FWF Cluster of Excellence: Bilateral Artificial Intelligence

Mon, 01 Jan 2024 00:00:00 +0000

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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. But what exactly is Broad AI? As opposed to Narrow AI, which is characterized by task specific skills, Broad AI aims at solving a wide array of problems, rather than being limited to a single task or domain. By combining sub-symbolic AI (machine learning, ML) with symbolic AI (knowledge representation and reasoning, KRR), Bilateral AI provides the means to develop the foundations of the capabilities and skill acquisition for problem solving by a Broad AI. Harnessing the full potential of both symbolic and sub-symbolic approaches can open new avenues for AI that are better at solving new problems, adapting to a wide variety of environments, having better reasoning skills, and being more efficient in terms of both computation and data use. These key features allow for a vast range of use cases for Broad AI, starting with drug development and medicine, over planning and scheduling, to autonomous traffic management and recommendation systems. With fairness, transparency, and explainability as top priorities, developing Broad AI is also essential for addressing ethical concerns and ensuring a positive impact on our society. These concerns play a central role as cross-cutting aspect in our project. The Broad AI resulting from the bilateral AI approach would use its own sensory perceptions to perform abstractions and engage in a logical thinking process. The AI could then, for example, organize a trip, minimize carbon emissions, or renovate a house as cost- effectively and ecologically as possible. In other words, AI could perform complex planning taking all aspects into account. Sepp Hochreiter, Director of Research: Broad AI could potentially improve our everyday lives as well as system-relevant aspects and processes - such as energy, transportation and healthcare - by becoming more environmentally sustainable, efficient and resource-friendly.

Parameterized Graph Drawing

Sun, 01 Jan 2023 00:00:00 +0000

Funding organization: Vienna Science and Technology Fund, WWTF
Project number: ICT22-029 (Information and Communication Technology 2022)

Project Team

Robert Ganian (Principal Investigator)

Martin Nöllenburg (Principal Investigator)

Simon Dominik Fink (Postdoctoral Researcher)

Thomas Depian (PhD Student)

Alexander Firbas (PhD Student)

Research Statement

The project is centered around two well-established fields of information and communication technology: (1) graph drawing and visualization, which deals with the construction and analysis of geometric representations of graphs and networks subject to specific layout conventions, and (2) parameterized complexity analysis, which offers the tools to design efficient algorithms as well as lower bounds custom-tailored to the specific structural properties of relevant inputs. Recent advances have highlighted the huge potential for the application of parameterized techniques on graph drawing and visualization problems. The two PIs of this proposal – Robert Ganian and Martin Nöllenburg – have already spearheaded an initial push to bring the two fields closer together, and this proposal will allow them to bring these efforts into fruition by targeting and resolving some of the most prominent questions in this intersection.

The project focuses on developing the tools and frameworks that will facilitate the parameterized analysis of central problems in graph drawing and visualization. The work is split into four fundamental themes, covering Extension Problems, Linear and Layered Layouts, Geometric Graph Representations and Bridges to Network Visualization. The output of each theme will include not only new algorithms but also tight lower bounds and, where relevant, implementations, significantly advancing the state of the art in these increasingly prominent fields of research.

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FWF Cluster of Excellence: Bilateral Artificial Intelligence

Parameterized Graph Drawing

Project Team

Research Statement