Skip to main content
Log in

Hybrid Intelligence

  • Catchword
  • Published:
Business & Information Systems Engineering Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. For further work on this topic see Dellermann et al. (2019).

  2. https://deepmind.com (accessed 19 Mar 2019).

  3. https://ai.google/research/teams/brain/pair (accessed 19 Mar 2019).

References

  • Agrawal A, Gans J, Goldfarb A (2018) Prediction machines: the simple economics of artificial intelligence. Harvard Business Press, Boston

    Google Scholar 

  • 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):105–120

    Article  Google Scholar 

  • Bellman R (1978) An introduction to artificial intelligence: can computers think?. Boyd & Fraser, San Francisco

    Google Scholar 

  • Berdahl A, Torney CJ, Ioannou CC, Faria JJ, Couzin ID (2013) Emergent sensing of complex environments by mobile animal groups. Science 339(6119):574–576

    Article  Google Scholar 

  • Bostrom N (2017) Superintelligence. Dunod, Paris

    Google Scholar 

  • Brand C (1996) The g factor: general intelligence and its implications. Wiley, Hoboken

    Google Scholar 

  • 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, USA

  • Gardner HE (2000) Intelligence reframed: multiple intelligences for the 21st century. Hachette, London

    Google Scholar 

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    Google Scholar 

  • Gottfredson LS (1997) Mainstream science on intelligence: an editorial with 52 signatories, history, and bibliography. Intelligence 24(1):13–23

    Article  Google Scholar 

  • Kahneman D (2011) Thinking, fast and slow. Macmillan, London

    Google Scholar 

  • Kamar E (2016) Hybrid workplaces of the future. XRDS 23(2):22–25

    Article  Google Scholar 

  • Kurzweil R (1990) The age of intelligent machines. MIT Press, Cambridge

    Google Scholar 

  • Lee JD, See KA (2004) Trust in automation: designing for appropriate reliance. Hum Factor 46(1):50–80

    Article  Google Scholar 

  • Leimeister JM (2010) Collective intelligence. Bus Inf Syst Eng 2(4):245–248

    Article  Google Scholar 

  • Malone TW, Bernstein MS (2015) Handbook of collective intelligence. MIT Press, Cambridge

    Google Scholar 

  • McAfee A, Brynjolfsson E (2017) Machine, platform, crowd: harnessing our digital future. WW Norton & Company, New York

    Google Scholar 

  • Medsker LR (2012) Hybrid intelligent systems. Springer, Heidelberg

    Google Scholar 

  • Meehl PE (1954) Clinical versus statistical prediction: a theoretical analysis and a review of the evidence. University of Minnesota Press, Minneapolis

    Book  Google Scholar 

  • 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):529–533

    Article  Google Scholar 

  • Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R (2011) Cognitive computing. Commun ACM 54(8):62–71

    Article  Google Scholar 

  • Moravec H (1988) Mind children: The future of robot and human intelligence. Harvard University Press, Cambridge

    Google Scholar 

  • Poole DL, Mackworth AK (2017) Artificial intelligence: foundations of computational agents, 2nd edn. Oxford University Press, Oxford

    Book  Google Scholar 

  • Russell SJ, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited, London

    Google Scholar 

  • Searle JR (1980) Minds, brains, and programs. Behav Brain Sci 3(3):417–424

    Article  Google Scholar 

  • 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):484–489

    Article  Google Scholar 

  • 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/1707.06742

  • Sternberg RJ (1985) Beyond IQ: a triarchic theory of human intelligence. Cambridge University Press, Cambridge, England

    Google Scholar 

  • 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 

  • Wechsler D (1964) Die Messung der Intelligenz Erwachsener. Huber, Bern

    Google Scholar 

  • 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):686–688

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Marco Leimeister.

Additional information

Accepted after two revisions by Prof. Weinhardt.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dellermann, D., Ebel, P., Söllner, M. et al. Hybrid Intelligence. Bus Inf Syst Eng 61, 637–643 (2019). https://doi.org/10.1007/s12599-019-00595-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12599-019-00595-2

Keywords

Navigation