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
Can a Machine be Conscious? Towards Universal Criteria for Machine Consciousness
Nur Aizaan Anwar, Cosmin Badea
Multi Class Depression Detection Through Tweets using Artificial Intelligence
Muhammad Osama Nusrat, Waseem Shahzad, Saad Ahmed Jamal
Food Development through Co-creation with AI: bread with a "taste of love"
Takuya Sera, Izumi Kuwata, Yuki Taya, Noritaka Shimura, Yosuke Motohashi
Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
Shayne Longpre, Robert Mahari, Naana Obeng-Marnu, William Brannon, Tobin South, Katy Gero, Sandy Pentland, Jad Kabbara
Transparent AI: Developing an Explainable Interface for Predicting Postoperative Complications
Yuanfang Ren, Chirayu Tripathi, Ziyuan Guan, Ruilin Zhu, Victoria Hougha, Yingbo Ma, Zhenhong Hu, Jeremy Balch, Tyler J. Loftus, Parisa Rashidi, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Azra Bihorac
Enhancing AI Diagnostics: Autonomous Lesion Masking via Semi-Supervised Deep Learning
Ting-Ruen Wei, Michele Hell, Dang Bich Thuy Le, Aren Vierra, Ran Pang, Mahesh Patel, Young Kang, Yuling Yan
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
Sarah Dean, Evan Dong, Meena Jagadeesan, Liu Leqi
Exploring the landscape of large language models: Foundations, techniques, and challenges
Milad Moradi, Ke Yan, David Colwell, Matthias Samwald, Rhona Asgari
CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models
Minjung Shin, Donghyun Kim, Jeh-Kwang Ryu
Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review
Ahmed Maged, Salah Haridy, Herman Shen
Taxonomy to Regulation: A (Geo)Political Taxonomy for AI Risks and Regulatory Measures in the EU AI Act
Sinan Arda
Large Language Models Meet User Interfaces: The Case of Provisioning Feedback
Stanislav Pozdniakov, Jonathan Brazil, Solmaz Abdi, Aneesha Bakharia, Shazia Sadiq, Dragan Gasevic, Paul Denny, Hassan Khosravi
Exploring Augmentation and Cognitive Strategies for AI based Synthetic Personae
Rafael Arias Gonzalez, Steve DiPaola
Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty
Ubaid Azam, Imran Razzak, Shelly Vishwakarma, Hakim Hacid, Dell Zhang, Shoaib Jameel
A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories
Meriam Zribi, Paolo Pagliuca, Francesca Pitolli
Learning Wireless Data Knowledge Graph for Green Intelligent Communications: Methodology and Experiments
Yongming Huang, Xiaohu You, Hang Zhan, Shiwen He, Ningning Fu, Wei Xu
TEL'M: Test and Evaluation of Language Models
George Cybenko, Joshua Ackerman, Paul Lintilhac