Large Language Models (LLMs) — Models like GPT-4, Claude, Gemini that understand and generate human language with remarkable fluency
Multimodal AI — Models that process and generate multiple data types (text, image, audio, video) simultaneously
AI Agents — Autonomous systems that use LLMs for planning, tool use, and task completion (AutoGPT, Claude Agents)
Reinforcement Learning from Human Feedback (RLHF) — Training AI to align with human preferences
Few-Shot & Zero-Shot Learning — Models that generalize from minimal examples
Retrieval-Augmented Generation (RAG) — Combining LLMs with external knowledge bases for accurate, up-to-date responses
Cognitive Computing
Cognitive computing aims to simulate human thought processes in a computerized model. Key characteristics:
Adaptive — Learns from changing information and goals
Interactive — Communicates naturally with humans
Iterative — States what it knows and asks for clarification when uncertain
Contextual — Understands context and meaning
Frontier Models
Frontier models are the most advanced AI systems at the cutting edge of capabilities. They exhibit emergent abilities not present in smaller models — like in-context learning, chain-of-thought reasoning, and tool use. Companies include OpenAI, Google DeepMind, Anthropic, and Meta.
AI Safety
As AI systems become more powerful, ensuring they remain safe and aligned with human values becomes critical. Key concerns:
Alignment — Ensuring AI goals match human values
Control — Maintaining human oversight of AI systems
Robustness — AI systems that perform safely under distribution shift
Interpretability — Understanding how AI models arrive at decisions
AI Tools Ecosystem
LangChain / LlamaIndex — Frameworks for building LLM-powered applications
Hugging Face Spaces — Deploy and share AI demos
Ollama / llama.cpp — Run LLMs locally on consumer hardware
Vector Databases — Pinecone, Weaviate, Chroma for RAG systems
MLOps — MLflow, Kubeflow, DVC for ML lifecycle management
Practice Task: Research one advanced topic (e.g., RAG, RLHF, multimodal AI) in depth. Build a simple RAG pipeline using Python: use a vector database (Chroma) to store documents and query them with an LLM (via Hugging Face or OpenAI API). Document the architecture and results.