Artificial Intelligence
Artificial intelligence (AI) research focuses on creating systems capable of performing tasks that typically require human intelligence, with current efforts concentrating on improving model alignment with human values, enhancing transparency and accountability in AI systems, and mitigating risks associated with bias and malicious use. Prominent approaches involve large language models (LLMs), deep learning architectures like nnU-Net, and reinforcement learning techniques, often applied within specific domains such as healthcare, cybersecurity, and scientific research. The widespread adoption of AI across diverse fields necessitates rigorous investigation into its ethical implications, safety, and societal impact, driving ongoing research to develop more robust, reliable, and responsible AI systems.
Papers
Artificial Intelligence: Arguments for Catastrophic Risk
Adam Bales, William D'Alessandro, Cameron Domenico Kirk-Giannini
AI Does Not Alter Perceptions of Text Messages
N'yoma Diamond
A Decision Theoretic Framework for Measuring AI Reliance
Ziyang Guo, Yifan Wu, Jason Hartline, Jessica Hullman
Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models
Yunhong He, Jianling Qiu, Wei Zhang, Zhengqing Yuan
Beyond principlism: Practical strategies for ethical AI use in research practices
Zhicheng Lin
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
Jan-Philipp Redlich, Friedrich Feuerhake, Joachim Weis, Nadine S. Schaadt, Sarah Teuber-Hanselmann, Christoph Buck, Sabine Luttmann, Andrea Eberle, Stefan Nikolin, Arno Appenzeller, Andreas Portmann, André Homeyer
On the Emergence of Symmetrical Reality
Zhenliang Zhang, Zeyu Zhang, Ziyuan Jiao, Yao Su, Hangxin Liu, Wei Wang, Song-Chun Zhu
Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students
Chengbo Zheng, Kangyu Yuan, Bingcan Guo, Reza Hadi Mogavi, Zhenhui Peng, Shuai Ma, Xiaojuan Ma
Empathy and the Right to Be an Exception: What LLMs Can and Cannot Do
William Kidder, Jason D'Cruz, Kush R. Varshney
Clinical Melanoma Diagnosis with Artificial Intelligence: Insights from a Prospective Multicenter Study
Lukas Heinlein, Roman C. Maron, Achim Hekler, Sarah Haggenmüller, Christoph Wies, Jochen S. Utikal, Friedegund Meier, Sarah Hobelsberger, Frank F. Gellrich, Mildred Sergon, Axel Hauschild, Lars E. French, Lucie Heinzerling, Justin G. Schlager, Kamran Ghoreschi, Max Schlaak, Franz J. Hilke, Gabriela Poch, Sören Korsing, Carola Berking, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Eva Krieghoff-Henning, Titus J. Brinker
Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators
Wiebke Hutiri, Oresiti Papakyriakopoulos, Alice Xiang
Bridging Generative Networks with the Common Model of Cognition
Robert L. West, Spencer Eckler, Brendan Conway-Smith, Nico Turcas, Eilene Tomkins-Flanagan, Mary Alexandria Kelly
A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams
Shahroz Tariq, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris
Design and Implementation of Hardware Accelerators for Neural Processing Applications
Shilpa Mayannavar, Uday Wali