A causal theory for studying the cause-and-effect relationships of genes
By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.
Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
By enabling users to chat with an older version of themselves, Future You is aimed at reducing anxiety and guiding young people to make better choices.
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.