AI Terminology Explained: Your Comprehensive Dictionary
AI Dictionary:
AI Agent (Artificial Intelligence Agent): A software program or system designed to perceive its environment (through sensors or data inputs), process that information (reasoning), make decisions, and take actions (through effectors or interacting with other systems) to achieve specific goals, often with a degree of autonomy. Think of it as an AI entity that does things in an environment.
Agentic AI: A paradigm or type of AI focused on building systems composed of AI agents that can autonomously make decisions, plan, and execute complex tasks with limited human oversight to achieve defined objectives. It emphasizes the “agency,” or ability to act independently, of the AI.
Autonomous Agent: An AI agent that can operate and make decisions independently without constant human supervision. The degree of autonomy can vary.
Environment: The context in which an AI agent operates. This can be a digital environment (like a database, a website, or a software system) or a physical one (like a factory floor for a robot).
Perception: An AI agent’s ability to receive and interpret information from its environment using sensors (in physical agents) or data inputs/APIs (in software agents).
Sensors: The components or interfaces that allow an AI agent to perceive its environment (e.g., cameras, microphones, data feeds, APIs).
Actuators (or Effectors): The components or interfaces that allow an AI agent to take action within its environment (e.g., robotic arms, sending emails, updating a database, displaying information on a screen).
Reasoning: The AI agent’s internal process of processing perceived information, applying logic, accessing knowledge, and evaluating options to decide on a course of action.
Decision-Making Mechanism: The part of the AI agent’s architecture responsible for processing input and determining the appropriate action based on its goals, knowledge, and perception.
Knowledge Base: A repository of information that an AI agent can access and utilize to inform its reasoning and decision-making. This can include pre-programmed knowledge, learned information, or external data sources.
Memory: An AI agent’s ability to store and recall past experiences, interactions, or information, allowing it to maintain context and improve performance over time.
Planning Module: A component that enables an AI agent to develop a sequence of steps or a strategy to achieve a specific goal.
Action Module: The component that translates the AI agent’s decisions into actual executions or interactions within the environment.
Learning Agent: An AI agent that improves its performance over time based on its experiences and interactions, often using machine learning techniques.
Simple Reflex Agent: The most basic type of AI agent that makes decisions based only on the current perception of the environment, using a set of predefined condition-action rules. It has no memory.
Model-Based Reflex Agent: An AI agent that maintains an internal model or representation of the environment to help it make decisions, even if the environment is not fully observable. It uses memory to track the state of the world.
Goal-Based Agent: An AI agent that operates with explicit goals and plans a sequence of actions to achieve those goals.
Utility-Based Agent: A more sophisticated agent that considers not just achieving a goal, but also the “utility” or desirability of the outcome. It evaluates different possible actions and chooses the one expected to maximize its utility function (e.g., balancing multiple objectives).
Multi-Agent System (MAS): A system composed of multiple AI agents that interact with each other and their shared environment to achieve individual or collective goals.
Agentic Workflow: A process or sequence of tasks that is designed to be executed autonomously by one or more AI agents.
Agent-to-Human Handoff: The seamless transfer of a task, interaction, or decision-making authority from an AI agent to a human operator, typically when the task exceeds the agent’s capabilities or requires human judgment.
Human-in-the-Loop (HITL): An approach where human input or intervention is incorporated at various stages of an AI agent’s operation or learning process, ensuring human oversight and improving accuracy or ethical alignment.
Orchestration (AI Orchestration): The coordination, management, and sequencing of multiple AI agents or AI systems to work together harmoniously to achieve a larger objective or complete a complex workflow.
Swarm Intelligence: An approach inspired by the collective behavior of decentralized, self-organizing systems (like ant colonies or bird flocks) where multiple simple agents interact locally to achieve complex global behavior.
Digital Labor / Digital Worker: Terms sometimes used to describe AI agents or agentic systems that perform tasks previously done by human employees.
AI Abbreviations & Core Concepts:
- AI (Artificial Intelligence): The overarching field of creating computer systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and language understanding.
- ML (Machine Learning): A subset of AI that focuses on enabling systems to learn from data, identify patterns, and make decisions or predictions without being explicitly programmed for every possible scenario.
- DL (Deep Learning): A subset of Machine Learning that uses artificial neural networks with multiple layers (hence “deep”) to analyze complex patterns in large datasets, particularly effective for tasks like image and speech recognition.
