1.7. Key Concepts & References

Key Concepts

• Artificial Intelligence (AI) – Simulation of human intelligence in machines (learning, reasoning, perceiving, creating).
• Machine Learning (ML) – Subset of AI where systems learn patterns from data instead of explicit programming.
  • Supervised Learning – Training with labeled data
  • Unsupervised Learning – Pattern-finding without labels.
• Reinforcement Learning (RL) – Trial-and-error learning guided by rewards and penalties.
• Deep Learning (DL) – A form of ML using neural networks with multiple layers to recognize complex patterns (e.g., images, speech, translation).
• Generative Models (GM) – Models that can create new content (text, images, audio).
• Large Language Models (LLMs) – Deep learning models trained on vast text datasets to predict and generate human-like language (e.g., ChatGPT, Gemini).
• Chain-of-Thought (CoT) – A reasoning approach where AI generates intermediate steps before producing answers.
• Long-range Reasoning Models (LRMs) – Designed for extended, multi-step reasoning with long context retention.
• Retrieval-Augmented Generation (RAG) – Combines generation with external information retrieval for more accurate outputs.
• Multimodal Models (MCM) – AI systems that process and generate across multiple input types (text, images, audio, video).
• Multi-Component Prompting (MCP) – Structured prompting strategy for guiding AI toward more accurate, creative results.
• Agentic AI – Adaptive, goal-directed AI agents capable of planning, reflection, and autonomous task execution (e.g., AutoGPT).
• Artificial Narrow Intelligence (ANI) – Task-specific AI (today’s dominant form).
• Artificial General Intelligence (AGI) – Hypothetical AI capable of reasoning and learning across domains like a human.
• Artificial Superintelligence (ASI) – Speculative future AI surpassing human intelligence in all respects.
• LLM Chaining or prompt chaining – this technique links a series of prompts together to progressively guide an LLM toward a desired output. Each prompt builds upon the last, allowing for structured reasoning, more coherent text, and complex workflows that approximate multi-stage human thinking.
• Mixture of Experts (MoE) – architectures distribute computations across multiple specialized “expert” sub-models. A gating mechanism determines which experts to activate for a given input, allowing the system to scale efficiently while maintaining strong performance. MoE systems help optimize resource use and improve model interpretability by segmenting problem domains.
• Temperature Settings – a hyperparameter that controls randomness in text generation.
  • Low temperatures (0.0–0.5) yield deterministic, precise, and repeatable outputs.
  • Higher temperatures (>1.0) increase creativity, and unpredictability in responses.
  • Allows users to adjust AI output between reliability and originality depending on the task.
• Reinforcement Learning with Human Feedback (RLHF) – a training method where human evaluators provide feedback on model outputs, helping the system learn desirable behaviors and avoid harmful ones. It combines supervised fine-tuning with reinforcement learning, aligning model responses with human values, preferences, and safety considerations. RLHF has been crucial in making state-of-the-art LLMs usable and trustworthy.

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