Glossary

Abductive logic programming is a subfield of AI that deals with the use of logic programming to solve problems by formulating and testing hypotheses.

Abductive reasoning is a form of logical reasoning that starts with an observation or set of observations and then seeks to find the simplest and most likely explanation for the set of observations.

An abstract data type is a data type that is not concrete, but rather is defined by a set of values and operations.

Abstraction is the process of hiding the details of an implementation from the user. In software engineering, abstraction is used to hide the details of an implementation from the user.

Accelerating change in AI refers to the rapid pace at which AI technologies are evolving and being adopted in various fields. This is resulting in a growing number of AI applications and higher demand for AI talent.

Action language is a language used to describe the actions that a agent can perform.

Action model learning is a process in AI whereby a computer system is able to learn how to perform a task by observing and imitating a human example.

Action selection is the process of choosing which action to take in a given situation.

A function that determines whether a neuron should be activated or not, based on the input it receives.

An adaptive algorithm is a type of algorithm that changes its behavior based on feedback or new information.

A type of artificial neural network that is used to model complex input-output relationships by combining fuzzy logic with neural networks.

A heuristic is a rule of thumb that is used to make decisions, solve problems, or learn new information. Heuristics are used when exact solutions are not possible or practical. Admissible heuristics are those that always lead to a solution that is as good as or better than the solutions that could be found using other heuristics.

Affective computing is a branch of AI that deals with the study and design of systems and devices that can recognize, interpret, process, and simulate human emotions.

Agent architecture in AI is a framework that defines the components and interactions of an intelligent agent.

A computer hardware accelerator is a device that is used to improve the performance of a computer or other type of electronic device.

A problem is AI-complete if it requires the same amount of effort to solve using AI as it would using any other method.

AIML is a markup language used by Artificial Intelligence applications to create natural language interfaces.

A set of rules or steps that are followed in order to solve a problem.

Algorithmic efficiency in AI is the ability of an algorithm to solve a problem in the shortest amount of time possible.

Algorithmic probability is a branch of AI that deals with the probability of events occurring based on an algorithm.

AlphaGo is a computer program that plays the board game Go.

Ambient intelligence (AmI) is a term used to describe a vision of the future in which computing is ubiquitous and integrated into the environment, and where intelligent systems interact seamlessly with people to enhance their quality of life.

The analysis of algorithms is the process of determining the amount of resources (time, space, etc.) required to solve a problem using a given algorithm.

Analytics in AI is the process of analyzing data to extract useful information that can be used to improve decision making.

Answer set programming is a declarative programming paradigm in which a program consists of a set of rules. These rules are used to compute the set of desired output values.

An anytime algorithm is an algorithm that produces a sequence of solutions, with each successive solution being better than the previous one. The algorithm is designed to be run for a limited amount of time, and the best solution found so far is returned when time runs out.

An application programming interface (API) is a set of routines, protocols, and tools for building software applications. It specifies how software components should interact and lets programmers specify how they want their software to work.

Approximate string matching is a method for finding strings that are similar to a given string.

Approximation error is the difference between the value of a function at a certain point and the value of its approximation at that point.

An argumentation framework is a set of rules and procedures for how agents in an artificial intelligence system can exchange arguments and counterarguments.

Artificial general intelligence (AGI) is a subfield of AI research dedicated to creating a machine that can reason, learn, and solve problems like a human.

AIS is a subfield of AI that deals with the design of computer systems that can identify and respond to potentially harmful inputs in a way that is similar to the human immune system.

The situated approach to artificial intelligence is a view of AI that emphasizes the importance of an agent's environment in its ability to perform intelligent behavior.

Artificial intelligence (AI) is a branch of computer science that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously.

An artificial neural network (ANN) is a machine learning algorithm that is used to model complex patterns in data. ANNs are similar to the brain in that they are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data.

The Association for the Advancement of Artificial Intelligence (AAAI) is a scientific society devoted to advancing the scientific understanding of artificial intelligence (AI) and promoting its responsible use.

Asymptotic computational complexity is a measure of the efficiency of an algorithm as the input size increases.

Attributional calculus is a mathematical framework for reasoning about the causes of events. It is used in AI to identify the causes of events and to predict the consequences of actions.

Augmented reality (AR) is a technology that superimposes a computer-generated image on a user's view of the real world, providing a composite view.

AutoGPT refers to techniques and systems that automate interacting with large language models (LLMs) like GPT-3.

Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them.

Automated planning and scheduling in AI is the process of using computers to automatically plan and schedule actions or events. This can include planning and scheduling tasks, resources, and events.

