Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/AI_Brain / daemon/datasets/meta_learning_capabilities.json |
| Date | Not specified in source |
| Repository | Janady13/AI_Brain |
| Revision | 5cd0bbf744b0aee9445913337e852889d9a8098e |
| Branch | codex/build-online-github-ani-7v4kug |
| Path or archive member | daemon/datasets/meta_learning_capabilities.json |
| Original SHA-256 | c29b204bfba88b600fae024061dcd09d56a8b27db616da95e52fe3c79e618eaa |
| Git blob SHA | c58ff35909fd13bb04b8e5defabe66c2f841eff3 |
| Reading-copy SHA-256 | c29b204bfba88b600fae024061dcd09d56a8b27db616da95e52fe3c79e618eaa |
Recovered source transcript
Download complete source text ↓68 lines · 3.9 KB
[
{
"name": "Autonomous Feedback Loops",
"category": "reinforcement_learning",
"description": "Continuously tunes marketing experiments with reward shaping, policy updates, and offline evaluation.",
"signal_keywords": ["reinforcement", "policy", "reward", "loop"],
"learning_modes": ["offline rl", "batch policy improvement", "counterfactual evaluation"],
"data_sources": ["campaign telemetry", "crm outcomes", "attribution logs"],
"feedback_channels": ["reward shaping", "safety critics", "guarded exploration"],
"tooling": ["ray rllib", "amazon sagemaker rl", "azure personalizer"],
"metrics": ["policy_return", "exploration_rate", "stability_index"],
"deployment_modes": ["shadow deployment", "progressive rollout", "multi-armed bandits"],
"base_maturity": "advanced"
},
{
"name": "Meta-Learning Playbooks",
"category": "meta_learning",
"description": "Learns how to learn new channels rapidly using few-shot adaptation and hyperparameter search.",
"signal_keywords": ["meta", "few-shot", "hyperparameter", "adapt"],
"learning_modes": ["maml", "reptile", "transfer learning"],
"data_sources": ["historical experiments", "benchmark corpora", "synthetic blends"],
"feedback_channels": ["episodic evaluation", "learning rate schedulers", "curriculum design"],
"tooling": ["pytorch lightning", "weights & biases sweeps", "google vertex ai"],
"metrics": ["adaptation_speed", "few_shot_accuracy", "generalization_gap"],
"deployment_modes": ["meta-initialization", "auto ml", "prompt bootstrapping"],
"base_maturity": "elite"
},
{
"name": "Continuous Experiment Graph",
"category": "knowledge_distillation",
"description": "Transfers learnings across campaigns and compresses policies into lightweight deployment artifacts.",
"signal_keywords": ["distillation", "teacher", "student", "compression"],
"learning_modes": ["knowledge distillation", "policy compression", "graph propagation"],
"data_sources": ["experiment graph", "knowledge store", "analytics warehouse"],
"feedback_channels": ["validation cohorts", "drift detection", "human review"],
"tooling": ["neptune.ai", "mlflow", "azure ml"],
"metrics": ["distillation_loss", "cohort_lift", "policy_size"],
"deployment_modes": ["edge deployment", "on-device agents", "microservices"],
"base_maturity": "advanced"
},
{
"name": "Self-Healing Learning Mesh",
"category": "self_supervised",
"description": "Applies self-supervised objectives to unify marketing signals, detect drift, and auto-correct models.",
"signal_keywords": ["self-supervised", "contrastive", "drift", "self-heal"],
"learning_modes": ["contrastive learning", "masked modeling", "representation alignment"],
"data_sources": ["web analytics", "product telemetry", "support transcripts"],
"feedback_channels": ["drift alarms", "self-healing patches", "anomaly queues"],
"tooling": ["hugging face transformers", "databricks", "snowflake ml"],
"metrics": ["representation_quality", "drift_delay", "auto_fix_rate"],
"deployment_modes": ["continuous training", "stream processing", "feature store streaming"],
"base_maturity": "advanced"
},
{
"name": "Strategic Learning Governor",
"category": "governance",
"description": "Coordinates active learning, governance approvals, and compliance aware retraining for marketing AI.",
"signal_keywords": ["governor", "compliance", "active", "governance"],
"learning_modes": ["active learning", "human in the loop", "policy gating"],
"data_sources": ["governance ledger", "compliance annotations", "risk reports"],
"feedback_channels": ["approval workflows", "risk scoring", "model cards"],
"tooling": ["monte carlo data", "aria", "openlineage"],
"metrics": ["time_to_approval", "governance_coverage", "risk_score"],
"deployment_modes": ["governed pipeline", "review gates", "audit trails"],
"base_maturity": "foundational"
}
]