Augmented Intelligence: A Human Productivity and Performance Amplifier in Systems Engineering and Engineered Human–Machine Systems

Abstract

With the resurgence of artificial intelligence (AI) paced primarily by recent advances in machine learning and deep learning, systems engineering is turning to AI to introduce flexibility in the development process, facilitate exploration and search, and enhance team productivity. As importantly, systems engineering is beginning to leverage AI in engineered systems to increase system resilience, safety, and security. More recently, however, AI is beginning to be viewed as a means to augment rather than replace humans. This perspective alters the role of AI from that of autonomous intelligence to one of augmented intelligence (AugI), a conceptualization of AI that emphasizes AI's role in augmenting or amplifying human intelligence rather than replacing it. Inherent in this view is the recognition that AI and human working together can perform certain tasks better than either could alone. To this end, the chapter presents a methodological framework for effectively exploiting AugI in systems engineering as well as in engineered human–machine systems.

Leads

Azad M. Madni

University of Southern California

Publications

Araya , D. ( 2019 ). 3 things you need to know about augmented intelligence . Forbes .
Ashby , R. ( 1956 ). An Introduction to Cybernetics . Chapman and Hall .
Bansal , G. Nushi , B. Kamar , E. , et al. ( 2019 ). Updates in human-AI teams: understanding and addressing the performance/compatibility tradeoff . Paper Presented at the 3rd Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence , Proceedings of the AAAI Conference on Artificial Intelligence , Honolulu, US-HI (27 January–1 February). Vol. 33(01), pp. 2429 – 2437
Bird , S. ( 2017 ). Why AI must be redefined as ‘Augmented Intelligence’ .
Chakraborti , T. & Kambhampati , S. ( 2018 ). “ Algorithms for the greater good! On mental modeling and acceptable Symbiosis in human-agent collaboration .” arXiv.1801.09854[cs.AI].
Crigger , E. , Reinbold , K. , Hanson , C. et al. ( 2022 ). Trustworthy augmented intelligence in healthcare . Journal of Medical Systems 46 : 12 .
Davis , S. and Lessard , A. ( 2018 ). The AI Revolution Begins with Augmented Intelligence . Signafire White Paper.
DeChurch , L.A. and Mesmer-Magnus , J.R. ( 2010 ). The cognitive underpinnings of effective teamwork: a meta-analysis . Journal of Applied Psychology 95 ( 1 ): 32 .
Engelbart , D.C. ( 1962 ). Augmenting Human Intellect: A Conceptual Framework . SRI Summary Report AFOSR-3223 . Stanford Research Institute .
Galbraith , J.K. ( 2015 ). The New Industrial State , 9 . Princeton University Press .
Kamar , E. ( 2016 ). Directions in hybrid intelligence: complementing AI systems with human intelligence . Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI) , New York, US-NY (9–15 July), pp. 4070 - 4073 . AAAI Press .
Kasparov , G. ( 2017 ). Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins . Public Affairs .
Kuo , C.C.J. and Madni , A.M. ( 2023 ). Green learning: introduction, examples and outlook . Journal of Visual Communication and Image Representation 90 : 103685 .
Licklider , J.C.R. ( 1960 ). Man-computer Symbiosis, transactions on human factors . Electronics HFE-1 : 4 – 11 .
Madni , A.M. ( 1988 ). The role of human factors in expert systems design and acceptance . Human Factors Journal 30 ( 4 ): 395 – 414 .
Madni , A.M. ( 2010 ). Integrating humans with software and systems: technical challenges and a research agenda . Systems Engineering 13 ( 3 ): 232 – 245 , Autumn (Fall).
Madni , A.M. ( 2011 ). Integrating humans with and within software and systems: challenges and opportunities . (Invited Paper). CrossTalk, Journal of Defense Software Engineering “People Solutions,” May/June.
Madni , A.M. ( 2020 ). Exploiting augmented intelligence in systems engineering and engineered systems . Insight Special Issue, Systems Engineering and AI
Madni , A.M. and Jackson , S. ( 2009 ). Towards a conceptual framework for resilience engineering . IEEE Systems Journal 3 ( 2 ): 181 – 191 .
Madni , A.M. and Madni , C.C. ( 2004 ). Context-driven collaboration during mobile C2 operations . Proceedings of the Society for Modeling and Simulation International . pp. 18–22.
Madni , A.M. and Madni , C.C. ( 2018 ). Architectural framework for exploring adaptive human-machine teaming options in simulated dynamic environments . MDPI Systems , special issue on “Model-Based Systems Engineering” 6 ( 4 ): 44 .
Madni , A.M. and Sievers , M. ( 2018 ). Model-based systems engineering: motivation, current status, and research opportunities . Systems Engineering , Special 20th Anniversary Issue 21 ( 3 ): 172 – 190 .
Madni , A.M. , Samet , M.G. , and Freedy , A. ( 1982 ). A trainable on-line model of the human operator in information acquisition tasks . IEEE Transactions of Systems, Man, and Cybernetics 12 ( 4 ): 504 – 511 .
Madni , A.M. , Madni , C.C. , and Salasin , J. ( 2002 ). ProACT™: process-aware zero latency system for distributed, collaborative enterprises . NCOSE International Symposium 12 ( 1 ): 783 – 790 .
Madni , A.M. , Madni , C.C. , and Lucero , D.S. ( 2019 ). Leveraging digital twin technology in model-based systems engineering . MDPI Systems , special issue on “Model-Based Systems Engineering,” 7 ( 1 ): 7 .