"Neuro-symbolic Artificial Intelligence (NSAI) denotes a research paradigm and technological framework that synthesizes the capabilities of contemporary Machine Learning, most notably Deep Learning, with the representational and inferential strengths of symbolic AI. By integrating data-driven statistical learning with explicit knowledge structures and logical reasoning, NSAI seeks to overcome the limitations inherent in either approach when used in isolation. Symbolic: Logic, Ontologies. Neural Networks: Structure, Weights."
"Within this paradigm, the term "symbolic" refers to computational methodologies grounded in the explicit encoding of knowledge through formal languages, logical predicates, ontologies, and rule-based systems. Such symbolic representations, ranging from mathematical expressions and logical assertions to programming constructs, enable machines to manipulate discrete symbols, enforce constraints, and derive conclusions via structured inference. Symbolic AI thus emphasizes the classification of entities and the articulation of their relationships within machine-readable knowledge frameworks that support transparent, logically grounded reasoning processes."
Neuro-symbolic Artificial Intelligence synthesizes contemporary machine learning, notably deep learning, with symbolic representations and logical inference to leverage strengths of both approaches. Symbolic methods encode knowledge explicitly using formal languages, predicates, ontologies, and rule-based systems that enable discrete symbol manipulation, constraint enforcement, and transparent reasoning. Sub-symbolic neural networks capture information implicitly through patterns of weighted connections and distributed representations adjusted during training. Neural models excel at extracting correlations from unstructured data and scale well in data-rich environments. Sub-symbolic systems, however, often struggle to generalize beyond training distributions and can produce erroneous or fabricated outputs, commonly termed hallucinations, and uncontrolled bias.
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