Summary of Mapping the Neuro-symbolic Ai Landscape by Architectures: a Handbook on Augmenting Deep Learning Through Symbolic Reasoning, By Jonathan Feldstein et al.
Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning
by Jonathan Feldstein, Paulius Dilkas, Vaishak Belle, Efthymia Tsamoura
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a survey on neuro-symbolic AI, which combines statistical and symbolic techniques to overcome the limitations of each approach. The paper focuses on neural networks as statistical methods and discusses how they have outperformed logical or neural models alone in recent years. However, despite its promise, neuro-symbolic AI is still in its early stages and has not been widely adopted by machine learning practitioners. The survey maps different families of frameworks based on their architectures, highlighting the strengths and weaknesses of each approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neuro-symbolic AI is a way to make computers smarter by combining two types of thinking: symbolic (like logical rules) and statistical (like how neural networks learn). Right now, this field is still growing and hasn’t been widely used yet. This paper looks at the different ways people are using these two approaches together, showing which ones work well together and how they can be improved. |
Keywords
» Artificial intelligence » Machine learning