Automated reasoning is a subfield of AI that deals with the question of how to get computers to reason automatically.

Autonomic computing is a self-managing approach to computing in which systems can automatically configure, optimize, and heal themselves.

A car that is able to drive itself using artificial intelligence.

A robot that is capable of carrying out tasks without human intervention.

BPTT is a neural network training algorithm that is used to train recurrent neural networks. It is a variation of the backpropagation algorithm that is used to train standard feedforward neural networks.

Backpropagation is a method used in artificial neural networks to calculate the error gradient of the network.

Backward chaining is a technique used in artificial intelligence to solve problems by working backwards from the goal state to the current state.

A bag-of-words model in computer vision is a model where visual features are represented as a bag of words.

A bag-of-words model is a simple technique for natural language processing where a text is represented as a bag of words.

Batch normalization is a technique used to improve the training of deep neural networks by normalizing the input data at each layer.

Bayesian programming is a method of AI programming that uses Bayesian inference to update beliefs about the state of the world based on new evidence.

Bees algorithm is a swarm intelligence algorithm that is inspired by the foraging behavior of bees. The algorithm is used to solve optimization problems.

Behavior informatics is the study of how people interact with technology, and how those interactions can be used to improve the design of technology.

A behavior tree is a graphical representation of a sequence of actions and conditions that determine how an AI agent behaves.

The belief-desire-intention software model (BDI) is a model of the mental states of an intelligent agent. The model consists of three components - beliefs, desires, and intentions.

The bias–variance tradeoff is the problem of minimizing the sum of the bias and the variance of an estimator.

Big data in AI is a term used to describe the large amount of data that is used to train machine learning models.

Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends to infinity.

A binary tree is a data structure that allows two nodes to be linked together by a path from the root to the leftmost child, and from the leftmost child to the rightmost child. The path is called a path from the root to the leftmost child, and from the leftmost child to the rightmost child.

A blackboard system is a type of artificial intelligence architecture that uses a central repository (blackboard) of information to which various modules can contribute. The blackboard is used to store both data and results of computations, and the modules can access and modify this information.

A Boltzmann machine is a type of stochastic artificial neural network that can learn a probability distribution over a set of inputs.

The Boolean satisfiability problem is the problem of determining whether a given Boolean formula can be satisfied by a given assignment of truth values to the variables of the formula.

Brain technology in AI is a technology that enables machines to simulate human intelligence.

The branching factor is the number of possible moves that can be made from a given position in a game.

A brute-force search in AI is a search algorithm that systematically checks every possible solution until it finds the one that works best.

A capsule neural network (CapsNet) is a neural network that uses capsules to group together related information. Capsules are groups of neurons that work together to represent information about a particular part of an image or a particular object.

Case-based reasoning is a problem-solving approach that relies on previous solutions to similar problems.

A chatbot is a computer program that simulates human conversation, usually through artificial intelligence, to provide customer service or other online assistance.

Cloud robotics is a field of robotics that deals with the design and implementation of robots that are connected to the internet and can be controlled remotely.

Cluster analysis is a technique for finding groups of similar objects in a data set.

A cobweb (clustering) is a method of unsupervised learning where data points are clustered together based on similarity.

A cognitive architecture is a blueprint for a type of artificial intelligence that is designed to simulate the human mind.

Cognitive computing is a branch of AI that deals with creating machines that can learn and think like humans.

Cognitive science is the study of the mind and its processes, including perception, attention, memory, language, and reasoning. It covers a wide range of topics from artificial intelligence (AI) and neuroscience to psychology and linguistics.

Combinatorial optimization is a subfield of AI that deals with the problem of finding an optimal solution from a finite set of possibilities, using a combination of heuristic search and mathematical optimization techniques.

A committee machine is a type of artificial intelligence algorithm that combines the predictions of multiple models to produce a more accurate result.

Commonsense knowledge is a branch of artificial intelligence that deals with the ability of computers to understand and work with common sense knowledge, i.e. the kind of knowledge that is not formal or logical, but is instead based on everyday experience.

Commonsense reasoning is the ability to make deductions based on everyday knowledge.

Computational chemistry in AI is the study of how to use computers to model and simulate chemical systems.

Computational complexity theory is a branch of AI that deals with the study of the resources required to solve problems.

Computational creativity is a branch of AI that deals with the creation of new, original ideas or solutions.

Computational cybernetics is a subfield of AI that deals with the design and analysis of computational systems that can learn and adapt. It is concerned with the ways in which these systems can be made to behave in ways that are similar to the way humans and other animals learn and adapt.

