About General Primitive Intelligent Agents

Author: Wang Jiao Cheng

What is a Universal Primitive Agent (UPA)? Core Idea
- Goal: To create a "Universal Artificial Intelligence" (AGI/Artificial General Intelligence) that can handle anything.
- Core Idea: No "behemoth" models! General intelligence should consist of countless super simple, super small "intelligent building blocks" (Primitives, such as [PERC-101] perceiving time, [INF-205] making simple inferences, [MEM-303] temporarily remembering things).
- How does it work? Just like playing intelligent Lego:
  - Encounter a new task? Select the necessary primitive building blocks on-site.
  - Combine these primitive building blocks as needed for work (Composition).
  - The combination method is also decided on-site (no need to pre-train a large model).
What is UPAS? System Design for Implementing UPA
- UPAS = The Realization of UPA (the system that implements it).
- Core Components:
  - Primitive Library: A large warehouse storing all "building blocks" (Primitives). Each primitive has a unique ID (e.g., [INF-205]).
  - Intelligent Assembly Worker: Dynamic Neural-Symbolic Composition Engine (DNSC):
    - A task arrives → Break it down into the smallest steps like drawing a flowchart from the manual (Recursive Task Decomposition).
    - For each small step → Instantly select suitable building blocks (Primitives) from the library (using neural networks for quick matching).
    - "Weld" these primitive building blocks together (using clear logical rules to connect the process).
    - Mark the combination: 【[PERC-101]⊗[INF-205]】 (the symbol ⊗ indicates neural + symbolic combination).
  - Self-Learning Ability (Learning & Evolution):
    - Discover new situations when breaking down tasks (no ready-made building blocks)? → Collect data on-site, train new primitives (e.g., [NEW-ACT-808]) and add them to the primitive library! The system can auto-expand.
  - Transparent Operation (Transparency/Explainability):
    - Full record: "How was the task broken down? Which building blocks were used at each step? What was the result?" Like a transparent assembly log.
    - Monitoring mechanism (Emergence Monitoring): Identify unexpected "new effects" (Emergent Behavior) after recognizing building block combinations.
  - Overall Manager: Entropy-Reduction Adaptive Engine:
    - Assess task complexity (Task Entropy).
    - Simple tasks → Follow predefined blueprints (Predefined Composition Pathways).
    - Complex tasks → Deploy the Intelligent Assembly Worker (DNSC) + possibly enable self-learning to create new building blocks.
Why seek help from Neuromorphic Hardware for acceleration? Making the system "live" more efficiently
- Problem: UPAS needs to manage/combine massive primitive building blocks quickly and with low power consumption? Traditional computer hardware (CPU/GPU) can't handle it! High energy consumption, not agile enough.
- Neuromorphic Chips (e.g., Intel Loihi, IBM TrueNorth):
  - Like the human brain: The basic unit is artificial neurons, communicating via spikes (similar to neural signals).
  - Ultra-low Power: Only consumes power when "something happens" (spikes come/go), resting otherwise (Event-Driven).
  - Inherently Multi-threaded: All units work together, particularly suitable for serving a bunch of small primitives simultaneously.
  - Perfect Fit for UPAS:
    - Each primitive building block → Mapped to one (or a group of) hardware units on the chip for execution.
    - Communication between primitives → Becomes "signaling" (spike communication) within the chip, extremely fast!
    - Energy-saving → Allows UPAS to fit into phones, drones, sensors, enabling long-term operation.
  - Effect: Task processing speed skyrockets (Low Latency), energy consumption plummets (Energy Efficiency)! Complex combination operations on neuromorphic chips feel like having a physical cheat.
Is Quantum-Neuromorphic Hybrid Architecture feasible? The future's big move
- Source of the Idea:
  - Neuromorphic chips → Super-fast execution of primitive tasks, ultra-low power, solving detailed work and communication issues.
  - Quantum Computing (QC) → A "super cheat" for solving specific problems: Can instantly find the optimal solution among countless possibilities or handle specific complex mathematical structures (like combinatorial optimization, quantum simulation). Neuromorphic chips struggle with this.
- Core Idea: Let them team up! (Hybrid Processing)
  - Quantum Processor (QPU) as the "super strategist":
    - When UPAS encounters a super difficult problem (e.g., finding the best response path during a global crisis), the quantum processor steps in.
    - Using quantum algorithms (e.g., Grover's Search Algorithm) with exponential parallelism, it instantly explores massive options, filtering out the most promising strategies or pointing out core directions (Combinatorial Optimization).
  - Neuromorphic Chips as the "lightning strike team":
    - Receive the streamlined golden strategy from the quantum strategist.
    - Immediately drive massive primitive building blocks, executing tasks with neuromorphic chips super fast and energy-efficiently.
  - Need a translator & communicator: Quantum-Classical Interface (QCI)
    - The quantum processor and neuromorphic chips speak different "languages" (quantum states vs. spikes).
    - Requires specialized interface hardware (e.g., optical interconnects, superconducting microwave photon converters) to translate information and reduce noise interference between low-temperature/room-temperature environments.
    - Needs a Hybrid Programming Framework to facilitate developers in leveraging capabilities from both sides.
- Is it feasible? Conclusion: Huge challenges, but full of hope! (Feasibility: Challenging but Promising)
  - Strong synergy: Quantum (solving tough problems) + Neuromorphic (efficient execution), perfectly complementary! Solving the combinatorial explosion problem (Combinatorial Explosion).
  - Clear need: The flexibility of UPAS brings enormous computational demands, and the hybrid architecture is just the right remedy.
  - Clear technological path: Research on interface technology, noise-resistant quantum gates, compilers, etc. has already begun.
  - Transformative potential: This could be the key path for UPAS to break through computational limits and achieve truly human-like or even superhuman intelligence! Imagine its applications in drug design, ultra-secure systems, planetary-scale IoT!