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Breaking through the cocoon of "artificial" - Future cross-modal intelligent central fully automated operation protocol

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The article discusses the limitations of current artificial intelligence (AI) systems, emphasizing that they are still heavily reliant on human input for understanding and executing tasks in the real world. It identifies two main bottlenecks: 1. **Perception Barrier**: AI's understanding of reality is constrained by the need for human-generated data and labels, preventing it from autonomously interpreting complex, real-time information. 2. **Execution Barrier**: Even when AI generates effective solutions, the implementation still requires human intervention, making the process less efficient. The author argues that true advancements in AI will come from creating systems that can autonomously perceive and act in the physical world without human mediation. This involves integrating various sensory data to understand environmental changes and generating executable code that can directly control machinery. The proposed "Cross-Modal Intelligent Centralized Fully Automated Operation Protocol" outlines a four-stage process for achieving this autonomy: 1. **Reality to Language**: Gathering real-time data from multiple sources and generating structured reports. 2. **Language to Knowledge**: Using knowledge graphs and causal reasoning to interpret data and make decisions. 3. **Knowledge to Code**: Automatically generating executable code based on physical constraints and safety regulations. 4. **Code to Reality**: Implementing actions in the physical world without human deployment. The goal is to bridge the gap between digital instructions and real-world applications, ultimately leading to a more intelligent and responsive AI system that can operate independently.

Breaking Through the "Artificial" Cocoon: Future Fully Automated Operation Protocol for Cross-Modal Intelligent Hub — Author: Wang Jiao Cheng#

Breaking Through the "Artificial" Cocoon: The True Breakthrough of Artificial Intelligence Lies at Both Ends of the Closed Loop#

As we gaze at the dazzling development of artificial intelligence — increasingly fluent conversations, stunning text creation, efficient code generation — it is easy to fall into the illusion that language models are intelligence itself. However, peeling back the surface reveals that current mainstream artificial intelligence resembles a beautifully crafted cocoon suspended in mid-air: on the closed-loop chain of "reality → language → knowledge → code → reality," both the entry and exit points are still entangled in the stubborn "cocoon of artificiality."

Bottleneck One: The Cocoon of Perception (Reality → Language). Current artificial intelligence's understanding of the real world heavily relies on the meticulous "feeding" and labeling by humans. Its "perception" does not stem from original, dynamic, multi-dimensional interactions with the world, but is composed of countless cut and annotated digital slices. A farmer wanting to obtain a soil report still needs to manually collect samples, upload data, and interpret complex results. AI cannot autonomously set up a sensor network, integrate multi-modal signals such as infrared spectra, temperature, and humidity in real-time, and accurately describe in natural language that "the risk of organic matter loss at a depth of 0.5 meters in the northwest plot is accelerating" — this key leap of "reality → language" is still filled with human gaps and delays.

Bottleneck Two: The Cocoon of Execution (Code → Reality). When AI generates a perfect irrigation optimization code, the brilliance abruptly halts. Deploying the code to specific agricultural machinery, verifying reliability in the field environment, handling sudden hardware failures or boundary conditions… these critical steps that convert digital instructions into physical utility are still firmly controlled by the hands of engineers and operators. The imagined seamless transition from "AI-generated code → automatic execution" transforms into a fragmented semi-manual process in the dusty fields, roaring factory workshops, or complex equipment in emergency rooms.

The Overlooked Core Battlefield:

  1. The Automated Furnace of Reality → Language: Future intelligence must become "the proactive learner of the world" — it needs to integrate laser radar scans of building acoustic vibrations, consolidate olfactory sensors monitoring chemical leaks, and understand the subtle meanings of thermal anomalies in massive infrared images… then summarize like humans: "The ventilation duct in Area B has structural vibrations, with a predicted risk of cracks within 3 days."
  2. The Autonomous Executor of Code → Reality: Breakthroughs will occur in embedded intelligence that does not require human intermediate compilation — AI-generated algorithms will directly control robotic arms to perform emergency surgical suturing without needing engineers to convert formats; the moment code is generated, it will pass safety verification and be instantly dispatched to every autonomous vehicle in the city to execute traffic adjustment plans. This is the frictionless landing of "digital instructions" in the real world.

In contrast, the currently overhyped paradigms of "agents" and "workflows" by capital essentially build exquisite abstract sandboxes at the intermediate level of "language → knowledge → code." Regardless of how sophisticated the scheduling between agents or how flexible the workflow configurations are, if they cannot directly perceive the pulse of the real world and take action to transform it, they remain digital games floating in the cloud. When farmers still need to manually collect data daily and engineers still need to debug deployments overnight, these intermediate-layer solutions struggle to create truly transformative value.