Computational humor is a branch of AI that deals with the automatic generation and recognition of humor.

Computational intelligence (CI) is a subfield of artificial intelligence (AI) that deals with the design and development of intelligent computer systems. CI research is characterized by a focus on computational models of natural intelligence, as opposed to more traditional rule-based or logic-based approaches.

Computational learning theory is a subfield of artificial intelligence that deals with the design and analysis of machine learning algorithms.

Computational linguistics is the study of how to create computer programs that can process and understand human language.

Computational mathematics in AI is the study of mathematical problems that can be solved using computers. This includes problems in optimization, numerical analysis, and statistics.

Computational neuroscience is the study of the brain and nervous system using mathematical models and computer simulations.

Computational number theory in AI is the study of algorithms for performing arithmetic operations on numbers, typically integers. It is a branch of mathematics that is concerned with the properties of integers, such as divisibility and factorization.

A computational problem is a task that can be solved by a computer.

Computational statistics in AI is the application of statistical methods to data analysis and decision making in artificial intelligence systems.

A technology that uses computers to assist in the creation of designs.

Computer science in AI is the study of how to create intelligent computer systems. This includes developing algorithms, designing architectures, and devising ways to make computers learn from data.

Computer vision is a branch of AI that deals with how computers can be made to gain high-level understanding from digital images or videos.

Concept drift is a phenomenon in machine learning where the performance of a model degrades over time due to changes in the underlying data distribution.

Connectionism is a neural network approach to artificial intelligence.

A consistent heuristic is a function that always returns the same value for the same input.

A constrained conditional model (CCM) is a type of AI model that is used to find the best possible solution to a problem within a set of constraints.

Constraint logic programming is a subfield of AI that deals with the use of logic to solve problems with constraints.

Constraint programming is a subfield of AI that deals with the problems of finding solutions to constraints.

A constructed language is a language that has been created by humans rather than developing naturally.

Control theory is a branch of AI that deals with the design and analysis of algorithms that can be used to control systems.

A convolutional neural network is a type of neural network that is used in image recognition and classification.

A crossover is a genetic algorithm that combines two different solutions to create a new solution.

A darkforest is a type of AI algorithm that is used to identify objects in images.

The Dartmouth workshop in AI was a conference held in 1956 that is credited with launching the field of artificial intelligence.

Data augmentation is a technique used to artificially increase the size of a training dataset by creating modified versions of existing data. This is done by applying random transformations to the data, such as rotation, translation, and scaling.

Data fusion is a process of combining data from multiple sources to produce a more accurate representation of a phenomenon than any of the individual data sources.

Data integration in AI is the process of combining data from multiple sources into a single, coherent view. This allows businesses to make better decisions by having a more complete picture of their customers, products, and operations.

Data mining is a process of extracting patterns from data.

Data science is the process of extracting knowledge from data. It is a interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.

A data set is a collection of data that is used to train a machine learning algorithm.

A data warehouse is a database used for reporting and data analysis. It is a central repository of data that can be used to answer business questions.

Datalog is a declarative programming language for querying relational databases. It is based on the logical language Prolog, and extends Prolog with features for efficient query processing.

A decision boundary is a line or surface that separates different regions in data space.

A decision support system (DSS) is a computer program that aids decision-making by using artificial intelligence (AI) techniques. DSSs use data from various sources to support decision-makers in solving complex problems.

Decision theory is a mathematical framework for making optimal decisions in the face of uncertainty.

Decision tree learning is a method of machine learning that is used to create a model that predicts the value of a target variable based on several input variables.

Declarative programming is a programming paradigm that expresses the logic of a computation without describing its control flow.

A deductive classifier is a type of AI algorithm that uses deductive reasoning to generate a classifier.

Deep Blue was a chess computer developed by IBM. It is known for being the first computer to beat a world chess champion in a match when it defeated Garry Kasparov in 1997.

Deep learning is a subset of machine learning that uses algorithms to model high-level abstractions in data.

DeepMind Technologies is a leading artificial intelligence company that specializes in deep learning and reinforcement learning algorithms.

Default logic is a non-monotonic logic that allows for the expression of exceptions.

A description logic is a formalism used for knowledge representation and reasoning in artificial intelligence.

Developmental robotics (DevRob) is a subfield of AI that deals with robots that are capable of autonomously acquiring new skills and knowledge through experience, similar to the way humans and other animals develop.

A diagnosis is a decision made by an AI system about the nature of something, typically based on data.

A dialogue system is a computer system that is designed to simulate a human conversation with another human.

Dimensionality reduction is a technique used to reduce the number of features in a data set while retaining as much information as possible.

