266 plain definitions for the terms that come up when you are actually building and shipping AI systems.
A framework in AI/ML for solving problems declaratively using abductive reasoning, allowing for incompletely defined...
In AI/ML, abductive reasoning is inferring the most likely explanation from incomplete or ambiguous data.
A mathematical model defining a data type by its behavior, operations, and values from a user's perspective.
Simplifying complex systems by focusing on essential features, ignoring irrelevant details in AI/ML contexts.
In AI/ML, it refers to the rapid advancement of technology, leading to faster, more impactful innovations.
A formalism in AI for modeling state transitions resulting from actions within dynamic systems.
Learning how actions affect an environment, informing software agents' decision-making in AI/ML.
Deciding the next action for intelligent systems based on current state, goals, and available actions.
Determines a neural network node's output based on its input, influencing the network's ability to learn complex patterns.
A strategy where the model identifies uncertain data points for labeling to optimize the annotation process.
Dynamically adjusts its strategy during runtime based on feedback to optimize performance in AI/ML tasks.
Integrates neural networks and fuzzy logic to approximate nonlinear functions with learning capabilities.
A heuristic that never overestimates the cost to reach a goal in pathfinding and search algorithms.
Inputs to machine learning models intentionally designed to cause the model to make a mistake.
Computing that recognizes, interprets, and simulates human emotions using interdisciplinary approaches.
Design framework for intelligent agents, outlining component arrangement for decision-making and behavior execution.
Hardware designed to speed up AI tasks, particularly in neural networks, machine vision, and machine learning.
Problems as complex as achieving general human-level intelligence, unsolvable by simple algorithms.
A step-by-step procedure for calculations, data processing, and automated reasoning in computing and AI/ML.
Measures an algorithm's resource usage, including time and space, crucial for optimizing AI/ML performance.
A method from algorithmic information theory for assigning prior probabilities to observations, developed by Ray Solomonoff.
AI program by Google DeepMind that plays Go, first to beat a professional human player on a full-sized board.
Electronic environments that are sensitive and responsive to the presence of people.
The study of algorithm performance in terms of time and space complexity.
The discovery, interpretation, and communication of meaningful patterns in data.
The process of adding metadata to a dataset, essential for training AI/ML models.
Outsourcing the data labeling process to specialized service providers, leveraging their expertise and resources.
Systematic errors introduced during the labeling process, affecting the fairness and performance of models.
Measures and techniques to maximize the output of the annotation process with minimal input, crucial for scaling AI...
A set of rules and standards for how data should be labeled, ensuring consistency and accuracy across annotators.
The oversight of the entire annotation workflow, from task allocation to progress tracking to quality control.
The ability to efficiently expand the data labeling process to accommodate growing datasets, a critical aspect for evolving AI...
The sequence of steps and processes involved in annotating data, from initial setup to final review and approvals.
A declarative programming approach for solving NP-hard search problems using stable model semantics.
An algorithm that can provide a valid solution even if interrupted before completion.
A set of protocols and tools for building and integrating application software.
Finding strings that closely match a pattern, rather than exactly.
The difference between an exact value and its approximation.
A structure for dealing with and reasoning about conflicting information.
AI with human-like cognitive abilities across a wide range of domains and tasks.
Algorithms inspired by the human immune system's mechanisms for adaptive learning and memory.
Machine-based systems that emulate human cognitive functions such as learning, problem-solving, and decision-making.
An XML dialect for developing natural language conversational agents.
Computing systems inspired by the biological neural networks that constitute animal brains.
A society promoting research, education, and responsible use of artificial intelligence.
A method for updating neural network weights by propagating errors backward from output to input.
An extension of backpropagation for training recurrent neural networks on sequence data.
An inference method that starts from the goal and works backward to deduce the required facts.
A text representation model that disregards order and grammar, focusing on word frequency.
Treating image features as "words" for classification, using vectors of feature occurrence counts.
A method to normalize neural network inputs, improving stability and performance by adjusting and scaling activations.
Specifying and solving problems using probabilistic models under conditions of uncertainty.
An optimization algorithm inspired by the foraging behavior of honey bees.
The study and analysis of behaviors through informatics to derive insights and intelligence.
A hierarchical model for structuring decision-making and task execution in AI, robotics, and game development.
A model for programming intelligent agents based on their beliefs, desires, and intentions.
A technique for natural language processing pre-training, demonstrating the importance of contextual word meanings.
Systematic errors in data or models that can lead to unfair outcomes.
Strategies and techniques to reduce or eliminate bias in data and models, ensuring fairness and equity in AI applications.
A fundamental tradeoff in machine learning between model simplicity (bias) and responsiveness to data (variance).
The tendency of an artificial neural network to completely and abruptly forget previously learned information upon learning new information.
