Difference between revisions of "Neuro-Symbolic"
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[https://www.bing.com/news/search?q=Neural-Symbolic+Neuro-Symbolic+Artificial+Intelligence+AI%3d%228%22 ...Bing News] | [https://www.bing.com/news/search?q=Neural-Symbolic+Neuro-Symbolic+Artificial+Intelligence+AI%3d%228%22 ...Bing News] | ||
| − | * [[ | + | * [[Neuro-Symbolic]] ... [[Symbolic Artificial Intelligence]] |
* [[Neuro-Symbolic Concept Learner (NS-CL)]] | * [[Neuro-Symbolic Concept Learner (NS-CL)]] | ||
* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | * [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | ||
Revision as of 11:39, 9 May 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Neuro-Symbolic ... Symbolic Artificial Intelligence
- Neuro-Symbolic Concept Learner (NS-CL)
- Singularity ... Sentience ... AGI ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Neural-Symbolic | SingularityNET
- Neuro-Symbolic Networks: Introduction to a New Information Processing Principle | Rosemarie Velik and Dietmar Bruckner
- Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems | Ioannis Hatzilygeroudis and Jim Prentzas
Neuro-Symbolic artificial intelligence refers to a field of research and applications that combines machine learning methods based on artificial neural networks, such as deep learning, with symbolic approaches to computing and artificial intelligence (AI). The general promise of Neuro-Symbolic AI lies in the hopes of a best-of-both worlds scenario, where the complementary strengths of neural and symbolic approaches can be combined in a favorable way. On the neural side, the desirable strengths would include trainability from raw data and robustness against faults in the underlying data, while on the symbolic side one would like to retain the inherently high explainability and provable correctness of these systems, as well as the ease of making use of deep human expert knowledge in their design and function. Neuro-Symbolic Artificial Intelligence | K. Saker, L. Zhou, A. Eberhart, & Pascal Hitzler - Kansas State University - arXiv