A discrete system is a system where the state space is discrete.

A distributed artificial intelligence (DAI) system is a type of AI system where intelligent agents are located in different places and can communicate and cooperate with each other to solve problems.

Dynamic epistemic logic (DEL) is a subfield of AI that studies how agents reason about knowledge and belief in dynamic environments.

Eager learning is a type of machine learning where the algorithm is trained using a dataset and then immediately tested on a separate dataset.

The Ebert test is a test used to determine whether a machine is capable of human-like intelligence.

An echo state network is a recurrent neural network with a random hidden layer. The hidden layer is "echoing" the input, so that the network can learn temporal dependencies.

An embodied agent is an artificial intelligence system that is designed to interact with the physical world. Embodied agents typically have some kind of body or physical form, and they are able to interact with their environment through sensors and actuators.

Embodied cognitive science is a field of AI that studies how the body and mind work together to produce intelligent behavior. It is based on the idea that the mind is not a separate entity from the body, but rather that the two are intimately intertwined.

Ensemble averaging is a technique used in machine learning to improve the accuracy of a model by combining the predictions of multiple models.

Error-driven learning is a type of learning in which the AI system is presented with a set of training data, and the system adjusts its parameters in order to minimize the error between the predicted output and the actual output.

The ethics of artificial intelligence is a branch of ethics that deals with the moral and ethical issues that arise from the use of artificial intelligence.

An evolutionary algorithm (EA) is a type of artificial intelligence that uses a process of natural selection to find solutions to problems.

Evolutionary computation is a subfield of AI that deals with the design and analysis of algorithms based on the principles of natural selection and evolution.

A function that maps an input to a class label, where the function evolves over time as it is exposed to new data.

Existential risk from artificial general intelligence is the risk of human extinction or permanent civilizational decline due to the development of artificial general intelligence.

An expert system is a computer system that emulates the decision-making ability of a human expert.

A fast-and-frugal tree is a decision tree that is designed to be both fast and frugal, meaning that it is able to make decisions quickly and with a minimum of resources.

Feature extraction is a process of reducing the amount of data in a dataset while retaining as much information as possible.

Feature learning is a technique for automatically discovering useful representations of data. This is typically done by training a machine learning model to perform a task using a dataset where the input data has been transformed into a set of features. The model can then be used to transform new data into the same set of features, which can be used to perform the same task.

Feature selection is a process in machine learning where a model is trained to identify which input features are most relevant to the model's output. This process can improve the model's accuracy and reduce the amount of data that the model needs to be trained on.

Federated learning is a type of machine learning where data is distributed across multiple devices and the model is trained on these devices.

First-order logic is a formal system used in mathematics, philosophy, linguistics, and computer science. It is characterized by its use of quantifiers, which are variables that range over objects, such as people or things, and allow for the expression of general statements, such as "there exists someone who likes all dogs".

Fluent is an artificial intelligence technology company that specializes in providing virtual customer assistants.

A formal language is a language that is characterized by a strict set of rules that govern the structure of expressions within the language.

Forward chaining is a data-driven approach to problem solving that involves starting with known information and working toward a desired goal.

A frame language is a language used to describe the structure of data in a frame-based system.

A frame problem is a problem in artificial intelligence that occurs when an AI system cannot identify all relevant factors in a given situation. This can lead to the AI making suboptimal or even incorrect decisions.

A frame is a collection of data describing a particular aspect of the world, including objects, events, and relations between them.

A friendly artificial intelligence is an AI system that has been designed to be safe and beneficial for humans.

Futures studies in AI is the study of how artificial intelligence will impact the future of humanity.

A fuzzy control system is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1.

Fuzzy logic is a type of AI that uses imprecise or incomplete data to make decisions.

A fuzzy rule is a statement of the form "If X is A, then Y is B", where X and Y are variables, and A and B are fuzzy sets.

A fuzzy set is a set in which the membership of each element is a real number between 0 and 1.

Game theory is a branch of AI that deals with the strategic interaction between intelligent agents.

A general game playing (GGP) agent is one that can reason about and play any game given only its rules.

A generative adversarial network (GAN) is a type of artificial intelligence algorithm used to generate new data samples from a training dataset. It is composed of two neural networks, a generator and a discriminator, that compete with each other in a zero-sum game. The generator creates new data samples that are similar to the training data, while the discriminator tries to distinguish between the generated samples and the real training data.

A genetic algorithm is a search heuristic that is inspired by Charles Darwin’s theory of natural selection. This algorithm mimics the process of natural selection to find the best solution to a problem.