A type of neural network design that attempts to better model hierarchical relationships in data than standard CNNs.
The primary component of a computer that performs most of the processing inside the computer.
A software application used to conduct an on-line chat conversation via text or text-to-speech.
A supervised learning task that maps an input to a specific category or label.
The task of grouping a set of objects in such a way that objects in the same group are more similar to each other.
The use of computerized models to simulate the human thought process in complex situations.
A method used by recommender systems to make predictions about the interests of a user by collecting preferences from many users.
A field of AI that enables computers to derive meaningful information from digital images, videos and other visual inputs.
A class of statistical modeling methods often applied in pattern recognition and machine learning for structured prediction.
A deep learning algorithm which can take in an input image and assign importance to various aspects/objects in the image.
A mathematical formula used to measure how well a machine learning model is performing.
A technique used for assessing how the results of a statistical analysis will generalize to an independent data set.
A parallel computing platform and application programming interface model created by Nvidia.
A technique used to increase the amount of data by adding slightly modified copies of already existing data.
The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data.
A collection of related sets of information that is composed of separate elements but can be manipulated as a unit by a computer.
A flowchart-like structure in which each internal node represents a test on an attribute, each branch represents the outcome of the test.
Part of a broader family of machine learning methods based on artificial neural networks with representation learning.
A subfield of machine learning that combines reinforcement learning and deep learning.
The transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties.
The part of a Generative Adversarial Network (GAN) that attempts to distinguish between real data and data produced by the generator.
A field of computer science that studies systems whose components are located on different networked computers.
A regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data.
Both a mathematical optimization method and a computer programming method.
A form of regularization used to avoid overfitting when training a learner with an iterative method, such as gradient descent.
A scalar value that is associated with a linear system of equations (such as a matrix equation).
A non-zero vector that changes at most by a scalar factor when that linear transformation is applied to it.
A relatively low-dimensional space into which you can translate high-dimensional vectors.
The process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem.
One complete pass of the entire training dataset through the machine learning algorithm.
Another name for a loss function or cost function.
A first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.
A subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm.
A computer system that emulates the decision-making ability of a human expert.
A measure of a test's accuracy, defined as the harmonic mean of precision and recall.
A technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
A statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.
An error in binary classification in which a test result incorrectly indicates the absence of a condition when it is actually present.
An error in binary classification in which a test result incorrectly indicates the presence of a condition when it is actually absent.
The process of using domain knowledge to extract features (characteristics, properties, attributes) from raw data via data mining techniques.
In machine learning, pattern recognition and image processing, it starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant.
An artificial neural network where connections between the nodes do not form a cycle.
A type of machine learning problems where the model is trained with only a few examples for each class.
The process of taking a pre-trained model and adapting it to a new, but related, task by training it further on a smaller dataset.
The initial step in the training of a neural network where the input data is passed through the network to generate an output.
A form of many-valued logic in which the truth values of variables may be any real number between 0 and 1.
A gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al.
A stochastic process, such that every finite collection of those random variables has a multivariate normal distribution.
See Artificial General Intelligence (AGI).
A class of machine learning frameworks where two neural networks contest with each other in a game.
Artificial intelligence capable of generating text, images, or other media, using generative models.
A metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms.
The absolute lowest point of a function over its entire domain.
A first-order iterative optimization algorithm for finding a local minimum of a differentiable function.
A class of artificial neural networks for processing data that can be represented as graphs.
A phenomenon where an AI model generates output that is incorrect, nonsensical, or not based on any factual evidence.
In information theory, the Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different.
A technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution.
A layer in an artificial neural network between the input layer and the output layer, where artificial neurons take in a set of weighted inputs and produce an output through an activation function.
A statistical Markov model in which the system being modeled is assumed to be a Markov process with unobservable (i.e. hidden) states.
A method of cluster analysis which seeks to build a hierarchy of clusters.
A British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks.
A popular metaphor for an introduction to the vast and complex world of artificial intelligence.
A model that requires human interaction to work properly, especially in the context of training machine learning models.
The ability of software to identify objects, places, people, writing and actions in images.
In AI/ML mathematics, an integral that has either or both limits of integration as infinity, or an integrand that becomes infinite at one or more points in the interval.
The set of assumptions that the learner uses to predict outputs given inputs that it has not encountered before.
The process of using a trained machine learning model to make predictions on new, unseen data.
The process of obtaining information system resources that are relevant to an information need from a collection of those resources.
The first layer of an artificial neural network that receives the primary input data.
A family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training.
Anything which perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or may use knowledge.
The degree to which a human can understand the cause of a decision from an AI model.
A statistic used for gauging the similarity and diversity of sample sets.
