MIT researchers designed a computationally efficient algorithm for machine learning with symmetric data that also requires fewer data for training than conventional approaches. Their work could inform the design of faster, more accurate machine-learning models for tasks like discovering new drugs or identifying astronomical phenomena. 

When it comes to susceptibility to influence on social media, “It’s not just about who you are—it’s about where you are in a network, and who you’re connected to,” said Luca Luceri, a lead scientist at USC’s Information Sciences Institute (ISI). A new study by Luceri and his team finds that the likelihood someone will be influenced online isn’t spread evenly across a social platform. Instead, it clusters.

They call this the Susceptibility Paradox; it’s a pattern in which users’ friends are, on average, more influenceable than the users themselves. And it may help explain how behaviors, trends, and ideas catch on—and why some corners of the internet are more vulnerable to influence than others.

The National Science Foundation has awarded $5 million over five years to the University of California, Davis, to run the Artificial Intelligence Institutes Virtual Organization, a community hub for new and existing AI institutes established by the federal government. AIVO is part of a $100 million public-private investment in AI announced by NSF July 29. 

Researchers have devised a new machine learning method to improve large-scale climate model projections and demonstrated that the new tool makes the models more accurate at both the global and regional level. This advance should provide policymakers with improved climate projections that can be used to inform policy and planning decisions.