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Technologies of the Future

  • Mitt Nowshade Kabir
Chapter
Part of the Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth book series (DIG)

Abstract

Continuous emergence of new technologies is one of the principal reasons for the transformation of the economy to the knowledge-based one. In this chapter, we highlighted many profoundly promising technologies that are already reshaping our life, society, and the economy. We discussed here how ICT was the power behind the radical conversion of the economy, and how some new and emerging technologies are entirely revamping not just the economic relationships in the society but catapulting the society to a new level. While there exist many technologies that work as a catalyst for the change, we described some key technologies that are already having game-changing effects or going to have tremendous impact in the near future such as artificial intelligence, 5G mobile technology, virtual and augmented technologies, nanotechnology, quantum computing, 3D printing, and the Internet of things. We specifically accentuated the importance of AI and machine learning technologies here.

References

  1. ABI Research and Qualcomm: Augmented and Virtual Reality: The First Wave of 5G Killer Apps. White Paper (2017). https://www.qualcomm.com/news/onq/2017/02/01/vr-and-arare-pushing-limits-connectivity-5g-our-rescue.
  2. Ahamad, S., Nair, M., & Varghese, B. (2013, May). A survey on crypto currencies. In 4th International Conference on Advances in Computer Science, AETACS (pp. 42–48). Citeseer.Google Scholar
  3. Akyildiz, I. F., & Jornet, J. M. (2010). The internet of nano-things. IEEE Wireless Communications, 17(6), 58–63.CrossRefGoogle Scholar
  4. Auld, J., Sokolov, V., & Stephens, T. S. (2017). Analysis of the effects of connected–automated vehicle technologies on travel demand. Transportation Research Record: Journal of the Transportation Research Board, 2625, 1–8.CrossRefGoogle Scholar
  5. Baum, R. (2003). Drexler and Smalley make the case for and against ‘molecular assemblers’. Chemical and Engineering News, 81(48), 37–42.Google Scholar
  6. Boschert, S., & Rosen, R. (2016). Digital twin—The simulation aspect. In Mechatronic futures (pp. 59–74). Cham: Springer.Google Scholar
  7. Bouwmeester, D., Pan, J. W., Mattle, K., Eibl, M., Weinfurter, H., & Zeilinger, A. (1997). Experimental quantum teleportation. Nature, 390(6660), 575.CrossRefGoogle Scholar
  8. Brynjolfsson, E., & McAfee, A. (2011). The big data boom is the innovation story of our time. The Atlantic, 21.Google Scholar
  9. Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-based expert systems. Reading, MA: Addison Wesley.Google Scholar
  10. Buterin, V. (2014). A next-generation smart contract and decentralized application platform. White Paper.Google Scholar
  11. Cao, Q., Han, S. J., Tersoff, J., Franklin, A. D., Zhu, Y., Zhang, Z., et al. (2015). End-bonded contacts for carbon nanotube transistors with low, size-independent resistance. Science,350(6256), 68–72.CrossRefGoogle Scholar
  12. Carbonell, J. G., Michalski, R. S., & Mitchell, T. M. (1983). An overview of machine learning. In Machine learning (Vol. I, pp. 3–23). Portola Valley, CA: Tioga.Google Scholar
  13. Chapelle, O., Scholkopf, B., & Zien, A. (2009). Semi-supervised learning (O. Chapelle, et al., eds.; 2006) [book reviews]. IEEE Transactions on Neural Networks, 20(3), 542.Google Scholar
  14. Chawla, D., & Kumar, D. A. (2016). A review paper on study of Mote Technology: Smart Dust. In National Conference in Innovations in Micro-electronics, Signal Processing and Communication Technologies. Google Scholar
  15. Chen, H., & Chau, M. (2004). Web mining: Machine learning for web applications. Annual Review of Information Science and Technology, 38(1), 289–329.CrossRefGoogle Scholar
  16. Chen, Y. J., Groves, B., Muscat, R. A., & Seelig, G. (2015). DNA nanotechnology from the test tube to the cell. Nature Nanotechnology, 10(9), 748.CrossRefGoogle Scholar
  17. Chuen, L. D. K., & Linda, L. (2018). Inclusive FinTech: Blockchain, cryptocurrency and ICO. Singapore: World Scientific.Google Scholar
