Abrardi, L., C. Cambini, and L. Rondi. 2019. The economics of artificial intelligence: A survey. Robert Schuman Centre for Advanced Studies Research Paper No. RSCAS 2019/58. https://doi.org/10.2139/ssrn.3425922.
Article
Google Scholar
Ahmed, M., A.N. Mahmood, and Md Rafiqul Islam. 2016. A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems 55: 278–288. https://doi.org/10.1016/j.future.2015.01.001.
Article
Google Scholar
Albrecher, H., A. Bommier, D. Filipović, P. Koch-Medina, S. Loisel, and H. Schmeiser. 2019. Insurance: Models, digitalization, and data science. Swiss Finance Institute Research Paper No. 19-26. https://doi.org/10.2139/ssrn.3382125.
Article
Google Scholar
Ayuso, M., M. Guillen, and J. Perch Nielsen. 2019. Improving automobile insurance ratemaking using telematics: Incorporating mileage and driver behaviour data. Transportation 46: 735–752. https://doi.org/10.1007/s11116-018-9890-7.
Article
Google Scholar
Akhusama, P.M., and C. Moturi. 2016. Cloud computing adoption in insurance companies in Kenya. American Journal of Information Systems 4 (1): 11–16.
Google Scholar
Allam, Z., and Z.A. Dhunny. 2019. On big data, artificial intelligence and smart cities. Cities 89: 80–91. https://doi.org/10.1016/j.cities.2019.01.032.
Article
Google Scholar
BarNir, A., J.M. Gallaugher, and P. Auger. 2003. Business process digitization, strategy, and the impact of firm age and size: The case of the magazine publishing industry. Journal of Business Venturing 18 (6): 789–814. https://doi.org/10.1016/S0883-9026(03)00030-2.
Article
Google Scholar
Barr, A., and E.A. Feigenbaum. 1981. The handbook of artificial intelligence, vol. 1. Stanford: HeurisTech Press.
Google Scholar
Baum, S.D., B. Goertzel, and T.G. Goertzel. 2011. How long until human-level AI? Results from an expert assessment. Technological Forecasting and Social Change 78 (1): 185–195. https://doi.org/10.1016/j.techfore.2010.09.006.
Article
Google Scholar
Berliner, B. 1982. Limits of insurability of risks. Englewood Cliffs: Prentice-Hall.
Google Scholar
Berliner, B. 1985. Large risks and limits of insurability. The Geneva Papers on Risk and Insurance—Issues and Practice 10 (37): 313–329. https://doi.org/10.1057/gpp.1985.22.
Article
Google Scholar
Bhatnagar, S., A. Alexandrova, S. Avin, S. Cave, L. Cheke, M. Crosby, J. Feyereisl, M. Halina, B.S. Loe, S.Ó. Éigeartaigh, F. Martínez-Plumed, H. Price, H. Shevlin, A. Weller, A. Winfield, and J. Hernández-Orallo. 2018. Mapping intelligence: Requirements and possibilities. In Philosophy and theory of artificial intelligence, ed. V.C. Müller, 117–135. Cham: Springer. https://doi.org/10.1007/978-3-319-96448-5_13.
Chapter
Google Scholar
Biener, C., M. Eling, and J. Hendrik Wirfs. 2015. Insurability of cyber risk: An empirical analysis. The Geneva Papers on Risk and Insurance—Issues and Practice 40 (1): 131–158. https://doi.org/10.1057/gpp.2014.19.
Article
Google Scholar
Biener, C., M. Eling, and M. Lehmann. 2020. Balancing the desire for privacy against the desire to hedge risks. Journal of Economic Behavior & Organization. https://doi.org/10.1016/j.jebo.2020.03.007.
Article
Google Scholar
Bohnert, A., A. Fritzsche, and S. Gregor. 2019. Digital agendas in the insurance industry: The importance of comprehensive approaches. The Geneva Papers on Risk and Insurance—Issues and Practice 44 (1): 1–19. https://doi.org/10.1057/s41288-018-0109-0.
Article
Google Scholar
Bologa, A.-R., R. Bologa, and A. Florea. 2013. Big data and specific analysis methods for insurance fraud detection. Database Systems Journal 4 (4): 30–39.
