Essential AI terms explained
Discover essential AI terms explained in plain English. Learn the basics and unlock your AI potential with this handy guide.
Algorithm: A set of step-by-step instructions that a computer follows to complete a task.
Artificial general intelligence (AGI): AI with human-like cognitive abilities.
Artificial intelligence (AI): Computer systems designed to do tasks that normally require human intelligence.
Augmented intelligence: AI that assists or enhances human decision-making rather than replacing it.
Automation: The use of technology to perform tasks with minimal human input.
Bias: When an algorithm unfairly favors certain outcomes, often reflecting human or data-based bias.
Chatbot: A computer program that simulates human conversation, often used in customer service.
Classification: The process of categorizing data into groups or labels.
Clustering: Grouping data points into clusters based on similarities.
Computer vision: AI technology that allows computers to "see" and interpret images.
Data: Information used for analysis and decision-making in AI.
Data mining: Discovering patterns and relationships in large data sets.
Deep learning: A subset of machine learning that uses neural networks with many layers to process data.
Ethics in AI: Moral considerations surrounding AI's impact on people and society.
Explainable AI: AI systems designed to provide understandable explanations for their decisions.
Generative AI: AI that can create new content (e.g., text, images, audio).
Human-in-the-loop (HITL): Involving humans in AI decision-making to ensure accuracy and oversight.
Inference: Using a trained model to make predictions or decisions based on new data.
Labeling: Tagging data with relevant information for training AI models.
Large language model (LLM): A type of AI trained on vast amounts of text data to generate language outputs.
Machine learning (ML): AI that learns from data to improve its performance over time.
Model: A mathematical representation of a real-world process or system used in AI.
Natural language processing (NLP): AI technology that understands and processes human language.
Neural network: A network of interconnected nodes inspired by the human brain, used in deep learning.
Prompt engineering: Crafting prompts to guide AI outputs effectively.
Reinforcement learning: AI that learns by trial and error, receiving feedback in the form of rewards or penalties.
Responsible AI: Developing AI systems that prioritize fairness, transparency, and accountability.
Supervised learning: AI that learns from labeled data.
Tokens: In AI language models, text is broken down into tokens, which are the basic units of meaning.
Training: The process of teaching an AI model using data.
Unsupervised learning: AI that learns from data without pre-labeled examples.
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