{"id":35644,"date":"2025-09-08T13:30:11","date_gmt":"2025-09-08T11:30:11","guid":{"rendered":"https:\/\/www.risc-software.at\/together-for-the-future-of-symbolic-ai\/"},"modified":"2026-06-24T17:05:33","modified_gmt":"2026-06-24T15:05:33","slug":"together-for-the-future-of-symbolic-ai","status":"publish","type":"post","link":"https:\/\/www.risc-software.at\/en\/together-for-the-future-of-symbolic-ai\/","title":{"rendered":"Together for the future of symbolic AI."},"content":{"rendered":"\n

PIONEER TOGETHER.<\/h2>\n\n\n\n

Hagenberg. The RISC Institute of the Johannes Kepler University Linz and the resulting RISC Software GmbH are working closely together to develop a new generation of artificial intelligence: symbolic AI. <\/em><\/p>\n\n\n\n

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Difference to data-based AI<\/h2>\n\n\n\n

The currently dominant AI is based on machine learning and large amounts of data. Symbolic AI, on the other hand, uses mathematical rules, logical and algebraic structures and formal models. This makes it explainable, independent of specific data models and resistant to change. This is precisely what is crucial when AI is used in medicine, industry or research. It is also important to note that the two approaches do not contradict each other, but complement each other. <\/p>\n<\/div>\n\n\n\n

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f.l.t.r. Wolfgang Freiseisen, Bruno Buchberger, Carsten Schneider – \u00a9 RISC Software GmbH, Reprint free of charge<\/p>\n<\/div>\n<\/div>\n\n\n

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“Symbolic computing is not a niche tool, but a central foundation of modern artificial intelligence – and its importance will grow rapidly in the coming years.”<\/em><\/p>\n\n\n\n

– Wolfgang Freiseisen, CEO of RISC Software GmbH<\/em><\/p>\n<\/div>\n<\/div>\n\n\n

\"Wolfgang<\/figure>\n<\/div>\n<\/div>\n\n\n

Cooperation in projects<\/h2>\n\n\n\n

One example of this close cooperation is the InProSSA project. Here, symbolic and data-based methods are combined to develop safe and comprehensible AI systems. At the same time, symbolic AI also plays an important role in international research. For example, the RISC Institute is working with the Deutsches Elektronen-Synchrotron (DESY) to analyze data from the Large Hadron Collider at CERN <\/p>\n\n\n

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“Symbolic methods make it possible to model complex systems in science and technology with structural precision and to gain important insights and properties from them – from particle physics to software verification. Together, we are bringing these technologies from research into application.”<\/em><\/p>\n\n\n\n

– Prof. Dr. Carsten Schneider, Director of the RISC Institute<\/em><\/p>\n<\/div>\n<\/div>\n\n\n

\"Carsten<\/figure>\n<\/div>\n<\/div>\n\n\n

Applications in medicine and industry<\/h2>\n\n\n\n

RISC Software GmbH uses symbolic AI in areas such as medical diagnostics and simulation-based decision-making. In these applications, it is particularly important that AI systems work comprehensibly and reliably. <\/p>\n\n\n\n

The RISC Institute is leading a new initiative to create a publishing forum in which, for the first time, the two fields of Symbolic Computation (SC) and Machine Learning (ML) are consciously linked as the two complementary and cooperative approaches to AI: The SCML: A Publishing Forum for Symbolic Computation and Machine Learning<\/a>. The forum is part of the “Journal of Symbolic Computation” (Elservier Publishing Company), which was founded by RISC in 1985. <\/p>\n\n\n\n

An important pioneer of this development is Prof. Dr. Bruno Buchberger, founder of the RISC Institute and initiator of the Hagenberg Software Park. Today, he continues to contribute his experience and ideas to the strategic development of the institute and GmbH.<\/p>\n\n\n

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“When RISC and the Journal of Symbolic Computation were founded in 1965, I already formulated the field of “automatic programming” – which even then some people liked to see as “artificial intelligence” – as an essential part and important application of symbolic computation in the editorial of the journal, and I am pleased that the RISC Institute and the company will jointly advance the tool of SC together with ML in research, development and application in the new phase of AI.”<\/em><\/p>\n\n\n\n

-University Professor Dr. Bruno Buchberger<\/em><\/p>\n<\/div>\n<\/div>\n\n

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\"Bruno<\/figure>\n\n\n\n

(C)O\u00d6N\/Weihbold<\/p>\n<\/div>\n<\/div><\/div>\n<\/div>\n\n

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“Symbolic AI is not an alternative – it is the necessary further development of classic AI. We are shaping this future together.”<\/em><\/p>\n\n\n\n

– Prof. Dr. Carsten Schneider, Director of the RISC Institute & Wolfgang Freiseisen, CEO of RISC Software GmbH<\/em><\/p>\n<\/div>\n<\/div>\n\n

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Forschungsprojekt InProSSa<\/a><\/p>\n

SCML: A Publishing Forum for Symbolic Computation and Machine Learning<\/a>.<\/p>\n <\/div>\n <\/div>\n<\/div>\n\n\n\n

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\n \u00dcber das RISC Institut und die RISC Software GmbH <\/h3>\n <\/span>\n <\/div>\n
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Das Forschungsinstitut f\u00fcr Symbolisches Rechnen (RISC)<\/strong> wurde 1987 von Bruno Buchberger gegr\u00fcndet und ist ein Institut der Johannes Kepler Universit\u00e4t (JKU)<\/strong> in Linz. Es geh\u00f6rt zum Institut f\u00fcr Mathematik der Technisch-Naturwissenschaftlichen Fakult\u00e4t und ist Teil des Softwareparks Hagenberg<\/strong>.<\/p>\n

Aus dem RISC Institut ging 1992 die RISC Software GmbH<\/strong> hervor. Sie ist seit \u00fcber 30 Jahren f\u00fchrend in der angewandten Forschung und Produktentwicklung. Als au\u00dferuniversit\u00e4re Forschungseinrichtung entwickelt das Unternehmen innovative Softwarel\u00f6sungen in Bereichen wie KI, digitale Zwillinge, Simulation und Optimierung \u2013 von der Grundlagenforschung bis zur praxisnahen Anwendung. RISC Software begleitet Organisationen in Industrie und Medizin erfolgreich auf ihrem digitalen Weg.<\/p>\n

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Ein besonderer Fokus liegt auf der kontinuierlichen Weiterentwicklung der Mitarbeiter*innen \u2013 durch gezielte Weiterbildung, interdisziplin\u00e4ren Austausch und enge Kooperationen mit der Wissenschaft.<\/p>\n

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RISC HAGENBERG. PIONEER NOW.<\/p>\n <\/div>\n <\/div>\n<\/div>\n\n\n\n\n

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<\/path><\/svg><\/span> Press release as PDF<\/a><\/div>\n<\/div>\n\n\n\n
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Contact us
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