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LLM (Large Language Model) – A large-scale language model trained on terabytes of text and built with billions of parameters, which determine its ability to analyze, interpret, and generate natural language. Thanks to its deep statistical understanding of language, an LLM can answer questions, write text, carry conversations, translate, summarize, and more. Examples include GPT-4, Gemini, and Claude. Their performance depends heavily on training data volume, architecture (e.g., Transformer), and task-specific optimization.

Machine Learning – An approach in AI where models learn patterns from data without being explicitly programmed. Instead of following hardcoded rules, the system analyzes examples (input and correct outputs) and learns to predict or classify new data. ML underpins technologies like computer vision, natural language processing, and recommendation engines.

Multimodal Model – An AI model capable of working with multiple data types simultaneously — text, images, audio, video, or sensor data. This allows it to integrate and reason across different modalities. For example, a multimodal model can read a recipe, recognize ingredients in an image, and describe the cooking process. Used in education, healthcare, entertainment, and robotics.

MCP (Model Context Protocol) – A specialized protocol that enables language or agent models to securely interact with external tools and services like calendars, emails, CRM systems, or databases. MCP ensures controlled and context-restricted access, protecting data and preventing inappropriate actions. It’s crucial for AI integration into workflows and task automation.

Neural Network – A mathematical model inspired by the structure and function of the human brain. It consists of interconnected “neurons” (artificial nodes) that transmit signals through layers — from input to output. Neural networks can detect complex patterns in data and are the foundation of deep learning. Widely used in image recognition, language processing, forecasting, and content generation.

Overfitting – A phenomenon where a model performs well on training data but fails to generalize to new data. It occurs when the model becomes too tailored to training specifics, including noise or irrelevant patterns, reducing its effectiveness in real-world scenarios.

Prompt – A text-based input or instruction given to a language model to elicit a response or complete a task. Prompts can be simple (a sentence) or complex (a structured scenario with context and examples). Response quality depends on how well-crafted the prompt is.

Prompt Engineering – The practice of designing clear, structured, and effective prompts to elicit optimal responses from AI models.

Retrieval-Augmented Generation (RAG) – A hybrid approach in generative AI that combines a language model with an external information retrieval system (e.g., documents, databases, web content). The model first fetches relevant context, then uses it to generate more accurate and factual outputs. RAG reduces hallucinations and enhances trust in AI-generated responses.

Reinforcement Learning – A machine learning method where an agent learns through trial and error, receiving rewards or penalties based on its actions. The goal is to maximize cumulative reward by optimizing decision sequences. Used in robotics, autonomous driving, finance, and games like chess. Unlike supervised learning, RL doesn’t require predefined correct answers — the model learns from experience.

Supervised Learning – A machine learning approach where the model is trained on labeled data — each example has input features and a correct output. The model learns to generalize patterns for accurate predictions on new data. Common in classification (e.g., spam detection) and regression (e.g., price prediction). Performance depends on data quality and quantity.

Synthetic Data – Artificially generated data that mimics the structure and features of real data, without including personal or confidential information. Created via ML models or simulations, it’s especially useful when access to real data is limited (e.g., in medicine or cybersecurity). Used for training, testing, and validation while preserving privacy.

Token – The smallest unit of text processed by a language model. A token might be a word, part of a word (e.g., a root or suffix), punctuation mark, or symbol. Tokenization breaks input into such elements, enabling the model to interpret and generate language efficiently. Accurate token recognition is essential for good natural language understanding.

Token Limit – The maximum number of tokens a model can process in a single input or session. It defines the length of input and output the model can handle. Limits vary by model — e.g., 8K or 128K tokens. A higher limit allows for more context and improves performance on complex or long tasks.

Training – The process through which an AI model learns to recognize patterns, structures, and relationships in provided data. Training quality depends on the diversity and richness of data, model architecture, and optimization algorithms. Once trained, the model can apply its knowledge to unseen data.

Transformer – A neural network architecture foundational to modern language models like GPT, BERT, and T5. Its key feature is the attention mechanism, which lets the model weigh the importance of different input elements based on context — enabling powerful, scalable models that deeply understand language and logic.

Transfer Learning – A machine learning technique where a model trained on one task is reused for a different, often related task. Instead of training from scratch, it builds on previously learned knowledge, saving time and data, and enabling effective adaptation.

Unsupervised Learning – A type of ML where the model works with unlabeled data — without predefined answers. The model discovers hidden patterns, clusters, or relationships on its own. It’s the basis for many recommendation systems, behavior analysis tools, and data preprocessing techniques.

Vision (Computer Vision) – A field of AI that enables machines to “see” — analyze and interpret visual data from the world. It powers object recognition, image classification, motion tracking, and visual reasoning, mimicking human perception.

Weights – Numerical values in a neural network that determine the influence of input elements on the model’s output. Each connection between neurons has a weight, which multiplies the input signal. Weights are the core of how models make decisions.

Weak AI – A form of AI designed to perform specific, narrowly defined tasks. Unlike AGI, it lacks self-awareness, general reasoning, or the ability to learn beyond its scope. It performs programmed functions based on learned algorithms. Examples include voice assistants (Siri, Alexa), translation systems, recommendation engines, and image recognition tools.

Explainability – The ability of an AI system to provide clear, logical explanations for its decisions or predictions. Crucial in high-stakes areas like medicine, finance, law, cybersecurity, and autonomous systems, where trust and accountability are essential.

YOLO (You Only Look Once) – A leading real-time object detection algorithm that processes an image in one pass to instantly locate and classify objects. Used in self-driving cars (detecting pedestrians, signs), surveillance (identifying threats), and medical imaging (localizing anomalies).

Zero-Shot Learning – A machine learning approach where a model can perform tasks or classify categories it hasn’t seen during training — without any examples provided for that specific category. It generalizes knowledge to handle novel situations.