Abstract
The text discusses the concept of hybrid intelligence, which is a form of collaboration between machines and humans. It describes how this concept can be used in manufacturing to help improve productivity. The text also discusses how this concept can be used to help humans learn from machines. There is a debate in the intelligence community about the role of humans vs. machines. Machine intelligence can do some things better than humans, such as processing large amounts of data, but is not good at tasks that require common sense or empathy. Augmented intelligence emphasizes the assistive role of machine intelligence, while hybrid intelligence posits that humans and machines are part of a common loop, where they adapt to and collaborate with each other. The text discusses the implications of increasing machine involvement in organizational decision-making, specifically mentioning two challenges: negative effects on human behavior and flaws in machine decision-making. It argues that, in order for machine intelligence to improve decision-making processes, humans and machines must collaborate. The chapter argues that hybrid intelligence is the most likely scenario for decision-making in the future factory. The chapter discusses the advantages of this approach and how it can be used to improve quality control in a production system. The transformer-based language model called GPT-3 can be used to generate summaries of text. This task is difficult for machines because they have to understand sentiment and meaning in textual data. The model is also a “few-shot learner,” which means that it is able to generate a text based on a limited amount of examples. Transformer-based language models are beneficial because they are able to take the context of the processed words into consideration. This allows for a more nuanced understanding of related words and concepts within a given text.
[Abstract generated by machine intelligence with GPT-3. No human intelligence applied.]
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Notes
- 1.
Zheng et al. (2017) also describe a second concept of human and machine collaboration: “cognitive computing-based hybrid-augmented intelligence.” While out of the scope of this chapter, it is worth mentioning. Cognitive computing-based hybrid-augmented intelligence refers to a machine that “mimics the function of the human brain and improves computer’s capabilities of perception, reasoning, and decision-making. In that sense, [it] is a new framework of computing with the goal of more accurate models of how the human brain/mind senses, reasons, and responds to stimulus, especially how to build causal models, intuitive reasoning models, and associative memories in an intelligent system” (Zheng et al., 2017: 154).
References
Abdel-Karim, B. M., Pfeuffer, N., Rohde, G., & Hinz, O. (2020). How and what can humans learn from being in the loop? Invoking contradiction learning as a measure to make humans smarter. Künstliche Intelligenz, 34(2), 199–207. https://doi.org/ghqvr8
Agrawal, A., Gans, J., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2), 31–50. https://doi.org/ggnh5t
Bailey, D., & Barley, S. R. (2020). Beyond design and use: How scholars should study intelligent technologies. Information and Organization, 30(2), 100286. https://doi.org/hm62
Baker, L., & Hui, F. (2017, April). Innovations of AlphaGo. Research blog by Deepmind. https://deepmind.com/blog/article/innovations-alphago
Berditchevskaia, A., & Baeck, P. (2020). The future of minds and machines: How AI can enhance collective intelligence. Nesta Report.
Bouschery, S., Blazevic, V., & Piller, F. (2022). Artificial intelligence as an actor in hybrid innovation teams: An assessment of the GPT-3 language model. Forthcoming as a Catalyst Paper in the Journal of Product Innovation Management.
Brauner, P., Dalibor, M., Jarke, M., Kunze, I., Koren, I., Lakemeyer, G., … Ziefle, M. (2022). A computer science perspective on digital transformation in production. ACM Transactions on Internet of Things, 3(2), 1–32.
Brecher, C., Eckel, H. M., Motschke, T., Fey, M., & Epple, A. (2019). Estimation of the virtual work piece quality by the use of a spindle-integrated process force measurement. CIRP Annals, 68(1), 381–384. https://doi.org/hm63
Brecher, C., Özdemir, D., & Weber, A. R. (2016). Integrative production technology: Theory and applications. In C. Brecher & D. Özdemir (Eds.), Integrative production technology (pp. 1–17). Springer. https://doi.org/hhn9
Brecher, C., et al. (2017). Learning production systems. In Proceedings of the 29th AWK Aachener Werkzeugmaschinen-Kolloquium (pp. 135–161). Apprimus.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A. (2020). Language models are few-shot learners. ArXiv:2005.14165.
Cowgill, B., & Tucker, C. E. (2020). Algorithmic fairness and economics. Columbia Business School Research Paper.
De Cremer, D. (2020). Leadership by algorithm: Who leads and who follows in the AI era. Harriman House.
