Knowing Knowledge: Epistemological Study of Knowledge in Transformers
Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search for strategi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/2/677 |
| _version_ | 1797446762243293184 |
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| author | Leonardo Ranaldi Giulia Pucci |
| author_facet | Leonardo Ranaldi Giulia Pucci |
| author_sort | Leonardo Ranaldi |
| collection | DOAJ |
| description | Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search for strategic reference points evoke essential 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. In this paper, we try to outline the origin of knowledge and how modern artificial minds have inherited it. |
| first_indexed | 2024-03-09T13:46:10Z |
| format | Article |
| id | doaj.art-718762f021d94ba586e6d474dfd9916d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:46:10Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-718762f021d94ba586e6d474dfd9916d2023-11-30T20:59:54ZengMDPI AGApplied Sciences2076-34172023-01-0113267710.3390/app13020677Knowing Knowledge: Epistemological Study of Knowledge in TransformersLeonardo Ranaldi0Giulia Pucci1Department of Innovation and Information Engineering, Guglielmo Marconi University, Via Plinio 44, 00193 Rome, ItalyDepartment of History, Cultural Heritage, Education and Society, University of Rome Tor Vergata, Viale del Politecnico, 1, 00133 Rome, ItalyStatistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search for strategic reference points evoke essential 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. In this paper, we try to outline the origin of knowledge and how modern artificial minds have inherited it.https://www.mdpi.com/2076-3417/13/2/677artificial intelligenceinterpretable AIhuman-centric AI |
| spellingShingle | Leonardo Ranaldi Giulia Pucci Knowing Knowledge: Epistemological Study of Knowledge in Transformers Applied Sciences artificial intelligence interpretable AI human-centric AI |
| title | Knowing Knowledge: Epistemological Study of Knowledge in Transformers |
| title_full | Knowing Knowledge: Epistemological Study of Knowledge in Transformers |
| title_fullStr | Knowing Knowledge: Epistemological Study of Knowledge in Transformers |
| title_full_unstemmed | Knowing Knowledge: Epistemological Study of Knowledge in Transformers |
| title_short | Knowing Knowledge: Epistemological Study of Knowledge in Transformers |
| title_sort | knowing knowledge epistemological study of knowledge in transformers |
| topic | artificial intelligence interpretable AI human-centric AI |
| url | https://www.mdpi.com/2076-3417/13/2/677 |
| work_keys_str_mv | AT leonardoranaldi knowingknowledgeepistemologicalstudyofknowledgeintransformers AT giuliapucci knowingknowledgeepistemologicalstudyofknowledgeintransformers |