Working with AI involves a variety of concepts and terminology. Here are some of the most important terms to know:
Core AI Concepts
- Artificial Intelligence (AI): The broad field focused on creating machines or systems capable of simulating human intelligence, including learning, reasoning, perception, and decision-making[1][2].
- Artificial General Intelligence (AGI): A type of AI that would match or exceed the intellectual capabilities of a human. Current AI, known as "narrow AI," excels only at specific tasks[1][3].
- Machine Learning (ML): A subset of AI involving algorithms that enable systems to learn and improve from data without being explicitly programmed for every task[2][3].
- Deep Learning: A form of machine learning using neural networks with many layers to model complex patterns in data, especially for tasks like image and speech recognition[4][3].
- Neural Network: Computing systems structured to mimic the human brain, consisting of nodes (neurons) organized into layers to process and analyze data[4][2].
Types of AI and Learning
- Supervised Learning: Machine learning approach where models are trained using labeled data, meaning the input and correct output are both provided[5][3].
- Unsupervised Learning: Here, models find patterns and relationships in data without pre-existing labels, useful for clustering and anomaly detection[5][6].
- Reinforcement Learning: The model learns through trial and error, receiving feedback through rewards or penalties to optimize actions towards a specific goal[5][3].
Natural Language and Interaction
- Natural Language Processing (NLP): Enables computers to understand, interpret, and generate human language[2][5].
- Large Language Model (LLM): A type of AI trained on vast amounts of text to generate human-like language and respond contextually (like GPT models)[7][3].
- Chatbot: A program that simulates conversation, often utilizing NLP and machine learning for responses[8].
- Prompt: The specific input or instruction given to an AI system to produce a response or output[2].
Data and Ethics
- Dataset: A structured collection of data examples used to train or validate AI models[8].
- Label: An identifier attached to each piece of training data to instruct the AI on expected outcomes[4].
- Bias: Systematic errors or unfairness in models, often resulting from imbalances in training data. Bias can cause AI to produce discriminatory or inaccurate results[8][9].
- AI Ethics and Guardrails: Frameworks, rules, and protocols to ensure AI operates responsibly, respects privacy, avoids bias, and aligns with human values[7][8].
Other Important Terms
- Algorithm: A step-by-step set of rules or a logical process used to solve problems, make decisions, or process data within AI systems[1][8].
- Hyperparameter: External parameters set before the machine learning process (such as learning rate), affecting how the model learns[7][4].
- Generative AI: Methods that create new content, such as text, images, or audio, based on learned patterns from training data[7][3].
- Hallucination: When an AI model generates plausible-sounding but incorrect or fabricated information[7][8].
- Computer Vision: The ability of AI systems to interpret and process visual information from the world, such as images or videos[10][5].
These terms offer a solid foundation for understanding and effectively working with artificial intelligence in practice. As the field evolves rapidly, familiarizing yourself with these basics will help navigate its advancements and applications[1][7][3].
