Research output per year
Research output per year
Project Details
Description
The project “Bilateral AI” aims at lifting artificial intelligence (AI) to the next level. Current AI systems are in a sense narrow. They center on a specific application or task such as object or speech recognition. Our project will combine two of the most important types of AI which have been developed separately so far: symbolic and sub-symbolic AI. While symbolic AI works with clearly defined logical rules, sub-symbolic AI (such as ChatGPT) is based on training a machine with the help of large datasets to create intelligent behavior. This integration, resulting in a Broad AI, is intended to mirror something that humans do naturally: the simultaneous use of cognition and reasoning skills.
| Status | Active |
|---|---|
| Effective start/end date | 01.10.2024 → 30.09.2029 |
Collaborative partners
- Johannes Kepler University Linz (lead)
- IST Institute of Science and Technology Austria (Project partner)
- Graz University of Technology (Project partner)
- Wirtschaftsuniversität Wien (Project partner)
- University of Klagenfurt (Project partner)
- Vienna University of Technology (Project partner)
Fields of science
- 101013 Mathematical logic
- 603109 Logic
- 102 Computer Sciences
- 102031 Theoretical computer science
- 102030 Semantic technologies
- 102011 Formal languages
- 102022 Software development
- 102001 Artificial intelligence
- 101019 Stochastics
- 103029 Statistical physics
- 101018 Statistics
- 101017 Game theory
- 101016 Optimisation
- 202017 Embedded systems
- 101015 Operations research
- 101014 Numerical mathematics
- 101029 Mathematical statistics
- 101028 Mathematical modelling
- 101026 Time series analysis
- 101024 Probability theory
- 102025 Distributed systems
- 202037 Signal processing
- 202036 Sensor systems
- 202035 Robotics
- 102003 Image processing
- 102006 Computer supported cooperative work (CSCW)
- 102032 Computational intelligence
- 102004 Bioinformatics
- 101027 Dynamical systems
- 102013 Human-computer interaction
- 305907 Medical statistics
- 202002 Audiovisual media
- 101004 Biomathematics
- 502007 E-commerce
- 305905 Medical informatics
- 505002 Data protection
- 101031 Approximation theory
- 102010 Database systems
- 102035 Data science
- 102033 Data mining
- 506002 E-government
- 305901 Computer-aided diagnosis and therapy
- 102019 Machine learning
- 106007 Biostatistics
- 102018 Artificial neural networks
- 106005 Bioinformatics
- 102028 Knowledge engineering
- 202007 Computer integrated manufacturing (CIM)
- 102016 IT security
- 102015 Information systems
- 509018 Knowledge management
- 102014 Information design
- 202006 Computer hardware
JKU Focus areas
- Digital Transformation
- Sustainable Development: Responsible Technologies and Management
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(Semantic) Feature Model Differences with (Q)SAT
Heisinger, S., Heisinger, M. & Seidl, M., 17 Jun 2025, SLE '25: Proceedings of the 18th ACM SIGPLAN International Conference on Software Language Engineerin. p. 189-198 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review
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MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery.
Schimunek, J., Luukkonen, S. & Klambauer, G., 30 Apr 2025, In: Journal of Chemical Information and Modeling. 65, 9, p. 4243-4250 8 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Solution Counts of Some Prominent Quantified Boolean Formulas Families
Plank, A., Kauers, M. & Seidl, M., 14 May 2025, Proceedings of ACM SAC Conference (SAC’25). New York, p. 1035-1042 8 p. (Proceedings of the ACM Symposium on Applied Computing).Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review
Open Access
Activities
- 1 Contributed talk
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(Semantic) Feature Model Differences with (Q)SAT
Heisinger, S. (Speaker)
13 Jun 2025Activity: Talk or presentation › Contributed talk › science-to-science