A genetic operator is a function that is used to create a new individual in a population of individuals for a genetic algorithm.

Glowworm swarm optimization is a method of AI that uses a swarm of glowworms to find the best solution to a problem.

A graph is a discrete mathematics structure that consists of a set of vertices (or nodes) and a set of edges connecting them.

A graph database is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.

Graph theory is the study of graphs and their properties. In AI, graph theory is used to represent and solve problems involving relationships between objects.

Graph traversal is a process of visiting each node in a graph, usually from a starting node, and keeping track of which nodes have been visited.

A graph is a data structure that consists of a set of nodes (vertices) and a set of edges connecting them.

The halting problem is a problem in computer science that asks whether it is possible to determine, given a description of a program and an input, whether the program will finish running or continue to run forever.

A heuristic is a technique used to solve a problem more quickly when classic methods are too slow, or when no exact solution exists.

A hyper-heuristic is a search method that uses a set of heuristics to guide the search for a solution to a problem.

The IEEE Computational Intelligence Society (IEEE-CIS) is a professional society of the Institute of Electrical and Electronics Engineers (IEEE) focused on artificial intelligence (AI), machine learning (ML), and computational intelligence (CI) research and applications.

Incremental learning is a method of training artificial intelligence (AI) systems whereby new data is incrementally added to a pre-existing dataset and the AI system is retrained on the combined dataset. This allows the AI system to continually learn from new data and improve its performance over time.

An inference engine is a component of an AI system that applies logical reasoning to arrive at conclusions based on a set of given facts.

Information integration is the process of combining data from multiple sources into a single, coherent view. This can be done manually, but is often automated using software that can identify and merge data from multiple sources.

IPL is a programming language designed for artificial intelligence research.

IA is a process of using AI technology to enhance human cognitive abilities. This can be done through a variety of means, such as providing humans with access to more information, or helping them to process and understand information more effectively.

The intelligence explosion is a hypothetical future event in which machines become capable of improving their own intelligence, and thus design even more intelligent machines. This could potentially lead to an exponential increase in intelligence, and eventually to a Singularity.

An intelligent agent is a software program that is able to autonomously perform tasks in order to achieve a goal.

Intelligent control is a subfield of AI that deals with the design of intelligent agents, which are systems that can reason and act autonomously.

An intelligent personal assistant is a software agent that can perform tasks or services for an individual based on commands or questions.

Interpretation (logic) in AI is the process of analyzing and understanding the meaning of data, in order to draw conclusions from it.

Intrinsic motivation is a form of motivation that comes from within oneself, as opposed to extrinsic motivation, which comes from external factors.

An issue tree is a graphical representation of the relationships between various issues. It is used to help identify and analyze the relationships between different issues, and to find solutions to problems.

A junction tree algorithm is a method for finding the optimal solution to a problem by breaking it down into smaller subproblems.

A kernel method is a method used in machine learning to estimate the value of a function at a given point by using a kernel, which is a function that returns the inner product of two vectors.

KL-ONE is a knowledge representation system used in artificial intelligence. It is based on the formalism of description logics.

In AI, knowledge acquisition is the process of extracting knowledge from data. This can be done manually, through a process of observation and experimentation, or automatically, using a variety of techniques such as machine learning.

A knowledge-based system (KBS) is a computer system that uses artificial intelligence (AI) techniques to store and retrieve knowledge.

Knowledge engineering is the process of designing and building computer systems that can acquire, represent, and reason with knowledge.

Knowledge extraction is the process of extracting knowledge from data.

KIF is a knowledge representation language used in AI that is based on first-order logic.

Knowledge representation is the process of encoding information about the world in a form that a computer can use to solve problems. Reasoning is the process of using the knowledge to draw new conclusions or solve problems.

Lazy learning is a machine learning method where generalization from a training set is delayed until a query is made to the system, as opposed to in eager learning, where the system is trained and generates a model before receiving any queries.

Lisp is a family of computer programming languages with a long history and a distinctive, fully parenthesized prefix notation. Originally specified in 1958, Lisp is the second-oldest high-level programming language in widespread use today. Only Fortran is older, by one year.

LLMOps refers to the processes involved in building, training, and deploying these large language models for practical applications.

Logic programming is a type of programming in which programmers define the rules of the program in the form of logical statements.

A long short-term memory (LSTM) is a type of recurrent neural network that is capable of learning long-term dependencies.

Machine learning is a subset of artificial intelligence in which computers are trained to learn from data, identify patterns and make predictions without being explicitly programmed to do so.

