Leela AI Skill

Manufacturing Intelligence -- from theory to industrial application.

Overview

This skill describes how Leela AI applies MOOLLM principles to real-world manufacturing intelligence. Leela takes the theoretical foundations of Minsky, Papert, and Drescher and deploys them on factory floors.

Core Technology

Neural-Symbolic Vision

Traditional computer vision is pattern matching. Leela's neural-symbolic system is causal reasoning.

neural_symbolic:
  layer_1: neural
    - object detection (what is there?)
    - pose estimation (how is it positioned?)
    - motion tracking (where is it going?)
    
  layer_2: symbolic
    - context inference (what situation is this?)
    - causal reasoning (why is this happening?)
    - SQL queries over temporal event database
    - prediction (what will happen next?)
    - explanation (human-readable "why")
    
  layer_3: pda  # LLM interface layer
    - generate: natural language → SQL
    - perform: execute queries
    - interpret: results → meaning
    - explain: causation in plain language
    - visualize: charts, timelines, maps
    - remember: query history, preferences

The neural layer provides perception. The symbolic layer provides reasoning. The PDA layer provides natural language interface -- neural at the surface, symbolic in the protocol.

Schema Mechanism (Drescher)

Every inference follows Drescher's schema pattern:

schema:
  context: [observable conditions]
  action: [event that occurred]
  result: [observed outcome]
  
  learning:
    marginal_attribution: 
      - which context features predict result?
    synthetic_items:
      - inferred entities not directly observed
    generalization:
      - when does this schema apply elsewhere?

Edge Computing Architecture

Intelligence at the edge, not in the cloud:

edge_architecture:
  edgebox:
    location: factory floor
    latency: <50ms
    capabilities: [inference, alerting, logging]
    
  cloud:
    purpose: training, aggregation, analytics
    latency: acceptable for non-real-time
    
  principle: |
    Real-time decisions happen at the edge.
    Learning and optimization happen in the cloud.
    Data sovereignty stays with the customer.

Applications

1. Safety Monitoring

safety_monitoring:
  purpose: Prevent accidents through predictive awareness
  
  examples:
    - pedestrian_in_vehicle_zone
    - ppe_compliance (hard hats, vests, glasses)
    - ergonomic_risk (repetitive motion, lifting posture)
    - near_miss_detection (close calls before accidents)
    
  output:
    alert: real-time notification
    explanation: why this is a safety concern
    recommendation: suggested action
    audit: logged for compliance

2. Process Optimization

process_optimization:
  purpose: Improve efficiency through observation and inference
  
  examples:
    - cycle_time_analysis
    - bottleneck_detection
    - idle_time_measurement
    - workflow_optimization
    
  output:
    insight: what is happening
    causation: why it is happening
    recommendation: how to improve
    simulation: what-if scenarios

3. Predictive Maintenance

predictive_maintenance:
  purpose: Fix equipment before it fails
  
  signals:
    visual: vibration patterns, wear indicators, alignment
    thermal: heat signatures indicating friction or failure
    acoustic: sound patterns indicating mechanical issues
    
  schema:
    context: [equipment state, operational history]
    action: [detected anomaly]
    result: [predicted failure mode]
    
  output:
    prediction: what will fail, when
    explanation: why we predict this
    recommendation: maintenance action
    confidence: certainty level

4. DevOps Automation

devops:
  purpose: Apply MOOLLM patterns to infrastructure
  
  patterns:
    files_as_state:
      - infrastructure as code
      - git as audit trail
      - YAML as configuration
      
    coherence_engine:
      - detect configuration drift
      - propose remediation
      - explain changes
      
    speed_of_light:
      - batch operations
      - parallel deployment
      - minimal round-trips

MOOLLM Integration

Rooms as Zones

# Factory zone as MOOLLM room
zone:
  id: assembly_line_3
  type: [production, monitored, indoor]
  
  contains:
    - equipment: [robot_arm_1, conveyor_2, station_7]
    - personnel: [operator_badge_1234]
    - cameras: [cam_3a, cam_3b, cam_3c]
    
  exits:
    - to: staging_area
    - to: quality_check
    
  atmosphere:
    safety_status: green
    production_status: active
    alert_level: none

Characters as Entities

# Forklift as MOOLLM character
entity:
  id: forklift_07
  type: [vehicle, autonomous, tracked]
  
  location: loading_dock_2
  state: stationary
  
  current_task: awaiting_clearance
  
  relationships:
    operator: badge_5678
    cargo: pallet_1234
    
  needs:
    fuel: 0.73
    maintenance: 0.15  # due soon

Skills as Inference Rules

# Safety protocol as MOOLLM skill
skill:
  id: pedestrian_safety
  
  activation:
    context: pedestrian detected in vehicle zone
    
  action:
    - alert vehicle operators
    - log safety event
    - track pedestrian until zone_clear
    
  advertisement:
    provides: pedestrian_zone_monitoring
    satisfies: [safety, compliance, awareness]

The Team

| Team Member | Role | Background | |-------------|------|------------| | Henry Minsky | CTO | MIT AI Lab, NTT DoCoMo, Google Nest. Marvin Minsky's son. | | Dr. Cyrus Shaoul | Chief Evangelist | Computational neuroscientist, Digital Garage co-founder/CTO | | Dr. Milan Singh Minsky | VP Product | Venture-backed startups, RayVio co-founder | | Sheung Li | VP Applications | Machine vision in manufacturing | | Dr. Steve Kommrusch | Senior AI Research Scientist | Deep learning, AMD/HP/National Semiconductor | | Don Hopkins | AI Architect | The Sims, NeWS, pie menus, MOOLLM |

The theory meets the practice. Minsky's ideas, refined through Hopkins's implementation experience and Kommrusch's deep learning expertise, deployed on factory floors.

Ethical Framework

Transparency

transparency:
  principle: Every inference is explainable
  
  implementation:
    - causal_chains: visible in audit log
    - confidence_levels: always reported
    - uncertainty: acknowledged, not hidden
    - limitations: documented

Privacy

privacy:
  principle: Data sovereignty and minimal collection
  
  implementation:
    - edge_processing: data stays local when possible
    - anonymization: faces blurred by default
    - retention: minimal, configurable
    - consent: clear signage, worker awareness

Human Agency

human_agency:
  principle: AI advises, humans decide
  
  implementation:
    - critical_decisions: require human approval
    - recommendations: clearly labeled as suggestions
    - override: always possible
    - accountability: human remains responsible

Integration Points

| System | Integration | |--------|-------------| | SCADA | Sensor data ingestion | | MES | Production event correlation | | ERP | Business context enrichment | | CMMS | Maintenance recommendation routing | | Safety Systems | Alert escalation |

Deployment Model

deployment:
  edge:
    edgeboxes: industrial compute at the source
    latency: <50ms for real-time inference
    resilience: operates offline if cloud disconnected
    
  cloud:
    platform: customer choice (AWS, GCP, Azure, on-prem)
    purpose: training, aggregation, dashboard
    sovereignty: customer owns their data
    
  hybrid:
    edge_to_cloud: telemetry, events, learning data
    cloud_to_edge: model updates, configuration

References

• Drescher, G. (1991). Made-Up Minds. MIT Press.
• Minsky, M. (1985). Society of Mind. Simon & Schuster.
• MOOLLM Skills
• Schema Mechanism
• leela.ai