APPROXIMATELY CORRECT MACHINE INTELLIGENCE LAB

Building intelligent systems applicable in the real world requires more than prediction. Driving decisions requires causal insights. Reliability requires models that are provably robust under clear assumptions. Deploying data-driven technology in society requires accounting for the complex dynamics and feedback loops mediating this interaction. Aligning with social desiderata such as fairness requires a philosophically coherent treatment. ACMI lab studies core machine learning methods, their applications in healthcare, and their social impacts. We seek to address these outer loop questions, while leveraging breakthroughs in representation learning to address the diverse raw data sources that deep learning has made accessible.

Also known as: ACMI Lab