Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence
Open accessAbstract
What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’ open innovation process. It conceptually establishes a direct and autonomous learning interaction model. The key factor of this model is that the process to respond to entries from external environments through interactions between autonomous learning and direct learning as well as to rearrange internal knowledge is incessant. When autonomous learning happens, the units of knowledge determinations that arise from indirect learning are separated. They induce not only broad autonomous learning made through the horizontal combinations that surpass the combinations that occurred in direct learning but also in-depth autonomous learning made through vertical combinations that appear so that new knowledge is added. The core of the interaction model between direct and autonomous learning is the variability of the boundary between proven knowledge and hypothetical knowledge, limitations in knowledge accumulation, as well as complementarity and conflict between direct and autonomous learning. Therefore, these should be considered when introducing the interaction model between direct and autonomous learning into navigations, cleaning robots, search engines, etc. In addition, we should consider the relationship between direct learning and autonomous learning when building up open innovation strategies and policies.
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AI applications are being used in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing.
“AI applications are being used in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing [6,7].”
From The sustainability of artificial intelligence: an urbanistic viewpoint from the lens of smart and sustainable cities (2020) · cites this paper as [6]
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Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Bounded rationality and organizational learning
Simon, H. A. (1991) · Organization Science · doi:10.1287/orsc.2.1.125 · Cited by 3,028 · see citing works
(2006) · doi:10.1093/oso/9780199290727.001.0001 · Cited by 2,688 · see citing works
Knowledge tracing: Modeling the acquisition of procedural knowledge
Corbett, A. T., & Anderson, J. R. (1995) · User Modelling and User-Adapted Interaction · doi:10.1007/bf01099821 · Cited by 2,324 · see citing works
Unsupervised learning of finite mixture models
Figueiredo, M. A. T., & Jain, A. K. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.990138 · Cited by 2,119 · see citing works
Bounded Rationality and Organizational Learning
Simon, H. A. (1999) · Reflections The SoL Journal · doi:10.1162/152417399570142 · Cited by 2,027 · see citing works
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
Niebles, J. C., Wang, H., & Fei-Fei, L. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0122-4 · Cited by 1,475 · see citing works
Inductive Reasoning and Bounded Rationality
Arthur, W. B. (1994) · American Economic Review · Cited by 1,376 · see citing works
EXPLORATORY LEARNING, INNOVATIVE CAPACITY, AND MANAGERIAL OVERSIGHT.
McGrath, R. G. (2001) · Academy of Management Journal · doi:10.5465/3069340 · Cited by 1,312 · see citing works
Characteristics of dissociable human learning systems
Shanks, D. R., & St. John, M. F. (1994) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00035032 · Cited by 1,163 · see citing works
Towards a Theory of Open Innovation: Three Core Process Archetypes
Gassmann, O., & Enkel, E. (2004) · Alexandria (UniSG) (University of St.Gallen) · Cited by 1,076 · see citing works
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1654.001.0001 · Cited by 1,033 · see citing works
On interpreting the effects of repetition: Solving a problem versus remembering a solution
Jacoby, L. L. (1978) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(78)90393-6 · Cited by 667 · see citing works
Kessler, G., & Bikowski, D. (2010) · Computer Assisted Language Learning · doi:10.1080/09588220903467335 · Cited by 347 · see citing works
Jacobs, D. M., & Michaels, C. F. (2007) · Ecological Psychology · doi:10.1080/10407410701432337 · Cited by 290 · see citing works
Evaluating an adaptive network model of human learning
Gluck, M. A., & Bower, G. H. (1988) · Journal of Memory and Language · doi:10.1016/0749-596x(88)90072-1 · Cited by 256 · see citing works
Belussi, F., Sammarra, A., & Sedita, S. R. (2010) · Research Policy · doi:10.1016/j.respol.2010.01.014 · Cited by 252 · see citing works
Novel Adaptive Nonlinear Predistorters Based on the Direct Learning Algorithm
Zhou, D., & DeBrunner, V. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.882058 · Cited by 250 · see citing works
Dynamics from open innovation to evolutionary change
Yun, J. J., Won, D., & Park, K. (2016) · Journal of Open Innovation Technology Market and Complexity · doi:10.1186/s40852-016-0033-0 · Cited by 242 · see citing works
Self-citationRobot shaping: developing autonomous agents through learning
Dorigo, M., & Colombetti, M. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90047-7 · Cited by 238 · see citing works
Consistent individual differences in human social learning strategies
Molleman, L., van den Berg, P., & Weissing, F. (2014) · Nature Communications · doi:10.1038/ncomms4570 · Cited by 128 · see citing works
Talking shop: Aspects of autonomous learning: An interview with Leslie Dickinson
Dickinson, L. (1993) · ELT Journal · doi:10.1093/elt/47.4.330 · Cited by 122 · see citing works
Natural Language Processing – IJCNLP 2005
Dale, R., Wong, K.-F., Su, J., et al. (2005) · Lecture notes in computer science · doi:10.1007/11562214 · Cited by 108 · see citing works
Autonomous learning of sequential tasks: experiments and analyses
Sun, R., & Peterson, T. S. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.728364 · Cited by 89 · see citing works
Discovery as Autonomous Learning from the Environment
Shen, W. (1993) · Machine Learning · doi:10.1007/bf00993064 · Cited by 75 · see citing works
Understanding the effect of the learning–forgetting phenomenon to duration of projects construction
Lam, K. C., Lee, D., & Hu, T. (2001) · International Journal of Project Management · doi:10.1016/s0263-7863(00)00025-9 · Cited by 64 · see citing works
Self-citationDemand articulation in the open-innovation paradigm
Kodama, F., & Shibata, T. (2015) · Journal of Open Innovation Technology Market and Complexity · doi:10.1186/s40852-015-0003-y · Cited by 57 · see citing works
Promotion of university students’ collaborative skills in open innovation environment
Oganisjana, K. (2015) · Journal of Open Innovation Technology Market and Complexity · doi:10.1186/s40852-015-0021-9 · Cited by 38 · see citing works
Yun, J. J., Cooke, P. N., Kodama, F., Phillips, F. Y., Gupta, A. K., Gamboa, F. J. C., Krishna, V. V., Lee, K., Lee, K., Witt, U., et al. (2016) · Journal of Open Innovation Technology Market and Complexity · doi:10.1186/s40852-016-0049-5 · Cited by 29 · see citing works
Self-citationJeon, J.-H., Kim, S.-K., & Koh, J. (2015) · Journal of Open Innovation Technology Market and Complexity · doi:10.1186/s40852-015-0026-4 · Cited by 28 · see citing works
Learner Course Recommendation in e-Learning Based on Swarm Intelligence
González, A. B. G., & García‐Peñalvo, F. J. (2020) · Gredos (University of Salamanca) · doi:10.3217/jucs-014-16-2737 · Cited by 17 · see citing works
(1996) · Hong Kong University Press eBooks · doi:10.1515/9789882202856 · Cited by 7 · see citing works
Flagged first, then by citations
Research works printed but not linked: 11
- Saul, L.K.; Roweis, S.T. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res. 2003, 4, 119-155.
