Traditional systems process each image in isolation. A self-learning world model continuously learns from your camerasâbuilding memory, detecting anomalies, and predicting changes.
| Capability | Traditional Vision | Self-Learning World Model | The Difference |
|---|---|---|---|
| Learning | No learningâprocesses frames independently | Continuously learns patterns from every observation | Gets smarter over time |
| Memory | Statelessâno memory between frames | Scene Memory + Evidence Memory | Remembers what's normal |
| Prediction | No prediction capability | JEPA predicts future states in embedding space | Knows what to expect |
| Anomaly Detection | Rule-based, high false positives | Learned baseline â automatic novelty scoring | Meaningful alerts only |
| Multimodal Fusion | Separate pipelines per camera type | Unified 512-dim embeddings across all sources | One model for all cameras |
Every observation teaches the world model. It learns what's normal, detects deviations, and improves predictionsâautomatically.
Every frame from every camera is encoded into a unified embedding space. A truck looks the same whether captured by satellite, drone, or CCTVâthe meaning is preserved.
Three layers: Visual Ingestion unifies all camera types. World Model Core learns and predicts. Intelligence Delivery outputs at any frequency.
Visual sources capture at different frequenciesâfrom weekly satellite passes to real-time video. The world model learns patterns at every timescale.
Higher-frequency sources serve ALL lower-frequency intelligence needs. A single CCTV stream can produce monthly trends AND real-time alerts.
The self-learning system that powers continuous intelligence.
Learns 'what's normal here'âtypical scenes, expected objects, routine patterns. Updated only when something genuinely new is observed.
Stores 'what exactly happened'âevery detection with timestamps and confidence scores. Enables forensic queries across time.
Predicts in embedding space (meaning), not pixel space (appearance). Multi-timescale forecasting: fast (frame-to-frame), medium (seconds-minutes), slow (hours-days). When prediction â reality â anomaly detected.
Not every observation becomes permanent memory. The novelty gate scores each observation: routine (auto-expires) vs. novel (permanently stored). This prevents memory bloat while capturing everything important.
Our self-learning world model is built on open foundations. Powering the future of visual intelligence.
The open source memory layer that enables self-learning in AI systems. Scene Memory + Evidence Memory architecture.
Visit Website âContribute to the world model. Star, fork, and build with our multimodal camera intelligence stack.
View on GitHub âAPI references, integration guides, and tutorials for connecting your cameras to the world model.
Read the Docs âBuilding the future of geospatial AI together. Contribute code, ideas, or feedback.