Auflistung nach Schlagwort "ML"
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- KonferenzbeitragAn Anthropomorphic Approach to establish an Additional Layer of Trustworthiness of an AI Pilot(Software Engineering 2022 Workshops, 2022) Regli, Christoph; Annighoefer, BjörnAI algorithms promise solutions for situations where conventional, rule-based algorithms reach their limits. They perform in complex problems yet unknown at design time, and highly efficient functions can be implemented without having to develop a precise algorithm for the problem at hand. Well-tried applications show the AI’s ability to learn from new data, extrapolate on unseen data, and adapt to a changing environment — a situation encountered in fl ight operations. In aviation, however, certifi cation regulations impede the implementation of non-deterministic or probabilistic algorithms that adapt their behaviour with increasing experience. Regulatory initiatives aim at defining new development standards in a bottom-up approach, where the suitability and the integrity of the training data shall be addressed during the development process, increasing trustworthiness in eff ect. Methods to establish explainability and traceability of decisions made by AI algorithms are still under development, intending to reach the required level of trustworthiness. This paper outlines an approach to an independent, anthropomorphic software assurance for AI/ML systems as an additional layer of trustworthiness, encompassing top-down black-box testing while relying on a well-established regulatory framework.
- KonferenzbeitragKünstliche Intelligenz in den Fingerspitzen(Workshops der 21. Fachtagung Bildungstechnologien (DELFI), 2023) Witt, ClemensIn diesem Beitrag werden zwei kollaborative Lernspiele für Multitouchdisplays im Themenfeld „Künstliche Intelligenz“ vorgestellt. Diese erlauben Schüler:innen der Sekundarstufe I eine eigenständige und spielerische Auseinandersetzung mit den Problemlöseprozessen des maschi-nellen Lernens. Durch ihre systemunabhängige Implementierung können sie auf beliebigen Endgeräten genutzt und in vielfältigen Spielszenarien eingesetzt werden.
- TextdokumentA Platform Framework for the Adoption and Operation of ML-based Smart Services in the Data Ecosystem of Smart Living(INFORMATIK 2022, 2022) Kortum,Henrik; Kohl,Tobias; Hubertus,Dominik; Hinz,Oliver; Thomas,OliverSmart services utilizing machine learning (ML) take a more and more important position in our daily lives. As a result, the need for a large smart living data ecosystem has emerged that links the most diverse areas of life with each other. This ecosystem is characterized by a multitude of different actors, a heterogeneous system, product and service landscape as well as high data protection requirements. To provide real added value and holistic solutions in this tension field, the orchestration of different subservices is necessary, bundling the functionality of individual smart devices and models. For this goal to be achieved, a framework that considers the complex challenges of the ecosystem focusing on the adoption and operation of smart services is required. Here our paper makes a key contribution. Based on requirements from the literature and concrete smart living use cases, we derive a platform framework for this data ecosystem.