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
Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org
Olivier Binette, Sokhna A York, Emma Hickerson, Youngsoo Baek, Sarvo Madhavan, Christina Jones
Automatic Neural Network Hyperparameter Optimization for Extrapolation: Lessons Learned from Visible and Near-Infrared Spectroscopy of Mango Fruit
Matthew Dirks, David Poole