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It is often used as a query language for deductive databases. In recent years, Datalog has found new application in data integration, information extraction, networking, program analysis, security, and cloud computing. decision boundary In the case of backpropagation-based artificial neural networks or perceptrons, the... | Wikipedia - Glossary of artificial intelligence - D | 345 | 1,803 | null |
decision tree learning Uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning. declarat... | Wikipedia - Glossary of artificial intelligence - D | 331 | 1,767 | null |
The company is based in London, with research centres in Canada, France, and the United States. Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans, as well as a neural Turing machine, or a neural network that may be able to acc... | Wikipedia - Glossary of artificial intelligence - D | 346 | 1,721 | null |
There are general, spatial, temporal, spatiotemporal, and fuzzy descriptions logics, and each description logic features a different balance between DL expressivity and reasoning complexity by supporting different sets of mathematical constructors. developmental robotics (DevRob) Also epigenetic robotics. A scientific ... | Wikipedia - Glossary of artificial intelligence - D | 348 | 1,861 | null |
It mainly consists of three major components: the forward process, the reverse process, and the sampling procedure. Three examples of generic diffusion modeling frameworks used in computer vision are denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. Dijks... | Wikipedia - Glossary of artificial intelligence - D | 306 | 1,759 | null |
DAI is closely related to and a predecessor of the field of multi-agent systems. double descent A phenomenon in statistics and machine learning where a model with a small number of parameters and a model with an extremely large number of parameters have a small test error, but a model whose number of parameters is abou... | Wikipedia - Glossary of artificial intelligence - D | 167 | 914 | null |
Section: E. eager learning A learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system. early stopping A regularization techn... | Wikipedia - Glossary of artificial intelligence - E | 337 | 1,773 | null |
embodied agent Also interface agent. An intelligent agent that interacts with the environment through a physical body within that environment. Agents that are represented graphically with a body, for example a human or a cartoon animal, are also called embodied agents, although they have only virtual, not physical, emb... | Wikipedia - Glossary of artificial intelligence - E | 336 | 1,877 | null |
An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the ... | Wikipedia - Glossary of artificial intelligence - E | 251 | 1,471 | null |
Section: F. fast-and-frugal trees A type of classification tree. Fast-and-frugal trees can be used as decision-making tools which operate as lexicographic classifiers, and, if required, associate an action (decision) to each class or category. feature An individual measurable property or characteristic of a phenomenon.... | Wikipedia - Glossary of artificial intelligence - F | 341 | 1,894 | null |
federated learning A machine learning technique that allows for training models on multiple devices with decentralized data, thus helping preserve the privacy of individual users and their data. first-order logic Also first-order predicate calculus or predicate logic. A collection of formal systems used in mathematics,... | Wikipedia - Glossary of artificial intelligence - F | 349 | 1,772 | null |
An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data. frame An artificial intel... | Wikipedia - Glossary of artificial intelligence - F | 334 | 1,873 | null |
futures studies The study of postulating possible, probable, and preferable futures and the worldviews and myths that underlie them. fuzzy control system 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 a... | Wikipedia - Glossary of artificial intelligence - F | 347 | 1,744 | null |
Section: G. game theory The study of mathematical models of strategic interaction between rational decision-makers. general game playing (GGP) General game playing is the design of artificial intelligence programs to be able to run and play more than one game successfully. generalization The concept that humans, other ... | Wikipedia - Glossary of artificial intelligence - G | 348 | 1,853 | null |
GPT models are usually also fine-tuned, for example with reinforcement learning from human feedback to reduce hallucination or harmful behaviour, or to format the output in a conversationnal format. genetic algorithm (GA) A metaheuristic inspired by the process of natural selection that belongs to the larger class of e... | Wikipedia - Glossary of artificial intelligence - G | 334 | 1,756 | null |
The objects correspond to mathematical abstractions called vertices (also called nodes or points) and each of the related pairs of vertices is called an edge (also called an arc or line). graph database (GDB) A database that uses graph structures for semantic queries with nodes, edges, and properties to represent and s... | Wikipedia - Glossary of artificial intelligence - G | 251 | 1,321 | null |
Section: H. hallucination A response generated by AI that contains false or misleading information presented as fact. heuristic A technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is ... | Wikipedia - Glossary of artificial intelligence - H | 328 | 1,680 | null |
Section: I. IEEE Computational Intelligence Society A professional society of the Institute of Electrical and Electronics Engineers (IEEE) focussing on "the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist system... | Wikipedia - Glossary of artificial intelligence - I | 350 | 1,960 | null |
IPL invented the concept of list processing, albeit in an assembly-language style. intelligence amplification (IA) Also cognitive augmentation, machine augmented intelligence, and enhanced intelligence. The effective use of information technology in augmenting human intelligence. intelligence explosion A possible outco... | Wikipedia - Glossary of artificial intelligence - I | 339 | 1,865 | null |
Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. interpretation An assignment of meaning to the symbols of a formal language. Many formal languages used in mathematics, log... | Wikipedia - Glossary of artificial intelligence - I | 276 | 1,515 | null |
