1. Large Language Models
LLMs are transformer-based models trained on vast text corpora to predict the next token. Pre-training learns general representations; fine-tuning (SFT, RLHF) adapts behavior. Key concepts: context window, temperature sampling, chain-of-thought prompting, retrieval-augmented generation (RAG).
2. Alignment & Responsible AI
Alignment ensures AI systems pursue intended goals. Techniques include RLHF, constitutional AI, red-teaming, and interpretability research. Deploy with content policies, rate limits, and human review for sensitive domains.
3. Multi-Agent Systems
Multiple agents coordinate or compete in shared environments. Applications: robotics swarms, game theory, auction design, distributed problem solving. Challenges: communication protocols, emergent behavior, equilibrium analysis.
4. Causal Inference in AI
Correlation ≠ causation. Structural causal models (Pearl's do-calculus) distinguish observational and interventional distributions. Causal ML improves robustness, fairness analysis, and decision-making under distribution shift.
Research Direction
Neuro-symbolic AI integrates neural learning with symbolic reasoning — promising for explainability and systematic generalization.