A fictional AI assistant from Marvel Comics, often used as a benchmark or inspiration for real-world AI assistants.
A statistical measure that calculates the likelihood of two events occurring together and at the same point in time.
Often discussed in the context of image compression and its effects on computer vision model performance.
A web-based interactive computing platform that allows users to create and share documents that contain live code, equations, visualizations and narrative text.
A way of executing computer code that involves compilation during execution of a program – at run time – rather than prior to execution.
A method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters.
A non-parametric classification method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover.
An algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables.
A class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM).
A technology used to store complex structured and unstructured information used by a computer system.
A knowledge base that uses a graph-structured data model or topology to integrate data.
The field of artificial intelligence dedicated to representing information about the world in a form that a computer system can use to solve complex tasks.
A measure of how one probability distribution is different from a second, reference probability distribution.
Adds the absolute value of magnitude of coefficient as penalty term to the loss function.
Adds the squared magnitude of coefficient as penalty term to the loss function.
In supervised learning, the 'answer' or 'result' portion of a data point.
A probabilistic model that is trained to predict the next word in a sequence.
A generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
An abstract multi-dimensional space used in machine learning where each point represents a feature or concept.
A group of neurons in an artificial neural network that process inputs and pass their outputs to the next group.
A tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function.
A standard approach in regression analysis to approximate the solution of overdetermined systems.
A linear approach for modelling the relationship between a scalar response and one or more explanatory variables.
An artificial recurrent neural network (RNN) architecture used in the field of deep learning.
A function that maps an event or values of one or more variables onto a real number intuitively representing some 'cost' associated with the event.
A field of inquiry devoted to understanding and building methods that 'learn' – that is, methods that leverage data to improve performance on some set of tasks.
A subfield of computational linguistics that investigates the use of software to translate text or speech from one language to another.
A stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
Provides a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker.
A rectangular array or table of numbers, symbols, or expressions, arranged in rows and columns.
A measure of errors between paired observations expressing the same phenomenon.
Measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value.
A programming technique in which computer programs have the ability to treat other programs as their data.
A decision rule used in artificial intelligence, decision theory, game theory, statistics, and philosophy for minimizing the possible loss for a worst case (maximum loss) scenario.
A probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs.
A mathematical representation of a real-world process, often used in machine learning to make predictions.
A broad class of computational algorithms that rely on repeated random sampling to obtain numerical results.
A computerized system composed of multiple interacting intelligent agents.
A class of feedforward artificial neural network (ANN).
A software process that transforms structured data into natural language.
An interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human languages.
A subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension.
A family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong independence assumptions between the features.
A network or circuit of biological neurons, or, in a modern sense, an artificial neural network.
An electrically excitable cell that communicates with other cells via specialized connections called synapses; its artificial counterpart is a node in an ANN.
A basic unit of a data structure, such as a linked list or tree data structure.
The process of scaling individual samples to have unit norm.
A computer vision technique that allows us to identify and locate objects in an image or video.
The function that is systemized to be either minimized or maximized in an optimization problem.
The problem-solving principle that "entities should not be multiplied beyond necessity", or that the simplest explanation is usually the correct one.
In reinforcement learning, learning about an optimal policy while following a different, behavioral policy.
In reinforcement learning, learning about the policy that the agent is currently following.
A process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction.
A library of programming functions mainly aimed at real-time computer vision.
The selection of a best element, with regard to some criterion, from some set of available alternatives.
A data point that differs significantly from other observations.
The production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably.
The probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct.
Variables internal to the model whose values are learned from data.
The process of marking up a word in a text as corresponding to a particular part of speech.
The automated recognition of patterns and regularities in data.
An algorithm for supervised learning of binary classifiers.
A measure used to evaluate the efficiency and effectiveness of a machine learning model.
A class of reinforcement learning algorithms that optimize the policy directly.
The fraction of relevant instances among the retrieved instances.
An output produced by a trained model when given a new input.
A technique for dimensionality reduction that identifies the most important features in a dataset.
A mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment.
The process of structuring text that can be interpreted and understood by a generative AI model.
A technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances.
A model-free reinforcement learning algorithm to learn the value of an action in a particular state.
Information that cannot be counted, measured or easily expressed using numbers.
Data that can be expressed as a number or quantified.
The intersection of quantum computing and artificial intelligence, aiming to speed up ML training.
A precise request for information retrieval from a database or search engine.
A collection of entities that are maintained in a sequence and can be modified by the addition of entities at one end and the removal of entities from the other end.
A statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model.
An ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time.
The fraction of the total amount of relevant instances that were actually retrieved.
A class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence.
A set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables.