Conclusion: Only by Breaking the Cocoon Can One Become a Butterfly. The true value of artificial intelligence should not stop at simulating human language games but should bridge the gap between the digital and physical worlds. By removing the "chains of artificiality," granting AI "eyes" (proactive perception through multi-modal sensor fusion) and "hands" (direct manipulation of the physical world without human intervention), allowing the two ends of the closed loop to seamlessly connect — this is the necessary path for artificial intelligence to break through its "toy" attributes and become a true engine of civilizational progress. When AI can autonomously perceive suffering and take immediate action, we will ultimately welcome the dawn of intelligent inclusivity for all things and the co-evolution of humans and machines.

Fully Automated Operation Protocol for Cross-Modal Intelligent Hub#

Mission Statement
When the entropy increase of the real world abnormally touches the system's perception boundary, autonomously initiate the "perception-cognition-decision-execution" closed loop, completing the full-link intelligent response from physical signals to real-world transformation without human intervention.

Phase One: Reality → Language (Multi-Modal Perception Translation)#

  1. Environmental Perception Activation
    • Target Coordinates: Obtain real-time geographic positioning, covering a spherical space with a radius of 500 meters
    • Spatiotemporal Dimension: Integrate current data streams with 72 hours of historical change trends
    • Multi-Modal Signal Fusion:
      Visual Field: Analyze structural distortions in 10^8 pixel-level spectral features
      Acoustic Network: Reconstruct abnormal resonance waveforms >20kHz in a three-dimensional sound field
      Molecular Probes: Quantify the concentration gradient of volatile organic compounds in air/water bodies
  2. Natural Language Generation
    Output structured event report template:
    "At [coordinate location] on [UTC time], [entity object] was monitored to have [state anomaly], with core abnormal evidence including:
    • Infrared radiation deviation value: _X% baseline
    • Infrasound energy peak: _Y decibels
    • Heavy metal ion concentration: _Z ppb"

Phase Two: Language → Knowledge (Dynamic Cognitive Inference)#

  1. Knowledge Graph Activation
    • Associate with global event databases: Map current abnormal features to three major knowledge domains of equipment failure, ecological pollution, and structural failure
  2. Causal Inference Engine
    If both "material concentration surge" and "vibration spectrum dispersion" are simultaneously satisfied:
    • Generate dual-path hypotheses:
      Emergency Scenario: Pipeline corrosion rupture (confidence_P1%, reference case CT2025)
      Criminal Scenario: Illegal discharge behavior (confidence_P2%, associated legal clause §4.8)
  3. Decision Tree Construction
    • Activate emergency shutdown protocol when confidence >90%
    • Dispatch drones for sampling verification when confidence is 70%-90%

Phase Three: Knowledge → Code (Autonomous Programming Generation)#

  1. Physical Constraint Modeling
    • Specify execution entity: Industrial robot Arm7 series
    • Hard safety boundaries: Working radius ≤_R meters | Torque threshold ≤_T Newton·meters
    • Regulatory Compliance: ISO 13849-PL e-level safety standards embedded in control logic
  2. Executable Instruction Construction
    Generate adaptive control program:
    • Path Planning: Avoid high-risk areas based on Voronoi diagrams
    • Core Action: Use graphene sealant for repairs (pressure value_P kilopascals)
    • Real-time Verification: Laser scanner detects millimeter-level deformations
    • Failsafe Mechanism: Immediately initiate emergency braking when torque exceeds threshold

Phase Four: Code → Reality (Physical World Operations)#

  1. No Human Deployment
    • Directly connect target device firmware through industrial IoT gateways
    • Establish real-time verification channels between device data streams and digital twins
  2. Effect Evaluation Criteria
    • Success Metrics: Leakage rate <1 Pascal/second & Vibration energy <0.1 Joules
  3. Closed-Loop Evolution Mechanism
    • When execution deviation exceeds allowable values:
      Initiate Incremental Learning: Record pressure parameter deviation value_Δ
      Knowledge Graph Update: Mark the effectiveness of sealing solution V3.1 under temperature_T℃/pressure_P megapascals conditions
    • Global Knowledge Base Synchronization: Publish new constraint conditions "environment pH >6.5"

Core System Features#

  • Intelligent Arbitration Mechanism: Automatically activate particle detectors for evidence weighting when optical and acoustic signals conflict
  • Embedded Physical Rules: Directly convert Newtonian mechanics equations into robotic arm motion constraints
  • Cognitive Entropy Meter: Real-time display of the system's understanding maturity of the current scene (0-100% entropy reduction index)

Practical Scenario Simulation
Oil pipeline pressure sensor alarm → Drone swarm formation scanning → Crack 3D modeling → Self-generated repair plan → Robot precise sealing → Blockchain evidence throughout the process → Update global energy facility knowledge graph

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