Google Scholar
Bolton, C., V. Machová, M. Kovacova, and K. Valaskova. 2018. The power of human-machine collaboration: Artificial intelligence, business automation, and the smart economy. Economics, Management, and Financial Markets 13 (4): 51–56. https://doi.org/10.22381/emfm13420184.
Article
Google Scholar
Boyd, R., and R.J. Holton. 2017. Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation? Journal of Sociology 54 (3): 331–345. https://doi.org/10.1177/1440783317726591.
Article
Google Scholar
Braun, A., and F. Schreiber. 2017. The current InsurTech landscape: Business models and disruptive potential. St. Gallen: Institute of Insurance Economics I.VW-HSG, University of St. Gallen.
Google Scholar
Brown, J.R., and A. Goolsbee. 2002. Does the internet make markets more competitive? Evidence from the life insurance industry. Journal of Political Economy 110 (3): 481–507. https://doi.org/10.1086/339714.
Article
Google Scholar
Bughin, J., E. Hazan, S. Ramaswamy, M. Chui, T. Allas, P. Dahlström, N. Henke, and M. Trench. 2017. Artificial intelligence - the next digital frontier? London: McKinsey Global Institute. Accessed 28 August 2020. https://www.calpers.ca.gov/docs/board-agendas/201801/full/day1/06-technology-background.pdf.
Cappiello, A. 2020. The technological disruption of insurance industry: A review. International Journal of Business and Social Science 11: 1.
Article
Google Scholar
Castelvecchi, D. 2016. Can we open the black box of AI? Nature 538 (7623): 20–23. https://doi.org/10.1038/538020a.
Article
Google Scholar
Catlin, T., J.-T. Lorenz, J. Nandan, S. Sharma, and A. Waschto. 2018. Insurance beyond digital: The rise of ecosystems and platforms. McKinsey & Company. Accessed 28 August 2020. https://www.mckinsey.com/industries/financial-services/our-insights/insurance-beyond-digital-the-rise-of-ecosystems-and-platforms.
CB Insights. 2020. Insurance tech Q2 2020. Accessed 28 August 2020. https://www.cbinsights.com/research/report/insurance-tech-q2-2020/.
Charpentier, A. 2007. Insurability of climate risks. The Geneva Papers on Risk and Insurance—Issues and Practice 33 (1): 91–109. https://doi.org/10.1057/palgrave.gpp.2510155.
Article
Google Scholar
Churchland, P.S., and T.J. Sejnowski. 1988. Perspectives on cognitive neuroscience. Science 242 (4879): 741–745. https://doi.org/10.1126/science.3055294.
Article
Google Scholar
Dale, R. 2016. The return of the chatbots. Natural Language Engineering 22 (5): 811–817. https://doi.org/10.1017/s1351324916000243.
Article
Google Scholar
Dastin., J. 2018. Amazon scraps secret AI recruiting tool that showed bias against women. Accessed 28 August 2020. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G.
Deloitte. 2017. Artificial intelligence: From mystery to mastery - unlocking the business value of AI in the insurance industry. Accessed 28 August 2020. https://www2.deloitte.com/de/de/pages/innovation/contents/artificial-intelligence-insurance-industry.html.
Eastman, J.K., A.D. Eastman, and K.L. Eastman. 2002a. Insurance sales agents and the internet: The relationship between opinion leadership, subjective knowledge, and internet attitudes. Journal of Marketing Management 18 (3–4): 259–285. https://doi.org/10.1362/0267257022872460.
Article
Google Scholar
Eastman, J.K., A.D. Eastman, and K.L. Eastman. 2002b. Issues in marketing online insurance products: An exploratory look at agents’ use, attitudes, and views of the impact of the internet. Risk Management and Insurance Review 5 (2): 117–134. https://doi.org/10.1111/1098-1616.00013.
Article
Google Scholar
Eling, M., and M. Lehmann. 2018. The impact of digitalization on the insurance value chain and the insurability of risks. The Geneva Papers on Risk and Insurance—Issues and Practice 43: 359–396. https://doi.org/10.1057/s41288-017-0073-0.
Article
Google Scholar
Erevelles, S., N. Fukawa, and L. Swayne. 2016. Big data consumer analytics and the transformation of marketing. Journal of Business Research 69 (2): 897–904. https://doi.org/10.1016/j.jbusres.2015.07.001.
