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AI Used to Detect Anomalies in Power Grid Failures

Finding a fault in the power grid of a country can be like attempting to locate a needle in a huge haystack. ....

United States , Jie Chen , Enyan Dai , Megan Craig , International Conference On Learning Representations , Watson Ai Lab , Pennsylvania State University , Research Staff Member , Us Department Of Energy , Intelligence Used , Detect Anomalies , Power Grid , Senior Study Author , Pennsylvania State , International Conference , Graph Augmented Normalizing Flows , Anomaly Detection , Multiple Time Series ,

ML system proves adept at pinpointing power grid problems

ML system proves adept at pinpointing power grid problems
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Jie Chen , Enyan Dai , International Conference On Learning Representations , Watson Ai Lab , Pennsylvania State University , International Conference , Learning Representations ,

Using artificial intelligence to find anomalies hiding in massive datasets

Researchers at the MIT-IBM Watson AI lab have developed a computationally efficient method that could be used to identify anomalies in the U.S. power grid in real time. The novel technique augments a special type of machine-learning model with a powerful graph structure, and does not require any labeled data to train. ....

Jie Chen , Enyan Dai , International Conference On Learning Representations , Watson Ai Lab , Pennsylvania State University , Us Department Of Energy , Pennsylvania State , International Conference , Mit Ibm Watson Ai Lab , Ormalizing Flow Models ,

Using artificial intelligence to find anomalies hiding in massive datasets

Identifying a malfunction in the nation’s power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated ....

Jie Chen , Enyan Dai , International Conference On Learning Representations , Watson Ai Lab , Pennsylvania State University , Us Department Of Energy , Pennsylvania State , International Conference ,

New machine learning model could remove bias from social network connections

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UNIVERSITY PARK, Pa. Did you ever wonder how social networking applications like Facebook and LinkedIn make recommendations on the people you should friend or pages you should follow?
Behind the scenes are machine learning models that classify nodes based on the data they contain about users for example, their level of education, location or political affiliation. The models then use these classifications to recommend people and pages to each user. But there is significant bias in the recommendations made by these models known as graph neural networks (GNNs) as they rely on user features that are highly related to sensitive attributes such as gender or skin color. ....

Enyan Dai , Suhang Wang , International Conference On Web Search , Penn State College Of Information Sciences , National Science Foundation , Global Research Outreach , Samsung Advanced Institute Of Technology , Penn State College , Information Sciences , Web Search , Data Mining , Samsung Advanced Institute , Technology Engineering Computer Science , சர்வதேச மாநாடு ஆன் வலை தேடல் , பென் நிலை கல்லூரி ஆஃப் தகவல் அறிவியல் , தேசிய அறிவியல் அடித்தளம் , உலகளாவிய ஆராய்ச்சி ஔட்‌ரீச் , பென் நிலை கல்லூரி , தகவல் அறிவியல் , வலை தேடல் , தகவல்கள் சுரங்க , தொழில்நுட்பம் பொறியியல் கணினி அறிவியல் ,