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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

Neuro-Symbolic Artificial Intelligence refers to an emerging field that combines neural networks and symbolic AI techniques to integrate the strengths of both approaches. It aims to bridge the gap between the connectionist (subsymbolic) and symbolic paradigms of AI, leveraging the representation and learning capabilities of neural networks along with the interpretability and reasoning abilities of symbolic AI.

  • Integration of neural networks and symbolic AI: Neuro-Symbolic AI seeks to combine neural networks, which excel at learning from raw data and capturing complex patterns, with symbolic AI, which focuses on logical reasoning and explicit knowledge representation. By integrating these two paradigms, researchers aim to create more powerful and interpretable AI systems that can handle both low-level perception and high-level reasoning tasks.
  • Hybrid models and architectures: Neuro-Symbolic AI involves the development of hybrid models and architectures that bring together neural networks and symbolic reasoning. These models can incorporate neural networks as components within a larger symbolic framework or use symbolic rules to guide the learning and decision-making processes of neural networks.
  • Knowledge-guided learning: In Neuro-Symbolic AI, symbolic knowledge is used to guide the learning process of neural networks. By incorporating prior knowledge or domain-specific rules, neural networks can be trained to capture relevant patterns and make informed decisions. This combination of symbolic knowledge and neural network learning enables more interpretable and explainable AI models.
  • Interpretability and explainability: One of the key advantages of Neuro-Symbolic AI is the potential for enhanced interpretability and explainability. By incorporating symbolic reasoning and explicit knowledge representation, the internal workings of the AI system can be made more understandable and transparent, enabling users to gain insights into the decision-making process.
  • Applications: Neuro-Symbolic AI has shown promise in various domains, including natural language understanding, robotics, healthcare, and scientific discovery. For example, in natural language understanding, neural networks can learn to process and generate text, while symbolic reasoning can be used to interpret and generate explanations for the underlying meaning.

Neuro-Symbolic Artificial Intelligence represents an exciting direction in AI research, aiming to combine the strengths of neural networks and symbolic reasoning. By integrating these two paradigms, Neuro-Symbolic AI seeks to create AI systems that are both capable of learning from data and reasoning with explicit knowledge. The field holds promise for developing more interpretable and explainable AI models, enabling the development of advanced AI systems that can tackle complex real-world problems while maintaining human-level understanding and reasoning.