Summary of A Survey on Verification and Validation, Testing and Evaluations Of Neurosymbolic Artificial Intelligence, by Justus Renkhoff et al.
A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence
by Justus Renkhoff, Ke Feng, Marc Meier-Doernberg, Alvaro Velasquez, Houbing Herbert Song
First submitted to arxiv on: 6 Jan 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 Neurosymbolic artificial intelligence (AI) combines symbolic and sub-symbolic AI strengths. A major challenge in evaluating sub-symbolic AI models is that they are “black boxes”, making testing and evaluation difficult. This survey explores how neurosymbolic applications can ease the validation and verification process. It evaluates two taxonomies of neurosymbolic AI, analyzes algorithms used as symbolic and sub-symbolic components, and provides an overview of current techniques for testing and evaluating these components. The study investigates how symbolic parts are used for testing and verification purposes in current neurosymbolic applications. Results show that neurosymbolic AI has great potential to ease the testing and evaluation process by leveraging symbolic AI possibilities. Current testing and evaluation methods are partly sufficient, but some approaches require adjustments or new techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about a type of artificial intelligence called neurosymbolic AI. It’s special because it combines two different ways AI works: symbolic and sub-symbolic. One problem with sub-symbolic AI is that you can’t understand how it makes decisions, making it hard to test or evaluate. This survey looks at how neurosymbolic applications can make testing and evaluation easier. The study evaluates different types of neurosymbolic AI and shows how they can be used for testing and verification purposes. Results suggest that neurosymbolic AI has the potential to make AI more trustworthy by using symbolic AI techniques. |
