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Tech News

From students crafting essays and engineers writing code to call center operators responding to customers, generative artificial intelligence tools have prompted a wave of experimentation over the past year. At MIT, these experiments have raised questions — some new, some ages old — about how these tools can change the way we live and work.  Can these tools make us better at our jobs, or might they make certain skills obsolete? How can we use these tools for good and minimize potential harm? The generative AI wave has elicited excitement, anxiety, and plenty of speculation about what’s to come, but […]
Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian approaches start with an assumption about the data’s patterns (prior probability), collecting evidence (e.g., new time series data), and continuously updating that assumption to form a posterior probability distribution. Traditional Bayesian approaches like Gaussian processes (GPs) and Structural Time Series are extensively used for modeling time series data, e.g., the commonly used Mauna Loa CO2 dataset. However, they often rely on domain experts to painstakingly select appropriate model components and may be computationally expensive. Alternatives such […]
Last summer, MIT President Sally Kornbluth and Provost Cynthia Barnhart issued a call for papers to “articulate effective roadmaps, policy recommendations, and calls for action across the broad domain of generative AI.” The response to the call far exceeded expectations with 75 proposals submitted. Of those, 27 proposals were selected for seed funding. In light of this enthusiastic response, Kornbluth and Barnhart announced a second call for proposals this fall. “The groundswell of interest and the caliber of the ideas overall made clear that a second round was in order,” they said in their email to MIT’s research community this […]

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