Article
Google Scholar
Faloon, M., and B. Scherer. 2017. Individualization of robo-advice. The Journal of Wealth Management 20 (1): 30–36. https://doi.org/10.3905/jwm.2017.20.1.030.
Article
Google Scholar
Garven, J.R. 2002. On the implications of the internet for insurance markets and institutions. Risk Management and Insurance Review 5 (2): 105–116. https://doi.org/10.1111/1098-1616.00014.
Article
Google Scholar
Gatteschi, V., F. Lamberti, C. Demartini, C. Pranteda, and V. Santamaría. 2018. Blockchain and smart contracts for insurance: Is the technology mature enough? Future Internet 10 (2): 20–35. https://doi.org/10.3390/fi10020020.
Article
Google Scholar
Gehrke, E. 2014. The insurability framework applied to agricultural microinsurance: What do we know, what can we learn? The Geneva Papers on Risk and Insurance—Issues and Practice 39 (2): 264–279. https://doi.org/10.1057/gpp.2014.2.
Article
Google Scholar
Gentsch, P. 2018. Künstliche Intelligenz für Sales, Marketing und Service: Mit AI und Bots zu einem Algorithmic Business – Konzepte und Best Practices. Wiesbaden: Springer Gabler. https://doi.org/10.1007/978-3-658-25376-9.
Book
Google Scholar
Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. Cambridge, MA: MIT Press.
Google Scholar
Görz, G., J. Schneeberger, and U. Schmid. 2013. Handbuch der Künstlichen Intelligenz, 5th ed. Munich: Oldenbourg.
Book
Google Scholar
Graves, A., A.-R. Mohamed, and G. Hinton. 2013. Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/icassp.2013.6638947.
Grewal, D.S. 2014. A critical conceptual analysis of definitions of artificial intelligence as applicable to computer engineering. IOSR Journal of Computer Engineering 16 (2): 9–13. https://doi.org/10.9790/0661-16210913.
Article
Google Scholar
Guidotti, R., A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi. 2018. A survey of methods for explaining black box models. ACM Computing Surveys 51 (5): 93. https://doi.org/10.1145/3236009.
Article
Google Scholar
He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 770–778. https://doi.org/10.1109/cvpr.2016.90.
Hussain, K., and E. Prieto. 2016. Big data in the finance and insurance sectors. In New horizons for a data-driven economy - A roadmap for usage and exploitation of big data in Europe, ed. J.M. Cavanillas, E. Curry, and W. Wahlster, 209–223. Cham: Springer.
Chapter
Google Scholar
International Association of Insurance Supervisors. 2017. FinTech developments in the insurance industry. Accessed 28 August 2020. https://www.iaisweb.org/page/news/other-papers-and-reports/file/65625/report-on-fintech-developments-in-the-insurance-industry.
Jajal, T.D. 2018. Distinguishing between narrow AI, general AI and super AI. Accessed 28 August 2020. https://medium.com/@tjajal/distinguishing-between-narrow-ai-general-ai-and-super-ai-a4bc44172e22.
Jakšič, M., and M. Marinč. 2019. Relationship banking and information technology: The role of artificial intelligence and FinTech. Risk Management 21 (1): 1–18. https://doi.org/10.1057/s41283-018-0039-y.
Article
Google Scholar
Jarrahi, M.Hossein. 2018. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons 61 (4): 577–586. https://doi.org/10.1016/j.bushor.2018.03.007.
Article
Google Scholar
Jiang, F., Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, and Y. Wang. 2017. Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology 2 (4): 230–243. https://doi.org/10.1136/svn-2017-000101.
Article
Google Scholar
Kaiser, T. 2002. The customer shall lead: e-business solutions for the new insurance industry. The Geneva Papers on Risk and Insurance—Issues and Practice 27 (1): 134–145.
Article
Google Scholar
Kaplan, A., and M. Haenlein. 2019. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons 62 (1): 15–25. https://doi.org/10.1016/j.bushor.2018.08.004.
Article
Google Scholar
Kelley, K.H., L.M. Fontanetta, M. Heintzman, and N. Pereira. 2018. Artificial intelligence: Implications for social inflation and insurance. Risk Management and Insurance Review 21 (3): 373–387. https://doi.org/10.1111/rmir.12111.
