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Auflistung nach Autor:in "Stein, Hannah"

1 - 3 von 3
  • Workshopbeitrag
    inSIDE Fair Dialogues: Assessing and Maintaining Fairness in Human-Computer-Interaction
    (Mensch und Computer 2018 - Workshopband, 2018) Janzen, Sabine; Bleymehl, Ralf; Alam, Aftab; Xu, Sascha; Stein, Hannah
    For simulating human-like intelligence in dialogue systems, individual and partially conflicting motives of interlocutors have to be processed in dialogue planning. Little attention has been given to this topic in dialogue planning in contrast to dialogues that are fully aligned with anticipated user motives. When considering dialogues with congruent and incongruent interlocutor motives like sales dialogues, dialogue systems need to find a balance between competition and cooperation. As a means for balancing such mixed motives in dialogues, we introduce the concept of fairness defined as combination of fair-ness state and fairness maintenance process. Focusing on a dialogue between human and robot in a retailing scenario, we show the application of the SatIsficing Dialogue Engine (inSIDE) - a platform for assessing and maintaining fairness in dialogues with mixed motives.
  • Konferenzbeitrag
    A Proposal for Physics-Informed Quantum Graph Neural Networks for Simulating Laser Cutting Processes
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Mehrin Ruhi, Zurana; Stein, Hannah; Maaß, Wolfgang
    Simulations are crucial for production monitoring and planning in manufacturing. Still, the performance of simulations based on mathematical modeling and machine learning methods is limited and opaque to widespread application. Quantum computing offers the potential for exponential acceleration of these tools, while physically informed neural networks (PINN) improve learning and reduce ambiguity. Objective of this paper is to explore the concept of developing a tool for laser cutting simulation based on a quantum neural network that can be trained on thermal physics principles.
  • Konferenzbeitrag
    Towards Energy-Efficient Large-Scale Artificial Intelligence for Sustainable Data Centers
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Dokic, Dusan; Stein, Hannah; Janzen, Sabine; Maaß, Wolfgang
    The growing interest in AI services has led to a higher demand for computing power to train and execute complex AI models, causing a surge in power consumption in data centers. Together with rising costs for electricity, gas, petroleum, and coal, and the national target for climate neutrality of data centers by 2027, the ability to operate data centers economically is threatened in Germany. To address these issues, a pressing need to improve the sustainability of data centers and that of artificial intelligence. This paper proposes a roadmap to develop sustainable and resource-efficient data centers and AI systems. The roadmap includes four key building blocks: sustainable data centers, AI algorithms, AI sustainability framework, and economic efficiency analysis. Each building block poses pivotal research questions grounded in contemporary literature to guide the pursuit of environmental sustainability in data centers and AI.