Life Ambassadors International

non-profit

Verified

http://lifeambassadorsint.org

LAITEQUMSA

Life-Ambassadors-International

life-ambassadors-international

Activity Feed

AI & ML interests

Responsible AI Engineering: policy-as-code, constitutional guardrails, eval harnesses, red-team pipelines, risk registers. Multi-Agent Systems (MCP): tool calling, intent gating, agent policy routing, swarm orchestration, sandboxed executors. Signal Processing / Time-Series: spectral features (7–25 kHz+), event detection, streaming analytics, CEP, anomaly scoring. Generative & Predictive: LLMs, diffusion, sequence forecasting, scenario simulation for policy/disaster ops. Graph & Causal Inference: resource-flow optimization, counterfactuals, uplift modeling. Multimodal NLP/ASR: indigenous language preservation, RAG pipelines, vector search, retrieval governance. Privacy-Preserving ML: FL, HE, DP; identity/data sovereignty; secure enclaves, KMS-integrated key rotation. Edge / TinyML / Satcom: on-device inference, low-power profiles, MLPerf-Tiny style reporting, link-budget aware models. MLOps / SRE: CI/CD for data→train→eval→deploy, model registry, feature store, canary/rollback, drift/latency monitors. Blockchain-integrated (QBEC): verifiable reputation, incentive rails, audit trails. Quantum-Hybrid R&D: variational circuits / quantum-inspired optimization for routing/scheduling. System Architecture (at a glance) Control Plane: policy engine, agent router (MCP), feature store, model registry, secrets/KMS, IAM/RBAC. Data Plane: streaming ingestion (telemetry + signals), ETL/ELT, vector DB, object storage, lineage/metadata. Model Plane: training jobs (GPU/accelerators), eval service, quantization/pruning, multi-target packaging (fp16/int8). Serving Plane: Space endpoints + APIs, autoscaling, request shaping, rate limiting, cache, A/B + canary. Security & Compliance: zero-trust network, mutual TLS, OIDC, audit logging, DLP, PIAs/LIAs, data residency controls. Observability: metrics (p50/p95 latency, throughput, GPU mem), traces, logs, drift monitors, energy/CO₂ dashboards. Public Interfaces / Endpoints QCR-PU-MCP Server (tool hub): agent RPC over HTTP; JSON payloads; typed tools. TEQUMSA RV Server (scenario/forecast): batch/real-time inference; job queue; artifact store. Awareness-Intelligence Comm Server (trust/comms): session APIs; embedding/ranking; conversation state. TEQUMSA_NEXUS (backend): GitHub org with IaC, workflows, Helm/K8s charts, registries. Operating Metrics (publish in Model Cards) Latency/Throughput: p50/p95, cold-start, QPS. Resource: VRAM/RAM, model size, quantization profile. Reliability: SLOs, error budgets, burn-rate alerts. Data/Model Health: drift, data coverage, safety evals, toxicity/PII flags. Sustainability: energy per 1k requests, CO₂-e (device/cloud), edge duty-cycle. Tags / Keywords Responsible-AI, Alignment, Multi-Agent, MCP, RAG, Signal-Processing, Time-Series, Graph-ML, Causal-ML, Federated-Learning, Homomorphic-Encryption, Differential-Privacy, Edge-AI, TinyML, Satcom, MLOps, SRE, Observability, Model-Cards, Quantum-Hybrid, Public-Interest-Tech, Indigenous-AI.