Dis-Cover AI Minds to Preserve Human Knowledge

Leonardo Ranaldi, Francesca Fallucchi and Fabio Massimo Zanzotto
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Leonardo Ranaldi: Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy
Francesca Fallucchi: Department of Innovation and Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy
Fabio Massimo Zanzotto: Department of Enterprise Engineering, University of Rome Tor Vergata, 00133 Rome, Italy

Future Internet, 2021, vol. 14, issue 1, 1-15

Abstract: Modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. The structure of symbols—the operations by which the intellectual solution is realized—and the search for strategic reference points evoke important issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped ahead with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we would investigate the syntax as possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning, and human knowledge.

Keywords: machine learning; natural language processing; deep learning; Psycholinguistics (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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