Convergence to real-time decision making
James M. TIEN
Convergence to real-time decision making
Real-time decision making reflects the convergence of several digital technologies, including those concerned with the promulgation of artificial intelligence and other advanced technologies that underpin real-time actions. More specifically, real-time decision making can be depicted in terms of three converging dimensions: Internet of Things, decision making, and real-time. The Internet of Things include tangible goods, intangible services, ServGoods, and connected ServGoods. Decision making includes model-based analytics (since before 1990), information-based Big Data (since 1990), and training-based artificial intelligence (since 2000), and it is bolstered by the evolving real-time technologies of sensing (i.e., capturing streaming data), processing (i.e., applying real-time analytics), reacting (i.e., making decisions in real-time), and learning (i.e., employing deep neural networks). Real-time includes mobile networks, autonomous vehicles, and artificial general intelligence. Central to decision making, especially real-time decision making, is the ServGood concept, which the author introduced in an earlier paper (2012). It is a physical product or good encased by a services layer that renders the good more adaptable and smarter for a specific purpose or use. Addition of another communication sensors layer could further enhance its smartness and adaptiveness. Such connected ServGoods constitute a solid foundation for the advanced products of tomorrow which can further display their growing intelligence through real-time decisions.
real-time decision making / services / goods / ServGoods / Big Data / Internet of Things / artificial intelligence / wireless communications
[1] |
Accenture (2017). Impact of Artificial Intelligence on Industry Growth by 2035. Report
|
[2] |
Anderson J M, Kalra N, Stanley K D, Sorenson P, Samaras C, Oluwatola O A (2014). Autonomous Vehicle Technology: a Guide for Policymakers. Santa Monica: The RAND Corporation
|
[3] |
Samuel A L (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3): 210–229
CrossRef
Google scholar
|
[4] |
Atkinson R D (2016). ‘It’s Going to Kill Us!’ and Other Myths About the Future of AI. Information Technology & Innovation Foundation
|
[5] |
Azimov I (1950). I, Robot. New York: Gnome Press
|
[6] |
Castro D, New J (2016). The Promise of Artificial Intelligence. Washington DC/Brussels: Center for Data Innovation
|
[7] |
Dreyfus H (1972). What Computers Can’t Do. New York: MIT Press
|
[8] |
Gholami B, Haddad W M, Bailey J M (2018). AI in the ICU: in the intensive care unit, artificial intelligence can keep watch. IEEE Spectrum, 55(10): 31–35
CrossRef
Google scholar
|
[9] |
Hendler J, Mulvehill A M (2016). Social Machines: the Coming Collision of Artificial Intelligence, Social Networking, and Humanity. New York: Apress
|
[10] |
House of Lords Select Committee on Artificial Intelligence (2018). Five Proposed Principles for an AI Code. House of Lords of the United Kingdom Report
|
[11] |
Kleene S C (1956). Representation of events in nerve nets and finite automata. In: Shannon C E, McCarthy J, eds. Automata Studies. Princeton: Princeton University Press, 3–41
|
[12] |
McCarthy J, Minsky M L, Rochester N, Shannon C E (1955). A proposal for the Dartmouth research project on artificial intelligence. Republished in 2006. AI Magazine, 27(4): 11–14
|
[13] |
McCulloch W S, Pitts W (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4): 115–133
CrossRef
Google scholar
|
[14] |
Minsky M (1961). Steps toward artificial intelligence. Proceedings of the IRE, 49(1): 8–30
CrossRef
Google scholar
|
[15] |
MIT (2017). 50 smartest companies in 2017. MIT Technology Review, 120(4): 54–57
|
[16] |
Mlodinow L (2012). Subliminal: How Your Unconscious Mind Rules Your Behavior. New York: Pantheon Books
|
[17] |
National Highway Traffic Safety Administration (2013). U.S. Department of Transportation Releases Policy on Automated Vehicle Development. Washington, DC: NHTSA
|
[18] |
Orwell G (1949). 1984. London: Secker and Warburg
|
[19] |
Parkinson B W, Spilker J J (1996). Global Positioning System: Theory and Applications. Reston: American Institute of Aeronautics and Astronautics
|
[20] |
Perry T S (2018). GPS’ navigator in chief. IEEE Spectrum, 55(5): 46–51
CrossRef
Google scholar
|
[21] |
Ross P E (2015). Diabetes has a new enemy: robo-pancreas. IEEE Spectrum, 52(6): 40–44
CrossRef
Google scholar
|
[22] |
Schmidhuber J (2015). Deep learning in neural networks: an overview. Neural Networks, 61: 85–117
CrossRef
Google scholar
|
[23] |
Tegmark M (2018). Life 3.0: Being Human in the Age of Artificial Intelligence. New York: Knopf Doubleday Publishing Group
|
[24] |
Tien J M (2003). Toward a decision informatics paradigm: a real-time information based approach to decision making. IEEE Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews, 33(1): 102–113
CrossRef
Google scholar
|
[25] |
Tien J M (2012). The next industrial revolution: integrated services and goods. Journal of Systems Science and Systems Engineering, 21(3): 257–296
CrossRef
Google scholar
|
[26] |
Tien J M (2013). Big Data: unleashing information. Journal of Systems Science and Systems Engineering, 22(2): 127–151
CrossRef
Google scholar
|
[27] |
Tien J M (2014). Overview of big data: a US perspective. Bridge, 44(4): 12–19
|
[28] |
Tien J M (2015). Internet of connected ServGoods: considerations, consequences and concerns. Journal of Systems Science and Systems Engineering, 24(2): 130–167
CrossRef
Google scholar
|
[29] |
Tien J M (2016). The sputnik of ServGoods: autonomous vehicles. Journal of Systems Engineering, 26(2): 10–38
|
[30] |
Tien J M (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4(2): 149–178
CrossRef
Google scholar
|
[31] |
Turing A (1950). Computing machinery and intelligence. Mind, LIX(236): 433–460
CrossRef
Google scholar
|
/
〈 | 〉 |