Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Discover AI Knowledge to Preserve Cultural Heritage

Version 1 : Received: 30 August 2021 / Approved: 3 September 2021 / Online: 3 September 2021 (12:53:42 CEST)

How to cite: Ranaldi, L.; Zanzotto, F.M. Discover AI Knowledge to Preserve Cultural Heritage. Preprints 2021, 2021090062. Ranaldi, L.; Zanzotto, F.M. Discover AI Knowledge to Preserve Cultural Heritage. Preprints 2021, 2021090062.


Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, 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. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a 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.


Machine Learning; Natural Language Processing; Deep Learning


Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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