AI Glossary

AI Ethics: The study of right and wrong in creating and using artificial intelligence. It deals with how AI affects people and society. Topics include fairness, privacy, and whether we can trust AI systems.

Activation Function: A mathematical rule used in neural networks. It helps decide what output a neuron should produce based on its input. Activation functions allow networks to learn complex patterns by adding non-linear features.

Adaptive Learning: A method in AI where systems change and improve their behavior based on new information or experiences. It helps machines perform better over time.

Agent Architecture: The design of an intelligent agent. It shows how the agent senses the environment, processes information, and takes actions. Different architectures lead to different behaviors and abilities.

Agent-Based Modeling: A way to simulate complex systems by using individual "agents." Each agent follows simple rules, but together they create complex behaviors. This method helps us understand things like crowd movements or market trends.

Algorithm: A set of step-by-step instructions to solve a problem or complete a task. Algorithms are like recipes for computers. They tell the computer exactly what to do, one step at a time.

Algorithmic Bias: Errors in AI decision-making caused by unfair or unrepresentative patterns in the training data. This can lead to biased outcomes in areas like hiring, lending, or facial recognition.

Artificial General Intelligence (AGI): This type of AI can understand, learn, and apply knowledge. It functions in a way similar to humans. Unlike AI that is good at one task, AGI would be able to handle many different tasks. It could adapt to new situations.

Artificial Intelligence: The branch of computer science focused on creating machines or software that can perform tasks requiring human intelligence. This includes abilities like learning, reasoning, problem-solving, and understanding language.

Artificial Neural Networks (ANNs): A type of machine learning model inspired by the structure and function of the human brain. ANNs consist of layers of interconnected nodes that process information to recognize patterns and make predictions.

Automation: The use of technology to perform tasks without human intervention. Automation allows machines and systems to operate on their own, making processes faster, more efficient, and often more accurate.

Autonomous Agents: Software programs or robots that can make decisions and act independently without human guidance. They observe their environment, process information, and take actions to achieve specific goals.

Autonomous Vehicles: Vehicles that can operate and navigate without a human driver. They use sensors, cameras, and artificial intelligence to understand their surroundings, avoid obstacles, and follow traffic laws.

BDI Agents (Belief-Desire-Intention Agents): These are intelligent agents that make decisions based on three components. They include Beliefs (information they have about the world), Desires (goals they want to achieve), and Intentions (plans they commit to). This model helps simulate human-like reasoning in artificial intelligence.

Backpropagation: A training method used in neural networks. It adjusts the connections in the network by moving backward from the output layer to the input layer. This process helps the network learn by reducing the difference between its predictions and the actual results.

Beliefs: Information or data that an agent holds about its environment. Beliefs help the agent understand what's happening around it, which influences its decisions and actions.

Bias: In machine learning, bias refers to errors that result from incorrect assumptions in the learning algorithm. High bias can cause a model to oversimplify and miss important patterns. In general terms, bias also means a tendency to prefer one thing over another, often in an unfair way.

Big Data: Extremely large and complex sets of data that are difficult to process using traditional methods. Big data requires special tools and techniques to store, analyze, and extract useful information.

Black Box AI: AI systems whose decision-making processes are not transparent or understandable to humans. This makes it hard to explain how the AI arrives at its conclusions.

Chatbots: Computer programs designed to simulate conversation with human users. They can answer questions, provide customer support, or assist with tasks through text or voice interactions.

Classification: A machine learning task where the goal is to assign items to specific categories or classes. For example, sorting emails into "spam" or "inbox" is a classification problem.

Clustering: A technique in machine learning used to group similar data points together. Unlike classification, clustering doesn't rely on predefined categories. Instead, it finds patterns and groups within the data itself.

Cognitive Agents: Intelligent agents that mimic human thinking processes. They can learn, reason, and solve problems by using models similar to how the human brain works.

Cognitive Computing: Technology that tries to simulate human thought processes in a computerized model. It uses techniques like data mining. Natural language processing helps computers understand and interact with the world more like humans do.

Computer Vision: A field of artificial intelligence that enables computers to interpret and understand visual information from the world. This includes tasks like recognizing objects in images or videos and understanding scenes.

Condition-Action Rules: Simple "if-then" statements that dictate how an agent or system should behave. For example, "If the sensor detects light, then turn off the night light." These rules help systems respond to specific situations.

Convolutional Neural Networks: A type of neural network especially good at processing grid-like data, such as images. They use a mathematical operation called convolution to detect features like edges, textures, and shapes in visual data.

Cross-Validation: A method used in machine learning to test how well a model performs on new, unseen data. It involves splitting a dataset into parts. The model is trained on some parts. Then, it is tested on other parts to check accuracy.

Data Mining: The process of discovering patterns, trends, and useful information from large sets of data. It helps organizations make informed decisions by analyzing data to find hidden connections.

