leela-ai

# Leela AI Skill > *Manufacturing Intelligence -- from theory to industrial application.* ## Overview This skill describes Leela AI's relationship to MOOLLM. Leela develops MOOLLM with an eye toward manufacturing intelligence, using it daily for practical devops, edgebox management, coding, debugging, and design work. The team is exploring how the theoretical foundations of Minsky, Papert, and Drescher might eventually deploy on factory floors. ## Leela and Gary Drescher Leela's foundations lie in Gary Drescher's work at MIT under Marvin Minsky and Seymour Papert. Drescher brought Jean Piaget's developmental psychology into computing: infants learn through sensorimotor experience and build *schemas* (context → action → result). Henry Minsky was exposed to this as a student; years later he reimplemented Drescher's algorithms and, with Cyrus Shaoul and Milan Minsky, founded Leela AI. The name *Leela* is Sanskrit for divine play — the play of creation, destruction, and re-creation. Key points: - **Schema mechanism**: Leela builds models of the world using schemas that reason about which actions are possible and what changes when an action is performed. Goals are achieved by chaining schemas (planner finds actions whose results match the goal). - **Self-supervised learning**: Leela learns from exploratory actions without labeled examples or explicit reward; it forms and tests hypotheses. In multi-goal grid-world experiments (Kommrusch et al., IWSSL 2020), Leela reached training targets in ~160N² steps vs DQN ~360N^2.7, and does not suffer catastrophic forgetting. - **Neurosymbolic extension**: Later work (Symbolic Guidance for Constructivist Learning, Neurosymbolic Learning on Video Data, Society of LLMs) combines the symbolic schema system with neural perception (object/pose detection, cortical columns, multi-LLM instances). *Society of LLMs* (Kommrusch & Minsky, IWSSL 2024) maps Drescher's schema mechanism onto multi-agent LLMs: curiosity-driven goals, multiple plans, training samples when plans differ and one succeeds, contextual sub-activation (one agent "thinking subconsciously"), and incremental LoRA updates; evaluation target ARC-AGI. Leela Core uses the hybrid for manufacturing video intelligence — causal reasoning and explainability on top of ConvNets. See: [schema-mechanism/](../schema-mechanism/), [reference/drescher-lineage.yml](reference/drescher-lineage.yml), [reference/publications.yml](reference/publications.yml), [reference/society-of-llms.yml](reference/society-of-llms.yml). ## 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 pixelated 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. - Kommrusch et al. (2020). Self-Supervised Learning for Multi-Goal Grid World: Comparing Leela and Deep Q Network. IWSSL, PMLR 131. - [MOOLLM Skills](../README.md) - [Schema Mechanism](../schema-mechanism/) - [reference/publications.yml](reference/publications.yml) — papers and case study - [leela.ai](https://leela.ai)