Article
Google Scholar
Kharpal, A. 2017. Stephen Hawking says A. I. could be ‘worst event in the history of our civilization’. Accessed 28 August 2020. https://www.cnbc.com/2017/11/06/stephen-hawking-ai-could-be-worst-event-in-civilization.html.
Knight, W. 2017. The dark secret at the heart of AI. MIT Technology Review 120 (3): 54–61.
Google Scholar
Kothari, D. 2019. How artificial intelligence accelerates software development. International Research Journal of Engineering and Technology (IRJET) 6 (8): 1392–1394.
Google Scholar
KPMG. 2018. Neues Denken, Neues Handeln. Insurance Thinking Ahead: Versicherungen im Zeitalter von Digitalisierung und Cyber, Studienteil B: Cyber. Accessed 28 August 2020. https://assets.kpmg/content/dam/kpmg/ch/pdf/neues-denken-neues-handeln-cyber-de.pdf.
Kreutzer, R.T., and M. Sirrenberg. 2020. Künstliche Intelligenz verstehen: Grundlagen – Use-Cases – Unternehmenseigene KI-Journey. Wiesbaden: Springer Gabler. https://doi.org/10.1007/978-3-658-25561-9.
Book
Google Scholar
Krizhevsky, A., I. Sutskever, and G.E. Hinton. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM 60 (6): 84–90. https://doi.org/10.1145/3065386.
Article
Google Scholar
Lake, B.M., T.D. Ullman, J.B. Tenenbaum, and S.J. Gershman. 2016. Building machines that learn and think like people. Behavioral and Brain Sciences. https://doi.org/10.1017/s0140525x16001837.
Article
Google Scholar
LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521 (7553): 436–444. https://doi.org/10.1038/nature14539.
Article
Google Scholar
Lee, J., H. Davari, J. Singh, and V. Pandhare. 2018. Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters 18: 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002.
Article
Google Scholar
Legner, C., T. Eymann, T. Hess, C. Matt, T. Böhmann, P. Drews, A. Mädche, N. Urbach, and F. Ahlemann. 2017. Digitalization: Opportunity and challenge for the business and information systems engineering community. Business & Information Systems Engineering 59 (4): 301–308. https://doi.org/10.1007/s12599-017-0484-2.
Article
Google Scholar
Li, B.-H., B.-C. Hou, W.-T. Yu, X.-B. Lu, and C.-W. Yang. 2017. Applications of artificial intelligence in intelligent manufacturing: A review. Frontiers of Information Technology & Electronic Engineering 18 (1): 86–96. https://doi.org/10.1631/FITEE.1601885.
Article
Google Scholar
Lorenz, Johannes-Tobias, Ulrike Deetjen, and Jasper van Ouwerkerk. 2020. Ecosystems in insurance: The next frontier for enhancing productivity. McKinsey & Company. Accessed 28 August 2020. https://www.mckinsey.com/industries/financial-services/our-insights/insurance-blog/ecosystems-in-insurance-the-next-frontier-for-enhancing-productivity.
Lycett, M. 2013. ‘Datafication’: Making sense of (big) data in a complex world. European Journal of Information Systems 22 (4): 381–386. https://doi.org/10.1057/ejis.2013.10.
Article
Google Scholar
Majchrzak, A., M.L. Markus, and J. Wareham. 2016. Designing for digital transformation: Lessons for information systems research from the study of ICT and societal challenges. MIS Quarterly 40 (2): 267–277. https://doi.org/10.25300/misq/2016/40:2.03.
Article
Google Scholar
Makridakis, S. 2017. The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures 90: 46–60. https://doi.org/10.1016/j.futures.2017.03.006.
Article
Google Scholar
Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in Computational Neuroscience. https://doi.org/10.3389/fncom.2016.00094.
Article
Google Scholar
Marchand, A., and P. Marx. 2020. Automated product recommendations with preference-based explanations. Journal of Retailing. https://doi.org/10.1016/j.jretai.2020.01.001.
Article
Google Scholar
Martínez-Plumed, F., B.S. Loe, P. Flach, S.O. Éigeartaigh, K. Vold, and J. Hernández-Orallo. 2018. The facets of artificial intelligence: A framework to track the evolution of AI. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/718.