The process of adding information in order to solve an ill-posed problem or to prevent overfitting.
An area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
An activation function defined as the positive part of its argument: f(x) = max(0, x).
A specific type of artificial neural network that uses skip connections, or shortcuts, to jump over some layers.
A form of business process automation technology based on metaphorical software robots or on artificial intelligence / digital workers.
An interdisciplinary branch of computer science and engineering that involves the design, construction, operation, and use of robots.
The ability of a system to resist or overcome adverse conditions.
An individual with deep knowledge and expertise in a specific field, demonstrated through education, certification, or...
Hypothetical AI that vastly exceeds the cognitive performance of humans in all domains of interest.
Machine learning task where a model is trained on labeled data to predict outputs from inputs.
Collective intelligence emerging from simple agents coordinating and interacting within decentralized, self-organized systems.
AI approach using human-readable symbols to represent problems, logic, and search for problem-solving.
Creating data labels automatically through simulations or generative models, enhancing datasets without manual labeling.
Creating artificial data mimicking real data's statistical properties for training models or ensuring privacy.
Intelligence in machines designed to be genuine and autonomous, not merely an imitation of human intelligence.
Neuroscience subfield studying neural circuits' structure, function, and their integration into brain systems.
Hypothetical future point where technological growth becomes uncontrollable, leading to transformative changes to civilization.
Reinforcement learning methods that update value estimates based on the difference between subsequent predictions.
Mathematical framework modeling the transformation of sensory data into motor responses by neuronal networks using tensors.
Open-source library for numerical computation and large-scale machine learning, using data flow graphs.
Branch of computer science that deals with the mathematical aspects of computing and computational theory.
Branch of computer science studying the limits and capabilities of computing machines and algorithms.
Probabilistic method for balancing exploration and exploitation in multi-armed bandit problems by sampling from belief...
Measure of the amount of computational time an algorithm takes to complete as a function of input size.
Breaking down text into smaller units (tokens) for analysis in NLP, crucial for data preprocessing.
Reusing existing dataset annotations in new, but similar, contexts to minimize additional labeling work.
Adapting a pre-trained model for a new, but related, task to reduce the need for extensive new data.
Deep learning model using self-attention to process sequential data, significant in natural language processing and beyond.
Movement advocating for enhancing human capabilities through advanced technologies.
Conceptual model describing discrete systems' states and transitions, used in computational theory.
Process of visiting each node in a tree data structure in a specific order, exactly once.
A formal language of fully quantified Boolean formulas where every variable is bound and the formula is true.
Abstract computational model capable of simulating any algorithm via tape manipulation based on predefined rules.
Test assessing a machine's ability to exhibit human-like intelligence in conversations, devised by Alan Turing.
Framework within programming languages for assigning types to program constructs, enhancing reliability and performance.
IBM's AI system for answering questions in natural language, developed under the DeepQA project.
AI designed to perform specific, narrow tasks without possessing consciousness, understanding, or self-awareness.
Utilizing imprecise or noisy labels to train models, reducing reliance on extensively labeled datasets.
International organization that develops open standards to ensure the long-term growth of the Web.
A set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms.
An open-source software library which provides a regularized gradient boosting framework.
Extensible Markup Language, sometimes used for Knowledge Representation or configuration of AI models.
A classic problem in neural networks where a single layer perceptron failed to solve the XOR logical operation.
A popular real-time object detection system.
A keyword used in some programming languages (like Python) to produce a sequence of values lazily, often used in data loaders.
A unit of data storage equal to 10^24 bytes, representing the massive scale of modern AI training datasets.
266 terms, from Abductive Logic Programming to Zero-shot Learning.
Knowing the word is the easy part. We build the systems these terms describe, run them on your data, and hand them over to your team.
Plain definitions for the terms that come up when building production AI systems — 101 entries covering machine learning, agents, evaluation and reinforcement learning. Each one is written to be understood without the surrounding jargon.
Engineers and technical leads who keep meeting a term in papers or vendor decks and want a straight answer rather than a paragraph of hedging.
One sentence that answers what it is, then context on where it actually matters. If a term is contested we say so rather than picking a side silently.
Yes. Every entry has its own page and its own URL, so you can link a specific definition in a doc or a code comment.
Yes, with attribution to ReinforcedX and a link to the term page. They are written to be quotable.
Terms are added as they become load-bearing in real work. We remove entries that turned out to be vendor coinage rather than concepts.
Probably. Tell us which one and, if it is genuinely used in production work, it gets added.
The glossary defines; the AI systems guides show the concepts working together in a real architecture. The two are meant to be read alongside each other.
It assumes general software literacy but no ML background. If you can read an API doc you can read these.
Yes — that is the day job. Book a working session and we will map the concepts onto your actual system.
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