{"id":11590,"date":"2024-07-09T13:52:55","date_gmt":"2024-07-09T13:52:55","guid":{"rendered":"https:\/\/incose.nl\/?page_id=11590"},"modified":"2024-08-13T14:09:53","modified_gmt":"2024-08-13T14:09:53","slug":"tno","status":"publish","type":"page","link":"https:\/\/incose.nl\/en\/incose-nl-workshop-2024\/tno\/","title":{"rendered":"TNO"},"content":{"rendered":"
\"INCOSE-NL<\/div><\/div><\/div><\/div><\/div><\/div><\/section>
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Frank Benders<\/h2>\n<\/div><\/div>

Principal Systems Engineer <\/em><\/p>\n<\/div><\/div>

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Michael Borth<\/h2>\n<\/div><\/div>

Senior Research Fellow <\/em><\/p>\n<\/div><\/div><\/div><\/div>

AI inside \u2013 What does that take in System Engineering?<\/strong><\/h2>\n

13:00 – 14:30<\/p>\n<\/div><\/div>

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Abstract
\n<\/b>Would you trust an AI-based system with your life? Under all conditions and circumstances?
\nTo trust a real-world system like that is beyond AI. It is about trust in the quality of both the system and the work that made it happen \u2013 and that is the domain of system engineering and lifecycle management. In these disciplines, we reduce the operational risk, ensure that a system does what its designers intended and keeps doing that. But as we learn in hard lessons: reaching these objectives gets a lot harder once AI is involved.
\nTo succeed, we need engineers to understand the challenges AI brings into their field \u2013 and the AI community to think about systems, not algorithms. At TNO, we bring this forward and invite you to join us in exploring how AI challenges System Engineering.
\nWe use system science, looking at why control engineering likes its state space very different from what learning systems go for, investigate how adaptive AI invalidates stability assumptions, which hinders Verification, Validation, and Preventive Maintenance, and re-evaluate architecting and design processes that now has to handle likelihoods instead of deterministic functions.
\nBased on this problem understanding, we confer possible system engineering approaches in this workshop. Interestingly, such techniques not only need to tackle future, AI-based systems \u2013 in all likelihood, they will also have some AI inside. So, we will discuss with you, being at the heart of System Engineering:\u202f\u00a0What do you consider applicable, feasible, and helpful to engineer trustworthy AI inside?<\/p>\n

Bio Frank Benders EngD<\/em>\u00a0<\/strong>
\nFrank Benders is a Principal Systems Engineer at TNO and one of the Scientific Leads of TNO\u2019s Artificial Intelligence program Appl.AI. Addressing challenges in several domains, his current focus is on autonomous systems and robotics for Automotive, Defense Safety & Security, and Civil Engineering.\u00a0<\/em>
\nDuring his career at TNO he integrated knowledge and expertise in transdisciplinary teams, always looking at big societal challenges. Frank has a master’s degree in electrical engineering and a EngD degree in Software Technology both from the Eindhoven Technical University.<\/em><\/p>\n

Bio Michael Borth<\/em>\u00a0<\/strong>
\nMichael Borth is a Senior Research Fellow of TNO. Addressing challenges that come with the introduction of autonomous behavior into vehicles and smart systems in general, he leads the AI & Data Science Team of the Integrated Vehicle Safety Department as well as TNO\u2019s flagship on System Engineering and Lifecycle Management of AI-based system.\u202f<\/em>
\nIn this, he fuses his expertise in AI which he gained at Daimler Research and Technology with his experience in system architecting for automotive, but also for smart high-tech systems, which he investigated at ESI in Eindhoven.\u202fMichael holds a PhD in Computer Science from the University of Ulm and pursues probabilistic and causal system modelling since these days.<\/em><\/p>\n<\/div><\/div>

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