Summary of Neurosymbolic Conformal Classification, by Arthur Ledaguenel et al.
Neurosymbolic Conformal Classification
by Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed research explores the intersection of neurosymbolic AI and conformal prediction to develop trustworthy AI systems that provide theoretical guarantees about their output. The study combines neural network learning capabilities with symbolic reasoning abilities to enable the system to comply with prior knowledge. Additionally, it introduces conformal prediction techniques that transform unique predictions into confidence sets with statistical guarantees regarding the presence of true labels. This research aims to mitigate the fragility of ML systems and provide a more robust approach for designing trustworthy AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence has made tremendous progress in recent years, but one major challenge remains: making sure these smart machines can be trusted. Right now, AI systems are not very reliable because they can make mistakes or behave unexpectedly when faced with new situations. To address this issue, researchers have been exploring different approaches to create more trustworthy AI. Two promising methods are neurosymbolic AI and conformal prediction. Neurosymbolic AI combines the strengths of neural networks (good at learning patterns) with symbolic systems (good at reasoning). This fusion can provide theoretical guarantees about the system’s output. Conformal prediction is a technique that turns a single prediction into multiple predictions, or confidence sets, which includes statistical guarantees about whether the true label is included. This research combines these two methods to create more reliable AI. |
Keywords
» Artificial intelligence » Neural network
