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
Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset
Janis Goldzycher, Paul Röttger, Gerold Schneider
Integrating behavior analysis with machine learning to predict online learning performance: A scientometric review and empirical study
Jin Yuan, Xuelan Qiu, Jinran Wu, Jiesi Guo, Weide Li, You-Gan Wang
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
Mateusz Łajszczak, Guillermo Cámbara, Yang Li, Fatih Beyhan, Arent van Korlaar, Fan Yang, Arnaud Joly, Álvaro Martín-Cortinas, Ammar Abbas, Adam Michalski, Alexis Moinet, Sri Karlapati, Ewa Muszyńska, Haohan Guo, Bartosz Putrycz, Soledad López Gambino, Kayeon Yoo, Elena Sokolova, Thomas Drugman
T-RAG: Lessons from the LLM Trenches
Masoomali Fatehkia, Ji Kim Lucas, Sanjay Chawla
Teenagers and Artificial Intelligence: Bootcamp Experience and Lessons Learned
Uzay Macar, Blake Castleman, Noah Mauchly, Michael Jiang, Asma Aouissi, Salma Aouissi, Xena Maayah, Kaan Erdem, Rohith Ravindranath, Andrea Clark-Sevilla, Ansaf Salleb-Aouissi
Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring
Alexandra Zytek, Wei-En Wang, Sofia Koukoura, Kalyan Veeramachaneni