Cognitive Intelligence
Cognitive intelligence research aims to understand and model the complex processes underlying intelligent behavior, encompassing learning, reasoning, and problem-solving. Current research focuses on developing and evaluating computational models, particularly large language models (LLMs) and other deep learning architectures, using various benchmarks and novel datasets designed to assess different aspects of intelligence, such as reasoning, knowledge representation, and even theory of mind. This work has implications for advancing artificial intelligence, improving our understanding of human cognition, and informing the development of more robust and ethical AI systems for diverse applications. Furthermore, the field is actively exploring how to measure and define intelligence itself, moving beyond traditional metrics to encompass more nuanced aspects of cognitive abilities.
Papers
Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
Luis M. Lopez-Ramos, Florian Leiser, Aditya Rastogi, Steven Hicks, Inga Strümke, Vince I. Madai, Tobias Budig, Ali Sunyaev, Adam Hilbert
Intellectual Property Protection for Deep Learning Model and Dataset Intelligence
Yongqi Jiang, Yansong Gao, Chunyi Zhou, Hongsheng Hu, Anmin Fu, Willy Susilo
Ensemble-based, large-eddy reconstruction of wind turbine inflow in a near-stationary atmospheric boundary layer through generative artificial intelligence
Alex Rybchuk, Luis A. Martínez-Tossas, Stefano Letizia, Nicholas Hamilton, Andy Scholbrock, Emina Maric, Daniel R. Houck, Thomas G. Herges, Nathaniel B. de Velder, Paula Doubrawa
Metacognitive Monitoring: A Human Ability Beyond Generative Artificial Intelligence
Markus Huff, Elanur Ulakçı