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
Generative AI for Music and Audio
Hao-Wen Dong
Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems
Mackenzie Weygandt Mathis
Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework
Mengshuo Jia, Zeyu Cui, Gabriela Hug
Whack-a-Chip: The Futility of Hardware-Centric Export Controls
Ritwik Gupta, Leah Walker, Andrew W. Reddie
Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model
Aslan S. Dizaji
Exploring Accuracy-Fairness Trade-off in Large Language Models
Qingquan Zhang, Qiqi Duan, Bo Yuan, Yuhui Shi, Jialin Liu
AI-Driven Agents with Prompts Designed for High Agreeableness Increase the Likelihood of Being Mistaken for a Human in the Turing Test
U. León-Domínguez, E. D. Flores-Flores, A. J. García-Jasso, M. K. Gómez-Cuellar, D. Torres-Sánchez, A. Basora-Marimon
Federated Continual Learning for Edge-AI: A Comprehensive Survey
Zi Wang, Fei Wu, Feng Yu, Yurui Zhou, Jia Hu, Geyong Min
Suspected Undeclared Use of Artificial Intelligence in the Academic Literature: An Analysis of the Academ-AI Dataset
Alex Glynn
Test Security in Remote Testing Age: Perspectives from Process Data Analytics and AI
Jiangang Hao, Michael Fauss
No Free Delivery Service: Epistemic limits of passive data collection in complex social systems
Maximilian Nickel
Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm
Rushabh Solanki, Elliot Creager
AI-powered Digital Framework for Personalized Economical Quality Learning at Scale
Mrzieh VatandoustMohammadieh, Mohammad Mahdi Mohajeri, Ali Keramati, Majid Nili Ahmadabadi
Transforming the Hybrid Cloud for Emerging AI Workloads
Deming Chen, Alaa Youssef, Ruchi Pendse, André Schleife, Bryan K. Clark, Hendrik Hamann, Jingrui He, Teodoro Laino, Lav Varshney, Yuxiong Wang, Avirup Sil, Reyhaneh Jabbarvand, Tianyin Xu, Volodymyr Kindratenko, Carlos Costa, Sarita Adve, Charith Mendis, Minjia Zhang, Santiago Núñez-Corrales, Raghu Ganti, Mudhakar Srivatsa, Nam Sung Kim, Josep Torrellas, Jian Huang, Seetharami Seelam, Klara Nahrstedt, Tarek Abdelzaher, Tamar Eilam, Huimin Zhao, Matteo Manica, Ravishankar Iyer, Martin Hirzel, Vikram Adve, Darko Marinov, Hubertus Franke, Hanghang Tong, Elizabeth Ainsworth, Han Zhao, Deepak Vasisht, Minh Do, Fabio Oliveira, Giovanni Pacifici, Ruchir Puri, Priya Nagpurkar
Existential Conversations with Large Language Models: Content, Community, and Culture
Murray Shanahan, Beth Singler
Automating Sonologists USG Commands with AI and Voice Interface
Emad Mohamed, Shruti Tiwari, Sheena Christabel Pravin