ORNL researchers develop ‘multitasking’ AI tool to extract cancer data in record time

To better leverage cancer data for research, scientists at ORNL are developing an artificial intelligence (AI)-based natural language processing tool to improve information extraction from textual pathology reports. In a first for cancer pathology reports, the team developed a multitask convolutional neural network (CNN)—a deep learning model that learns to perform tasks, such as identifying key words in a body of text, by processing language as a two-dimensional numerical dataset.

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LLNL computer scientists explore deep learning to improve efficiency of ride-hailing and autonomous electric vehicles

Computer scientists at Lawrence Livermore National Laboratory are preparing the future of commuter traffic by applying Deep Reinforcement Learning — the same kind of goal-driven algorithms that have defeated video game experts and world champions in the strategy game Go — to determine the most efficient strategy for charging and driving electric vehicles used for ride-sharing services.

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NUS deep-learning AI system puts Singapore on global map of big data analytics

⎯ A team of researchers from the National University of Singapore (NUS) has put Singapore on the global map of Artificial Intelligence (AI) and big data analytics. Their open-source project, called Apache SINGA, “graduated” from the Apache Incubator on 16 October 2019 and is now Southeast Asia’s first Top-Level Project (TLP) under the Apache Software Foundation, the world’s largest open-source software community.

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InnovationXLab Summit brings industry, national laboratories together around artificial intelligence

The recent InnovationXLab℠ Summit on AI raised the profile of the national laboratories’ work in AI and forged new partnerships between industry and the national labs.

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AI technique does double duty spanning cosmic and subatomic scales

While high-energy physics and cosmology seem worlds apart in terms of sheer scale, physicists and cosmologists at Argonne are using similar machine learning methods to address classification problems for both subatomic particles and galaxies.

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ORNL develops, deploys AI capabilities across research portfolio

To accelerate promising artificial intelligence applications in diverse research fields, ORNL has established a labwide AI Initiative. This internal investment brings the lab’s AI expertise, computing resources and user facilities together to facilitate analyses of massive datasets.

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