Critical Lesson
"Critical Lesson" research focuses on extracting valuable insights and best practices from diverse applications of artificial intelligence and machine learning. Current efforts concentrate on improving model performance and reliability across various domains, employing techniques like deep learning, retrieval-augmented generation, and mixture-of-experts models, while also addressing challenges in data quality, explainability, and ethical considerations. These lessons learned are crucial for advancing AI development, enhancing the trustworthiness of AI systems, and improving the design of human-centered AI applications across fields ranging from healthcare to robotics.
Papers
Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks
Mohamad Fazelnia, Viktoria Koscinski, Spencer Herzog, Mehdi Mirakhorli
Lessons Learned in Quadruped Deployment in Livestock Farming
Francisco J. Rodríguez-Lera, Miguel A. González-Santamarta, Jose Manuel Gonzalo Orden, Camino Fernández-Llamas, Vicente Matellán-Olivera, Lidia Sánchez-González