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Hybrid Intelligence

  • Dominik Dellermann
  • Philipp Ebel
  • Matthias Söllner
  • Jan Marco LeimeisterEmail author
Catchword

Introduction

Research has a long history of discussing what is superior in predicting certain outcomes: statistical methods or the human brain. This debate has repeatedly been sparked off by the remarkable technological advances in the field of artificial intelligence (AI), such as solving tasks like object and speech recognition, achieving significant improvements in accuracy through deep-learning algorithms (Goodfellow et al. 2016), or combining various methods of computational intelligence, such as fuzzy logic, genetic algorithms, and case-based reasoning (Medsker 2012). One of the implicit promises that underlie these advancements is that machines will 1 day be capable of performing complex tasks or may even supersede humans in performing these tasks. This triggers new heated debates of when machines will ultimately replace humans (McAfee and Brynjolfsson 2017). While previous research has proved that AI performs well in some clearly defined tasks such as playing chess, playing Go...

Keywords

Hybrid intelligence Artificial intelligence Machine learning Human-computer collaboration Machines as teammates Future of work 

References

  1. Agrawal A, Gans J, Goldfarb A (2018) Prediction machines: the simple economics of artificial intelligence. Harvard Business Press, BostonGoogle Scholar
  2. Amershi S, Cakmak M, Knox WB, Kulesza T (2014) Power to the people: the role of humans in interactive machine learning. AI Mag 35(4):105–120CrossRefGoogle Scholar
  3. Bellman R (1978) An introduction to artificial intelligence: can computers think?. Boyd & Fraser, San FranciscoGoogle Scholar
  4. Berdahl A, Torney CJ, Ioannou CC, Faria JJ, Couzin ID (2013) Emergent sensing of complex environments by mobile animal groups. Science 339(6119):574–576CrossRefGoogle Scholar
  5. Bostrom N (2017) Superintelligence. Dunod, ParisGoogle Scholar
  6. Brand C (1996) The g factor: general intelligence and its implications. Wiley, HobokenGoogle Scholar
  7. Dellermann D, Calma A, Lipusch N, Weber T, Weigel S, Ebel P (2019) The future of human-ai collaboration: a taxonomy of design knowledge for hybrid intelligence systems. In: Hawaii international conference on system sciences (HICSS). Hawaii, USAGoogle Scholar
  8. Gardner HE (2000) Intelligence reframed: multiple intelligences for the 21st century. Hachette, LondonGoogle Scholar
  9. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, CambridgeGoogle Scholar
  10. Gottfredson LS (1997) Mainstream science on intelligence: an editorial with 52 signatories, history, and bibliography. Intelligence 24(1):13–23CrossRefGoogle Scholar
  11. Kahneman D (2011) Thinking, fast and slow. Macmillan, LondonGoogle Scholar
  12. Kamar E (2016) Hybrid workplaces of the future. XRDS 23(2):22–25CrossRefGoogle Scholar
  13. Kurzweil R (1990) The age of intelligent machines. MIT Press, CambridgeGoogle Scholar
  14. Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Factor 46(1):50–80CrossRefGoogle Scholar
  15. Leimeister JM (2010) Collective intelligence. Bus Inf Syst Eng 2(4):245–248CrossRefGoogle Scholar
  16. Malone TW, Bernstein MS (2015) Handbook of collective intelligence. MIT Press, CambridgeGoogle Scholar
  17. McAfee A, Brynjolfsson E (2017) Machine, platform, crowd: harnessing our digital future. WW Norton & Company, New YorkGoogle Scholar
  18. Medsker LR (2012) Hybrid intelligent systems. Springer, HeidelbergGoogle Scholar
  19. Meehl PE (1954) Clinical versus statistical prediction: a theoretical analysis and a review of the evidence. University of Minnesota Press, MinneapolisCrossRefGoogle Scholar
  20. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533CrossRefGoogle Scholar
  21. Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R (2011) Cognitive computing. Commun ACM 54(8):62–71CrossRefGoogle Scholar
  22. Moravec H (1988) Mind children: The future of robot and human intelligence. Harvard University Press, CambridgeGoogle Scholar
  23. Poole DL, Mackworth AK (2017) Artificial intelligence: foundations of computational agents, 2nd edn. Oxford University Press, OxfordGoogle Scholar
  24. Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited, LondonGoogle Scholar
  25. Searle JR (1980) Minds, brains, and programs. Behav Brain Sci 3(3):417–424CrossRefGoogle Scholar
  26. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489CrossRefGoogle Scholar
  27. Simard PY, Amershi S, Chickering DM, Pelton AE, Ghorashi S, Meek C, Ramos G, Suh J, Verwey J, Wang M, Wernsing J (2017) Machine teaching: a new paradigm for building machine learning systems. CoRR abs/1707.06742Google Scholar
  28. Sternberg RJ (1985) Beyond IQ: a triarchic theory of human intelligence. Cambridge University Press, Cambridge, EnglandGoogle Scholar
  29. Ullman T, Tenenbaum J, Gershman SJ (2017) Building machines that learn and think like people. Behav Brain Sci.  https://doi.org/10.1017/S0140525X16001837 Google Scholar
  30. Wechsler D (1964) Die Messung der Intelligenz Erwachsener. Huber, BernGoogle Scholar
  31. Woolley AW, Chabris CF, Pentland A, Hashmi N, Malone TW (2010) Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004):686–688CrossRefGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Dominik Dellermann
    • 1
  • Philipp Ebel
    • 2
  • Matthias Söllner
    • 2
    • 3
  • Jan Marco Leimeister
    • 1
    • 2
    Email author
  1. 1.Research Center for IS Design (ITeG), Information SystemsUniversity of KasselKasselGermany
  2. 2.Institute of Information ManagementUniversity of St. GallenSt. GallenSwitzerland
  3. 3.Information Systems and Systems EngineeringUniversity of KasselKasselGermany

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