Hybrid Intelligence
Hybrid intelligence is a novel approach towards artificial general intelligence by combining human intelligence and intelligence demonstrated by machines. The basic rationale is to try to combine the complementary strengths of heterogeneous intelligences (i.e. human and artificial agents) into a socio-technological ensemble.[1]. Hybrid intelligence is defined as the ability to accomplish complex goals by combining human intelligence and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Research on hybrid intelligence is an interdisciplinary field from computer science, information systems, neuro science, cognitive psychology, business engineering. The topic was heavily influenced by the doctoral thesis of Dominik Dellermann[2]
Related Concepts
Hybrid intelligence is defined as the ability to accomplish complex goals by combining human intelligence and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Research on hybrid intelligence is an interdisciplinary field from computer science, information systems, neuro science, cognitive psychology, business engineering.
Intelligence
Various definitions and dimensions (e.g. social, logical, spatial, musical etc.) of the term intelligence exist in multiple research disciplines such as psychology, cognitive science, neuro science, human behavior, education, or computer science. For the purpose of our research we use an inclusive and generic definition of describing general intelligence as the ability to accomplish complex goals, learn, reason, and adaptively perform effective actions within an environment[3].
Human intelligence
The sub-dimension of human intelligence that is related to the human species, defines the mental capabilities of human beings to learn, reason, and adaptively perform effective actions within an environment based on their knowledge. This allows humans to adapt to changing environments and act towards achieving their goals. One commonly popular definition of human intelligence by Wechsler[4] explains intelligence as composed or global ability of an individual to act purposefully, think reasonably, and to effectively deal with its environment.
Collective Intelligence
The second related human intelligence concept is collective intelligence. Collective intelligence refers to “groups of individuals acting collectively in ways that seem intelligence”[5]. Even though the term “individuals” leaves room for interpretation, researchers in this domain usually refer to the concept of wisdom of crowds and thus a combined intelligence of individual human agents[6]. This concept describes that in certain conditions, a group of average people can outperform any individual of the group or even a single expert due to error reduction and knowledge aggregation[5][7].
Artificial Intelligence
The sub-field of general intelligence that relates to machines is called artificial intelligence. By this term we mean systems that perform “[…] activities that we associate with human thinking, activities such as decision-making, problem solving, learning […]"[8]. Although, various definitions exist for this, it generally covers the idea to create machines that can accomplish complex goals. This includes facets such as natural language processing, perceiving objects, storing of knowledge, applying this knowledge for solving problems, machine learning to adapt to new circumstances and act in its environment[9]. Various approaches exist to achieve AI that are more or less associated with the understanding and replication of intelligence. For instance, the field of Cognitive Computing “[…] aims to develop a coherent, unified, universal mechanism inspired by the mind’s capabilities. […] we seek to implement a unified computational theory of the mind.”[10]. Therefore, interdisciplinary researcher teams rely on the reverse-engineering of human learning to create machines that “learn and think like people”[11].
Elements of Hybrid Intelligence
Definition
Hybrid Intelligence is defined as the "[...] ability to achieve complex goals by combining human and artificial intelligence, thereby reaching superior results to those each of them could have accomplished separately, and continuously improve by learning from each other."[12] Several core concepts of this definition are noteworthy:
Collectively
Hybrid Intelligence covers the fact that tasks are performed collectively. This means that the activities conducted by each part are dependent; however, are not necessarily always aligned to achieve a common goal (e.g. teaching an AI adversarial tasks such as playing games)[13].
Collectively
Hybrid Intelligence covers the fact that tasks are performed collectively. This means that the activities conducted by each part are dependent; however, are not necessarily always aligned to achieve a common goal (e.g. teaching an AI adversarial tasks such as playing games)[13].
