AI Dictionary

A step-by-step procedure or formula used to solve a problem or accomplish a task. In AI, algorithms help make decisions based on data.

The capability of a machine to imitate human intelligence. This can include tasks such as decision-making, language understanding, and problem-solving.

A system that operates and makes decisions on its own without human intervention. Examples include self-driving cars.

The process of analyzing data to discover patterns or insights. AI can be used to enhance these analyses.

Identifying unusual patterns or outliers in data. Often used in security or fraud detection.

A set of tools and protocols that allows different software applications to communicate with each other. AI functionalities can often be accessed via APIs.

Computational models inspired by the human brain's structure. They are used in machine learning to process complex data inputs.

In neural networks, a mathematical function applied to a neuron's output, determining its firing strength.

Attempts to fool machine learning models by feeding deceptive input data, often slightly altered from authentic data.

An enhanced version of reality created by adding digital information on an image or video of something being viewed through a device.

A method used to train neural networks by adjusting the model's weights based on the error of its predictions.

In AI, it refers to an algorithm's tendency to consistently make certain kinds of errors, often due to the data it was trained on. It can also refer to a term in neural networks that helps nodes to activate.

A type of decision-making task in AI where the outcome is one of two classes, like `yes` or `no`.

Short for robot, a bot is a software application that performs automated tasks, often simulating human actions.

Extremely large data sets that may be analyzed using AI to reveal patterns, trends, and associations.

A probabilistic model that represents a set of variables and their conditional dependencies via a directed acyclic graph.

Processing high volumes of data at once, rather than processing it item by item in real-time.

A machine learning technique that focuses on improving the accuracy of any given learning algorithm by converting weak learners into strong learners.

A straightforward, exhaustive search approach to solve a computational problem. Often not the most efficient method.

A reference point in the context of machine learning. It refers to the performance of a relatively simple model against which more complex models are compared.

A software application designed to simulate human conversation. It can interact with users and answer questions using predefined rules or machine learning.

A type of machine learning task where data is categorized into labeled groups. An example is determining if an email is spam or not.

An unsupervised machine learning technique that groups similar data points together based on certain features, without having prior labels for the groups.

A type of deep learning algorithm mainly used for image processing and computer vision tasks.

Simulating human thought processes in machines using AI. It involves self-learning through data mining, pattern recognition, and natural language processing.

A range of values used to estimate the true population parameter. In AI, it can represent the reliability of a model's predictions.

A technique in machine learning to evaluate model performance by partitioning the original training dataset into a training set to train the model, and a validation set to evaluate it.

A technique used in recommendation systems where predictions are made based on patterns of similar users or items.

Storing and accessing data and programs over the internet instead of a local computer. AI models can be trained and deployed in the cloud.

The practice of protecting systems, networks, and data from theft, damage, or unauthorized access. AI can enhance cybersecurity efforts by detecting and responding to threats.

The process of examining large databases to generate new information and discover patterns. Often used in business for insights.

A subset of machine learning that uses neural networks with many layers (hence `deep`) to analyze various factors of data. It's especially effective for large datasets and complex tasks like image recognition.

A graphical representation used in decision-making and machine learning. It illustrates potential outcomes and choices.

A collection of data used in machine learning to train and test AI models. It often includes inputs and their corresponding outputs.

The process of reducing the number of random variables in a dataset, typically by obtaining a set of principal variables. Useful in AI for simplifying datasets without losing important information.

A type of model that distinguishes between different categories of data. It learns the boundaries between categories rather than learning about the categories themselves.

Training machine learning models on data sources that are distributed across multiple locations. The models can learn from diverse datasets without centralizing the data.

A technique in neural networks where randomly selected neurons are ignored during training, helping to prevent overfitting.

A method used to solve problems by breaking them down into simpler subproblems. It avoids redundant calculations and can speed up computations.

The process of cleaning and converting raw data into a format that can be used by AI and machine learning models.

An approach in machine learning where multiple models are trained to solve the same problem and then combined (or `ensembled`) to make more accurate predictions.

In the context of training a machine learning model, an epoch is one complete pass through the entire training dataset.

A measure used to evaluate the performance of a machine learning model. Examples include accuracy, precision, and recall.

Algorithms inspired by the process of natural selection. They are used to find approximate solutions to optimization and search problems in AI.

The conversion of discrete data types, like words, into continuous vectors. Common in natural language processing to represent words as vectors of numbers.

A technique in machine learning where training is halted before it completes, especially if the model is no longer improving on a held-out validation dataset. Helps prevent overfitting.

Part of an autoencoder neural network, the encoder compresses the input data into a compact representation, which the decoder then expands.

A computer system that emulates the decision-making ability of a human expert in a specific domain. It uses a 'knowledge base' of facts and heuristics to make decisions.

