What is neuro symbolic ai?
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Neuro-Symbolic Artificial Intelligence (AI) integrates symbolic and sub-symbolic systems to enhance predictive model performance and explainability. It combines traditional symbolic techniques with neural networks, addressing tasks like sequential decision making under uncertainty. Neuro-symbolic AI infuses knowledge about context information into deep learning models, such as for Human Activity Recognition (HAR), to mitigate labeled data scarcity and model opacity issues. By leveraging deductive databases and knowledge graphs, neuro-symbolic systems can deduce meaningful relationships among entities, improving link prediction accuracy. These hybrid approaches bridge the gap between symbolic reasoning and neural networks, offering a promising direction in AI research for tasks requiring both data-driven learning and symbolic reasoning capabilities.
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| Papers (4) | Insight |
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08 Jun 2023 | Neuro-Symbolic AI combines deep learning with symbolic reasoning to enhance Human Activity Recognition by infusing context knowledge without requiring symbolic reasoning during classification, aiding deployment on resource-constrained devices. |
| Neuro-symbolic AI combines symbolic techniques with neural networks. It applies to decision-making under uncertainty, as in neuro-symbolic POMDPs, integrating neural perception with symbolic decision-making processes. | |
| Neuro-symbolic AI combines traditional symbolic techniques with neural networks, as seen in neuro-symbolic POMDPs for decision-making under uncertainty in this paper. | |
| Neuro-Symbolic AI integrates symbolic and sub-symbolic systems to enhance predictive models' performance and explainability, combining knowledge graphs and deductive databases for improved link prediction. |
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