University (JKU) Linz, the LIT AI Lab bundles JKU’s world-class expertise in artificial intelligence (AI) for shaping and advancing AI research and its industrial applications.
The LIT Lab is committed to scientific excellence. Our focus is on theoretical and experimental research in machine learning, logical reasoning, and computational perception. We train the next generations of AI researchers and educate engineers at various academic levels in AI technology.
“Artificial Intelligence (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behaviour – understanding language, learning, reasoning, solving problems, and so on.”
(Barr & Feigenbaum, 1981)
Our Goals, Mission, and Team: Learn More About Us.
Created in 2017 as a permanent research institution, the LIT AI Lab started with three seed groups conducting core AI research:
and two further research groups:
The expertise of these three groups is now complemented by
at our Graduate School of Artifical Intelligence. The doctoral program benefits from the synergies of these six groups, focusing on interdisciplinary research to advance AI. In order to broaden the application areas of our AI research in the LIT AI Lab, we collaborate closely with other JKU institutes that conduct related research as well as with international companies.
ELISE, a network of artificial intelligence research hubs, is based on a high level research focused om disseminating its findings and expertise in academia, the business community, and society. The project launched the first Open Call for the best SMEs and start-up companies that are focusing on AI-based services and/or applications.
Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications
In our new paper, we identify and counter domain shifts in machine learning models for COVID-19 diagnosis from blood tests:
Two topics – 30 minutes: Univ.-Prof. Dr. Martina Seidl: Competitions as Scientific Method and Dr. He Sun: Design of Electrochemical Flow-cell for Efficient Electrocatalytic CO2 Conversion