Papers

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Hwang, T. (2018). Computational power and the social impact of artificial intelligence. Ssrn Electronic Journal. https://doi.org/10.2139/ssrn.3147971
David, B. (2017). Computer technology and probable job destructions in Japan: An evaluation. Journal Of The Japanese And International Economies, 43, 77 - 87. https://doi.org/10.1016/j.jjie.2017.01.001
Williamson, B. (2017). Computing brains: Learning algorithms and neurocomputation in the smart city. Information, Communication & Society, 20(1), 81 - 99. https://doi.org/10.1080/1369118X.2016.1181194
Welfare, K. S., Hallowell, M. R., Shah, J. A., & Riek, L. D.. (2019). Consider the human work experience when integrating robotics in the workplace. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (75 - 84). https://doi.org/10.1109/HRI.2019.8673139
Poon, S. S., Thomas, R. C., Aragon, C. R., & Lee, B.. (2008). Context-linked virtual assistants for distributed teams (B. Begole & McDonald, D. W., Trans.). In the ACM 2008 conferenceProceedings of the ACM 2008 conference on Computer supported cooperative work - CSCW '08 (361). https://doi.org/10.1145/1460563.1460623
Poon, S. S., Thomas, R. C., Aragon, C., & Lee, B.. (2008). Context-linked virtual assistants for distributed teams: an astrophysics case study. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (361–370).
Nathan, L. F. (2018). Creativity, the arts, and the future of work (pp. 283 - 310; J. W. Cook, Ed.). In (pp. 283 - 310). https://doi.org/10.1007/978-3-319-78580-6_9
Sopilnyk, L., Shevchuk, A., & Kopytko, V.. (2018). Cryptocurrency, Artificial Intelligence and basic income as innovative technological system. Path Of Science, 4(8), 2024 - 2030. https://doi.org/10.22178/pos10.22178/pos.37-6
Bohannon, J. (2017). The cyberscientist. Science, 357(6346), 18 - 21. https://doi.org/10.1126/science.357.6346.18
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Data Science for Undergraduates. (2018). Data Science for Undergraduates. https://doi.org/10.17226/25104
Møller, N. L. Holten, Bjørn, P., Villumsen, J. Christoffe, Hancock, T. C. Hansen, Aritake, T., & Tani, S.. (2017). Data tracking in search of workflows (C. P. Lee, Poltrock, S., Barkhuus, L., Borges, M., & Kellogg, W., Trans.). In the 2017 ACM ConferenceProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW '17 (2153 - 2165). https://doi.org/10.1145/2998181.2998296
Markus, L. M. (2017). Datification, organizational strategy, and is research: What’s the score? . The Journal Of Strategic Information Systems, 26(3), 233 - 241. https://doi.org/10.1016/j.jsis.2017.08.003
Günther, W. Arianne, Mehrizi, M. H. Rezazad, Huysman, M., & Feldberg, F.. (2017). Debating big data: A literature review on realizing value from big data. The Journal Of Strategic Information Systems, 26(3), 191 - 209. https://doi.org/10.1016/j.jsis.2017.07.003
Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N. N. C., et al.. (2017). A deep learning approach for quantifying tumor extent. Scientific Reports, 7(1). https://doi.org/10.1038/srep46450
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T.. (2017). Deep learning for healthcare: review, opportunities and challenges. Briefings In Bioinformatics, 19(6), 1236 - 1246. https://doi.org/10.1093/bib/bbx044

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