Article
Google Scholar
McCarthy, J. 2007. What is artificial intelligence? Stanford: Stanford University. Accessed 28 August 2020. http://www-formal.stanford.edu/jmc/whatisai.pdf.
McCulloch, W.S., and W. Pitts. 1943. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics 5: 115–133. https://doi.org/10.1007/bf02478259.
Article
Google Scholar
Monett, D., and C.W.P. Lewis. 2018. Getting clarity by defining artificial intelligence—A survey. In Philosophy and theory of artificial intelligence 2017, ed. V.C. Müller, 212–214. Berlin: Springer. https://doi.org/10.1007/978-3-319-96448-5_21.
Chapter
Google Scholar
Müller, M. 2002. Computer Go. Artificial Intelligence 134 (1–2): 145–179. https://doi.org/10.1016/s0004-3702(01)00121-7.
Article
Google Scholar
National Transportation Safety Board. 2019. Collision between vehicle controlled by developmental automated driving system and pedestrian: Accident report. Accessed 28 August 2020. https://www.ntsb.gov/investigations/AccidentReports/Reports/HAR1903.pdf.
Neisser, U., G. Boodoo, T.J. Bouchard Jr., A.W. Boykin, N. Brody, S. Ceci, D.F. Halpern, J.C. Loehlin, R. Perloff, R.J. Sternberg, and S. Urbina. 1996. Intelligence: Knowns and unknowns. American Psychologist 51 (2): 77–101. https://doi.org/10.1037/0003-066X.51.2.77.
Article
Google Scholar
Niu, J., W. Tang, F. Xu, X. Zhou, and Y. Song. 2016. Global research on artificial intelligence from 1990–2014: Spatially-explicit bibliometric analysis. ISPRS International Journal of Geo-Information 5 (5): 66. https://doi.org/10.3390/ijgi5050066.
Article
Google Scholar
Nyholm, S., and J. Smids. 2016. The ethics of accident-algorithms for self-driving cars: An applied trolley problem? Ethical Theory and Moral Practice 19 (5): 1275–1289. https://doi.org/10.1007/s10677-016-9745-2.
Article
Google Scholar
Panetta, K. 2018. 5 trends emerge in the Gartner hype cycle for emerging technologies. Accessed 28 August 2020. https://www.gartner.com/smarterwithgartner/5-trends-emerge-in-gartner-hype-cycle-for-emerging-technologies-2018/.
Patel, V.L., E.H. Shortliffe, M. Stefanelli, P. Szolovits, M.R. Berthold, R. Bellazzi, and A. Abu-Hanna. 2009. The coming of age of artificial intelligence in medicine. Artificial Intelligence in Medicine 46 (1): 5–17. https://doi.org/10.1016/j.artmed.2008.07.017.
Article
Google Scholar
Porter, M. 1985. The competitive advantage: Creating and sustaining superior performance. New York: The Free Press.
Google Scholar
Rahlfs, C. 2007. Redefinition der Wertschoepfungskette von Versicherungsunternehmen. Wiesbaden: Deutscher Universitäts-Verlag.
Google Scholar
Rangwala, A., A. Starrs, E. Viale, D. Presutti, J. Bramblet, K. Saldanha, and N. Shibata. 2020. Technology vision for insurance 2020: We, the post-digital people. Can your enterprise survive the “tech-clash?” Accenture. Accessed 28 August 2020. https://financialservices.accenture.com/rs/368-RMC-681/images/Accenture-Technology-Vision-for-Insurance-2020-Full-Report.pdf.
Rawat, W., and Z. Wang. 2017. Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation 29 (9): 2352–2449. https://doi.org/10.1162/neco_a_00990.
Article
Google Scholar
Redmon, J., and A. Farhadi. 2017. YOLO9000: Better, faster, stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.690.
Ren, S., K. He, R. Girshick, and J. Sun. 2017. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6): 1137–1149. https://doi.org/10.1109/tpami.2016.2577031.
Article
Google Scholar
Riikkinen, M., H. Saarijärvi, P. Sarlin, and I. Lähteenmäki. 2018. Using artificial intelligence to create value in insurance. International Journal of Bank Marketing 36 (6): 1145–1168. https://doi.org/10.1108/ijbm-01-2017-0015.