Machine listening is a subfield of AI that deals with teaching computers how to interpret and understand audio data. This can involve tasks such as speech recognition, speaker identification, and sound source localization.

Machine perception is the ability of machines to interpret and understand sensory data. This includes the ability to identify objects, faces, and emotions from images and videos, as well as to identify sounds and to understand spoken language.

Machine vision is a branch of AI that deals with giving computers the ability to see and interpret the world in the same way that humans do.

A Markov chain is 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.

A Markov decision process (MDP) is a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker.

Mathematical optimization is a subfield of mathematics that deals with the problem of finding the best possible solution to a given mathematical problem.

Mechanism design is a subfield of artificial intelligence that deals with the design of intelligent systems. It is concerned with the creation of algorithms and architectures that enable intelligent systems to perform tasks such as planning, scheduling, and resource allocation.

Mechatronics is the combination of mechanical engineering, electronics, and computer science to create intelligent machines.

Metabolic network reconstruction and simulation is the process of using artificial intelligence algorithms to create a model of a cell's metabolic network and then simulate how the cell would respond to various stimuli.

A metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or uncertain information.

Model checking is a method of verifying the correctness of a model of a system.

A Monte Carlo tree search is a method used in artificial intelligence to find the best move in a game by using random simulations.

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents.

Multi-swarm optimization is a technique used in AI to optimize a function by iteratively improving a set of candidate solutions.

A mutation is a random change to the genetic code of a chromosome.

Mycin is a rule-based expert system for diagnosing infections and selecting antibiotics.

A naive Bayes classifier is a simple machine learning algorithm that is used to predict the class of an object based on the class probabilities of other objects.

Naive semantics is a simple approach to understanding the meaning of natural language sentences that relies on the compositionality of meaning.

Name binding is the process of associating a name with an entity, such as a variable, in order to identify it.

Named-entity recognition (NER) is a sub-task of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

A named graph is a graph that has been given a name.

Natural language generation is a subfield of artificial intelligence that deals with the generation of natural language text by computers.

Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Natural language programming is a subfield of AI that deals with the ability of computers to understand and process human language.

A network motif is a small, recurring pattern of connectivity within a complex network.

Neural machine translation is a type of AI that is used to translate text from one language to another.

A neural Turing machine (NTM) is a neural network architecture that can learn to perform complex tasks by reading and writing to an external memory.

A neuro-fuzzy system is a type of artificial intelligence that uses both neural networks and fuzzy logic.

Neurocybernetics is a field of AI that deals with the design and control of intelligent systems that interact with the brain and nervous system.

Neuromorphic engineering is a branch of AI that deals with the design and development of artificial neural networks.

A node is a point in a network where lines or pathways intersect or branch. In computer science, a node is an individual computer or other device within a network.

A nondeterministic algorithm is an algorithm that, given a particular input, can exhibit different behaviors on different runs, even if the input is the same.

Nouvelle AI is a subfield of AI that deals with the creation of intelligent agents.

A problem is NP-complete if it is in the class NP and it is as hard as the hardest problems in NP.

A problem is NP-hard if it is at least as hard as the hardest problems in NP.

In computational complexity theory, NP is a complexity class used to describe certain types of decision problems. NP is the set of all decision problems for which the answer can be checked by a polynomial-time algorithm, that is, an algorithm that runs in O(nk) time for some constant k.

Occam's razor is a principle that states that the simplest explanation is usually the correct one. In AI, this principle is often used to choose between different models or algorithms.

Offline learning is a type of AI where the system is not constantly being trained with new data. Instead, it is trained with a set of data and then left to learn on its own.

Online machine learning is a subfield of machine learning that focuses on developing algorithms that can learn from data that is continuously being streamed.

Ontology learning is a process of automatically extracting structured information from unstructured or semi-structured data sources.

Open Mind Common Sense is a cognitive architecture that aims to provide a computational model of human common sense. It is designed to capture the knowledge that humans use to reason about the world, including knowledge about physics, psychology, and social interactions.

Open-source software (OSS) is software that is available for free and can be modified by anyone.

OpenAI is a research company that focuses on artificial intelligence (AI) in order to promote friendly AI.

OpenCog is an artificial intelligence project aimed at creating a general artificial intelligence framework.

Partial order reduction is a technique used in AI to reduce the search space of a problem by considering only partial orders of the variables. This can be done by using a heuristic function to order the variables, or by using a constraint satisfaction algorithm to find a consistent ordering of the variables.

A POMDP is a decision process in which an agent must make decisions in an environment where some of the information is hidden.

Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.

Pathfinding is the process of finding a path from one point to another.

