Part II · Gallery — Hybrid / Neuro-symbolic AI

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Combines paradigms: the statistical intuition of neural networks with the structure and rigor of symbols, search, or external tools. It is the architecture of the most capable AI systems in production today.

🤖 Agent (Agentic AI) Hybrid · Multimodal + tools · LLM + reasoning/action loop

  • What it is: an LLM that plans, uses tools, observes results, and iterates

    toward a goal.

  • Examples: Claude Code, computer use agents, AutoGPT, ReAct pipelines.
  • Algorithmic basis: ReAct, tool use, persistent memory, planning,

    reflection (self-critique).

  • Peak: 2023–present.
  • Capabilities / modes: intellectual + executive; integrates reasoning and action.
  • → Part IV: chapter planned.

📚 RAG — Retrieval-Augmented Generation Hybrid · Language · LLM + vector search

  • What it is: retrieves relevant documents and injects them into the LLM's

    context to answer with sources.

  • Examples: enterprise assistants, conversational search, Q&A over docs.
  • Algorithmic basis: embeddings + ANN (HNSW), re-ranking, grounding.
  • Peak: 2023–present.
  • Capabilities / modes: intellectual; reduces hallucination, cites sources.
  • → Part IV: chapter planned.

🧠📐 Neuro-symbolic Hybrid · Reasoning · Neural network + logic/solver

  • What it is: couples neural perception with verifiable symbolic reasoning.
  • Examples: AlphaGeometry, AlphaProof, QA systems with a reasoner.
  • Algorithmic basis: network for intuition/proposition + solver for verification.
  • Peak: 2023–present (the frontier of reliable reasoning).
  • Capabilities / modes: logical-mathematical with guarantees.
  • → Part IV: chapter planned.

🎯 Recommendation System Hybrid · Ranking/personalization · Embeddings + factorization + networks

  • What it is: predicts preferences and ranks items for each user.
  • Examples: feeds (YouTube, TikTok), e-commerce, streaming.
  • Algorithmic basis: collaborative filtering, matrix factorization, two-tower,

    learning-to-rank.

  • Peak: 2010–present (the economic engine of the web).
  • Capabilities / modes: predictive/personalization at scale.
  • → Part IV: chapter planned.

🦾 Embodied Robotics Hybrid · Embodied/control · Vision + RL + control + (LLM planner)

  • What it is: AI that perceives and acts in the physical world, integrating

    modalities.

  • Examples: robotic manipulation, autonomous vehicles, vision-language-action.
  • Algorithmic basis: perception (CNN/ViT) + policy (RL) + planning;

    recent VLA models.

  • Peak: 2016–present.
  • Capabilities / modes: bodily-kinesthetic + spatial + executive.
  • → Part IV: chapter planned.

Salient sciences and mathematics: combines those of all paradigms — logic and proof theory (symbolic side), optimization and linear algebra (neural side), control theory and game theory (agents), information theory (search). Strength: joins robustness/verifiability with flexibility; weakness: engineering and integration complexity.