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Abductive Strategies in Human Cognition and in Deep Learning Machines

Abductive Strategies in Human Cognition and in Deep Learning Machines

Lorenzo Magnani
Copyright: © 2023 |Pages: 12
ISBN13: 9781799892205|ISBN10: 1799892204|EISBN13: 9781799892212
DOI: 10.4018/978-1-7998-9220-5.ch046
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MLA

Magnani, Lorenzo. "Abductive Strategies in Human Cognition and in Deep Learning Machines." Encyclopedia of Data Science and Machine Learning, edited by John Wang, IGI Global, 2023, pp. 804-815. https://doi.org/10.4018/978-1-7998-9220-5.ch046

APA

Magnani, L. (2023). Abductive Strategies in Human Cognition and in Deep Learning Machines. In J. Wang (Ed.), Encyclopedia of Data Science and Machine Learning (pp. 804-815). IGI Global. https://doi.org/10.4018/978-1-7998-9220-5.ch046

Chicago

Magnani, Lorenzo. "Abductive Strategies in Human Cognition and in Deep Learning Machines." In Encyclopedia of Data Science and Machine Learning, edited by John Wang, 804-815. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-7998-9220-5.ch046

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Abstract

Locked and unlocked strategies are at the center of this article as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. The author maintains that these differences lead to important consequences when we analyze computational AI programs, such as AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by locked abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning because they are limited in eco-cognitive openness, which instead qualifies human thinkers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead unlocked.

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