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AIĀ Glossary

A - F

autonomous car

->

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

automated planning and scheduling

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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.

augmented reality (AR)

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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.

AI-complete

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

action selection

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

AIML

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

Artificial intelligence, situated approach

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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.

action model learning

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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.

analysis of algorithms

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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.

approximate string matching

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

abductive reasoning

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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.

algorithmic probability

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

argumentation framework

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

attributional calculus

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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.

abstraction (software engineering)

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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.

action language

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

anytime algorithm

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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.

artificial neural network (ANN)

->

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.

artificial immune system (AIS)

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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.

abstract data type

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

abductive logic programming (ALP)

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

algorithmic efficiency

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

accelerating change

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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.

ambient intelligence (AmI)

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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.

activation function

->

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

admissible heuristic

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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.

automata theory

->

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

approximation error

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

algorithm

->

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

affective computing

->

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.

analytics

->

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

autonomous robot

->

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

adaptive algorithm

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

artificial general intelligence (AGI)

->

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.

agent architecture

->

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

asymptotic computational complexity

->

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

artificial intelligence (AI)

->

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.

AlphaGo

->

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

application programming interface (API)

->

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.

autonomic computing (AC)

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

automated reasoning

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

AI accelerator (computer hardware)

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

adaptive neuro fuzzy inference system (ANFIS)

->

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

Association for the Advancement of Artificial Intelligence (AAAI)

->

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.

answer set programming (ASP)

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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.

bag-of-words model in computer vision

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

bees algorithm

->

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

backward chaining

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

batch normalization

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

branching factor

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

behavior tree (artificial intelligence, robotics and control)

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

Big O notation

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

brain technology

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

behavior informatics (BI)

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

blackboard system

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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.

Bayesian programming

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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.

Boolean satisfiability problem

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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.

Boltzmann machine

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

backpropagation through time (BPTT)

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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.

binary tree

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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.

brute-force search

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

biasā€“variance tradeoff

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The biasā€“variance tradeoff is the problem of minimizing the sum of the bias and the variance of an estimator.

belief-desire-intention software model (BDI)

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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.

backpropagation

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

bag-of-words model

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

big data

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

cognitive computing

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

cognitive architecture

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

constraint programming

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

consistent heuristic

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

convolutional neural network

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

computational learning theory

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

computational chemistry

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

computer-automated design (CAutoD)

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

computational problem

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

crossover (genetic algorithm)

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

commonsense reasoning

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

cognitive science

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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.

commonsense knowledge (artificial intelligence)

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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.

computational humor

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

computational cybernetics

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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 mathematics

->

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.

case-based reasoning (CBR)

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

computer vision

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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

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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.

computational neuroscience

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

constrained conditional model (CCM)

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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.

computational complexity theory

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

committee machine

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

computational intelligence (CI)

->

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 creativity

->

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

cloud robotics

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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.

constructed language

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

connectionism

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Connectionism is a neural network approach to artificial intelligence.

Cobweb (clustering)

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

cluster analysis

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

control theory

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

constraint logic programming

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

chatbot

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

computational statistics

->

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

G - L

graph (abstract data type)

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

game theory

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

genetic operator

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

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

graph traversal

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.

graph database (GDB)

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

genetic algorithm (GA)

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

generative adversarial network (GAN)

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.

general game playing (GGP)

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

graph (discrete mathematics)

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

graph theory

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.

hyper-heuristic

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

heuristic (computer science)

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

halting problem

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.

interpretation (logic)

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

issue tree

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.

intelligence amplification (IA)

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.

Information Processing Language (IPL)

IPL is a programming language designed for artificial intelligence research.

information integration (II)

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.

intrinsic motivation (artificial intelligence)

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

inference engine

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.

intelligence explosion

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.

intelligent control

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

incremental learning

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.

IEEE Computational Intelligence Society

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.

intelligent agent (IA)

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

intelligent personal assistant

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

junction tree algorithm

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

knowledge extraction

Knowledge extraction is the process of extracting knowledge from data.