An area of AI focused on creating models that are transparent and can be easily understood by humans.

The fraction of predictions that a classification model gets wrong.

An individual measurable property of the data being observed. In machine learning, features are used as input variables to make predictions.

The process of transforming raw data into a set of features or representations that can be easily used by AI models.

The process of selecting, transforming, or creating the right variables (features) to enhance the performance of a machine learning model.

Adjusting the parameters of an already trained machine learning model to improve its performance or adapt it to a slightly different task.

A system of logic that deals with approximate values, rather than fixed and exact ones. It's used in AI to reason with uncertain or vague information.

A type of artificial neural network where the connections between nodes do not form cycles. It's one of the simplest forms of neural networks.

In neural networks, a layer where each neuron is connected to every neuron in the previous and subsequent layers.

In classification tasks, when the model incorrectly predicts the positive class.

In classification tasks, when the model incorrectly predicts the negative class.

Distinguishing between very similar classes in a classification task. For example, identifying bird species in images.

A class of AI algorithms used in unsupervised machine learning, involving two networks competing against each other to generate new, synthetic instances of data.

An optimization algorithm used to minimize the error in a machine learning model by iteratively adjusting its parameters.

A field of mathematics used to study networks of interconnected nodes and edges. Relevant in AI for structures like neural networks.

A method for tuning hyperparameters of a model by trying out every possible combination of parameters.

A simple, intuitive algorithm that makes the locally optimal choice at each stage with the hopes of finding the global optimum.

The ability of an AI model to perform well on unseen data after being trained on a limited dataset.

An optimization technique that mimics the process of natural selection to generate high-quality solutions for complex problems.

A type of deep learning model that operates on data structured as graphs, capturing relationships between nodes.

The accuracy of a dataset, considered to be the real and true value. Used as a benchmark in the validation of AI model predictions.

A visual way of interacting with a computer or AI system, using items like windows, icons, and buttons.

A practical approach or shortcut used to speed up the process of finding a satisfactory solution, where traditional methods may be too slow.

A parameter whose value is set before the learning process begins, used to control the training process of a machine learning model.

In a neural network, layers between the input and output layers where computations are performed and features are extracted.

A method in statistics used to determine if a certain statement about a data set is true. In AI, it can be used to validate model assumptions.

A learning rule that states: If two neurons are activated simultaneously, the strength of the synapses between them increases.

A type of computer hardware designed to perform some functions more efficiently than is possible in software running on a general-purpose CPU.

The ability of a computer to receive and interpret handwritten input from sources such as paper documents or touchscreens.

A process of converting a large chunk of data into a smaller size that represents the original data. Useful in AI for quick data lookups and retrieval.

A situation in regression analysis where the variability of error terms is not constant across all levels of independent variables.

A method in unsupervised machine learning that builds a hierarchy of clusters by either a bottom-up or top-down approach.

The process of using a trained machine learning model to make predictions on new, unseen data.

The initial layer in a neural network that receives the raw data for processing.

The process of replacing missing data with substituted values to maintain the consistency and quality of the dataset.

A single data point or example in a dataset.

A process that repeats a series of steps over and over until a specific condition is met or a particular result is achieved.

The process of obtaining information, often from large databases, that matches a certain criteria or query.

A task in computer vision where an algorithm is trained to assign a label to an input image from a fixed set of categories.

A type of machine learning where the model makes predictions based on patterns in the data, as opposed to using explicit rules.

A system that perceives its environment, reasons about it, and takes actions to achieve specific goals.

A type of machine learning where humans interact and provide feedback to the learning system during its training.

Variability in latency or other system measures over time. In AI, it can refer to intentional introduction of variation during data augmentation.

The likelihood of two events happening at the same time. Useful in AI models that consider multiple variables.

A high-performance programming language known for its ease-of-use and speed, often utilized in numerical and scientific computing, including AI.

A lightweight data-interchange format that's easy for humans to read and write and easy for machines to parse and generate. Used in many AI APIs for data exchange.

An open-source web application that allows for interactive computing and data visualization, popular in AI and data science fields.

A matrix representing all the first-order partial derivatives of a vector-valued function. In deep learning, it's often used in the context of optimization and understanding model behavior.

A widely-used programming language that can also be employed to create AI and machine learning applications.

A commonly used method of compression for photographic images. In AI, it's relevant for image processing and computer vision tasks.

An unsupervised machine learning algorithm used to group data into a specified number (k) of clusters based on similarity.

A function used in support vector machines (a type of machine learning algorithm) to transform data into a higher-dimensional space.

A database used for knowledge sharing and management. It can store complex structured and unstructured information used by computer systems.

A graph that uses a network structure to represent and link knowledge, capturing relationships between entities or concepts.