De Silva, M., Howells, J., & Meyer, M. (2018). Innovation intermediaries and collaboration: Knowledge–based practices and internal value creation. Research Policy, 47(1), 70–87. https://doi.org/gcshf5
Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business and Information Systems Engineering, 61(5), 637–643. https://doi.org/ggkxz4
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. ArXiv, 1810, 04805. https://doi.org/hm65
Endsley, M. R. (1987). The application of human factors to the development of expert systems for advanced cockpits. Proceedings of the Human Factors Society Annual Meeting, 31(12), 1388–1392. https://doi.org/fzdz4g
Fan, W., Wallace, L., Rich, S., & Zhang, Z. (2006). Tapping the power of text mining. Communications of the ACM, 49(9), 76–82. https://doi.org/b7f48c
Groensund, T., & Aanestad, M. (2020). Augmenting the algorithm: Emerging human-in-the-loop work configurations. Journal of Strategic Information Systems, 29(2), 101614. https://doi.org/gjjp64
Hirsch-Kreinsen, H., & Ittermann, P. (2021). Digitalization of work processes: A framework for human-oriented work design. In The palgrave handbook of workplace innovation (pp. 273–293). Palgrave Macmillan.
Iansiti, M., & Lakhani, K. R. (2020). Putting AI at the firm’s core. Harvard Business Review, 98(1), 59–67.
Kamar, E. (2016 July). Directions in hybrid intelligence: Complementing AI systems with human intelligence. In Proceedings of the twenty-fifth international joint conference on artificial intelligence (pp. 4070–4073).
Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022). To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organization Science, 33(1), 126–148. https://doi.org/gn3jks
Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 1603–1612).
Liddy, E. D. (2018). Natural language processing for information retrieval. In J. D. McDonald & M. Levine-Clark (Eds.), Encyclopedia of library and information sciences (Vol. 5, 4th ed., pp. 3346–3355). CRC Press.
Liebenberg, M., & Jarke, M. (2020). Information systems engineering with digital shadows: Concept and case studies. In S. Dustdar, E. Yu, C. Salinesi, D. Rieu, & V. Pant (Eds.), Advanced information systems engineering. CAiSE 2020 (Lecture notes in computer science) (Vol. 12127). Springer. https://doi.org/hhph
Long, J. B., & Ehrenfeld, J. M. (2020). The role of augmented intelligence (AI) in detecting and preventing the spread of novel coronavirus. Journal of Medical Systems, 44(3), 1–2. https://doi.org/ggp6f3
Mütze-Niewöhner, S., Mayer, C., Harlacher, M., Steireif, N., & Nitsch, V. (2022). Work 4.0: Human-centered work design in the digital age. In W. Frenz (Ed.), Handbook industry 4.0: Law, technology, society. Springer.
Pan, Y. (2016). Heading toward artificial intelligence 2.0. Engineering, 2(4), 409–413. https://doi.org/gfwwrf
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. Open AI blog, 1(8), 9.
Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/ggw7h2
Raj, M., & Seamans, R. (2019). Primer on artificial intelligence and robotics. Journal of Organization Design, 8(1), 1–14. https://doi.org/hm67
Shrestha, Y. R., Ben-Menahem, S., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/gf48d3
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/dw3pfg
van der Aalst, W. M. (2016). Process mining: Data science in action. Springer.
van der Aalst, W. M. (2020). On the Pareto principle in process mining, task mining, and robotic process automation. In Proceedings of the 9th international conference on Data Science, Technology and Applications (DATA 2020) (pp. 5–12). https://doi.org/hm7b
van der Aalst, W. M. (2021). Hybrid Intelligence: To automate or not to automate, that is the question. International Journal of Information Systems and Project Management, 9(2), 5–20. https://doi.org/gk92bq
van der Aalst, W. M., Hinz, O., & Weinhardt, C. (2021). Resilient digital twins. Business and Information Systems Engineering, 63(6), 615–619. https://doi.org/gmv8sh
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st conference on neural information processing systems. NIPS.
Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409. https://doi.org/gfztxx
Waardenburg, L., Huysman, M., & Sergeeva, A. V. (2022). In the land of the blind, the one-eyed man is king: Knowledge brokerage in the age of learning algorithms. Organization Science, 33(1), 59–82. https://doi.org/gntnhp
Xi, T., Benincá, I. M., Kehne, S., Fey, M., & Brecher, C. (2021). Tool wear monitoring in roughing and finishing processes based on machine internal data. International Journal of Advanced Manufacturing Technology, 113(11), 3543–3554. https://doi.org/gndbwx
Zheng, N. N., Liu, Z. Y., Ren, P. J., Ma, Y. Q., Chen, S. T., Yu, S. Y., & Wang, F. Y. (2017). Hybrid-augmented intelligence: Collaboration and cognition. Frontiers of Information Technology and Electronic Engineering, 18(2), 153–179. https://doi.org/gg6r35
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Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC-2023 Internet of Production—390621612.
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Piller, F.T., Nitsch, V., van der Aalst, W. (2022). Hybrid Intelligence in Next Generation Manufacturing: An Outlook on New Forms of Collaboration Between Human and Algorithmic Decision-Makers in the Factory of the Future. In: Piller, F.T., Nitsch, V., Lüttgens, D., Mertens, A., Pütz, S., Van Dyck, M. (eds) Forecasting Next Generation Manufacturing. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-031-07734-0_10
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