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The data challenges in Industrial AI include quality, quantity, management, interoperability, ownership and governance. The last challenge in particular lies in dealing with Generative AI models, which can produce synthetic data but introduce new risks in terms of data authenticity and accountability.
Oct 31, 2024
Sep 3, 2022 · This paper summarizes challenges for AI Systems Engineering. Two examples of AI systems engineering are provided: the TRUMPF Sorting Guide and ABB BatchInsight.
The industry's need for systems engineering is to expand the methods and procedures known today in sys- tems engineering to include the aspect of data economics.
This talk summarizes important experiences we cultivated in several projects where we developed AI methods for industrial customers like chemical production ...
Sep 4, 2024 · The top five AI challenges that businesses will face are data-related, ethical concerns, regulatory and legal, bias and transparency.
Jun 11, 2024 · This talk summarizes important experiences we cultivated in several projects where we developed AI methods for industrial customers.
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Oct 19, 2023 · The Challenges of Implementing AI in Manufacturing · Implementation costs: · Data silos and disconnected systems: · Data management and security: ...
Dec 16, 2024 · By 2024, AI will be increasingly challenged with problems relating to privacy and personal data protection, algorithm bias and transparency ethics.
Missing data is a prevalent problem in data sets. In industrial use cases, faulty sensors or errors during data integration are common causes for systematically ...
Sep 16, 2022 · With Dr Julius Pfrommer, Head of Department, Fraunhofer IOSB Many AI projects in industry produce a proof-of-concept, but do not go into ...