GPT: Wittgenstein’s Connectionist Machine
DOI:
https://doi.org/10.22169/revint.v20.e25do2en1Keywords:
Artificial Intelligence, Generative Artificial Intelligence, Artificial Neural Networks, Educational Technologies, Philosophy of MindAbstract
The emergence of generative technologies has highlighted the power of the connectionist paradigm to deliver AI systems highly proficient at handling human language, thereby revealing pertinent philosophical ramifications. This essay proposes a discussion of how connectionism - represented by GPT (Generative Pretrained Transformer) models - has supplanted the symbolic research paradigm in Artificial Intelligence (AI), in light of authors such as Gottlob Frege, Daniel Dennett, and John Searle, but above all drawing on Ludwig Wittgenstein’s dual philosophy of language, as discussed by William Frawley, which treats meaning both as a logic-based, computed form and as a use-based form of action. Complementing this central idea, it also asks whether generative technologies might, to some extent, offer a solution to David Hume’s puzzle concerning the possibility of ideas and impressions “thinking about themselves”, in alignment with Dennett’s reflections.
Downloads
References
ALAMMAR, J. The Illustrated Retrieval Transformer.
Jay Alammar, 3 jan. 2022. Disponível em: https://jalammar.github.io/illustrated-retrieval-transformer/. Acesso em: 15 ago. 2024.
ANTHROPIC. Introducing the next generation of Claude. Disponível em: https://www.anthropic.com/news/claude-3-family. Acesso em: 15 ago. 2024.
BENGIO, Y.; LECUN, Y.; HINTON, G. Deep learning for AI. Communications of the ACM, v. 64, n. 7, p. 58-65, 2021. DOI: https://doi.org/10.1145/3448250. Disponível em: https://dl.acm.org/doi/pdf/10.1145/3448250. Acesso em: 15 ago. 2024.
BROWN, T. B. et al. Language Models are Few-Shot Learners. arXiv, v. 33, p. 1877-1901, 2020. Disponível em: https://arxiv.org/pdf/2005.14165. Acesso em: 15 ago. 2024.
BUBECK, S. et al. Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv, v. 1, p. 1877-1901, 2023. DOI: https://doi.org/10.48550/arXiv.2303.12712. Disponível em: https://arxiv.org/pdf/2303.12712. Acesso em: 15 ago. 2024.
BUCHHOLZ, K. Compreender Wittgenstein. 2. ed. Petrópolis: Vozes, 2009.
CHURCHLAND, P. M. Matéria e Consciência. São Paulo: Editora UNESP, 2004.
DENNETT, D. C. Brainstorms: escritos filosóficos sobre a mente e a psicologia. São Paulo: UNESP, 2006.
DEVLIN, J. et al. BERT: Pre-training of deep bidirectional transformers for language understanding. In: NAACL HLT 2019 - 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: Human Language Technologies. Proceedings […], v. 1, n. Mlm, p. 4171-4186, 2019. DOI: 10.18653/v1/N19-1423. Disponível em: https://aclanthology.org/N19-1423.pdf. Acesso em: 15 ago. 2024.
ELHAGE, et al. A Mathematical Framework for Transformer Circuits. Transformer Circuits Thread, 2022. Disponível em: https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html. Acesso em: 15 ago. 2024.
FANN, K. T. El Concepto de Filosofía en Wittgenstein. 3. ed. Madrid: Editorial Tecnos, 2013.
FRAWLEY, W. Vygotsky e a Ciência Cognitivas: Linguagem e interação das mentes social e computacional. Porto Alegre: Artes Médicas Sul, 2000.
FREGE, G. Ensayos de Semántica y Filosofia de La Lógica. 2. ed. Madrid: Tecnos, 2013.
GEORGIEV, P. et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv, v. 1, p. 1-154, 2024. DOI: https://doi.org/10.48550/arXiv.2403.05530. Disponível em: https://arxiv.org/pdf/2403.05530. Acesso em: 15 ago. 2025.
GOODFELLOW, I. et al. Generative adversarial networks. arXiv, v. 27, 2014. DOI: https://doi.org/10.48550/arXiv.1406.2661. Disponível em: https://arxiv.org/pdf/1406.2661. Acesso em: 15 ago. 2024.
GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. Cambridge-MA: MIT Press, 2016.
HAYKIN, S. Redes Neurais: Princípios e prática. Porto Alegre: Bookman, 2001.
HINTON, G. E.; OSINDERO, S.; TEH, Y.-W. A Fast-Learning Algorithm for Deep Belief Nets. Neural Comput., v. 18, n. 7, p. 1527-1554, 2006. DOI: 10.1162/neco.2006.18.7.1527.
