Artificial Intelligence

Lunga, Dalton D.

doi:10.22224/gistbok/2019.4.16

Artificial Intelligence

Journal Article · Tue Oct 01 04:00:00 EDT 2019 · Geographic Information Science & Technology Body of Knowledge

DOI:https://doi.org/10.22224/gistbok/2019.4.16· OSTI ID:1607204

^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1607204

Journal Information:: Geographic Information Science & Technology Body of Knowledge, Journal Name: Geographic Information Science & Technology Body of Knowledge Journal Issue: Q4 Vol. 2019; ISSN 2577-2848

Publisher:: University Consortium for Geographic Information Science (UCGIS)Copyright Statement

Country of Publication:: United States

Language:: English

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Modernizing the Bradford Hill criteria for assessing causal relationships in observational data Cox, Louis Anthony Critical Reviews in Toxicology, Vol. 48, Issue 8 https://doi.org/10.1080/10408444.2018.1518404	journal	September 2018
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Multiagent cooperation and competition with deep reinforcement learning Tampuu, Ardi; Matiisen, Tambet; Kodelja, Dorian ETH Zurich https://doi.org/10.3929/ethz-b-000130290	text	January 2017

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