Approximate and Situated Causality in Deep Learning
Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, t...
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| Tipo de documento: | artigo |
| Data de publicação: | 2020 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositório: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglês |
| OAI Identifier: | oai:ddd.uab.cat:240278 |
| Acesso em linha: | https://ddd.uab.cat/record/240278 https://dx.doi.org/urn:doi:10.3390/philosophies5010002 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Causality Deep learning Machine learning Counterfactual Explainable AI Blended cognition Mechanisms System |
| Resumo: | Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well as about a situated causality. |
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