Pages tagged "ai-fundamentals"

2 pages tagged with ai-fundamentals. ← all tags

• Embeddings — How AI Turns Meaning Into Numbers Embeddings are numerical representations of text (or images, audio, video) that preserve semantic similarity. Two texts about the same topic have similar embedding vectors, even when they use different words. Embeddings are the foundational layer under semantic search, RAG, recommendation systems, and most AI applications that need to match meaning rather than match keywords.
• Hallucination — When AI Confidently Invents Things Hallucination is when AI generates content that is plausible-sounding but false. The structural cause is that LLMs predict probable next tokens rather than retrieve true facts. Inside the model's training distribution, hallucination is rare; outside it (uncommon topics, specific named entities, recent events) it's reliable. Hallucination is what makes the jagged-frontier asymmetry dangerous — wrong answers look identical to right ones.