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
Turing Representational Similarity Analysis (RSA): A Flexible Method for Measuring Alignment Between Human and Artificial Intelligence
Mattson Ogg, Ritwik Bose, Jamie Scharf, Christopher Ratto, Michael Wolmetz
2-Factor Retrieval for Improved Human-AI Decision Making in Radiology
Jim Solomon, Laleh Jalilian, Alexander Vilesov, Meryl Mathew, Tristan Grogan, Arash Bedayat, Achuta Kadambi
An AI-Driven Data Mesh Architecture Enhancing Decision-Making in Infrastructure Construction and Public Procurement
Saurabh Mishra, Mahendra Shinde, Aniket Yadav, Bilal Ayyub, Anand Rao
NüshuRescue: Revitalization of the endangered Nüshu Language with AI
Ivory Yang, Weicheng Ma, Soroush Vosoughi
Towards the Ultimate Programming Language: Trust and Benevolence in the Age of Artificial Intelligence
Bartosz Sawicki, Michał Śmiałek, Bartłomiej Skowron
Responsible AI Governance: A Response to UN Interim Report on Governing AI for Humanity
Sarah Kiden, Bernd Stahl, Beverley Townsend, Carsten Maple, Charles Vincent, Fraser Sampson, Geoff Gilbert, Helen Smith, Jayati Deshmukh, Jen Ross, Jennifer Williams, Jesus Martinez del Rincon, Justyna Lisinska, Karen O'Shea, Márjory Da Costa Abreu, Nelly Bencomo, Oishi Deb, Peter Winter, Phoebe Li, Philip Torr, Pin Lean Lau, Raquel Iniesta, Gopal Ramchurn, Sebastian Stein, Vahid Yazdanpanah
What fifty-one years of Linguistics and Artificial Intelligence research tell us about their correlation: A scientometric review
Mohammed Q. Shormani
Artificial intelligence contribution to translation industry: looking back and forward
Mohammed Q. Shormani
The AI Interface: Designing for the Ideal Machine-Human Experience (Editorial)
Aparna Sundar, Tony Russell-Rose, Udo Kruschwitz, Karen Machleit
Generative AI Literacy: Twelve Defining Competencies
Ravinithesh Annapureddy, Alessandro Fornaroli, Daniel Gatica-Perez
Zero-Forget Preservation of Semantic Communication Alignment in Distributed AI Networks
Jingzhi Hu, Geoffrey Ye Li
Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance
Philipp Brauner, Felix Glawe, Gian Luca Liehner, Luisa Vervier, Martina Ziefle
PREBA: A Hardware/Software Co-Design for Multi-Instance GPU based AI Inference Servers
Gwangoo Yeo, Jiin Kim, Yujeong Choi, Minsoo Rhu
A Unified Platform for At-Home Post-Stroke Rehabilitation Enabled by Wearable Technologies and Artificial Intelligence
Chenyu Tang, Ruizhi Zhang, Shuo Gao, Zihe Zhao, Zibo Zhang, Jiaqi Wang, Cong Li, Junliang Chen, Yanning Dai, Shengbo Wang, Ruoyu Juan, Qiaoying Li, Ruimou Xie, Xuhang Chen, Xinkai Zhou, Yunjia Xia, Jianan Chen, Fanghao Lu, Xin Li, Ninglli Wang, Peter Smielewski, Yu Pan, Hubin Zhao, Luigi G. Occhipinti
Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
Majid Ghasemi, Amir Hossein Mousavi, Dariush Ebrahimi
Methods to Assess the UK Government's Current Role as a Data Provider for AI
Neil Majithia, Elena Simperl
Emergence of Self-Identity in AI: A Mathematical Framework and Empirical Study with Generative Large Language Models
Minhyeok Lee
Abductive Symbolic Solver on Abstraction and Reasoning Corpus
Mintaek Lim, Seokki Lee, Liyew Woletemaryam Abitew, Sundong Kim
The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?
Jérémie Sublime