The online interactive magazine of the Association for the Advancement of Artificial Intelligence

Reports of the Association for the Advancement of Artificial Intelligence’s 2021 Spring Symposium Series

The Association for the Advancement of Artificial Intelligence’s 2021 Spring Symposium Series was held virtually from March 22-24, 2021. There were ten symposia in the program: Applied AI in Healthcare: Safety, Community, and the Environment, Artificial Intelligence for K-12 Education, Artificial Intelligence for Synthetic Biology, Challenges and Opportunities for Multi-Agent Reinforcement Learning, Combining Machine Learning and Knowledge Engineering, Combining Machine Learning with Physical Sciences, Implementing AI Ethics, Leveraging Systems Engineering to Realize Synergistic AI/Machine-Learning Capabilities, Machine Learning for Mobile Robot Navigation in the Wild, and Survival Prediction: Algorithms, Challenges and Applications. This report contains summaries of all the symposia.

Will Machine Learning Outgrow Human Labeling?

Will Machine Learning Outgrow Human Labeling?

by Mike Schaekermann, Christopher M. Homan, Lora Aroyo, Praveen Paritosh, Kurt Bollacker, Chris Welty

Some machine learning (ML) rhetoric seems to imply an assumption or expectation that, at some point, machines will outgrow the need for human labeled data. Today’s reliance on such labeling is a sort of dirty little secret of AI, and some view it as a necessary means to a larger end. This bet is an attempt to formalize that attitude into a concrete question, whose answer can be measured over time.