Section: K. kernel method In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). The general task of pattern analysis is to find and study general types of relations (e.g., cluster analysis, rankings, principal components, correla... | Wikipedia - Glossary of artificial intelligence - K | 340 | 1,825 | null |
knowledge distillation The process of transferring knowledge from a large machine learning model to a smaller one. knowledge engineering (KE) All technical, scientific, and social aspects involved in building, maintaining, and using knowledge-based systems. knowledge extraction The creation of knowledge from structured... | Wikipedia - Glossary of artificial intelligence - K | 314 | 1,665 | null |
In the same way KIF is meant to facilitate sharing of knowledge across different systems that use different languages, formalisms, platforms, etc. knowledge representation and reasoning (KR² or KR&R) The field of artificial intelligence dedicated to representing information about the world in a form that a computer sys... | Wikipedia - Glossary of artificial intelligence - K | 242 | 1,350 | null |
Section: L. language model A probabilistic model that manipulates natural language. large language model (LLM) A language model with a large number of parameters (typically at least a billion) that are adjusted during training. Due to its size, it requires a lot of data and computing capability to train. Large language... | Wikipedia - Glossary of artificial intelligence - L | 316 | 1,566 | null |
Section: M. machine vision (MV) The technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision is a term encompassing a large number of technologies, software and hardware ... | Wikipedia - Glossary of artificial intelligence - M | 324 | 1,905 | null |
machine listening Also computer audition (CA). A general field of study of algorithms and systems for audio understanding by machine. machine perception The capability of a computer system to interpret data in a manner that is similar to the way humans use their senses to relate to the world around them. mechanism desi... | Wikipedia - Glossary of artificial intelligence - M | 323 | 1,744 | null |
Metaheuristics sample a set of solutions which is too large to be completely sampled. model checking In computer science, model checking or property checking is, for a given model of a system, exhaustively and automatically checking whether this model meets a given specification. Typically, one has hardware or software... | Wikipedia - Glossary of artificial intelligence - M | 327 | 1,620 | null |
Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. multilayer perceptron (MLP) In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear activation functions, org... | Wikipedia - Glossary of artificial intelligence - M | 289 | 1,471 | null |
Section: N. naive Bayes classifier In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. naive semantics An approach used in computer science for representing basic knowledge about... | Wikipedia - Glossary of artificial intelligence - N | 338 | 1,691 | null |
named-entity recognition (NER) Also entity identification, entity chunking, and entity extraction. A subtask of information extraction that seeks to locate and classify named entity mentions in unstructured text into pre-defined categories such as the person names, organizations, locations, medical codes, time expressi... | Wikipedia - Glossary of artificial intelligence - N | 322 | 1,698 | null |
natural language programming An ontology-assisted way of programming in terms of natural-language sentences, e.g. English. network motif All networks, including biological networks, social networks, technological networks (e.g., computer networks and electrical circuits) and more, can be represented as graphs, which in... | Wikipedia - Glossary of artificial intelligence - N | 338 | 1,822 | null |
An NTM has a neural network controller coupled to external memory resources, which it interacts with through attentional mechanisms. The memory interactions are differentiable end-to-end, making it possible to optimize them using gradient descent. An NTM with a long short-term memory (LSTM) network controller can infer... | Wikipedia - Glossary of artificial intelligence - N | 347 | 1,611 | null |
The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors. node A basic unit of a data structure, such as a linked list or tree data structure. Nodes contain data and also may link to other nodes. Links between ... | Wikipedia - Glossary of artificial intelligence - N | 347 | 1,725 | null |
Section: O. Occam's razor Also Ockham's razor or Ocham's razor. The problem-solving principle that states that when presented with competing hypotheses that make the same predictions, one should select the solution with the fewest assumptions; the principle is not meant to filter out hypotheses that make different pred... | Wikipedia - Glossary of artificial intelligence - O | 332 | 1,658 | null |
The automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. OpenAI The for-profit corporation Op... | Wikipedia - Glossary of artificial intelligence - O | 349 | 1,845 | null |
Section: P. partial order reduction A technique for reducing the size of the state-space to be searched by a model checking or automated planning and scheduling algorithm. It exploits the commutativity of concurrently executed transitions, which result in the same state when executed in different orders. partially obse... | Wikipedia - Glossary of artificial intelligence - P | 339 | 1,712 | null |
This field of research is based heavily on Dijkstra's algorithm for finding a shortest path on a weighted graph. pattern recognition Concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data i... | Wikipedia - Glossary of artificial intelligence - P | 312 | 1,640 | null |
principal component analysis (PCA) A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. This transfo... | Wikipedia - Glossary of artificial intelligence - P | 343 | 1,807 | null |
It can be used to create systems that help make decisions in the face of uncertainty. Programming languages used for probabilistic programming are referred to as "Probabilistic programming languages" (PPLs). production system A computer program typically used to provide some form of AI, which consists primarily of a se... | Wikipedia - Glossary of artificial intelligence - P | 343 | 1,803 | null |
