Skip to main content

AI for Humanity: The Global Challenges

  • Chapter
  • First Online:
Reflections on Artificial Intelligence for Humanity

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12600))

Abstract

This chapter summarizes four global challenges for AI: health, education, the environment, and science. In each area, AI has enormous potential to enhance human well-being, yet very substantial obstacles remain in both basic research and global deployment. Beyond these four areas, we ask whether reliance on AI to solve our problems is a viable strategy for humanity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    There is no consensus on exactly to define this phrase, but we find it useful, nonetheless, as a hypothetical construct in discussing possible future developments. For the sake of argument, assume that such an entity could successfully carry out essentially any task that it would be reasonable to ask a human or collection of humans “skilled in the art” to carry out. It is likely, of course, that such machines would vastly exceed human capabilities in terms of scale and speed.

  2. 2.

    On the specifics of AI for social sciences and humanities, see Chap. 12.

References

  1. Albertsson, K., et al.: Machine learning in high energy physics community white paper. arXiv 1807.02876 (2019)

    Google Scholar 

  2. Ayache, N.: Towards a personalized computational patient. In: IMIA Yearbook of Medical Informatics. International Medical Informatics Association (2016)

    Google Scholar 

  3. Bloom, B.S.: The 2 sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educ. Res. 13, 4–16 (1984)

    Article  Google Scholar 

  4. 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. 209–254. Edinburgh University Press, Edinburgh (1969)

    Google Scholar 

  5. Carleo, G., et al.: Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019)

    Article  Google Scholar 

  6. 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)

    Google Scholar 

  7. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)

    Article  Google Scholar 

  8. 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, 1287–1289 (2019)

    Article  Google Scholar 

  9. Forster, E.M.: The Machine Stops. Sheba Blake (1909)

    Google Scholar 

  10. 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, 2402–2410 (2016)

    Article  Google Scholar 

  11. Guyton, A.C., Coleman, T.G., Granger, H.J.: Circulation: overall regulation. Ann. Rev. Phys. 34, 13–41 (1972)

    Article  Google Scholar 

  12. Herweijer, C., Combes, B., Gillham, J.: How AI can enable a sustainable future. PwC report (2018)

    Google Scholar 

  13. Hillis, D.: The first machine intelligences. In: Brockman, J. (ed.) Possible Minds: Twenty- Five Ways of Looking at AI. Penguin Press (2019)

    Google Scholar 

  14. 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, 847–56 (2015)

    Article  Google Scholar 

  15. 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, 11322–11326 (1992)

    Article  Google Scholar 

  16. King, R.D., et al.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004)

    Article  Google Scholar 

  17. 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)

    Google Scholar 

  18. 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

  19. Molléro, 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), 285–300 (2017). https://doi.org/10.1007/s10237-017-0960-0

    Article  Google Scholar 

  20. Novak, G.: Representations of knowledge in a program for solving physics problems. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence (1977)

    Google Scholar 

  21. Postelnicu, L.: Babylon inks 10-year partnership with Rwandan government. MobiHealth News, March 4 (2020)

    Google Scholar 

  22. 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, 195–204 (2019)

    Article  Google Scholar 

  23. Rolnick, D., Donti, P.L., Kaack, L.H., et al.: Tackling climate change with machine learning. arXiv:1906.05433 (2019)

  24. Russell, S.J.: Human Compatible. Penguin (2019)

    Google Scholar 

  25. Slagle, J.R.: A heuristic program that solves symbolic integration problems in freshman calculus. J. ACM 10(4), 507–520 (1963)

    Article  Google Scholar 

  26. Sleeman, D., Brown, J.S.: ITS 2020. LNCS, vol. 12149. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49663-0_9

  27. Solis, M.: New frontiers in robotic surgery. IEEE Pulse, 51–55 (2016)

    Google Scholar 

  28. Stokes, J.M., et al.: A deep learning approach to antibiotic discovery. Cell 180, 688–702 (2020)

    Article  Google Scholar 

  29. Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP. arXiv 1906.02243 (2019)

    Google Scholar 

  30. Suppes, P., Morningstar, M.: Computer- assisted instruction. Science 166, 343–50 (1969)

    Article  Google Scholar 

  31. Topol, E.: Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books (2019)

    Google Scholar 

  32. Turing, A.: Can digital machines think?. BBC Third Programme. Typescript available at turingarchive.org, Radio broadcast (1951)

    Google Scholar 

  33. Vinuesa, R., et al.: The role of artificial intelligence in achieving the sustainable development goals. Nat. Commun. 11, 233 (2020)

    Article  Google Scholar 

  34. 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. 171–180. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_18

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stuart Russell .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Maclure, J., Russell, S. (2021). AI for Humanity: The Global Challenges. In: Braunschweig, B., Ghallab, M. (eds) Reflections on Artificial Intelligence for Humanity. Lecture Notes in Computer Science(), vol 12600. Springer, Cham. https://doi.org/10.1007/978-3-030-69128-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69128-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69127-1

  • Online ISBN: 978-3-030-69128-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics