Backpropagation of Spirit: Hegelian Recollection and Human-A.I. Abductive Communities
This article examines types of abductive inference in Hegelian philosophy and machine learning from a formal comparative perspective and argues that Robert Brandom’s recent reconstruction of the logic of recollection in Hegel’s <i>Phenomenology of Spirit</i> may be fruitful for anticipat...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Philosophies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2409-9287/7/2/36 |
| _version_ | 1797225992718123008 |
|---|---|
| author | Rocco Gangle |
| author_facet | Rocco Gangle |
| author_sort | Rocco Gangle |
| collection | DOAJ |
| description | This article examines types of abductive inference in Hegelian philosophy and machine learning from a formal comparative perspective and argues that Robert Brandom’s recent reconstruction of the logic of recollection in Hegel’s <i>Phenomenology of Spirit</i> may be fruitful for anticipating modes of collaborative abductive inference in human/A.I. interactions. Firstly, the argument consists of showing how Brandom’s reading of Hegelian recollection may be understood as a specific type of abductive inference, one in which the past interpretive failures and errors of a community are explained hypothetically by way of the construction of a narrative that rehabilitates those very errors as means for the ongoing successful development of the community, as in Brandom’s privileged jurisprudential example of Anglo-American case law. Next, this Hegelian abductive dynamic is contrasted with the error-reducing backpropagation algorithms characterizing many current versions of machine learning, which can be understood to perform abductions in a certain sense for various problems but not (yet) in the full self-constituting communitarian mode of creative recollection canvassed by Brandom. Finally, it is shown how the two modes of “error correction” may possibly coordinate successfully on certain types of abductive inference problems that are neither fully recollective in the Hegelian sense nor algorithmically optimizable. |
| first_indexed | 2024-03-09T04:18:31Z |
| format | Article |
| id | doaj.art-f8d9268ed21141bf87844a492faf7707 |
| institution | Directory Open Access Journal |
| issn | 2409-9287 |
| language | English |
| last_indexed | 2024-04-24T14:17:50Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Philosophies |
| spelling | doaj.art-f8d9268ed21141bf87844a492faf77072024-04-03T08:20:03ZengMDPI AGPhilosophies2409-92872022-03-01723610.3390/philosophies7020036Backpropagation of Spirit: Hegelian Recollection and Human-A.I. Abductive CommunitiesRocco Gangle0Center for Diagrammatic and Computational Philosophy, Endicott College, Beverly, MA 01915, USAThis article examines types of abductive inference in Hegelian philosophy and machine learning from a formal comparative perspective and argues that Robert Brandom’s recent reconstruction of the logic of recollection in Hegel’s <i>Phenomenology of Spirit</i> may be fruitful for anticipating modes of collaborative abductive inference in human/A.I. interactions. Firstly, the argument consists of showing how Brandom’s reading of Hegelian recollection may be understood as a specific type of abductive inference, one in which the past interpretive failures and errors of a community are explained hypothetically by way of the construction of a narrative that rehabilitates those very errors as means for the ongoing successful development of the community, as in Brandom’s privileged jurisprudential example of Anglo-American case law. Next, this Hegelian abductive dynamic is contrasted with the error-reducing backpropagation algorithms characterizing many current versions of machine learning, which can be understood to perform abductions in a certain sense for various problems but not (yet) in the full self-constituting communitarian mode of creative recollection canvassed by Brandom. Finally, it is shown how the two modes of “error correction” may possibly coordinate successfully on certain types of abductive inference problems that are neither fully recollective in the Hegelian sense nor algorithmically optimizable.https://www.mdpi.com/2409-9287/7/2/36abductive inferencemachine learningG.W.F. HegelRobert Brandomhuman-A.I. interaction |
| spellingShingle | Rocco Gangle Backpropagation of Spirit: Hegelian Recollection and Human-A.I. Abductive Communities Philosophies abductive inference machine learning G.W.F. Hegel Robert Brandom human-A.I. interaction |
| title | Backpropagation of Spirit: Hegelian Recollection and Human-A.I. Abductive Communities |
| title_full | Backpropagation of Spirit: Hegelian Recollection and Human-A.I. Abductive Communities |
| title_fullStr | Backpropagation of Spirit: Hegelian Recollection and Human-A.I. Abductive Communities |
| title_full_unstemmed | Backpropagation of Spirit: Hegelian Recollection and Human-A.I. Abductive Communities |
| title_short | Backpropagation of Spirit: Hegelian Recollection and Human-A.I. Abductive Communities |
| title_sort | backpropagation of spirit hegelian recollection and human a i abductive communities |
| topic | abductive inference machine learning G.W.F. Hegel Robert Brandom human-A.I. interaction |
| url | https://www.mdpi.com/2409-9287/7/2/36 |
| work_keys_str_mv | AT roccogangle backpropagationofspirithegelianrecollectionandhumanaiabductivecommunities |