KL-ONE

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

kernel method

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.

knowledge Interchange Format (KIF)

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

knowledge engineering (KE)

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

knowledge acquisition

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.

knowledge-based system (KBS)

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

knowledge representation and reasoning

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.

logic programming

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

long short-term memory (LSTM)

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

Lisp (programming language) (LISP)

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.

lazy learning

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.

M - R

Markov decision process (MDP)

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.

machine listening

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.

mechanism design

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.

mutation (genetic algorithm)

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

Mycin

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

mathematical optimization

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

mechatronics

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

machine vision (MV)

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.

model checking

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

Markov chain

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.

metabolic network reconstruction and simulation

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.

machine perception

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 learning (ML)

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.

metaheuristic

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.

multi-agent system (MAS)

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

Monte Carlo tree search

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

multi-swarm optimization

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

name binding

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

neural Turing machine (NTM)

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.

NP-completeness

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

neurocybernetics

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

nouvelle AI

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

NP-hardness

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

named graph

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

neural machine translation (NMT)

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

neuromorphic engineering

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

NP (complexity)

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.

naive Bayes classifier

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.

nondeterministic algorithm

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.

naive semantics

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

natural language generation (NLG)

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

named-entity recognition (NER)

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.

neuro-fuzzy

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

natural language programming

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

node (computer science)

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.

natural language processing (NLP)

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.

network motif

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

ontology learning

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

OpenCog

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

offline learning

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.

Occam's razor

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.

open-source software (OSS)

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

online machine learning

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

OpenAI

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

Open Mind Common Sense

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.

predictive analytics

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

partially observable Markov decision process (POMDP)

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

Prolog

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

principle of rationality

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

production system (computer science)

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

partial order reduction

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.

pattern recognition

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.

probabilistic programming (PP)

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

programming language

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

predicate logic

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.

particle swarm optimization (PSO)

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

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

Python (programming language)

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

principal component analysis (PCA)

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.

propositional calculus

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

query language

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

qualification problem

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

quantifier (logic)

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

quantum computing

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.

region connection calculus

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.

rule-based system

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

Resource Description Framework (RDF)

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.

recurrent neural network (RNN)

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.

radial basis function network

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.

Rete algorithm

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.

reasoning system

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

restricted Boltzmann machine (RBM)

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.

reinforcement learning (RL)

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

R (programming language)

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

random forest

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

robotics

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.

reservoir computing

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.

S - Z

search algorithm

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

semantic reasoner

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.

subject-matter expert

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

semantic network

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

stochastic semantic analysis

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

similarity learning

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.

supervised learning

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

spatial-temporal reasoning

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

sensor fusion

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

situation calculus

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

software engineering

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

symbolic artificial intelligence

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

separation logic

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

semantic query

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

statistical relational learning (SRL)

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.

simulated annealing (SA)

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.

statistical classification

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.

speech recognition

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

superintelligence

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

self-management (computer science)

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.

swarm intelligence (SI)

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

SPARQL

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

software

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

spiking neural network (SNN)

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.

Stanford Research Institute Problem Solver (STRIPS)

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.

systems neuroscience

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

selection (genetic algorithm)

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

SLD resolution

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

stochastic optimization (SO)

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

support-vector machines

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.

synthetic intelligence (SI)

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.

semantics (computer science)

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

satisfiability

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

state (computer science)

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.

tree traversal

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

time complexity

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

theory of computation

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

TensorFlow

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.

Thompson sampling

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).

tensor network theory

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.

temporal difference learning

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

theoretical computer science (TCS)

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.

technological singularity

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.

transhumanism

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.

type system

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.

Turing machine

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.

transition system

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.

true quantified Boolean formula

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.

Turing test

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

unsupervised learning

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.

vision processing unit (VPU)

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.

World Wide Web Consortium (W3C)

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

weak AI

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.

Watson (computer)

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

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