The development and deployment of artificial intelligence (AI) is and will profoundly reshape human society, the culture and the composition of civilisations which make up human kind. All technological triggers tend to drive a hype curve which over time is realised by an output which is often unexpected, taking both pessimistic and optimistic perspectives and actions of drivers, contributors and enablers on a journey where the ultimate destination may be unclear. In this paper we hypothesise that this journey is not dissimilar to the personal journey described by the Kubler-Ross change curve and illustrate this by commentary on the potential of AI for drug discovery, development and healthcare and as an enabler for deep space exploration and colonisation. Recent advances in the call for regulation to ensure development of safety measures associated with machine-based learning are presented which, together with regulation of the rapidly emerging digital after-life industry, should provide a platform for realising the full potential benefit of AI for the human species.
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.
11. Lehman, J., Chen, J., Clune, J., Stanley, K. O. ES Is More Than Just a Traditional Finite-Difference Approximator, arXiv 1712.06568, 2018.
12. Hutson, M. Artificial intelligence can ‘evolve’ to solve problems, Science 2018, doi: 10.1126/science.aas9715.
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.
17. Kubler-Ross, E. On death and dying, Routledge, 1969.
18. By, R. Organisational change management: a critical review, Journal of Change Management 5, 2005, pp. 369-380.
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.
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.
21. Russell, S. J., Norvid, P. Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, 2003.
22. Hendler, J. Avoiding another AI winter, Intelligent systems, IEEE 23, 2008, pp. 2-4.
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.
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.
28. Hecht, D. The neural basis of optimism and pessimism, Experimental Neurobiology 22, 2013, pp. 173-199.
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.
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.
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.
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.
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.
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.
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.
37. Schneider, G., Fechner, U. Computer-based de novo design of drug-like molecules, Nature Reviews Drug Discovery 4, 2005, pp. 649-663.
38. Duch, W., Swaminathan, K., Meller, J. Artificial intelligence approaches for rational drug design and discovery, Current Pharmaceutical Design 13, 2007, pp. 1497-1508.
39. Olivecrona, M., Blaschke, T., Engkvist, O., Chen, H. Molecular de novo design through deep reinforcement learning, Journal of Cheminformatics 9, 2017, 48.
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.
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.
42. Hessler, G., Baringhaus, K.-H. Artificial intelligence in drug design, Molecules 23, 2018, 2520.
43. Benhenda, M. ChenGAN challenge for drug discovery: can AI reproduce natural chemical diversity? arXiv 1708.08227, 2017.
44. Popova, M., Isayev, O., Tropsha, A. Deep reinforcement for drug design, Science Advances 4, 2018, eaap7855.
45. Fleming, N. How artificial intelligence is changing drug discovery, Nature 557, 2018, pp. S55-S57.
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.
48. Jordan, A. M. Artificial intelligence in drug design – the storm before the calm? ACS Medicinal Chemistry Letters 9, 2018, pp. 1150-1152.
49. Lyu, J., Wang, S., Balius, T. E., et al. Ultra-large docking for discovering new chemotypes, Nature 566, 2019, pp. 224-229.
50. Topol, E. High-performance medicine: the convergence of human and artificial intelligence, Nature Medicine 25, 2019, pp. 44-56.
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.
52. Daniela, G., Dario, I. Artificial intelligence for space applications, Intelligent Computing Everywhere 2007, pp. 235-253.
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.
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.
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.
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.
78. Campa, R., Szocik, K., Braddock, M. Why space colonisation will be fully automated, Technological Forecasting and Social Change 2019; in press.
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.