Data Augmentation: Techniques used to increase the variety of training data by modifying existing data. Examples include flipping, rotating, or adding noise to images, which helps improve the robustness of AI models.

Dataset: A collection of related data organized for analysis. Datasets are used to train and test models in machine learning and often come in formats like tables or spreadsheets.

Decision Tree: A flowchart-like model used for making decisions or predictions. It splits data into branches based on features, helping to classify information or predict outcomes in a visual way.

Deep Learning: A type of machine learning that uses neural networks with many layers. It enables computers to learn from large amounts of data, recognizing complex patterns like images or speech.

Deliberative Agents: Intelligent agents that plan their actions by thinking ahead. They use models of the world to consider possible outcomes before deciding what to do, similar to human planning.

Desires: In AI, desires are the goals or objectives an agent wants to achieve. They guide the agent's actions and decisions toward fulfilling these aims.

Edge Computing: A method of processing data closer to its source, such as on devices or local servers, instead of relying on centralized data centers. This reduces latency and improves the efficiency of AI systems.

Environment: The surroundings or conditions in which an agent operates. It includes everything the agent can perceive or interact with, like data, objects, or other agents.

Expert Systems: Computer programs designed to mimic the decision-making ability of a human expert. They use a knowledge base of facts and rules to solve complex problems in areas like medicine or engineering.

Explainable AI: Artificial intelligence systems designed to make their decisions understandable to humans. This transparency helps build trust and allows users to see how conclusions are reached.

Feature Extraction: The process of identifying important pieces of information from raw data. In machine learning, these features help models learn by focusing on the most relevant aspects of the data.

Fuzzy Logic: A form of logic that deals with reasoning that is approximate rather than exact. It allows computers to handle uncertain or vague information, using degrees of truth instead of just true or false.

Generalization: The ability of a machine learning model to perform well on new, unseen data. A model with good generalization avoids overfitting and learns patterns that apply broadly.

Generative Adversarial Networks: A type of neural network involving two parts—a generator and a discriminator. They work against each other to create data that looks real, like convincing fake images.

Goal-Based Agents: Agents that act to achieve specific goals. They choose actions by considering how they will help reach their objectives, often planning several steps ahead.

Gradient Descent: An algorithm used to find the best settings for a machine learning model. It works by gradually adjusting parameters to minimize errors between the model's predictions and actual results.

Hybrid Agents: Agents that combine different methods or architectures to perform better. By mixing approaches, they can handle a wider range of tasks and adapt to various environments.

Hyperparameters: Settings in a machine learning model that are chosen before training begins. Examples include learning rate or the number of layers in a neural network. Adjusting them can affect how well the model learns.

Inference Mechanism: The part of a system that draws conclusions from data or a knowledge base. It uses logical rules to deduce new information, helping the system make decisions.

Intelligent Agents: Systems that perceive their environment and take actions to achieve goals. They can learn from experiences and adapt their behavior over time.

Intentions: In AI, intentions are the plans or actions an agent commits to in order to achieve its desires. They represent the chosen course the agent will follow to reach its goals.

Internal State: Information an agent keeps about itself or the environment that isn't immediately visible. This helps the agent make decisions based on past experiences or hidden factors.

Internet of Things (IoT): A network of connected devices that collect, share, and process data. IoT systems often use AI to analyze data and make decisions, such as in smart homes or industrial automation.

K-Means Clustering: A machine learning technique used to group similar data points into clusters. It divides data into "k" number of clusters, where each point belongs to the cluster with the nearest mean. This helps in finding patterns and structures in the data.

Knowledge Base: A collection of information and facts that a computer system uses to make decisions. It stores data, rules, and relationships, allowing the system to understand and reason about various topics.

Knowledge Representation: The method by which information is organized and stored in a computer system. It enables machines to process complex data and draw conclusions, much like how humans think.

Knowledge-Based Agents: Intelligent agents that use a knowledge base to make decisions. They apply logical reasoning to the information they have, helping them understand their environment and choose appropriate actions.

Learning Agents: Agents that improve their performance over time by learning from experiences. They can adapt to new situations by updating their knowledge and adjusting their behavior accordingly.

Machine Learning: A field of artificial intelligence where computers learn from data without being explicitly programmed. Machines use algorithms to find patterns and make decisions, improving as they receive more data.

Mobile Agents: Software programs that can move across different computer networks on their own. They carry out tasks by traveling from one system to another, often to gather information or perform computations remotely.

Model Training: The process of teaching a machine learning model to make accurate predictions or decisions. It involves feeding data into the model so it can learn patterns and adjust its internal parameters.

Model-Based Reflex Agents: Agents that use an internal model of the world to make decisions. They keep track of changes in the environment, helping them handle situations where not everything is immediately visible.

Multi-Agent Systems: Systems that consist of multiple agents interacting with each other. These agents may cooperate or compete to achieve individual or shared goals, leading to complex and dynamic behaviors.