HOPCROFT, J.; MOTWANI, R.; ULLMAN, J. Introduction To Automata Theory , Languages , and Languages. Boston-MA: Pearson Education, Inc, 2006.
HUME, D. Tratado da Natureza Humana. São Paulo: UNESP, 2000.
KITTLER, F. A. Gramophone, Film, Typewriter. [s. l.] Stanford University Press, 1999.
MANNING, C. D.; RAGHAVAN, P.; SCHÜTZE, H. An Introduction to Information Retrieval.
Cambridge, England: Cambridge University Press, 2009.
MANNING, C. D.; SCHÜTZE, H. Foundations of Statistical Natural Language Processing. [s. l.]: The MIT Pres, 1999.
MCCLELLAND, J. L.; RUMELHART, D.; HINTON, G. E. The Appeal of Parallel Distributed Processing. In: Parallel Distributed Processing: Exploration of the microstructure of cognition. Cambridge: MIT Press, 1986. p. 3-44.
MEDEIROS, L. F. de. Inteligência Artificial Aplicada: Uma abordagem introdutória. Curitiba: Intersaberes, 2018.
NIKOLAEV, D.; PADÓ, S. Investigating Semantic Subspaces of Transformer Sentence Embeddings through Linear Structural Probing. In: BLACKBOXNLP WORKSHOP: ANALYZING AND INTERPRETING NEURAL NETWORKS FOR NLP, 6, p. 142-154, 2023. Proceedings […], Association for Computational Linguistics, Singapore, 2023.
OLSSON, C. et al. In-context Learning and Induction Heads. Transformer Circuits Thread, Mar 8, 2022. Disponível em: https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html. Acesso em: 15 ago. 2024.
OPENAI. Hello GPT-4o. Disponível em: https://openai.com/index/hello-gpt-4o/. Acesso em: 13 abr. 2024.
PARR, T. The Definitive ANTLR 4 Reference. Dallas, Texas: The Pragmatic Programmers, LLC, 2012.
PLATÃO. Fedro. Tradução Maria Aparecida A. De Oliveira. São Paulo: Martin Claret, 2001.
RAFFEL, C. et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv, v. 1, 2019. DOI: https://doi.org/10.48550/arXiv.1910.10683. Disponível em: https://arxiv.org/pdf/1910.10683. Acesso em: 15 ago. 2024.
RUSSELL, S.; NORVIG, P. Inteligência Artificial - Tradução da 2a edição. Rio de Janeiro: Editora Campus, 2004.
RYLE, G. El Concepto de lo Mental. Barcelona: Ediciones Paidós Iberica, 2005.
SEARLE, J. Intencionalidade. 2. ed. São Paulo: Martins Fontes, 2002.
TEIXEIRA, J. de F. Robots, intencionalidade e inteligência artificial. Trans/Form/Ação, v. 14, p. 109-121, 1991. Disponível em: https://www.scielo.br/j/trans/a/w7DK95zgJFjWV7mw6NdhfRB/?format=pdf&lang=pt. Acesso em: 15 ago. 2024.
TOUVRON, H. et al. LLaMA: Open and Efficient Foundation Language Models. arXiv, v. 1, 2023. DOI: https://doi.org/10.48550/arXiv.2302.13971. Disponível em: https://arxiv.org/pdf/2302.13971. Acesso em: 15 ago. 2024.
VASWANI, A. et al. Attention is all you need. arXiv, v. 1, 2017. DOI: https://doi.org/10.48550/arXiv.1706.03762. Disponível em: https://arxiv.org/pdf/1706.03762. Acesso em: 15 ago. 2024.
WITTGENSTEIN, L. Tractatus Logico-Philosophicus. São Paulo: Editora da USP, 2010.
WITTGENSTEIN, L. Investigações Filosóficas. 7a. ed. Petrópolis: Vozes, 2012.
ZHAO, W. X. et al. A Survey of Large Language Models. 31 mar. 2023.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Luciano Frontino de Medeiros

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Os direitos autorais dos artigos publicados na Revista são de acordo com a licença CC-BY-ND - Creative Commons ( https://creativecommons.org/licenses/by-nd/4.0/legalcode)
Esta licença permite que outras pessoas reutilizem o trabalho para qualquer finalidade, inclusive comercialmente; no entanto, não pode ser compartilhado com outras pessoas de forma adaptada e o crédito deve ser fornecido ao autor.
Os direitos autorais dos artigos publicados na Revista são do autor, com os direitos de primeira publicação para a Revista

















