Papers
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. (2018). Comment on “Should we fear the robot revolution? (The correct answer is yes)” by Andrew Berg, Ed Buffie, and Felipe Zanna. Journal Of Monetary Economics, 97, 149 - 152. https://doi.org/10.1016/j.jmoneco.2018.05.012
. (2018). Computational power and the social impact of artificial intelligence. Ssrn Electronic Journal. https://doi.org/10.2139/ssrn.3147971
. (2018). The discourse approach to boundary identification and corpus construction for theory review articles. Journal Of The Association For Information Systems. Retrieved de https://www.researchgate.net/publication/325215971_Understanding_the_Elephant_The_Discourse_Approach_to_Boundary_Identification_and_Corpus_Construction_for_Theory_Review_Articles
. (2018). Discussion for JME special issue: APST paper. Journal Of Monetary Economics, 97, 68 - 70. https://doi.org/10.1016/j.jmoneco.2018.05.003
. (2018). eHealth - Making Health Care SmarterUse of Artificial Intelligence in Healthcare Delivery (). https://doi.org/http://dx.doi.org/10.5772/intechopen.74714
. (2018). Ethics of using Artificial Intelligence to augment drafting legal documents. Texas A&M Journal Of Property Law, 4(5). Retrieved de https://scholarship.law.tamu.edu/cgi/viewcontent.cgi?article=1080&context=journal-of-property-law
. (2018). Exploiting ability for human adaptation to facilitate improved human-robot interaction and acceptance. The Information Society, 34(3), 153 - 165. https://doi.org/10.1080/01972243.2018.1444255
. (2018). How do machine learning, robotic process automation, and blockchains affect the human factor in business process management?. Communications Of The Association For Information Systems, 297 - 320. https://doi.org/10.17705/1CAIS.04319
. (2018). The impact of technology and globalization on employment and equity. International Journal Of Global Sustainability, 3(1), 49. https://doi.org/10.5296/ijgs.v3i1.14127
. (2018). Improving palliative care with deep learning. Bmc Medical Informatics And Decision Making, 18(S4). https://doi.org/10.1186/s12911-018-0677-8
. (2018). iNaturalist as a tool to expand the research value of museum specimens. Applications In Plant Sciences, 6(11), e01193. https://doi.org/10.1002/aps3.1193
. (2018). Investigating ergonomics in the context of human-robot collaboration as a sociotechnical system (Vol. 784, pp. 127 - 135; ). In (Vol. 784, pp. 127 - 135). https://doi.org/10.1007/978-3-319-94346-6_12
. (2018). Machine intelligence: Blessing or curse? It depends on us! (pp. 25 - 39; ). In (pp. 25 - 39). https://doi.org/10.1007/978-3-319-90869-4_4
. (2018). Machine intelligence: Blessing or curse? It depends on us! (pp. 25 - 39; ). In (pp. 25 - 39). https://doi.org/10.1007/978-3-319-90869-4_4
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook (pp. 27 - 61; ). In (pp. 27 - 61). https://doi.org/10.1007/978-3-319-96978-7_2
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook (pp. 27 - 61; ). In (pp. 27 - 61). https://doi.org/10.1007/978-3-319-96978-7_2
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook (pp. 27 - 61; ). In (pp. 27 - 61). https://doi.org/10.1007/978-3-319-96978-7_2
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook (pp. 27 - 61; ). In (pp. 27 - 61). https://doi.org/10.1007/978-3-319-96978-7_2
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementUse of Machine Learning (ML) for Predicting and Analyzing Ecological and ‘Presence Only’ Data: An Overview of Applications and a Good Outlook (pp. 27 - 61; ). In (pp. 27 - 61). https://doi.org/10.1007/978-3-319-96978-7_2
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementMachine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide? (pp. 353 - 377; ). In (pp. 353 - 377). https://doi.org/10.1007/978-3-319-96978-7_18
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementMachine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide? (pp. 353 - 377; ). In (pp. 353 - 377). https://doi.org/10.1007/978-3-319-96978-7_18
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementMachine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide? (pp. 353 - 377; ). In (pp. 353 - 377). https://doi.org/10.1007/978-3-319-96978-7_18
. (2018). Machine Learning for Ecology and Sustainable Natural Resource ManagementMachine Learning and ‘The Cloud’ for Natural Resource Applications: Autonomous Online Robots Driving Sustainable Conservation Management Worldwide? (pp. 353 - 377; ). In (pp. 353 - 377). https://doi.org/10.1007/978-3-319-96978-7_18
. (2018). Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective. In , Machine Learning for Ecology and Sustainable Natural Resource Management (pp. 3 - 26). https://doi.org/10.1007/978-3-319-96978-7_1
. (2018). Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective. In , Machine Learning for Ecology and Sustainable Natural Resource Management (pp. 3 - 26). https://doi.org/10.1007/978-3-319-96978-7_1