An open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.

A type of supervised machine learning algorithm used for classification and regression. It predicts based on the input's k most similar training examples.

A type of database where data is represented as a collection of key-value pairs, where each key is unique. Useful in AI for rapid look-ups.

An online platform for predictive modeling and analytics competitions. They host competitions in which data scientists compete to produce the best models on various datasets.

A variable that isn't directly observed but is inferred from observed data. Commonly used in statistics and machine learning models.

A statistical method and type of machine learning model that predicts a continuous value based on one or more independent variables.

Despite its name, it's a classification algorithm used to predict a binary outcome based on one or more independent variables.

A mathematical function that calculates the difference between the predicted output and the actual output. Used in machine learning to optimize models.

A hyperparameter in machine learning algorithms that determines the step size at each iteration while moving towards a minimum of the loss function.

In supervised learning, it's the `answer` or `result` for a data point. For instance, in a dataset of images, the label might be whether the image contains a cat or not.

A type of recurrent neural network architecture particularly designed to recognize patterns over long durations of time.

In neural networks, a collection of nodes or neurons that process a set of inputs and produce a set of outputs.

A type of model used in natural language processing to predict the next word or character in a sequence.

A branch of mathematics concerning vector spaces and linear equations. Fundamental for many algorithms and techniques in machine learning.

A type of artificial intelligence where systems can learn and make decisions from data without being explicitly programmed to do so.

In AI, a representation of what has been learned from data. It's used to make predictions or decisions without being explicitly programmed for the task.

A type of artificial neural network with three or more layers. These layers can learn representations of data in complex, non-linear ways.

Data about data. It provides information about a dataset's content, quality, condition, origin, and other characteristics.

The process of providing an algorithm with data to learn from. The goal is to train the model so that it can make accurate predictions.

The process of assessing how well a machine learning model performs on a given task.

When a model is trained too closely to a particular dataset and performs poorly on new, unseen data.

When a model is too simple to capture the underlying structure of the data, leading to poor performance on both the training and test data.

A rectangular array of numbers arranged in rows and columns. Used extensively in AI algorithms, especially in neural networks.

A mathematical system that transitions from one state to another according to certain probabilistic rules. Used in AI for processes that are sequential in nature.

A computational model inspired by the human brain, made up of interconnected nodes (or `neurons`). Used in machine learning to recognize patterns and make predictions.

A field of AI focused on the interaction between computers and human language. It helps machines understand, interpret, and generate human language.

A fundamental unit of a neural network, similar to a neuron in the human brain. It receives input, processes it, and passes its output to the next layer of nodes.

Irrelevant or random data that doesn't provide useful information for analysis. In AI, noise can hinder the performance of models.

The process of scaling data to a standard range. In AI, it can help in speeding up the training process and achieving better performance.

An algorithmic task to find data points in a dataset that are closest to a given point. Commonly used in recommendation systems and clustering.

The ability of neural networks, both biological and artificial, to change their connections and behavior in response to new information.

A function applied to a neuron's output, introducing non-linearity to the model. Examples include the sigmoid and ReLU functions.

A probabilistic classifier based on Bayes' theorem, with an assumption of independence between features. Used in text classification and other applications.

An automated process for finding the best performing architecture for a neural network given a specific task.

A modeling error that occurs when a machine learning algorithm captures noise rather than the underlying pattern in the data. The model performs well on training data but poorly on new, unseen data.

The final layer in a neural network responsible for producing the desired results, such as predictions or classifications.

The process of adjusting a model’s parameters to improve its performance. In machine learning, it involves minimizing a loss function.

A data point that significantly differs from other data points in the dataset. Outliers can affect the performance of some AI models.

Also known as a loss or cost function, it measures the difference between the predicted and actual outcomes. The goal in training AI models is often to minimize this function.

A representation technique used to convert categorical data into binary format. Each category is represented as a binary vector.

A formal naming and definition of types, properties, and relationships of the entities that exist in a particular domain. Used in AI for knowledge representation and reasoning.

An organization focused on researching and developing friendly AI for the benefit of all. They have published several influential models and tools.

Occurs when a model is trained for too long, leading to overfitting where the model performs exceptionally well on the training data but poorly on new, unseen data.

Refers to solutions or functionalities that can be used immediately without the need for customization or further development. In AI, it can refer to pre-trained models or ready-to-use tools.

Zero-shot learning is a machine learning technique that allows AI systems to recognize or classify objects or concepts they have never seen before by generalizing from existing knowledge.

Yield optimization is the process of using AI algorithms to maximize the output or efficiency of a system, such as manufacturing processes or digital advertising campaigns.

In data visualization and charting, the Y-axis represents the vertical axis, often used to display dependent variables or numerical values.

