AI Practice and Technologies Interacting with Humans and the Real World — Revision Sheet

1. 📌 Essentials

• NLP enables machines to interpret, understand, and generate human language naturally.
• Classical NLP pipeline: tokenization, morphology/POS tagging, syntax, semantics, pragmatics.
• Word embeddings (static and contextual) represent words as vectors capturing meaning.
• Transformersized NLP with parallel processing and self-attention.
• Large Language Models (LLMs): BERT, GPT, T5, capable of understanding and generating language.
• Practical tools: spaCy (fast, rule-based), Hugging Face (state-of-the-art neural models).
• Responsible NLP: address bias, fairness, privacy, and energy consumption.
• Challenges include ambiguity, context-dependence, and intent recognition.
• Hierarchical flow: raw text → structured understanding → response or action.
• Future trends: instruction tuning, retrieval augmentation, multilingual models, safety.

2. 🧩 Key Structures & Components