Pattern recognition is a branch of machine learning that deals with the identification and classification of patterns in data. Pattern recognition can be used for a variety of tasks, such as image classification, object detection, and facial recognition.

Predicate logic is a formal system of logic that allows for the expression of complex propositions and relationships between objects, including the use of variables.

Predictive analytics is a branch of artificial intelligence that deals with making predictions about future events based on data and analytics.

A principal component is a linear combination of the original variables in a data set. Principal component analysis is a technique used to find the principal components in a data set.

The principle of rationality is the idea that agents should make decisions that are in their best interests.

Probabilistic programming is a subfield of AI that deals with the creation of models that can generate predictions based on probabilistic reasoning.

A production system is a computer program that uses a set of rules to generate a solution to a problem.

A programming language is a formal language that specifies a set of instructions that can be used to produce various kinds of output.

Prolog is a logic programming language associated with artificial intelligence and computational linguistics.

Prompt engineering is the practice of carefully designing the prompts that are inputted into large language models (LLMs) to produce better quality and more controlled outputs.

Propositional calculus is a branch of logic that deals with propositions, which are statements that can be either true or false.

Python is a programming language that is widely used in AI applications.

A qualification problem in AI is a problem that can be solved by a computer using AI techniques.

In logic, a quantifier is a type of operator that specifies how many times a statement must be true.

Quantum computing is a type of computing where information is processed using quantum bits instead of classical bits. This makes quantum computers much faster and more powerful than traditional computers.

A query language is a language used to make queries, or requests for information, from a database.

R is a programming language for statistical computing and graphics. It is a free and open-source software environment.

A radial basis function network is a type of neural network that uses radial basis functions as activation functions. Radial basis function networks are used for pattern recognition and classification tasks.

A random forest is a type of machine learning algorithm that builds a model of multiple decision trees to make predictions.

A reasoning system is a set of rules used to draw conclusions from a set of premises.

A recurrent neural network (RNN) is a type of neural network that is used to model sequential data. RNNs are similar to traditional neural networks, but they are designed to handle data that is in a sequential or time-series format.

A region connection calculus is a set of mathematical rules used to infer relationships between different regions in an image. This type of calculus is often used in computer vision and image processing applications.

Reinforcement learning is a type of machine learning that allows agents to learn how to optimize their behavior by interacting with their environment.

A reservoir computer is a type of neural network that uses a dynamic system, such as a liquid or gas, as its working memory. The system's dynamics are used to store and process information, making it well suited for tasks such as pattern recognition and time-series prediction.

The Resource Description Framework (RDF) is a standard model for data interchange on the Web. RDF is a directed, labeled graph data model that enables the representation of information from a variety of sources and vocabularies.

A restricted Boltzmann machine (RBM) is a type of energy-based model which is used to learn a probability distribution over a set of hidden variables, given a set of visible variables.

A Rete algorithm is a type of AI algorithm that is used to improve the efficiency of rule-based systems. It does this by creating a network of nodes, which represent rules or conditions, and then using this network to match new data against the rules.

Robotics in AI is the study of how to create robots that can think and act for themselves. This includes creating algorithms for robots to use to make decisions, as well as programming robots to be able to interact with their environment and with humans.

A rule-based system is a computer system that uses a set of rules to make decisions or perform actions.

The satisfiability problem is the problem of determining whether there exists an interpretation that satisfies a given Boolean formula.

A search algorithm is a method for finding a solution to a problem in a finite amount of time.

Selection is the process of choosing which individual will reproduce and pass on their genes to the next generation.

Self-management is the ability of a computer system to manage its own resources without human intervention. This includes tasks such as allocating resources, scheduling tasks, and monitoring performance.

A semantic network is a graphical representation of how words are related to each other.

A semantic query is a question that can be answered by extracting information from a text document.

A semantic reasoner is a computer program that uses a formal ontology to reason about the meaning of symbols in order to draw logical conclusions from a set of premises.

In computer science, semantics is the study of meaning in programming languages and formal logics.

Sensor fusion is the process of combining data from multiple sensors to estimate the state of a system.

Separation logic is a logical framework for reasoning about the safety of programs that manipulate heap-allocated data structures.

Similarity learning is a subfield of machine learning that deals with the problem of finding a similarity function that can be used to measure the similarity between two data points.

Simulated annealing is a technique used in AI to find an approximate solution to a problem by slowly changing a set of values in order to find a minimum or maximum value.

The situation calculus is a formalism for reasoning about actions and change in a dynamic world.

In AI, SLD resolution is a process of converting a first-order logic sentence into an equivalent sentence in propositional logic.

