{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:03:30Z","timestamp":1743113010031,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030691271"},{"type":"electronic","value":"9783030691288"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69128-8_8","type":"book-chapter","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T23:22:28Z","timestamp":1612826548000},"page":"116-126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI for Humanity: The Global Challenges"],"prefix":"10.1007","author":[{"given":"Jocelyn","family":"Maclure","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stuart","family":"Russell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,7]]},"reference":[{"key":"8_CR1","unstructured":"Albertsson, K., et al.: Machine learning in high energy physics community white paper. arXiv 1807.02876 (2019)"},{"key":"8_CR2","unstructured":"Ayache, N.: Towards a personalized computational patient. In: IMIA Yearbook of Medical Informatics. International Medical Informatics Association (2016)"},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3102\/0013189X013006004","volume":"13","author":"BS Bloom","year":"1984","unstructured":"Bloom, B.S.: The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ. Res. 13, 4\u201316 (1984)","journal-title":"Educ. Res."},{"key":"8_CR4","first-page":"209","volume-title":"Machine Intelligence 4","author":"BG Buchanan","year":"1969","unstructured":"Buchanan, B.G., Sutherland, G.L., Feigenbaum, E.A.: Heuristic DENDRAL: a program for generating explanatory hypotheses in organic chemistry. In: Meltzer, B., Michie, D., Swann, M. (eds.) Machine Intelligence 4, pp. 209\u2013254. Edinburgh University Press, Edinburgh (1969)"},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"045002","DOI":"10.1103\/RevModPhys.91.045002","volume":"91","author":"G Carleo","year":"2019","unstructured":"Carleo, G., et al.: Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019)","journal-title":"Rev. Mod. Phys."},{"key":"8_CR6","unstructured":"Erol, Y.B., Russell, S.J., Sivaganesan, A., Manley, G.T.: Combined state and parameter estimation of human intracranial hemodynamics. In: Proceedings of the NeurIPS-13 Workshop on Machine Learning for Clinical Data Analysis and Healthcare (2013)"},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017)","journal-title":"Nature"},{"key":"8_CR8","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1126\/science.aaw4399","volume":"363","author":"SG Finlayson","year":"2019","unstructured":"Finlayson, S.G., Bowers, J.D., Ito, J., Zittrain, J.L., Beam, A.L., Kohane, I.S.: Adversarial attacks on medical machine learning. Science 363, 1287\u20131289 (2019)","journal-title":"Science"},{"key":"8_CR9","unstructured":"Forster, E.M.: The Machine Stops. Sheba Blake (1909)"},{"key":"8_CR10","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316, 2402\u20132410 (2016)","journal-title":"J. Am. Med. Assoc."},{"key":"8_CR11","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1146\/annurev.ph.34.030172.000305","volume":"34","author":"AC Guyton","year":"1972","unstructured":"Guyton, A.C., Coleman, T.G., Granger, H.J.: Circulation: overall regulation. Ann. Rev. Phys. 34, 13\u201341 (1972)","journal-title":"Ann. Rev. Phys."},{"key":"8_CR12","unstructured":"Herweijer, C., Combes, B., Gillham, J.: How AI can enable a sustainable future. PwC report (2018)"},{"key":"8_CR13","unstructured":"Hillis, D.: The first machine intelligences. In: Brockman, J. (ed.) Possible Minds: Twenty- Five Ways of Looking at AI. Penguin Press (2019)"},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1016\/j.ijmedinf.2015.06.008","volume":"84","author":"F Khatun","year":"2015","unstructured":"Khatun, F., Heywood, A., Ray, P., Hanifi, S., Bhuiya, A., Liaw, S.T.: Determinants of readiness to adopt mhealth in a rural community of Bangladesh. Int. J. Med. Inform. 84, 847\u201356 (2015)","journal-title":"Int. J. Med. Inform."},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"11322","DOI":"10.1073\/pnas.89.23.11322","volume":"89","author":"R King","year":"1992","unstructured":"King, R., Muggleton, S., Lewis, R., Sternberg, M.: Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proc. Natl. Acad. Sci. 89, 11322\u201311326 (1992)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"8_CR16","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1038\/nature02236","volume":"427","author":"RD King","year":"2004","unstructured":"King, R.D., et al.