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
When Large Language Model Meets Optimization
Sen Huang, Kaixiang Yang, Sheng Qi, Rui Wang
Human-AI Safety: A Descendant of Generative AI and Control Systems Safety
Andrea Bajcsy, Jaime F. Fisac
Fusion Intelligence: Confluence of Natural and Artificial Intelligence for Enhanced Problem-Solving Efficiency
Rohan Reddy Kalavakonda, Junjun Huan, Peyman Dehghanzadeh, Archit Jaiswal, Soumyajit Mandal, Swarup Bhunia
Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation
Julia Barnett, Kimon Kieslich, Nicholas Diakopoulos
When AI Eats Itself: On the Caveats of Data Pollution in the Era of Generative AI
Xiaodan Xing, Fadong Shi, Jiahao Huang, Yinzhe Wu, Yang Nan, Sheng Zhang, Yingying Fang, Mike Roberts, Carola-Bibiane Schönlieb, Javier Del Ser, Guang Yang
Trustworthy AI in practice: an analysis of practitioners' needs and challenges
Maria Teresa Baldassarre, Domenico Gigante, Marcos Kalinowski, Azzurra Ragone, Sara Tibidò
Explainable AI for Ship Collision Avoidance: Decoding Decision-Making Processes and Behavioral Intentions
Hitoshi Yoshioka, Hirotada Hashimoto
Full Line Code Completion: Bringing AI to Desktop
Anton Semenkin, Vitaliy Bibaev, Yaroslav Sokolov, Kirill Krylov, Alexey Kalina, Anna Khannanova, Danila Savenkov, Darya Rovdo, Igor Davidenko, Kirill Karnaukhov, Maxim Vakhrushev, Mikhail Kostyukov, Mikhail Podvitskii, Petr Surkov, Yaroslav Golubev, Nikita Povarov, Timofey Bryksin
A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine
Hanguang Xiao, Feizhong Zhou, Xingyue Liu, Tianqi Liu, Zhipeng Li, Xin Liu, Xiaoxuan Huang
Adversarial Machine Learning Threats to Spacecraft
Rajiv Thummala, Shristi Sharma, Matteo Calabrese, Gregory Falco
LLM Theory of Mind and Alignment: Opportunities and Risks
Winnie Street
AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments
Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, Jeffrey Jopling, Michael Moor
Beyond traditional Magnetic Resonance processing with Artificial Intelligence
Amir Jahangiri, Vladislav Orekhov
Evaluating the Explainable AI Method Grad-CAM for Breath Classification on Newborn Time Series Data
Camelia Oprea, Mike Grüne, Mateusz Buglowski, Lena Olivier, Thorsten Orlikowsky, Stefan Kowalewski, Mark Schoberer, André Stollenwerk
Understanding and Evaluating Human Preferences for AI Generated Images with Instruction Tuning
Jiarui Wang, Huiyu Duan, Guangtao Zhai, Xiongkuo Min
Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer
Rajat Saxena, Bruce L. McNaughton
Adaptation of XAI to Auto-tuning for Numerical Libraries
Shota Aoki, Takahiro Katagiri, Satoshi Ohshima, Masatoshi Kawai, Toru Nagai, Tetsuya Hoshino
Large Language Models for Education: A Survey
Hanyi Xu, Wensheng Gan, Zhenlian Qi, Jiayang Wu, Philip S. Yu