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Artificial Intelligence and the Fight Against COVID-19

15 June 2020
In Innovation policy
30 min read
Juan Mateos-Garcia

Juan Mateos-Garcia

Juan Mateos-Garcia

Director of Data Analytics Practice

Juan Mateos-Garcia was the Director of Data Analytics at Nesta.

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Konstantinos Stathoulopoulos

Konstantinos Stathoulopoulos

Konstantinos Stathoulopoulos

Principal Researcher, Innovation Mapping

Konstantinos worked as a Principal Researcher on Nesta's Research Analysis and Policy team.

View profile
Joel Klinger

Joel Klinger

Joel Klinger

Data Engineering Senior Lead, Data Analytics

Joel is Nesta’s Data Engineering Senior Lead

View profile

Bibliography

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Executive summary Introduction Data sources and methodology Findings – Levels and evolution of activity Findings – Topical composition Findings – Geography Findings – Knowledge creation and combination Findings – Quality Conclusion Bibliography Resources

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.

Bryan, K., Lemus, J., & Marshall, G. (2020). Innovation During a Crisis: Evidence from Covid-19. Available at SSRN 3587973.

Bullock, J., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. ArXiv Preprint ArXiv:2003.11336.

Cockburn, I. M., Henderson, R., & Stern, S. (2018). The Impact of Artificial Intelligence on Innovation. National Bureau of Economic Research.

Frank, M. R., Wang, D., Cebrian, M., & Rahwan, I. (2019). The evolution of citation graphs in artificial intelligence research. Nature Machine Intelligence, 1(2), 79.

Gerlach, M., Peixoto, T. P., & Altmann, E. G. (2018). A network approach to topic models. Science Advances, 4(7), eaaq1360. https://doi.org/10.1126/sciadv.aaq1360

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

Klinger, J., Mateos-Garcia, J., & Stathoulopoulos, K. (2018). Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology. ArXiv Preprint ArXiv:1808.06355.

Marcus, G. (2018). Deep learning: A critical appraisal. ArXiv Preprint ArXiv:1801.00631.

Markoff, J. (2016). Machines of loving grace: The quest for common ground between humans and robots. HarperCollins Publishers.

Mateos-Garcia, J. (Forthcoming). Mapping Research & Innovation Missions.

Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Penguin UK.

Naudé, W. (2020a). Artificial Intelligence against COVID-19: An early review.

Naudé, W. (2020b). Artificial intelligence vs COVID-19: Limitations, constraints and pitfalls. Ai & Society, 1.

Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2016). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21.

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Malaysia; Pearson Education Limited,.

Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Hachette UK.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44.

van der Schaar, M., & Alaa, A. (2020). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.

Wang, K., Shen, Z., Huang, C., Wu, C.-H., Dong, Y., & Kanakia, A. (2020). Microsoft Academic Graph: When experts are not enough. Quantitative Science Studies, 1(1), 396–413.

Wooldridge, M. (2020). The Road to Conscious Machines. Penguin Books, Limited.

Younes, G. A., Ayoubi, C., Ballester, O., Cristelli, G., de Rassenfosse, G., Foray, D., Gaule, P., Pellegrino, G., van den Heuvel, M., & Webster, B. (2020). COVID-19_Insights from Innovation Economists. Center for Open Science.

Annex

List of terms similar to AI produced by word2vec:

['optimization_algorithm', 'convolutional_neural', 'automatic_detection', 'transfer_learning', 'semi_supervised', 'dnn', 'pre_trained', 'artificial_intelligence', 'natural_language', 'deep_learning', 'machine_learning', 'training_dataset', 'covid_net', 'classification', 'cnn', 'learning_based', 'deep_neural', 'neural_network', 'feature_extraction', 'deep_convolutional', 'supervised_learning', 'machine_learning_based', 'computational_intelligence', 'machine_intelligence']

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Authors

Juan Mateos-Garcia

Juan Mateos-Garcia

Juan Mateos-Garcia

Director of Data Analytics Practice

Juan Mateos-Garcia was the Director of Data Analytics at Nesta.

View profile
Konstantinos Stathoulopoulos

Konstantinos Stathoulopoulos

Konstantinos Stathoulopoulos

Principal Researcher, Innovation Mapping

Konstantinos worked as a Principal Researcher on Nesta's Research Analysis and Policy team.

View profile
Joel Klinger

Joel Klinger

Joel Klinger

Data Engineering Senior Lead, Data Analytics

Joel is Nesta’s Data Engineering Senior Lead

View profile

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