AI Case Studies: Potential for Human Health, Space Exploration and Colonisation and a Proposed Superimposition of the Kubler-Ross Change Curve on the Hype Cycle

1. Fagella, D. What is Artificial Intelligence? An Informed Definition, Emerj, 2017, retrieved on February 16^th 2019, https://emerj.com/ai-glossary-terms/what-is-artificial-intelligence-an-informed-definition/.Search in Google Scholar

2. Turing, A. M. Computing machinery and intelligence, Mind 49, 1950, pp. 433-460.10.1093/mind/LIX.236.433Search in Google Scholar

3. Definition of life in English. English Oxford living dictionaries, retrieved on February 16^th 2019, https://en.oxforddictionaries.com/definition/life.Search in Google Scholar

4. Life. The free dictionary, retrieved on February 16^th 2019, https://www.thefreedictionary.com/life.Search in Google Scholar

5. Sagan, D, Sagan, S., Margulis, L. Life biology. Encyclopaedia Brittanica, retrieved on February 16^th 2019, https://www.britannica.com/science/life.Search in Google Scholar

6. Chang, O, Lipson, D. Neural network quine, arXiv 1803.05859, 2018.10.1162/isal_a_00049Search in Google Scholar

7. Alasaarela, D. The Rise of Emotionally Intelligent AI, Machine Learnings 2017, retrieved on February 15^th 2019, https://machinelearnings.co/the-rise-of-emotionally-intelligent-ai-fb9a814a630e.Search in Google Scholar

8. Ghaffarzadeh, K. Mobile Robots and Drones in Material Handling and Logistics 2018-2038, IDTechEx, retrieved on February 15^th 2019, http://www.idtechex.com/research/reports/mobile-robots-and-drones-in-material-handling-and-logistics-2018-2038-000548.asp.Search in Google Scholar

9. Stanley, K. O., Clune J. Welcoming the Era of Deep Neuroevolution, Uber Engineering 2017, retrieved on February 17^th 2019, https://eng.uber.com/deep-neuroevolution/.Search in Google Scholar

10. Lehman, J., Chen, J., Clune, J., Stanley, K. O. Safe mutations for deep and recurrent neural networks through output gradients, arXiv 1712.06563, 2018.10.1145/3205455.3205473Search in Google Scholar

11. Lehman, J., Chen, J., Clune, J., Stanley, K. O. ES Is More Than Just a Traditional Finite-Difference Approximator, arXiv 1712.06568, 2018.10.1145/3205455.3205474Search in Google Scholar

12. Hutson, M. Artificial intelligence can ‘evolve’ to solve problems, Science 2018, doi: 10.1126/science.aas9715.10.1126/.aas9715Open DOISearch in Google Scholar

13. Conti, E., Madhavan, V., Such, F. P., Lehman, J., Stanley, K. O., Clune, J. Improving exploration in evolution strategies for deep reinforcement learning via a population of noveltyseeking agents. arXiv 1712.06560, 2018.Search in Google Scholar

14. Interpreting technology hype, Gartner, retrieved February 16^th 2019, https://www.gartner.com/en/research/methodologies/gartner-hype-cycle.Search in Google Scholar

15. Sicular, S., Brant, K. Hype Cycle for Artificial Intelligence, Gartner 2018, retrieved on February 17^th 2018, https://www.gartner.com/doc/3883863/hype-cycle-artificial-intelligence-Search in Google Scholar

6. Amara’s law, retrieved on February 17^th 2018, https://web.archive.org/web/20180410135130/https://spotlessdata.com/blog/amaras-law.Search in Google Scholar

17. Kubler-Ross, E. On death and dying, Routledge, 1969.Search in Google Scholar

18. By, R. Organisational change management: a critical review, Journal of Change Management 5, 2005, pp. 369-380.10.1080/14697010500359250Search in Google Scholar

19. Corr, C. A., Doka, A. J., Kastenbaum, R. Dying and its interpreters: a review of selected literature and some comments on the state of the field. OMEGA- Journal of Death and Dying 39, 1999, pp. 239-259.10.2190/3KGF-52BV-QTNT-UBMXSearch in Google Scholar

20. Stroebe, M., Schut, H., Boerner, K. Cautioning health-care professionals: bereaved persons are misguided through the stages of grief. OMEGA – Journal of Death and Dying 74, 2017, pp. 455-473.10.1177/0030222817691870537502028355991Search in Google Scholar

