What is Hybrid Intelligence?

Hybrid Intelligence is a new and better approach to AI. Based on UMNAI’s Explainable Neural Nets, Hybrid Intelligence provides a framework with which to develop and deploy fully-fledged Neuro-symbolic AI systems that build fit-for-purpose decisions from fit-to-data predictions. The framework delivers a novel model creation & training process, model optimisation and maintenance tools, and real-time explanations.


Hybrid Intelligence is a Neuro-symbolic approach to AI. It combines the strengths of symbolic reasoning with the predictive power of neural networks to give:

  • Full explainability: the explicit use of symbolic rules and logic enables a clear understanding of how the AI system arrives at its decisions, providing a trace of the reasoning process and precise and certain quantification of the influence of each feature on the output.
  • Integration of structured and unstructured data that allows for a more comprehensive and holistic analysis, leveraging the advantages of both types of data.
  • Flexibility and adaptability: to handle a wide range of complex problems and to adapt to different domains and tasks.
  • Improved generalization and robustness through the incorporation of symbolic reasoning.
  • Utilization of prior knowledge and expert insights into the learning process, leading to better outcomes and data efficiency.


A Hybrid Intelligence model comprises two fully integrated parts:

  1. Explainable Neural Net (XNN): forms the neural substrate of a Hybrid Intelligence model and is a graph neural model that represents knowledge hierarchically. XNNs have a wide, modular architecture and provide powerful predictive performance with full transparency and explainability.
  2. Explanation Structure Model (ESM): The ESM is a hyper-graph model that encapsulates and integrates the XNN, and provisions and organises symbolic logic, reasoning, labels, and relationships. ESMs also incorporate data engineering and pre-processing pipelines.

Together, the XNN and ESM provide the full neuro-symbolic AI model. Hybrid Intelligence models are fully transparent and interpretable, and they communicate explanations concurrently with predictions.


Hybrid Intelligence models are created directly from data using a unique model induction methodology that leverages several techniques including:

  • Statistical and Deep learning techniques
  • Information Theory
  • Causal Analysis
  • Symbolic Learning techniques

When appropriate, induction can also incorporate existing knowledge from black-box models and expert systems into the learning process.


Hybrid Intelligence leverages the transparency and modular structure of XNNs and ESMs to bring new and improved functionality to optimise and maintain models over their lifecycle.

  • Surgical model optimisation for specific performance objectives
  • Model compression with minimal effect on performance
  • Selective, as needed, model re-training and/or re-structuring
  • Targeted model re-training and re-structuring
  • Lifetime traceability of model structure, logic and behaviour changes


When queried, a Hybrid Intelligence model produces and communicates its prediction and the complete and precise explanation data for that prediction concurrently. The availability of real-time explanations creates an entirely new interventional space to:

  • Assess predictions in real-time to identify issues and sub-optimal behaviour
  • Intervene directly and seamlessly to turn fit-to-data predictions into fit-for-purpose decisions
  • Automate interventions in real-time workflows
  • Selectively de-automate to bring a human into the loop where appropriate
  • Curate explanations appropriately for different stakeholder needs
  • Monitor and anticipate model behaviour dynamically
  • Create accurate, verifiable and reproducible audit logs for each decision