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====== Key Terms for AI ======

^ **Term / Technology** ^ **Category** ^ **Definition / Description** ^
| **Artificial Intelligence (AI)** | Concept | Simulation of human intelligence by machines, especially computer systems. |
| **Machine Learning (ML)** | Concept | Algorithms that allow computers to learn patterns and make decisions with data. |
| **Deep Learning** | Subfield of ML | Uses neural networks with many layers to model complex representations. |
| **Natural Language Processing (NLP)** | Subfield of AI | Enables machines to understand and generate human language. |
| **Computer Vision** | Subfield of AI | AI techniques to interpret images and videos. |
| **Reinforcement Learning** | ML Methodology | Models learn by interacting with an environment and receiving feedback. |
| **Supervised Learning** | ML Type | Model is trained on labeled input-output pairs. |
| **Unsupervised Learning** | ML Type | Model learns from unlabeled data by identifying patterns. |
| **Transformer** | Model Architecture | Deep learning architecture using self-attention, core to GPT and BERT models. |
| **Token** | NLP Concept | A unit of text, like a word or subword, used in language models. |
| **Prompt Engineering** | AI Interaction Design | Crafting model inputs to steer the output toward desired results. |
| **Inference** | AI Operation | Running data through a trained model to get predictions or outputs. |
| **Training** | AI Operation | Teaching a model using data so it can make accurate predictions. |
| **Bias** | Ethical Concern | Systematic errors in AI that can lead to unfair or discriminatory results. |
| **Explainability** | AI Governance | Making AI models' decisions interpretable and understandable to humans. |
| **Tuning / Fine-tuning** | Model Optimization | Adjusting a pre-trained model for a specific task or domain. |
| **Overfitting** | ML Issue | When a model performs well on training data but poorly on new data. |
| **Model Context Protocol (MCP)** | Model Integration | Standardized interface or protocol designed to allow language models (LLMs) to access, interpret, and maintain context across multiple interactions, tools, and data sources. |

===== Key Technologies and Platforms =====

^ **Technology**  ^ **Category**  ^ **Description**  ^
| **OpenAI**  | Foundation Model Provider | Creator of ChatGPT and GPT models. Offers APIs for LLMs, embeddings, and other AI services.  |
| **Azure OpenAI Service**  | Cloud Platform  | Microsoft’s hosted version of OpenAI models with enterprise security, scaling, and governance.  |
| **Anthropic**  | Foundation Model Provider | AI company focused on safety and alignment, creator of the Claude model family.  |
| **Mistral (MCP)**  | Foundation Model Provider | European AI company building open-weight language models, known for fast inference and performance.  |
| **Agentic AI**  | AI Paradigm  | AI systems that act autonomously and can make decisions, take actions, and pursue goals across tools and environments. |
| **LangChain**  | Agentic Framework  | Framework to build agent-based AI applications using chains of prompts, tools, and models.  |
| **AutoGPT**  | Agentic Framework  | Autonomous AI agent that breaks down tasks into subtasks and self-prompt to complete goals.  |
| **Hugging Face**  | AI Platform / Model Hub  | Open-source platform hosting thousands of models, datasets, and transformers tools.  |
| **LLMOps**  | Operational Practice  | Managing, deploying, monitoring, and improving LLMs in production (similar to MLOps).  |
| **Retrieval-Augmented Generation (RAG)** | AI Technique  | Combines LLMs with external knowledge sources (e.g., vector databases) for more accurate and current answers.  |

[[ai_knowledge|AI Knowledge]]
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