21. Russell, S. J., Norvid, P. Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, 2003.Search in Google Scholar

22. Hendler, J. Avoiding another AI winter, Intelligent systems, IEEE 23, 2008, pp. 2-4.10.1109/MIS.2008.20Search in Google Scholar

23. Enwall, T. Why the pursuit of a “killer app” for home robots is fraught with peril, IEEE Spectrum 2018 retrieved on February 17^th 2019, https://spectrum.ieee.org/automaton/robotics/home-robots/why-the-pursuit-of-a-killer-app-for-home-robots-is-fraught-with-peril.Search in Google Scholar

24. Turck, M. Frontier AI: How far are we from artificial ‘general’ intelligence, really? Retrieved on February 16^th 2019, http://mattturck.com/frontierai/.Search in Google Scholar

25. European Parliamentary Research Service. Should we fear artificial intelligence? European Parliament Think Tank 2018, retrieved on February 14^th 2019, http://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_IDA(2018)614547.Search in Google Scholar

26. Fast, E., Horvitz, E. Long-term trends in the public perception of artificial intelligence, In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, Inc., Menlo Park, CA, 2017, pp. 963-969.10.1609/aaai.v31i1.10635Search in Google Scholar

27. Sharo, T., Korn, C. W., Dola, R. J. How unrealistic optimism is maintained in the face of reality, Nature Neuroscience 14, 2012, pp. 1475-1479.10.1038/nn.2949320426421983684Search in Google Scholar

28. Hecht, D. The neural basis of optimism and pessimism, Experimental Neurobiology 22, 2013, pp. 173-199.10.5607/en.2013.22.3.173380700524167413Search in Google Scholar

29. Stankevicius, A., Huys, Q. J. M., Kaira, A., Series, P. Optimism as a prior belief about the possibility of future reward, PLoS Computational Biology 10, 2014, e1003605.10.1371/journal.pcbi.1003605403104524853098Search in Google Scholar

30. Tufts center for the study of drug development, retrieved on February 17^th 2019, https://csdd.tufts.edu/.Search in Google Scholar

31. Hughes, J. P., Rees, S., Kalindjian, S. B, et al. Principles of early drug discovery, British Journal of Pharmacology 162, 2011, pp. 1239-1249.10.1111/j.1476-5381.2010.01127.x305815721091654Search in Google Scholar

32. Marsden, C. J., Eckersley, S., Hebditch, M. et al. The use of antibodies in small-molecule drug discovery, Journal of Biological Screening 19, 2014, pp. 829-838.10.1177/108705711452777024695620Search in Google Scholar

33. Perez, H. L., Cardarelli, P. M., Deshpande, S., et al. Antibody-drug conjugates: current status and future directions, Drug Discovery Today 19, 2014, pp. 869-881.10.1016/j.drudis.2013.11.00424239727Search in Google Scholar

34. Valeur, E., Jimonet, P. New modalities, technologies and partnerships in probe and lead generation: enabling a mode-of-action centric paradigm, Journal of Medicinal Chemistry 61, 2018, pp. 9004-9029.10.1021/acs.jmedchem.8b0037829851477Search in Google Scholar

35. Valeur, E., Gueret, S. M., Adihou, H., et al. New modalities for challenging targets in drug discovery, Angewandte Chemie International Edition 56, 2017, pp. 10294-10323.10.1002/anie.20161191428186380Search in Google Scholar

36. Monte, A. A., Brocker, C., Nebert, D. W., et al. Improved drug therapy: triangulating phenomics with genomics and metabolomics, Human Genomics 8, 2014, 16.10.1186/s40246-014-0016-9444568725181945Search in Google Scholar

37. Schneider, G., Fechner, U. Computer-based de novo design of drug-like molecules, Nature Reviews Drug Discovery 4, 2005, pp. 649-663.10.1038/nrd179916056391Search in Google Scholar

38. Duch, W., Swaminathan, K., Meller, J. Artificial intelligence approaches for rational drug design and discovery, Current Pharmaceutical Design 13, 2007, pp. 1497-1508.10.2174/13816120778076595417504169Search in Google Scholar

39. Olivecrona, M., Blaschke, T., Engkvist, O., Chen, H. Molecular de novo design through deep reinforcement learning, Journal of Cheminformatics 9, 2017, 48.10.1186/s13321-017-0235-x558314129086083Search in Google Scholar

40. Sellwood, M. A., Ahmed, M., Segler, M. H. S., Brown, N. Artificial intelligence in drug discovery, Future Medicinal Chemistry 10, 2018, pp. 2025-2028.10.4155/fmc-2018-021230101607Search in Google Scholar

