--- name: leela-ai description: "Manufacturing Intelligence — Leela AI applies MOOLLM to industry" license: MIT tier: 0 allowed-tools: [read_file, list_dir] protocol: LEELA-AI related: [moollm, manufacturing-intelligence, society-of-mind, k-lines, schema-mechanism, constructionism, simulator-effect, speed-of-light, representation-ethics, yaml-jazz] tags: [moollm, meta, company, manufacturing, industrial, neural-symbolic, drescher] --- # 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*. ```yaml 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: ```yaml 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: ```yaml 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 ```yaml 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 ```yaml 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 ```yaml 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 ```yaml 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 ```yaml # 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 ```yaml # 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 ```yaml # 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 ```yaml transparency: principle: Every inference is explainable implementation: - causal_chains: visible in audit log - confidence_levels: always reported - uncertainty: acknowledged, not hidden - limitations: documented ``` ### Privacy ```yaml 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 ```yaml 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 ```yaml 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](../README.md) - [Schema Mechanism](../schema-mechanism/) - [leela.ai](https://leela.ai)