By Adam Zewe, contributing writer for News.mit.edu
Ask a smart home device for the weather forecast, and it takes several seconds for the device to respond. One reason this latency occurs is because connected devices don’t have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it. The model is stored in a data center that may be hundreds of miles away, where the answer is computed and sent to the device.
By contributing writers for NIST.gov
A lack of oxygen can reduce even the most furious flame to smoldering ash. But when fresh air rushes in, say after a firefighter opens a window or door to a room, the blaze may be suddenly and violently resurrected. This explosive phenomenon, called backdraft, can be lethal and has been challenging for firefighters to anticipate.
By Shania Kennedy, contributing writer for Healthitanalytics.com
A new study published last week in PLOS Digital Health shows that an artificial intelligence (AI)-based predictive analytics tool can accurately forecast patient risk for deterioration and other adverse outcomes within six hours of hospitalization.
By Nate Farshchi, contributing writer for Builtin.com
Artificial intelligence (AI) and machine learning (ML) are ubiquitous in consumers’ lives, from the “up next” suggestions from your streaming service to routes suggested by your GPS when you plug an address into your phone for directions. Less visible impacts of AI and ML include the use of AI to control data center efficiency and cooling or the management of restaurant wait times, as some companies use AI to make decisions about how many burgers to cook for the day’s lunch rush.
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