From Reflex to Reflection: Two Tricks AI Could Learn from Us
Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-05-01
|
| Series: | Philosophies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2409-9287/4/2/27 |
| _version_ | 1797230123701764096 |
|---|---|
| author | Jean-Louis Dessalles |
| author_facet | Jean-Louis Dessalles |
| author_sort | Jean-Louis Dessalles |
| collection | DOAJ |
| description | Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection. |
| first_indexed | 2024-04-24T15:23:29Z |
| format | Article |
| id | doaj.art-7611f2bda5994c65b22c852763842b2f |
| institution | Directory Open Access Journal |
| issn | 2409-9287 |
| language | English |
| last_indexed | 2024-04-24T15:23:29Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Philosophies |
| spelling | doaj.art-7611f2bda5994c65b22c852763842b2f2024-04-02T07:16:24ZengMDPI AGPhilosophies2409-92872019-05-014227010.3390/philosophies4020027philosophies4020027From Reflex to Reflection: Two Tricks AI Could Learn from UsJean-Louis Dessalles0LTCI, Telecom Paris, Institut Polytechnique de Paris, 91120 Paris, FranceDeep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection.https://www.mdpi.com/2409-9287/4/2/27machine learningcomplexitysimplicitycognitioncontrast |
| spellingShingle | Jean-Louis Dessalles From Reflex to Reflection: Two Tricks AI Could Learn from Us Philosophies machine learning complexity simplicity cognition contrast |
| title | From Reflex to Reflection: Two Tricks AI Could Learn from Us |
| title_full | From Reflex to Reflection: Two Tricks AI Could Learn from Us |
| title_fullStr | From Reflex to Reflection: Two Tricks AI Could Learn from Us |
| title_full_unstemmed | From Reflex to Reflection: Two Tricks AI Could Learn from Us |
| title_short | From Reflex to Reflection: Two Tricks AI Could Learn from Us |
| title_sort | from reflex to reflection two tricks ai could learn from us |
| topic | machine learning complexity simplicity cognition contrast |
| url | https://www.mdpi.com/2409-9287/4/2/27 |
| work_keys_str_mv | AT jeanlouisdessalles fromreflextoreflectiontwotricksaicouldlearnfromus |