41. Segler, M. H. S., Kogej, T., Tyrchan, C., Waller, M. P. Generating focused molecule libraries for drug discovery with recurrent neural networks, ACS Central. Science 4, 2018, 120-131.10.1021/acscentsci.7b00512578577529392184Search in Google Scholar

42. Hessler, G., Baringhaus, K.-H. Artificial intelligence in drug design, Molecules 23, 2018, 2520.10.3390/molecules23102520622261530279331Search in Google Scholar

43. Benhenda, M. ChenGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv 1708.08227, 2017.10.1101/292177Search in Google Scholar

44. Popova, M., Isayev, O., Tropsha, A. Deep reinforcement for drug design, Science Advances 4, 2018, eaap7855.10.1126/sciadv.aap7885605976030050984Search in Google Scholar

45. Fleming, N. How artificial intelligence is changing drug discovery, Nature 557, 2018, pp. S55-S57.10.1038/d41586-018-05267-x29849160Search in Google Scholar

46. Mak, K.-K., Pichika, M. R. Artificial intelligence in drug development: present status and future prospects, Drug Discovery Today 2018, https://doi.org/10.1016/j.drudis.2018.11.014.10.1016/j.drudis.2018.11.01430472429Open DOISearch in Google Scholar

47. Pushpakom, S., Iorio, F., Eyers, P. A., et al. Drug repurposing: progress, challenges and recommendations, Nature Reviews Drug Discovery 18, 2019, pp. 41-58.10.1038/nrd.2018.16830310233Search in Google Scholar

48. Jordan, A. M. Artificial intelligence in drug design – the storm before the calm? ACS Medicinal Chemistry Letters 9, 2018, pp. 1150-1152.10.1021/acsmedchemlett.8b00500629584830613315Search in Google Scholar

49. Lyu, J., Wang, S., Balius, T. E., et al. Ultra-large docking for discovering new chemotypes, Nature 566, 2019, pp. 224-229.10.1038/s41586-019-0917-9638376930728502Search in Google Scholar

50. Topol, E. High-performance medicine: the convergence of human and artificial intelligence, Nature Medicine 25, 2019, pp. 44-56.10.1038/s41591-018-0300-730617339Search in Google Scholar

51. Havelund, K., Lowry, M., Penix, J. Formal analysis of a space craft controller using SPIN, IEEE Transactions on Software Engineering 27, 2001, pp. 749-765.10.1109/32.940728Search in Google Scholar

52. Daniela, G., Dario, I. Artificial intelligence for space applications, Intelligent Computing Everywhere 2007, pp. 235-253.10.1007/978-1-84628-943-9_12Search in Google Scholar

53. Weir, N., Fayyad, U. M., Djorgovski, G., Roden, J. The SKICAT system for processing and analysing digital imaging sky surveys, Publications of the Astronomical Society of the Pacific 107, 1995, pp. 1243-1254.10.1086/133683Search in Google Scholar

54. C. E. Petrillo, Totora, C., Chatterjee, S. et al. Finding strong gravitational lenses in the Kilo Degree Survey with convolutional neural networks, Monthly Notices of the Royal Astronomical Society 472, 2017, pp. 1129-1150.10.1093/mnras/stx2052Search in Google Scholar

55. Estlin, T. A., Bornstein, B. J., Gaines, D. M., et al. AEGIS automated targeting for the MER Opportunity Rover, ACM Transactions on Intelligent Systems and Technology (TIST) 3, 2012.10.1145/2168752.2168764Search in Google Scholar

56. Allwood, A. et al. Texture-specific elemental analysis of rocks and soils with PIXL: The Planetary Instrument for X-ray Lithochemistry on Mars 2020, IEEE Aerospace Conference Proceedings 2015.10.1109/AERO.2015.7119099Search in Google Scholar

57. Prosser, P., Rebolledo, J. D. AI is kicking space exploration into hyperdrive—here’s how, Singularity Hub 2018, retrieved on February 17^th 2019, https://singularityhub.com/2018/10/07/ais-kicking-space-exploration-into-hyperdrive-heres-how/#sm.000tm2cyt1cylenswso298wt2y6ec.Search in Google Scholar

58. Bradford, J., Schaffer, M., Talk, D. Torpor inducing transfer habitat for human stasis for Mars, SpaceWorks Enterprises 2016.Search in Google Scholar

59. Cerri, M., Tinganelli, W., Negrini, M. et al. Hibernation for space travel: Impact on radio protection, Life Sciences in Space Research 11, 2016, pp. 1-9.10.1016/j.lssr.2016.09.00127993187Search in Google Scholar

