The Neurosymbolic Frontier
Welcome to the core curriculum for Neurosymbolic AI. At iGentixAI, we believe the future of education isn't just about large-scale data; it's about the verifiable logic that interprets it.
This module bridges the gap between Deep Neural Networks (Perception) and Symbolic Reasoning (Logic).
By the end of this course, you will understand how to build systems that don't just "predict," but "understand" through formal rules.
01: Foundations & Traditions
We analyze the two pillars of AI: DNNs (Artificial Neural Networks) and KRR (Knowledge Representation & Reasoning).
While DNNs handle messy real-world data, KRR provides the "explainability" required for high-stakes EdTech applications.
03: Dual Process Theory
iGentixAI mimics the human brain's System 1 (Fast Intuition) and System 2 (Slow Logic). Neurosymbolic AI is the technical manifestation of this cognitive duality.
04: The Kautz Taxonomy
How do we ground symbols in vector space? We follow the Henry Kautz framework for integration.
| Kautz Level | Architecture Type | Use Case |
|---|---|---|
| Type 3 | Neuro | Symbolic | Vision-to-Logic diagnostics |
| Type 5 | Neuro-Loss Symbolic | Logical constraint training |
| Type 6 | Integrated Synthesis | The 2026 iGentix Standard |
05: iGentixAI Case Studies
Explore Amazon Guardrails: Using Large Language Models to convert policies into hard logic, ensuring zero-hallucination compliance.
Neuro-Lab Assessment
Apply your knowledge of symbol grounding and system integration.
Scenario: A student uses an AI that identifies a handwritten math equation (Perception) and then calculates the result using a formal algebraic solver (Logic). Which Kautz level is this?