  18. Clements, L. M., & Kockelman, K. M. (2017). Economic effects of automated vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2606, 106–114.CrossRefGoogle Scholar
  19. Crosby, M., Pattanayak, P., Verma, S., & Kalyanaraman, V. (2016). Blockchain technology: Beyond bitcoin. Applied Innovation, 2, 6–10.CrossRefGoogle Scholar
  20. Davenport, T. H., & Harris, J. G. (2005). Automated decision making comes of age. MIT Sloan Management Review, 46(4), 83.Google Scholar
  21. De Wolf, R. (2017). The potential impact of quantum computers on society. Ethics and Information Technology, 19(4), 271–276.CrossRefGoogle Scholar
  22. Fadel, M., Zibold, T., Décamps, B., & Treutlein, P. (2018). Spatial entanglement patterns and Einstein-Podolsky-Rosen steering in Bose-Einstein condensates. Science, 360(6387), 409–413.CrossRefGoogle Scholar
  23. Fedorovich, N. E., De Wijn, J. R., Verbout, A. J., Alblas, J., & Dhert, W. J. (2008). Three-dimensional fiber deposition of cell-laden, viable, patterned constructs for bone tissue printing. Tissue Engineering Part A, 14(1), 127–133.CrossRefGoogle Scholar
  24. Feynman, R. P. (2006). There’s plenty of room at the bottom. SPIE Milestone Series, MS, 182, 3.Google Scholar
  25. Giaretta, P., & Guarino, N. (1995). Ontologies and knowledge bases towards a terminological clarification. Towards Very Large Knowledge Bases: Knowledge Building & Knowledge Sharing, 25(32), 307–317.Google Scholar
  26. Giger, M. L. (2018). Machine learning in medical imaging. Journal of the American College of Radiology, 15(3), 512–520.CrossRefGoogle Scholar
  27. Glaessgen, E., & Stargel, D. (2012, April). The digital twin paradigm for future NASA and US Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA (p. 1818).Google Scholar
  28. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (pp. 2672–2680).Google Scholar
  29. Han, M., Zhang, X. S., Sun, X., Meng, B., Liu, W., & Zhang, H. (2014). Magnetic-assisted triboelectric nanogenerators as self-powered visualized omnidirectional tilt sensing system. Scientific Reports, 4, 4811.CrossRefGoogle Scholar
  30. Hastie, T., Tibshirani, R., & Friedman, J. (2009). Unsupervised learning. In The elements of statistical learning (pp. 485–585). New York, NY: Springer.Google Scholar
  31. Hayes-Roth, F., Waterman, D. A., & Lenat, D. B. (1983). Building expert system. Boston: Addison-Wesley. Google Scholar
  32. Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River, NJ: Pearson Education.Google Scholar
  33. Hejazian, M., Li, W., & Nguyen, N. T. (2015). Lab on a chip for continuous-flow magnetic cell separation. Lab on a Chip, 15(4), 959–970.CrossRefGoogle Scholar
  34. Huh, S., Cho, S., & Kim, S. (2017, February). Managing IoT devices using blockchain platform. In Proceedings of the 19th International Conference on Advanced Communication Technology (ICACT), 2017 (pp. 464–467). IEEE.Google Scholar
  35. Ilyas, M., & Mahgoub, I. (2016). Smart Dust: Sensor network applications, architecture and design. Boca Raton: CRC Press.Google Scholar
  36. ITU. (2012). New ITU standards define the internet of things and provide the blueprints for its development. http://www.itu.int/ITU-T/newslog/New+ITU+Standards+Define+The+Internet+Of+Things+And+Provide+The+Blueprints+For+Its+Development.aspx.
  37. Kaushik, B. K., & Majumder, M. K. (2015). Carbon nanotube: Properties and applications. Carbon Nanotube Based VLSI Interconnects, 17–37. Springer, India.Google Scholar
  38. Konar, A. (1999). Artificial intelligence and soft computing: Behavioral and cognitive modeling of the human brain. Boca Raton: CRC Press.CrossRefGoogle Scholar
  39. Kruse, R., Schwecke, E., & Heinsohn, J. (2012). Uncertainty and vagueness in knowledge based systems: Numerical methods. Berlin: Springer Science & Business Media.Google Scholar
  40. Kurzweil, R. (2004). The law of accelerating returns. In Alan Turing: Life and legacy of a great thinker (pp. 381–416). Berlin and Heidelberg: Springer.CrossRefGoogle Scholar
  41. Ladyman, J., Lambert, J., & Wiesner, K. (2013). What is a complex system? European Journal for Philosophy of Science, 3(1), 33–67.CrossRefGoogle Scholar