- ANN (Artificial Neural Network): A computational model inspired by the structure of the human brain, consisting of interconnected nodes (neurons) that process and transmit information. A fundamental building block for deep learning.
- NLP (Natural Language Processing): The branch of AI concerned with enabling computers to understand, interpret, and generate human language in a valuable way.
- LLM (Large Language Model): A type of AI model (often based on deep learning, specifically the Transformer architecture) trained on vast amounts of text data, allowing it to understand context, generate human-like text, translate languages, and answer questions.
- GenAI (Generative AI): A category of AI models capable of creating new content, such as text, images, music, code, or synthetic data, based on the data they were trained on.
- AGI (Artificial General Intelligence): A hypothetical type of AI that possesses human-like cognitive abilities across a wide range of tasks and domains, as opposed to being specialized for one specific task (Narrow AI).
- ANI (Artificial Narrow Intelligence): AI systems designed and trained for a specific task, like recognizing images, playing chess, or powering a chatbot. Most current AI is considered ANI.
- CV (Computer Vision): The field of AI that enables computers to “see” and interpret visual information from images and videos.
- RPA (Robotic Process Automation): Software robots designed to mimic human actions when interacting with digital systems to perform repetitive, rule-based tasks. Often enhanced with AI
AI Key Terms Explained Simply:
- Algorithm: A set of rules or instructions that an AI system follows to perform a task or solve a problem. In machine learning, these algorithms allow the system to learn from data.
- Model: The output of a machine learning algorithm after it has been trained on data. It represents what the algorithm has learned and is used to make predictions or decisions on new data.
- Training Data: The dataset used to teach an AI model. The quality and quantity of this data significantly impact the model’s performance.
- Prompt: The input or instruction given to an AI model, especially a generative AI or LLM, to guide its output. It can be a question, a command, or a piece of text to be continued.
- Token: In natural language processing, a token is a unit of text that an AI processes. This is often a word, but can also be a sub-word unit or punctuation mark.
- Hallucination: When an AI, particularly a generative model, produces information that is not factual, is nonsensical, or is not supported by its training data, but presents it as if it were true.
- Bias: Systematic errors or unfairness in an AI system’s output, often stemming from biases present in the data it was trained on or the way the algorithm was designed.
- Supervised Learning: A type of ML where the algorithm is trained on labeled data (input-output pairs), learning to map inputs to correct outputs.
- Unsupervised Learning: A type of ML where the algorithm learns from unlabeled data, finding patterns, structures, or relationships within the data on its own (e.g., clustering data).
- Reinforcement Learning (RL): A type of ML where an agent learns to make decisions by interacting with an environment, receiving rewards or penalties based on its actions to maximize its cumulative reward.
- Tensor: A mathematical object used to represent data in deep learning, similar to a multi-dimensional array.
- Activation Function: A mathematical function within a neural network neuron that determines whether the neuron “fires” and passes information to the next layer, introducing non-linearity.
- Backpropagation: A key algorithm used to train neural networks by adjusting the weights of the connections based on the error between the predicted output and the actual desired output.
- Overfitting: When an AI model performs exceptionally well on the data it was trained on but poorly on new, unseen data because it has essentially just memorized the training data rather than learning general patterns.
- Inference: The process of using a trained AI model to make predictions or decisions on new, unseen data.
- Embeddings: Dense vector representations of data (like words, images, or users) that capture their meaning or characteristics in a multi-dimensional space, allowing AI models to process them effectively
AI Slang and Informal Terms:
- Prompt Engineering: The art and science of crafting effective prompts to get the desired output from a generative AI model.
- Fine-tuning: Taking a pre-trained AI model (like a large language model) and training it further on a smaller, specific dataset to adapt it to a particular task or domain.
- Black Box: Refers to an AI model, particularly complex deep learning models, where it is difficult or impossible for humans to understand exactly why the AI made a particular decision or reached a specific conclusion.
- Agent: Often used to refer to an AI program designed to perform tasks autonomously or semi-autonomously, sometimes with the ability to plan and interact with an environment.
- MoE (Mixture of Experts): A type of neural network architecture where the model consists of several smaller “expert” networks, and a “gating” network learns to route different inputs to the most relevant expert(s). Sometimes informally referred to when discussing models that seem to combine different capabilities.
- Tokenizer: The component in NLP models that breaks down raw text into tokens.
- Vector Space: The multi-dimensional space where embeddings live, allowing mathematical operations to represent relationships between data points.
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