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247\u2013252 (2004)","journal-title":"Nature"},{"key":"8_CR17","unstructured":"Lewis, J., ray, P., Liaw, S.T.: Recent worldwide developments in eHealth and mHealth to more effectively manage cancer and other chronic diseases - a systematic review. In: IMIA Yearbook of Medical Informatics. International Medical Informatics Association (2016)"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Marolla, C.: Information and Communication Technology for Sustainable Development. AISC, vol. 933. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-13-7166-0_9","DOI":"10.1007\/978-981-13-7166-0_9"},{"issue":"1","key":"8_CR19","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10237-017-0960-0","volume":"17","author":"R Moll\u00e9ro","year":"2017","unstructured":"Moll\u00e9ro, R., Pennec, X., Delingette, H., Garny, A., Ayache, N., Sermesant, M.: Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models. Biomech. Model. Mechanobiol. 17(1), 285\u2013300 (2017). https:\/\/doi.org\/10.1007\/s10237-017-0960-0","journal-title":"Biomech. Model. Mechanobiol."},{"key":"8_CR20","unstructured":"Novak, G.: Representations of knowledge in a program for solving physics problems. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence (1977)"},{"key":"8_CR21","unstructured":"Postelnicu, L.: Babylon inks 10-year partnership with Rwandan government. MobiHealth News, March 4 (2020)"},{"key":"8_CR22","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","volume":"566","author":"M Reichstein","year":"2019","unstructured":"Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N.: Prabhat: deep learning and process understanding for data-driven earth system science. Nature 566, 195\u2013204 (2019)","journal-title":"Nature"},{"key":"8_CR23","unstructured":"Rolnick, D., Donti, P.L., Kaack, L.H., et al.: Tackling climate change with machine learning. arXiv:1906.05433 (2019)"},{"key":"8_CR24","unstructured":"Russell, S.J.: Human Compatible. Penguin (2019)"},{"issue":"4","key":"8_CR25","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1145\/321186.321193","volume":"10","author":"JR Slagle","year":"1963","unstructured":"Slagle, J.R.: A heuristic program that solves symbolic integration problems in freshman calculus. J. ACM 10(4), 507\u2013520 (1963)","journal-title":"J. ACM"},{"key":"8_CR26","doi-asserted-by":"publisher","unstructured":"Sleeman, D., Brown, J.S.: ITS 2020. LNCS, vol. 12149. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49663-0_9","DOI":"10.1007\/978-3-030-49663-0_9"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Solis, M.: New frontiers in robotic surgery. IEEE Pulse, 51\u201355 (2016)","DOI":"10.1109\/MPUL.2016.2606470"},{"key":"8_CR28","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","volume":"180","author":"JM Stokes","year":"2020","unstructured":"Stokes, J.M., et al.: A deep learning approach to antibiotic discovery. Cell 180, 688\u2013702 (2020)","journal-title":"Cell"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP. arXiv 1906.02243 (2019)","DOI":"10.18653\/v1\/P19-1355"},{"key":"8_CR30","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1126\/science.166.3903.343","volume":"166","author":"P Suppes","year":"1969","unstructured":"Suppes, P., Morningstar, M.: Computer- assisted instruction. Science 166, 343\u201350 (1969)","journal-title":"Science"},{"key":"8_CR31","unstructured":"Topol, E.: Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books (2019)"},{"key":"8_CR32","unstructured":"Turing, A.: Can digital machines think?. BBC Third Programme. Typescript available at turingarchive.org, Radio broadcast (1951)"},{"key":"8_CR33","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/s41467-019-14108-y","volume":"11","author":"R Vinuesa","year":"2020","unstructured":"Vinuesa, R., et al.: The role of artificial intelligence in achieving the sustainable development goals. Nat. Commun. 11, 233 (2020)","journal-title":"Nat. Commun."},{"key":"8_CR34","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-3-642-39112-5_18","volume-title":"Artificial Intelligence in Education","author":"MV Yudelson","year":"2013","unstructured":"Yudelson, M.V., Koedinger, K.R., Gordon, G.J.: Individualized Bayesian knowledge tracing models. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 171\u2013180. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-39112-5_18"}],"container-title":["Lecture Notes in Computer Science","Reflections on Artificial Intelligence for Humanity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69128-8_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T23:27:52Z","timestamp":1612826872000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69128-8_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030691271","9783030691288"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69128-8_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}