  42. Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering,6(4), 239–242.CrossRefGoogle Scholar
  43. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.CrossRefGoogle Scholar
  44. Marsland, S. (2015). Machine learning: An algorithmic perspective. Boca Raton, FL, USA: CRC Press.Google Scholar
  45. Matejka, J., Glueck, M., Bradner, E., Hashemi, A., Grossman, T., & Fitzmaurice, G. (2018, April). Dream lens: Exploration and visualization of large-scale generative design datasets. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (p. 369). ACM.Google Scholar
  46. McMenamin, P. G., Quayle, M. R., McHenry, C. R., & Adams, J. W. (2014). The production of anatomical teaching resources using three-dimensional (3D) printing technology. Anatomical Sciences Education, 7(6), 479–486.CrossRefGoogle Scholar
  47. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf.
  48. Oh, S. Y., & Bailenson, J. (2017). Virtual and augmented reality. In The international encyclopedia of media effects (pp. 1–16). Hoboken, NJ: Wiley.Google Scholar
  49. Rajkumar, R. R., Lee, I., Sha, L., & Stankovic, J. (2010, June). Cyber-physical systems: The next computing revolution. In Proceedings of the 47th Design Automation Conference (pp. 731–736). ACM.Google Scholar
  50. Schreiber, R., Do, J., Roller, E. M., Zhang, T., Schüller, V. J., Nickels, P. C., et al. (2014). Hierarchical assembly of metal nanoparticles, quantum dots and organic dyes using DNA origami scaffolds. Nature Nanotechnology, 9(1), 74.CrossRefGoogle Scholar
  51. Stephens, T. S., Gonder, J., Chen, Y., Lin, Z., Liu, C., & Gohlke, D. (2016). Estimated bounds and important factors for fuel use and consumer costs of connected and automated vehicles (No. NREL/TP-5400–67216). National Renewable Energy Laboratory (NREL), Golden, CO.Google Scholar
  52. Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 25(1), 161–198.CrossRefGoogle Scholar
  53. Takeda, Y., Mae, S., Kajikawa, Y., & Matsushima, K. (2009). Nanobiotechnology as an emerging research domain from nanotechnology: A bibliometric approach. Scientometrics, 80(1), 23–38.CrossRefGoogle Scholar
  54. Tang, B. (2017). The emergence of artificial intelligence in the home: Products, services, and broader developments of consumer oriented AI. Student Theses, Papers and Projects (Computer Science), 6.Google Scholar
  55. Taniguchi, N., Arakawa, C., & Kobayashi, T. (1974). On the basic concept of ‘nano-technology’. In Proceedings of the International Conference on Production Engineering, 1974–8 (Vol. 2, pp. 18–23).Google Scholar
  56. Wang, Y., & Kosinski, M. (2018). Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. Journal of Personality and Social Psychology, 114(2), 246.CrossRefGoogle Scholar
  57. Weller, C., Kleer, R., & Piller, F. T. (2015). Economic implications of 3D printing: Market structure models in light of additive manufacturing revisited. International Journal of Production Economics, 164, 43–56.CrossRefGoogle Scholar
  58. Whitesides, G. M. (2005). Nanoscience, nanotechnology, and chemistry. Small, 1(2), 172–179.CrossRefGoogle Scholar
  59. Wiig, K. (1994). The central management focus for intelligent-acting organizations. Schema Press.Google Scholar
  60. Wilde, M. M. (2013). Quantum information theory. Cambridge: Cambridge University Press.Google Scholar
  61. Wong, K. V., & Hernandez, A. (2012). A review of additive manufacturing. ISRN Mechanical Engineering,2012, 1.CrossRefGoogle Scholar
  62. Wortmann, F., & Flüchter, K. (2015). Internet of things. Business & Information Systems Engineering, 57(3), 221–224.CrossRefGoogle Scholar
  63. Xia, F., Yang, L. T., Wang, L., & Vinel, A. (2012). Internet of things. International Journal of Communication Systems, 25(9), 1101–1102.CrossRefGoogle Scholar
  64. Yam, K. L., Takhistov, P. T., & Miltz, J. (2005). Intelligent packaging: Concepts and applications. Journal of Food Science, 70(1), R1–R10.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Authors and Affiliations

  • Mitt Nowshade Kabir
    • 1
  1. 1.North YorkCanada

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