🔬 Research Summary by Meredith Ringel Morris, Director of Human-AI Interaction Research at Google DeepMind; she is also an Affiliate Professor at the University of Washington, and is an ACM Fellow and member of the ACM … [Read more...] about The Design Space of Generative Models
Technical Methods
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
🔬 Research Summary by Shangbin Feng, Chan Young Park, and Yulia Tsvetkov. Shangbin Feng is a Ph.D. student at University of Washington.Chan Young Park is a Ph.D. student at Carnegie Mellon University, studying … [Read more...] about From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
🔬 Research Summary by Stephen Casper, an MIT PhD student working on AI interpretability, diagnostics, and safety. [Original paper by Stephen Casper,* Xander Davies,* Claudia Shi, Thomas Krendl Gilbert, Jérémy … [Read more...] about Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
A Critical Analysis of the What3Words Geocoding Algorithm
🔬 Research Summary by Rudy Arthur, a Senior Lecturer in Data Science at the University of Exeter. [Original paper by Rudy Arthur] Overview: What3Words (W3W) is a geocoding app that has been aggressively … [Read more...] about A Critical Analysis of the What3Words Geocoding Algorithm
Self-Consuming Generative Models Go MAD
🔬 Research Summary by Josue Casco-Rodriguez and Sina Alemohammad. Josue is a 2nd-year PhD student at Rice University. He is interested in illuminating the intersection of machine learning and neuroscience from … [Read more...] about Self-Consuming Generative Models Go MAD