Papers

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Mcmahon, M., Mumper, D., Ihaza, M., & Farrar, D.. (2019). How Smart is your Manufacturing? Build Smarter with AI. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) (55 - 60). https://doi.org/10.1109/COMPSAC.2019.10183
Mehic, A. (2018). Industrial employment and income inequality: Evidence from panel data. Structural Change And Economic Dynamics, 45, 84 - 93. https://doi.org/10.1016/j.strueco.2018.02.006
Mendling, J., Decker, G., Hull, R., Reijers, H. A., & Weber, I.. (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
Meskó, B., Hetényi, G., & Győrffy, Z.. (2018). Will artificial intelligence solve the human resource crisis in healthcare?. Bmc Health Services Research, 18(1). https://doi.org/10.1186/s12913-018-3359-4
Metcalf, L., Askay, D. A., & Rosenberg, L. B.. (2019). Pooling knowledge through artificial swarm intelligence to improve business decision making. California Management Review, 61(4), 84 - 109. https://doi.org/10.1177/0008125619862256
Dahya, R., & Morris, A.. (2019). Toward a conceptual framework for understanding AI action and legal reaction (Vol. 11489, pp. 453 - 459; M. - J. Meurs & Rudzicz, F., Eds.). In (Vol. 11489, pp. 453 - 459). https://doi.org/10.1007/978-3-030-18305-9_44
N. Meyer, D. (1982). Office automation: A progress report. Office Technology And People, 1(1), 107 - 121. https://doi.org/10.1108/eb022608
Mikhaylov, S. Jankin, Esteve, M., & Campion, A.. (2018). Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration. Philosophical Transactions Of The Royal Society A: Mathematical, Physical And Engineering Sciences, 376(2128), 20170357. https://doi.org/10.1098/rsta.2017.0357
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
Mishel, L., & Bivens, J.. (2017). The zombie robot argument lurches on. Economy Policy Institute. Retrieved de http://epi.org/126750
Mlynar, J., Alavi, H. S., Verma, H., & Cantoni, L.. (2018). Towards a Sociological Conception of Artificial Intelligence. In Artificial General Intelligence (AGI). Presented at the Artificial General Intelligence (AGI). https://doi.org/10.1007/978-3-319-97676-1_13
Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A.. (2018). Robotic Process Automation for Auditing. Journal Of Emerging Technologies In Accounting, 15(1), 1 - 10. https://doi.org/10.2308/jeta-10589
Mohr, J. W., Wagner-Pacifici, R., & Breiger, R. L.. (2015). Toward a computational hermeneutics. Big Data & Society, 2(2), 205395171561380. https://doi.org/10.1177/2053951715613809
Mokyr, J., Vickers, C., & Ziebarth, N. L.. (2015). The history of technological anxiety and the future of economic growth: Is this time different?. Journal Of Economic Perspectives, 29(3), 31 - 50. https://doi.org/10.1257/jep.29.3.31
Moniz, A., & Krings, B. - J.. (2016). Robots working with humans or humans working with robots?. Societies, 6(3), 23. https://doi.org/10.3390/soc6030023
Moody, K. (2018). High tech, low growth: Robots and the future of work abstract. Historical Materialism, 26(4), 3 - 34. https://doi.org/10.1163/1569206X-00001745
Moore, M. M., Slonimsky, E., Long, A. D., Sze, R. W., & Iyer, R. S.. (2019). Machine learning concepts, concerns and opportunities for a pediatric radiologist. Pediatric Radiology, 49(4), 509 - 516. https://doi.org/10.1007/s00247-018-4277-7

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