Papers

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2019
Agrawal, A., Gans, J., & Goldfarb, A.. (2019). How artificial intelligence and machine learning can impact market design (pp. 567 - 586). In (pp. 567 - 586). https://doi.org/10.7208/chicago/9780226613475.003.0023
Duckworth, P., Graham, L., & Osborne, M.. (2019). Inferring work task automatability from AI expert evidence (V. Conitzer, Hadfield, G., & Vallor, S., Trans.). In the 2019 AAAI/ACM ConferenceProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society - AIES '19 (485 - 491). https://doi.org/10.1145/330661810.1145/3306618.3314247
Kühl, N., Goutier, M., Hirt, R., & Satzger, G.. (2019). Machine learning in Artificial Intelligence: Towards a common understanding (T. Bui, Tran.). In Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. Presented at the Hawaii International Conference on System SciencesProceedings of the 52nd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2019.630
Saba, L., Biswas, M., Kuppili, V., Godia, E. Cuadrado, Suri, H. S., Edla, D. Reddy, et al.. (2019). The present and future of deep learning in radiology. European Journal Of Radiology, 114, 14 - 24. https://doi.org/10.1016/j.ejrad.2019.02.038
Saba, L., Biswas, M., Kuppili, V., Godia, E. Cuadrado, Suri, H. S., Edla, D. Reddy, et al.. (2019). The present and future of deep learning in radiology. European Journal Of Radiology, 114, 14 - 24. https://doi.org/10.1016/j.ejrad.2019.02.038
Levine, A. B., Schlosser, C., Grewal, J., Coope, R., Jones, S. J. M., & Yip, S.. (2019). Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis. Trends In Cancer, 5(3), 157 - 169. https://doi.org/10.1016/j.trecan.2019.02.002
Kittur, A., Yu, L., Hope, T., Chan, J., Lifshitz-Assaf, H., Gilon, K., et al.. (2019). Scaling up analogical innovation with crowds and AI. Proceedings Of The National Academy Of Sciences, 116(6), 1870-1877. https://doi.org/10.1073/pnas.1807185116
Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., et al.. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings Of The National Academy Of Sciences, 116, 6531–6539. https://doi.org/10.1073/pnas.1900949116
Greco, C., Polonioli, A., & Tagliabue, J.. (2019). Why small data holds the key to the future of artificial intelligence. In 8th International Conference on Data Science, Technology and ApplicationsProceedings of the 8th International Conference on Data Science, Technology and Applications (340 - 347). https://doi.org/10.5220/0007956203400347
2018
Garvey, C. (2018). AI risk mitigation through democratic governance (J. Furman, Marchant, G., Price, H., & Rossi, F., Trans.). In the 2018 AAAI/ACM ConferenceProceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society - AIES '18 (366 - 367). https://doi.org/10.1145/3278721.3278801
Galily, Y. (2018). Artificial intelligence and sports journalism: Is it a sweeping change?. Technology In Society. https://doi.org/10.1016/j.techsoc.2018.03.001
Galanos, V. (2018). Artificial Intelligence Does Not Exist: Lessons from Shared Cognition and the Opposition to the Nature/Nurture Divide (Vol. 537, pp. 359 - 373; D. Kreps, Ess, C., Leenen, L., & Kimppa, K., Eds.). In (Vol. 537, pp. 359 - 373). https://doi.org/10.1007/978-3-319-99605-9_27
Galanos, V. (2018). Artificial Intelligence Does Not Exist: Lessons from Shared Cognition and the Opposition to the Nature/Nurture Divide (Vol. 537, pp. 359 - 373; D. Kreps, Ess, C., Leenen, L., & Kimppa, K., Eds.). In (Vol. 537, pp. 359 - 373). https://doi.org/10.1007/978-3-319-99605-9_27
Galanos, V. (2018). Artificial Intelligence Does Not Exist: Lessons from Shared Cognition and the Opposition to the Nature/Nurture Divide. In IFIP International Conference on Human Choice and Computers. Presented at the IFIP International Conference on Human Choice and Computers. Retrieved de https://doi.org/10.1007/978-3-319-99605-9_27
Johnson, K. W., Soto, J. Torres, Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., et al.. (2018). Artificial Intelligence in Cardiology. Journal Of The American College Of Cardiology, 71(23), 2668 - 2679. https://doi.org/10.1016/j.jacc.2018.03.521
Gries, T., & Naude, W.. (2018). Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter. In Maastricht Economic and social Research institute on Innovation and Technology. Retrieved de http://ftp.iza.org/dp12005.pdf
Jones, L. D., Golan, D., Hanna, S. A., & Ramachandran, M.. (2018). Artificial intelligence, machine learning and the evolution of healthcare. Bone & Joint Research, 7(3), 223 - 225. https://doi.org/10.1302/2046-3758.73.BJR-2017-0147.R1

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