Foundations
Learn the core ideas that make Hybrid Intelligence different from a conventional machine-learning pipeline. Neural learning, symbolic reasoning, the XNN and the ESM.
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01
What is Neuro-Symbolic AI?
Neuro-symbolic AI combines the ability of neural systems to learn statistical patterns with the explicit concepts, rules and relationships used in symbolic reasoning. The objective is not to place two independent systems side by side, but to create an architecture in which learnt patterns can be named, inspected and used within structured reasoning.
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02
What is Hybrid Intelligence?
Hybrid Intelligence is UMNAI's neuro-symbolic framework for building decision models whose predictions can be examined through explicit modules, partitions, rules, attributions and symbolic relationships.
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03
What are Explainable Neural Networks?
Explainable Neural Networks (XNNs) are modular predictive models that build explanation into their computation. Each module represents a feature or interaction, each partition defines a region of behaviour, and each active rule produces a contribution that can be traced to the final output.
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04
What is an Explanation Structure Model?
An Explanation Structure Model (ESM) is the explainable package around an XNN. It connects the data transformations, predictive model, symbolic identities, relationships and metadata needed to execute a query and organise its explanation.