60. Baird, D. NASA explores artificial intelligence for space communications 2017, Retrieved on February 17^th 2019, https://www.nasa.gov/feature/goddard/2017/nasa-explores-artificial-intelligence-for-space-communications.Search in Google Scholar

61. Chung, A., Ludivig, P., Potter, R. W. K., et al. Localization: or the importance of knowing where you are, Frontier Development Lab 2018, Handbook, pp. 38-39.Search in Google Scholar

62. Buchheim, J., Alexander, C. Pilot Study with the Crew Interactive MObile companioN (Cimon) (Mobile Companion), Erasmus Experiment Archive 2018.Search in Google Scholar

63. Pultarova, T. AI robot CIMON debuts at International Space Station, Space.com, retrieved on February 17^th 2019, https://www.space.com/42574-ai-robot-cimon-space-station-experiment.htmlSearch in Google Scholar

64. Pimm, S. L, Jenkins, C. N., Abell, R., et al. The biodiversity of species and their rates of extinction, distribution and protection, Science 344, 2014, https://doi.org/10.1126/science.124675210.1126/.1246752Open DOISearch in Google Scholar

65. Ceballos, G., Ehrlich, P. R., Barnosky, A. D., et al. Accelerated modern human-induced species losses: entering the sixth mass extinction, Science Advances, 1(5), 2015, e1400253.10.1126/sciadv.1400253464060626601195Search in Google Scholar

66. Clifford, C. Musk: ’Mark my words – A.I. is far more dangerous than nukes’, CNBC Make It 2018, retrieved on February 17^th 2019, https://www.cnbc.com/2018/03/13/elon-musk-at-sxsw-a-i-is-more-dangerous-than-nuclear-weapons.html.Search in Google Scholar

67. Galeon, D. Stephen Hawking: “I fear that AI may replace humans altogether”, WIRED 2017, retrieved on February 17^th 2019, https://futurism.com/stephen-hawking-ai-replace-humans/.Search in Google Scholar

68. Tegmark, M. Life 3.0. Being human in the age of artificial intelligence, Penguin Press, 2017.Search in Google Scholar

69. Rees, M. On the future prospects for humanity, Princeton University Press, 2018.10.1515/9780691184401Search in Google Scholar

70. Kaku, M. The future of humanity. Terraforming Mars, interstellar travel, immortality and our destiny beyond Earth, Penguin Books, 2018.Search in Google Scholar

71. Evans, C. 1.7 million U.S Facebook users will pass away in 2018. The Digital Beyond 2018, retrieved on February 17^th 2019, http://www.thedigitalbeyond.com/2018/01/1-7-million-u-sfacebook-users-will-pass-away-in-2018/.Search in Google Scholar

72. Weisberger, M. Lifelike ‘Sophia’ robot granted citizenship to Saudi Arabia, Live Science, retrieved on February 15^th 2019, https://www.livescience.com/60815-saudi-arabia-citizen-robot.html.Search in Google Scholar

73. De Quetteville, H. This young man died in April. So how did our writer have a conversation with him last month? The Telegraph, retrieved on February 17^th 2019, https://www.telegraph.co.uk/technology/2019/01/18/will-digital-soul/.Search in Google Scholar

74. Ohman, C., Floridi, L. The potential economy of death in the age of information: a critical approach to the digital afterlife industry, Minds and Machines 27, 2017, pp. 639-662.10.1007/s11023-017-9445-2Search in Google Scholar

75. Ohman, C., Floridi, L. An ethical framework for the digital afterlife industry, Nature Human Behaviour 2, 2018, pp. 318-320.10.1038/s41562-018-0335-230962597Search in Google Scholar

76. Chien, S., Wagstaff, K. L. Robotic space exploration agents, Science Robotics 2, 2017, eaan4831.10.1126/scirobotics.aan483133157898Search in Google Scholar

77. The pivotal role AI plays in the future of space travel. Ross, retrieved on February 17^th 2019, https://blog.rossintelligence.com/post/ai-space-travel.Search in Google Scholar

78. Campa, R., Szocik, K., Braddock, M. Why space colonisation will be fully automated, Technological Forecasting and Social Change 2019; in press.10.1016/j.techfore.2019.03.021Search in Google Scholar

79. Braddock, M., Campa, R., Szocik, K. Ergonomic constraints for astronauts: challenges and opportunities today and for the future, Proceedings of the International Conference on Ergonomics and Human Factors 2019, Stratford-Upon-Avon, 29 April-1 May 2019, 1st Edition.Search in Google Scholar