Superior results
This defines the fact that the system achieves a performance that none of the involved actors could have achieved in a certain task without the other[14][15]
Continuous learning
A central aspect is that over time this socio-technological system improves both as a whole and each single component (i.e. humans and machines) learn through experience from each other, thus improving performance in a certain task[16]
References
- ↑ Ece Kamar, Directions in Hybrid Intelligence: Complementing AI Systems with Human Intelligence (in German)CS1 maint: Unrecognized language (link)
- ↑ Dellermann, Dominik (2020). Accelerating Entrepreneurial Decision- Making with Hybrid Intelligence. Kindle Direct Publishing. ISBN 9781658588379. Search this book on
- ↑ Linda S. Gottfredson (January 1997), "Why g matters: The complexity of everyday life", Intelligence (in German), 24 (1), pp. 79–132, doi:10.1016/s0160-2896(97)90014-3, ISSN 0160-2896CS1 maint: Unrecognized language (link)
- ↑ C. Molz, R. Schulze, U. Schroeders, O. Wilhelm (October 2010), "TBS-TK Rezensionen", Psychologische Rundschau (in German), 61 (4), pp. 229–230, doi:10.1026/0033-3042/a000042, ISSN 0033-3042CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ 5.0 5.1 Malone, Thomas W. (2015), Handbook of Collective Intelligence. (in German), ISBN 978-0262331463, OCLC 928998019CS1 maint: Unrecognized language (link)
- ↑ Anita Williams Woolley, Christopher F. Chabris, Alex Pentland, Nada Hashmi, Thomas W. Malone (2010-10-29), "Evidence for a Collective Intelligence Factor in the Performance of Human Groups", Science (in German), 330 (6004), pp. 686–688, Bibcode:2010Sci...330..686W, doi:10.1126/science.1193147, ISSN 0036-8075, PMID 20929725CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ Dellermann, D.; Lipusch, N. & Ebel, P., "Heading for new shores: Crowdsourcing for entrepreneurial opportunity creation", European Conference of Information Systems (ECIS) (in German), 2018CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ Bellman, Richard, 1920-1984. (1978), An introduction to artificial intelligence : can computers think? (in German), San Francisco: Boyd & Fraser Pub. Co, ISBN 0878350667, OCLC 3933653CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ Norvig, Peter 1956- (2016), Artificial intelligence a modern approach (in German) (Third ed.), Boston: Pearson, ISBN 978-1292153964, OCLC 945899984CS1 maint: Unrecognized language (link)
- ↑ Modha, D. S., Ananthanarayanan, R., Esser, S. K., Ndirango, A., Sherbondy, A. J., & Singh, R., "Cognitive computing", Communications of the ACM (in German), 54 (8), pp. 62–71CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman (2017), "Building machines that learn and think like people", Behavioral and Brain Sciences (in German), 40, pp. e253, arXiv:1604.00289, doi:10.1017/S0140525X16001837, ISSN 0140-525X, PMID 27881212CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ Dellermann, Dominik; Ebel, Philipp; Söllner, Matthias; Leimeister, Jan Marco (2019-10-01). "Hybrid Intelligence" (PDF). Business & Information Systems Engineering. 61 (5): 637–643. doi:10.1007/s12599-019-00595-2. ISSN 1867-0202.
- ↑ 13.0 13.1 Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness (February 2015), "Human-level control through deep reinforcement learning", Nature (in German), 518 (7540), pp. 529–533, Bibcode:2015Natur.518..529M, doi:10.1038/nature14236, ISSN 0028-0836, PMID 25719670CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ Dominik Dellermann, Nikolaus Lipusch, Philipp Ebel, Karl Michael Popp, J. M. Leimeister (2017-12-10), Finding the Unicorn: Predicting Early Stage Startup Success Through a Hybrid Intelligence Method (in German) (ID 3159123), Rochester, NY: Social Science Research Network, doi:10.2139/ssrn.3159123, SSRN 3159123CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ Dellermann, Dominik; Lipusch, Nikolaus & Li, Mahei, "Combining Humans and Machine Learning: A Novel Approach for Evaluating Crowdsourcing Contributions in Idea Contests." (PDF), Multikonferenz Wirtschaftsinformatik (MKWI) (in German), 2018CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
- ↑ Joseph Chee Chang, Saleema Amershi, Ece Kamar (2017-05-02), Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets (in German), ACM, pp. 2334–2346, doi:10.1145/3025453.3026044, ISBN 9781450346559CS1 maint: Multiple names: authors list (link) CS1 maint: Unrecognized language (link)
External links
- Research Network on Hybrid Intelligence
- Research Center for Information System Design (ITeG) at the University of Kassel
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