Publication Date: April 29, 2011 Learn more at the CIS website Share this news article on: X Facebook LinkedIn Reddit Print
Transforming fusion from a scientific curiosity into a powerful clean energy source Driven to solve hard problems, Associate Professor Zachary Hartwig is advancing a new approach to commercial fusion energy. Read full story →
Researchers reduce bias in AI models while preserving or improving accuracy A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures. Read full story →
Cellular traffic congestion in chronic diseases suggests new therapeutic targets Chronic diseases like diabetes are prevalent, costly, and challenging to treat. A common denominator driving them may be a promising new therapeutic target. Read full story →
Revisiting reinforcement learning A detailed new look at dopamine signaling suggests neuroscientists’ model of reinforcement learning may need to be revised. Read full story →
Study: Some language reward models exhibit political bias Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data. Read full story →
Enabling AI to explain its predictions in plain language Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model. Read full story →
