Reconciling deep learning with symbolic artificial intelligence: representing objects and relations
Open accessAbstract
In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks. However, both paradigms have strengths and weaknesses, and a significant challenge for the field today is to effect a reconciliation. A central tenet of the symbolic paradigm is that intelligence results from the manipulation of abstract compositional representations whose elements stand for objects and relations. If this is correct, then a key objective for deep learning is to develop architectures capable of discovering objects and relations in raw data, and learning how to represent them in ways that are useful for downstream processing. This short review highlights recent progress in this direction.
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Combining connectionist and symbolic paradigms seems a favourable way to address the challenge of explainability in DL.
“Combining connectionist and symbolic paradigms seems a favourable way to address this challenge [169, 299, 312, 352, 353].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [352]
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Self-citationLarge language models in digital forensics: capabilities, challenges and future directions
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- Authors
- Garnelo, M., & Shanahan, M.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Shanahan, Murray
- ORCID
- Shanahan, Murray
- Venue
- Current Opinion in Behavioral Sciences
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 29
- Pages
- 17-23
- ISSN
- 2352-1546 (ISSN-L)
- OpenAlex
- W2930003055
- Cited by
- 255
- References
- 5
- Citations by year
- 2026: 14 · 2025: 36 · 2024: 37 · 2023: 41 · 2022: 46 · 2021: 32 · 2020: 34 · 2019: 15
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Behavioral Sciences (publishedVersion, cc-by-nc-nd)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- symbolic AI, deep learning, object representation, relational representation, compositional representations, connectionism, hybrid AI architecture, abstract representation, human-level artificial intelligence
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