Intelligence Augmentation
Build systems that amplify human reasoning.
Hybrid learning + applied engineering for IA: math foundations, model design, human-in-the-loop workflows, and production-grade guardrails.
Hybrid mode
Technical rigor + teaching clarity.
Grounded in local knowledge.
IA Core Process
- Define goal, constraints, and success metrics.
- Build math + ML foundations (linear algebra, calculus, stats).
- Model the task (NLP, CV, RL, retrieval).
- Design human-in-the-loop feedback and safety gates.
- Deploy, measure, and iterate with data.
loop:
observe()
reason()
recommend()
act_with_human()
evaluate()
refine()Math + Algorithms
Linear algebra — vectors, matrices, eigenspaces.
Calculus — gradients, optimization, backprop.
Probability — uncertainty, Bayes, inference.
Optimization — SGD, Adam, convexity.
Algorithms — search, graph reasoning, indexing.
IA Stack (Practical)
Data ingestion + evaluation harness
Embedding + retrieval + tool routing
Model policies + prompt templates
Human feedback + approvals
Monitoring + drift detection
Example Deliverables
Study plan + milestones
Annotated data + evaluation rubric
Tool-augmented assistant
Automated reporting dashboards