A examine by researchers on the universities of California, Berkeley and Texas at Austin highlights how off-label use of datasets can inject bias into artificial intelligence algorithms. UC Berkeley’s Michael Lustig mentioned the researchers traced their failure to replicate the results of a medical imaging study to a preprocessed dataset used to train the algorithm. The team processed raw images using two frequent data-processing pipelines that impression many open-access magnetic resonance imaging databases—commercial scanner software and data storage with JPEG compression. It skilled three picture reconstruction algorithms on these datasets, then quantified the accuracy of the reconstructed images versus the extent of data processing. The researchers mentioned though the algorithmically produced photographs look good, the shortcoming to reproduce them with uncooked knowledge highlights the danger of applying biased algorithms clinically. [newline]MeriTalk is staying near the leaders on the entrance strains, bringing you new ideas and lessons learned.
We will send you a fast reminder sooner or later, in case you change your mind. UCF and NASA Researchers Design Charged ‘Power Suits’ for Electric Vehicles and Spacecraft The lightweight, supercapacitor-battery hybrid composite materials supplies power and is as strong as metal. UCF’s Programming Team Wins Regionals, Again Earns Berth in 2022 North America Championship A total of seven programming groups from UCF competed on this weekend’s event, all ending within the high 15.
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