Natural Language Processing: A field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It allows machines to read text, hear speech, and determine meaning, helping in tasks like translation and sentiment analysis.

Neural Networks: Computing systems inspired by the human brain's network of neurons. They consist of interconnected nodes that process information by responding to inputs and learning from data. Neural networks are used to recognize patterns and make predictions.

Optimization: The process of improving a machine learning model by fine-tuning its parameters to minimize errors and maximize accuracy.

Overfitting: A problem in machine learning where a model learns the training data too well, including its noise and outliers. This causes the model to perform poorly on new, unseen data because it cannot generalize what it has learned.

Percepts: Pieces of information that an agent receives from its environment through sensors. Percepts help the agent understand its surroundings and decide how to act.

Planning Agents: Agents that make decisions by considering future actions and their consequences. They create plans to achieve their goals by predicting how different actions will affect the environment.

Predictive Analytics: The practice of using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. It helps organizations make informed decisions by forecasting trends and behaviors.

Preprocessing: The steps taken to prepare raw data for use in AI models. This includes cleaning, normalizing, or transforming data to make it suitable for analysis.

Random Forest: A machine learning method that uses multiple decision trees to make predictions. Each tree gives a result, and the final prediction is based on the majority vote or average. This approach improves accuracy and reduces the risk of overfitting.

Rational Agents: Agents that always act to achieve the best outcome or, when there is uncertainty, the best expected outcome. They make decisions based on logic and available information to meet their goals.

Reactive Agents: Agents that respond directly to changes in their environment without internal models or planning. They make quick decisions based on current perceptions, suitable for simple or rapidly changing situations.

Recurrent Neural Networks: A type of neural network where connections between nodes form a directed cycle. This structure allows the network to maintain a memory of previous inputs. It is useful for tasks like speech recognition and time-series prediction.

Regression: A statistical method used in machine learning to predict continuous outcomes. It models the relationship between input variables and a numerical output, helping to forecast values like prices or temperatures.

Reinforcement Learning: A type of machine learning where an agent learns by interacting with its environment. It receives rewards or penalties based on its actions and adjusts its behavior to maximize the total reward over time.

Robotics: A field that combines engineering and computer science to design, build, and operate robots. Robots are machines that can perform tasks automatically, often mimicking human actions.

Semi-Supervised Learning: A machine learning approach that uses a mix of labeled and unlabeled data. It is useful when labeled data is limited but unlabeled data is abundant.

Sentiment Analysis: A technique in natural language processing that determines the emotional tone behind a body of text. It identifies whether the expressed sentiment is positive, negative, or neutral.

Simple Reflex Agents: Agents that select actions based solely on the current situation. They use simple rules to respond to immediate perceptions, without considering past experiences or future consequences.

Speech Recognition: The ability of a machine or program to identify and process spoken language. It converts spoken words into text, enabling voice commands and dictation.

Strong AI: A theoretical form of artificial intelligence. It aims to understand and perform any intellectual task that a human can. It would possess consciousness and self-awareness, not just simulate intelligence.

Supervised Learning: A machine learning method where a model is trained on labeled data. The model learns to make predictions or decisions based on input-output pairs provided during training.

Support Vector Machine: A supervised learning algorithm used for classification and regression tasks. It finds the best boundary that separates different classes by maximizing the margin between data points.

Transfer Learning: A technique in machine learning. It involves using knowledge gained from solving one problem. This knowledge is applied to solve a different but related problem. It helps improve learning efficiency by reusing existing models.

Tokenization: The process of splitting text into smaller units, such as words, phrases, or characters, for natural language processing. It helps machines understand and analyze text more easily.

Turing Test: Alan Turing proposed this test. It determines if a machine can exhibit intelligent behavior indistinguishable from a human. The machine passes the test if a human evaluator cannot tell it apart from a human during a conversation.

Underfitting: A problem in machine learning where a model is too simple to capture the underlying pattern in the data. This results in poor performance on both training data and new, unseen data.

Unsupervised Learning: A type of machine learning where the model learns patterns from unlabeled data. It identifies structures or relationships within the data without guidance on what to find.

Utility-Based Agents: Agents that make decisions based on a utility function, which measures how desirable a particular outcome is. They aim to maximize their overall satisfaction or "utility" when choosing actions.

Variance: In machine learning, variance refers to how much a model's predictions change when trained on different datasets. High variance can lead to overfitting, where the model is too sensitive to the specifics of the training data.

Weak AI: Artificial intelligence designed to perform a specific task or set of tasks. Unlike strong AI, weak AI does not possess consciousness or general intelligence; it operates within predefined boundaries.

Weak Supervision: Training AI models using noisy, incomplete, or imprecise labels. This approach allows learning from less-than-perfect data and reduces the need for extensive manual labeling.

Zero-Shot Learning: AI systems use this technique to solve tasks without explicit training. They transfer knowledge from related tasks. It allows AI to generalize across different scenarios.