You Only Look Once (YOLO) is an AI object detection algorithm that can quickly and accurately detect and locate objects in images or videos, commonly used in applications like autonomous vehicles and surveillance systems.

Explainable AI (XAI) is an approach to artificial intelligence that aims to make AI systems more transparent and understandable, allowing users to comprehend how and why decisions are made.

In data visualization and charting, the X-axis represents the horizontal axis, often used to display independent variables or categories.

XML is a markup language that is used to structure and store data in a format that is both human-readable and machine-readable. It is commonly used for data exchange and configuration.

Workflow automation is the use of AI and technology to streamline and automate repetitive business processes, improving efficiency and reducing manual tasks.

Weak AI, also known as Narrow AI, refers to AI systems that are designed for specific tasks and do not possess general intelligence or consciousness.

Web scraping is the process of automatically extracting data from websites. AI can be used to analyze and process the scraped data for various purposes.

Wearable technology includes devices like smartwatches and fitness trackers that incorporate AI and sensors to collect and analyze data from users.

Virtual Reality (VR) is a technology that immerses users in a computer-generated environment, often used for simulations, training, and entertainment.

Voice recognition, also known as speech recognition, is the ability of an AI system to convert spoken language into text or perform actions based on voice commands.

A vision system in AI uses cameras and image processing techniques to interpret and understand visual information, enabling tasks like object detection and recognition.

Validation in AI involves assessing and confirming the accuracy and performance of AI models and systems to ensure they meet the intended requirements and standards.

Unsupervised learning is a machine learning technique where the AI system learns patterns and makes predictions from unlabeled data without explicit supervision.

The user interface (UI) is the graphical or visual part of software or applications that users interact with. It includes elements like buttons, menus, and screens.

Usability in AI refers to the ease with which users can interact with and understand a system or application. A high level of usability is essential for user satisfaction.

In AI, utility refers to the measure of the overall value or usefulness of a decision or action made by an AI system. It often involves considering factors such as cost and benefit.

TensorFlow is an open-source machine learning framework developed by Google for building and training AI models.

Transfer learning is a machine learning technique where a pre-trained model is used as a starting point for a new task, reducing the amount of data and training required.

Training data is the dataset used to teach an AI model during the machine learning process. It is essential for the model to learn patterns and make predictions.

The Turing Test is a measure of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human. It assesses a machine's capability to understand natural language.

Text analysis, also known as text mining, involves extracting valuable insights and information from text data, often used for tasks like sentiment analysis and information retrieval.

Supervised learning is a type of machine learning where the AI system is trained on labeled data, allowing it to learn patterns and make predictions based on examples.

SaaS is a cloud computing model where software applications are delivered over the internet as a service. Users can access these applications without the need for installation or maintenance.

Sentiment analysis, also known as opinion mining, is an AI technique that analyzes text data to determine the sentiment or emotion expressed within it, often used for customer feedback and social media analysis.

A smart assistant is an AI-powered virtual assistant that can perform tasks or answer questions for users, such as Siri, Alexa, or Google Assistant.

Simulation involves creating computer models that mimic real-world processes or scenarios. In AI, it can be used for testing and training AI systems.

Robotics involves the design and use of robots to perform tasks automatically. In AI, it often refers to the use of intelligent machines to perform physical tasks.

A recommendation system, or recommender system, is an AI technology that provides personalized suggestions or recommendations to users based on their past behavior and preferences.

Regression analysis is a statistical technique used in AI to understand the relationship between variables and make predictions based on historical data.

A rule-based system in AI uses a set of predefined rules to make decisions or solve problems. It's often used for tasks that can be expressed in if-then rules.

Quantum computing is an advanced form of computing that leverages the principles of quantum mechanics to perform complex calculations much faster than traditional computers.

In AI and databases, a query is a request for information from a database. It is a way to retrieve specific data or perform operations on data stored in a database.

Q-Learning is a reinforcement learning technique in AI that helps machines make decisions by learning the value of taking certain actions in specific situations.

Quality Assurance in AI refers to the process of ensuring that AI systems meet predetermined quality standards and perform as intended, typically involving testing and validation.

Pattern recognition is the process of identifying regularities or patterns in data to make predictions or classifications.

Predictive analytics uses historical data and statistical algorithms to forecast future outcomes and trends.

Natural Language Processing (NLP) is a field of AI that focuses on the interaction between computers and human language, enabling machines to understand and generate human language.

In AI, perception refers to the ability of machines to gather and interpret data from the environment, often through sensors or cameras.

Personalization is the process of tailoring AI-driven recommendations, content, or experiences to individual users based on their preferences and behaviors.

Probabilistic modeling involves using probability theory to model uncertainty and randomness in AI systems, allowing for more realistic decision-making.