Hybrid machine/human ML

1 Human-in-the-loop learning

There is probably a lot more going on in the world of human-in-the-loop learning than I am aware of. For now, see adaptive design of experiments.

2 Our robot regency

How long will it be worthwhile augmenting humans before it is more efficient to replace them? See the robot regency.

3 Incoming

• Lauren Oakden-Rayner, No Doctor Required: Autonomy, Anomalies, and Magic Puddings

• Machine learning for medical imaging: methodological failures and recommendations for the future

• Impact of artificial intelligence on pathologists’ decisions: an experiment

• Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions.

• How to Make Tech Products (that Don’t Cause Depression and War)

• Using Artificial Intelligence to Augment Human Intelligence

• Ethan Mollick, Centaurs and Cyborgs on the Jagged Frontier

• Tackling Collaboration Challenges in the Development of ML-Enabled Systems

  I highlight the findings of a study on which I teamed up with colleagues Nadia Nahar (who led this work as part of her PhD studies at Carnegie Mellon University) and Christian Kästner (also from Carnegie Mellon University) and Shurui Zhou (of the University of Toronto). The study sought to identify collaboration challenges common to the development of ML-enabled systems. Through interviews conducted with numerous individuals engaged in the development of ML-enabled systems, we sought to answer our primary research question: What are the collaboration points and corresponding challenges between data scientists and engineers? We also examined the effect of various development environments on these projects. Based on this analysis, we developed preliminary recommendations for addressing the collaboration challenges reported by our interviewees.

4 References