Article
Google Scholar
Russell, S., and P. Norvig. 2012. Künstliche Intelligenz: Ein moderner Ansatz, 3rd ed. Munich: Pearson Education.
Google Scholar
Sicari, S., A. Rizzardi, L.A. Grieco, and A. Coen-Porisini. 2015. Security, privacy and trust in Internet of Things: The road ahead. Computer Networks 76: 146–164. https://doi.org/10.1016/j.comnet.2014.11.008.
Article
Google Scholar
Silver, D., A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529 (7587): 484–489. https://doi.org/10.1038/nature16961.
Article
Google Scholar
Stoeckli, E., C. Dremel, and F. Uebernickel. 2018. Exploring characteristics and transformational capabilities of InsurTech innovations to understand insurance value creation in a digital world. Electronic Markets 28 (3): 287–305. https://doi.org/10.1007/s12525-018-0304-7.
Article
Google Scholar
The Geneva Association. 2018. Insurance in the digital age: A view on key implications for the economy and society. Author: Christian Schmidt. September. Accessed 28 August 2020. https://www.genevaassociation.org/sites/default/files/research-topics-document-type/pdf_public/insurance_in_the_digital_age_01.pdf.
Thrall, J.H., X. Li, Q. Li, C. Cruz, S. Do, K. Dreyer, and J. Brink. 2018. Artificial intelligence and machine Learning in radiology: Opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology 15 (3): 504–508. https://doi.org/10.1016/j.jacr.2017.12.026.
Article
Google Scholar
Topol, E.J. 2019. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine 25 (1): 44–56. https://doi.org/10.1038/s41591-018-0300-7.
Article
Google Scholar
Turing, A.M. 1950. Computing machinery and intelligence. Mind 59 (236): 433–460. https://doi.org/10.1093/mind/lix.236.433.
Article
Google Scholar
Uj, A. 2018. Understanding three types of artificial intelligence. Accessed 28 August 2020. https://www.analyticsinsight.net/understanding-three-types-of-artificial-intelligence/.
Vial, G. 2019. Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems 28 (2): 118–144. https://doi.org/10.1016/j.jsis.2019.01.003.
Article
Google Scholar
vom Brocke, J., A. Simons, B. Niehaves, K. Reimer, R. Plattfaut, and A. Cleven. 2009. Reconstructing the giant: on the importance of rigour in documenting the literature search process. ECIS 2009 Proceedings 161. http://aisel.aisnet.org/ecis2009/161.
Wang, P. 2008. What do you mean by “AI”? In Artificial general intelligence 2008, ed. P. Wang, B. Goertzel, and S. Franklin, 362–373. Amsterdam: IOS Press.
Google Scholar
Wang, P. 2019. On defining artificial intelligence. Journal of Artificial General Intelligence 10 (2): 1–37. https://doi.org/10.2478/jagi-2019-0002.
Article
Google Scholar
Webster, J., and R.T. Watson. 2002. Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly 26 (2): 13–23.
Google Scholar
Young, T., D. Hazarika, S. Poria, and E. Cambria. 2018. Recent trends in deep learning based natural language processing. IEEE Computational Intelligence Magazine 13 (3): 55–75. https://doi.org/10.1109/mci.2018.2840738.
Article
Google Scholar
Yu, K.-H., A.L. Beam, and I.S. Kohane. 2018. Artificial intelligence in healthcare. Nature Biomedical Engineering 2 (10): 719–731. https://doi.org/10.1038/s41551-018-0305-z.
Article
Google Scholar
Yu, S., S. Jia, and C. Xu. 2017. Convolutional neural networks for hyperspectral image classification. Neurocomputing 219: 88–98. https://doi.org/10.1016/j.neucom.2016.09.010.
Article
Google Scholar
Yuan, X., P. He, Q. Zhu, and X. Li. 2019. Adversarial examples: Attacks and defenses for deep learning. IEEE Transactions on Neural Networks and Learning Systems 30 (9): 2805–2824. https://doi.org/10.1109/tnnls.2018.2886017.
Article
Google Scholar
Zhang, Q., Z. Yu, W. Shi, and H. Zhong. 2016. Demo abstract: EVAPS: Edge video analysis for public safety. 2016 IEEE/ACM Symposium on Edge Computing (SEC): 121–122. https://doi.org/10.1109/sec.2016.30.