- Dianyu, Z. English learning strategies and autonomous learning. Foreign Lang. Educ. 2005, 1, 12.
- Dickinson, L. Talking shop aspects of autonomous learning. ELT J. 1993, 47, 330-336.
- Pemberton, R., Li, E.S.L., Or, W.W.F., Pierson, H.D., Eds.; Hong Kong University Press: Hong Kong, China, 1996; pp. 13-26.
- Bonifacio, M.; Bouquet, P.; Cuel, R. Knowledge nodes: The building blocks of a distributed approach to knowledge management. J. Univers. Comput. Sci. 2002, 8, 652-661.
- Simon, H.A. Theories of bounded rationality. In Decision and Organization; McGuire, C.B., Radner, R., Eds.; North-Holland Pub. Co.: Amsterdam, The Netherlands, 1972; Volume 1, pp. 161-176.
- Simon, H.A. Models of Bounded Rationality: Empirically Grounded Economic Reason; MIT Press: Cambridge, MA, USA, 1982.
- Ormrod, J.E.; Davis, K.M. Human Learning; Merrill: Princeton, NC, USA, 2004; pp. 1-5.
- Polanyi, M. Personal Knowledge: Towards a Post-Critical Philosophy; University of Chicago Press: Chicago, IL, USA, 2012.
- Dale, R., Wong, K., Su, J., Kwong, O.Y., Eds.; Springer-Verlag: Berlin, Germany; pp. 131-142.
- Sutton, R.S.; Barto, A.G. Reinforcement Learning: A Bradford Book; The MIT Press: Cambridge, MA, USA, 1998.
Cited by
Sorted by citations
Yigitcanlar, T., Desouza, K., Butler, L., & Roozkhosh, F. (2020) · Energies · doi:10.3390/en13061473 · Cited by 555 · see citing works
Self-citationThe Culture for Open Innovation Dynamics
Yun, J. J., Zhao, X., Jung, K., & Yigitcanlar, T. (2020) · Sustainability · doi:10.3390/su12125076 · Cited by 341 · see citing works
Self-citationYigitcanlar, T., & Cugurullo, F. (2020) · Sustainability · doi:10.3390/su12208548 · Cited by 337 · see citing works
Self-citationYigitcanlar, T., Mehmood, R., & Corchado, J. M. (2021) · Sustainability · doi:10.3390/su13168952 · Cited by 319 · see citing works
Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021) · Journal of Open Innovation: Technology, Market, and Complexity · doi:10.3390/joitmc7010071 · Cited by 245 · see citing works
Self-citationYigitcanlar, T., Li, R. Y. M., Beeramoole, P. B., & Paz, A. (2023) · Government Information Quarterly · doi:10.1016/j.giq.2023.101833 · Cited by 142 · see citing works
- Authors
- Yun, J., Lee, D., Ahn, H., Park, K., & Yigitcanlar, T.
- Affiliations
- Daegu Gyeongbuk Institute of Science and Technology (KR)
- Daegu Gyeongbuk Institute of Science and Technology (DGIST), 333, Techno Jungang Daero, Hyeonpung-Myeon, Dalseong-Gun, Daegu 711-873, Korea
- Sangji University (KR)
- School of Civil Engineering and Built Environment, Queensland University of Technology (QUT), 2 George Street, Brisbane, QLD 4001, Australia
- Corresponding author
- Yun, JinHyo
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2016-08-13
- Volume
- 8
- Issue
- 8
- Pages
- 797
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W2511103557
- Cited by
- 68
- References
- 32
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 9 · 2023: 7 · 2022: 9 · 2021: 11 · 2020: 10 · 2019: 3 · 2018: 6 · 2017: 6
- FWCI
- 12.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by-nc-sa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Science & Engineering Faculty
- Funders
- Daegu Gyeongbuk Institute of Science and Technology
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Innovation and Knowledge Management
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Cognitive Computing and Networks
- Keywords
- sustainable AI, self-directed learning, direct learning, open innovation, interaction model, cognitive learning, knowledge accumulation, open innovation strategy
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