Software engineering in AI is the process of designing, creating, testing, and maintaining software for artificial intelligence applications.

Software in AI is a set of instructions that tell a computer how to perform a task.

SPARQL is a query language for databases that allows for the retrieval of specific data from those databases.

Spatial-temporal reasoning is the ability to reason about space and time.

A process of converting spoken words to text, typically by means of a computer.

A spiking neural network (SNN) is a neural network that uses spikes, or discrete pulses of information, to communicate between neurons. SNNs are similar to traditional neural networks, but they can process information in a more efficient way.

STRIPS is a planning system for AI that was developed at Stanford Research Institute. It is based on the idea of representing actions and goals as sets of preconditions and effects.

A state is a representation of the current situation in an AI system. It includes all the information that is relevant to the current task.

Statistical classification is a method of machine learning where data is classified into groups based on similarities. This is done by training a classifier on a dataset, which is then used to predict the class of new data.

Statistical relational learning (SRL) is a subfield of machine learning that combines statistical and relational learning techniques to learn from complex, structured data. SRL algorithms are capable of learning complex relationships between variables and can handle data with missing values and hidden structure.

Stochastic optimization is a method of optimization that uses randomness to find an approximate solution to a problem.

A stochastic semantic analysis is a type of AI that uses statistical methods to analyze data.

A subject-matter expert in AI is a person who is an expert in a particular area of AI.

Superintelligence is a hypothetical AI system that is significantly smarter than any human.

Supervised learning is a type of machine learning algorithm that uses a known dataset to train a model to make predictions.

A support-vector machine is a supervised learning algorithm that can be used for both classification and regression tasks. The algorithm is based on finding a hyperplane that maximizes the margin between the two classes.

Swarm intelligence (SI) is a subfield of artificial intelligence (AI) that is concerned with the study of decentralized, self-organized systems.

Symbolic artificial intelligence is a subfield of AI that deals with the manipulation of symbols.

Synthetic intelligence (SI) is a subfield of AI that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously.

Systems neuroscience in AI is the study of how the brain processes information and how this can be applied to artificial intelligence.

The technological singularity is a hypothetical future event in which artificial intelligence (AI) will have surpassed human intelligence, leading to a rapid and exponential increase in technological development.

Temporal difference learning is a type of machine learning that is used to predict future events.

Tensor network theory is a branch of mathematics that deals with the representation of high-dimensional tensors. Tensors are mathematical objects that generalize matrices to higher dimensions. Tensor network theory provides a way to represent these high-dimensional objects using a lower-dimensional network. This theory has applications in machine learning, where it can be used to represent high-dimensional data.

TensorFlow is a free and open-source software library for data analysis and machine learning. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

Theoretical computer science (TCS) is a branch of computer science that deals with the theoretical foundations of computing and computer science, as well as their applications.

The theory of computation is the branch of mathematics that deals with the analysis of algorithms and the efficiency of computation.

Thompson sampling is a reinforcement learning algorithm that deals with the exploration-exploitation trade-off by balancing between exploration (of new options) and exploitation (of known good options).

Time complexity is a measure of the amount of time it takes for an algorithm to run.

Transhumanism in AI is the belief that artificial intelligence will eventually surpass human intelligence, and that humans should use technology to enhance their own cognitive and physical abilities.

A transition system is a mathematical model used to describe the behavior of a system that can be in one of a finite number of states. The system can change from one state to another in a finite number of steps.

A tree traversal is a method of visiting each node in a tree data structure in a specific order.

A true quantified Boolean formula (QBF) is a formula in which all variables are quantified, and in which the truth of the formula can be determined by evaluating the formula for all possible combinations of truth values for the quantified variables.

A Turing machine is a hypothetical machine conceived of by Alan Turing in 1936 that can perform any calculation that could be done by hand.

A Turing test is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.

A type system is a logical system that allows for the classification of objects into types. In AI, a type system can be used to classify objects in a scene, such as identifying all the people in an image.

Unsupervised learning is a type of machine learning algorithm that is used to find patterns in data. The algorithm is not given any labels or target values to learn from.

A vision processing unit (VPU) is a specialized type of microprocessor that is designed to rapidly process and interpret the large amounts of data that are generated by digital cameras and other image-sensing devices.

Watson is a computer system that can answer questions posed in natural language.

Weak AI is a term used to describe AI systems that are not as advanced as strong AI. Weak AI systems are designed to perform specific tasks, such as playing a game or solving a specific problem.

The World Wide Web Consortium (W3C) is an international community that develops standards for the World Wide Web.