In this sense, propositional logic is the foundation of first-order logic and higher-order logic. proximal policy optimization (PPO) A reinforcement learning algorithm for training an intelligent agent's decision function to accomplish difficult tasks. Python An interpreted, high-level, general-purpose programming lang... | Wikipedia - Glossary of artificial intelligence - P | 157 | 843 | null |
Section: Q. Q-learning A model-free reinforcement learning algorithm for learning the value of an action in a particular state. qualification problem In philosophy and artificial intelligence (especially knowledge-based systems), the qualification problem is concerned with the impossibility of listing all of the precon... | Wikipedia - Glossary of artificial intelligence - Q | 340 | 1,764 | null |
Section: R. R programming language A programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. radial basis fu... | Wikipedia - Glossary of artificial intelligence - R | 346 | 2,041 | null |
This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. regression analysis A set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or label in machine learning) and one or more... | Wikipedia - Glossary of artificial intelligence - R | 342 | 1,802 | null |
representation learning See feature learning. reservoir computing A framework for computation that may be viewed as an extension of neural networks. Typically an input signal is fed into a fixed (random) dynamical system called a reservoir and the dynamics of the reservoir map the input to a higher dimension. Then a si... | Wikipedia - Glossary of artificial intelligence - R | 334 | 1,814 | null |
Section: S. satisfiability In mathematical logic, satisfiability and validity are elementary concepts of semantics. A formula is satisfiable if it is possible to find an interpretation (model) that makes the formula true. A formula is valid if all interpretations make the formula true. The opposites of these concepts a... | Wikipedia - Glossary of artificial intelligence - S | 337 | 1,779 | null |
The notion of a semantic reasoner generalizes that of an inference engine, by providing a richer set of mechanisms to work with. The inference rules are commonly specified by means of an ontology language, and often a description logic language. Many reasoners use first-order predicate logic to perform reasoning; infer... | Wikipedia - Glossary of artificial intelligence - S | 342 | 1,873 | null |
A machine learning training paradigm characterized by using a combination of a small amount of human-labeled data (used exclusively in supervised learning), followed by a large amount of unlabeled data (used exclusively in unsupervised learning). sensor fusion The combining of sensory data or data derived from disparat... | Wikipedia - Glossary of artificial intelligence - S | 348 | 1,917 | null |
It is a refinement of resolution, which is both sound and refutation complete for Horn clauses. software A collection of data or computer instructions that tell the computer how to work. This is in contrast to physical hardware, from which the system is built and actually performs the work. In computer science and soft... | Wikipedia - Glossary of artificial intelligence - S | 344 | 1,922 | null |
It incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields. spiking neural network (SNN) An artificial neural network that more closely mimics a natural neural network. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their Operati... | Wikipedia - Glossary of artificial intelligence - S | 307 | 1,603 | null |
Note that SRL is sometimes called Relational Machine Learning (RML) in the literature. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphica... | Wikipedia - Glossary of artificial intelligence - S | 342 | 1,832 | null |
superintelligence A hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. Superintelligence may also refer to a property of problem-solving systems (e.g., superintelligent language translators or engineering assistants) whether or not these high-level intellect... | Wikipedia - Glossary of artificial intelligence - S | 316 | 1,726 | null |
The expression was introduced in the context of cellular robotic systems. symbolic artificial intelligence The term for the collection of all methods in artificial intelligence research that are based on high-level "symbolic" (human-readable) representations of problems, logic, and search. synthetic intelligence (SI) A... | Wikipedia - Glossary of artificial intelligence - S | 150 | 850 | null |
Section: T. technological singularity Also simply the singularity. A hypothetical point in the future when technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization. temporal difference learning A class of model-free reinforcement learning methods which learn ... | Wikipedia - Glossary of artificial intelligence - T | 347 | 1,909 | null |
Thompson sampling A heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists in choosing the action that maximizes the expected reward with respect to a randomly drawn belief. time complexity The computational complexity that describes the amount ... | Wikipedia - Glossary of artificial intelligence - T | 332 | 1,912 | null |
It consists of states and transitions between states, which may be labeled with labels chosen from a set; the same label may appear on more than one transition. If the label set is a singleton, the system is essentially unlabeled, and a simpler definition that omits the labels is possible. tree traversal Also tree sear... | Wikipedia - Glossary of artificial intelligence - T | 327 | 1,530 | null |
Turing test A test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human, developed by Alan Turing in 1950. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like response... | Wikipedia - Glossary of artificial intelligence - T | 343 | 1,694 | null |
Article: Granular computing. Granular computing is an emerging computing paradigm of information processing that concerns the processing of complex information entities called "information granules", which arise in the process of data abstraction and derivation of knowledge from information or data. Generally speaking,... | Wikipedia - Granular computing - Summary | 174 | 988 | null |
Section: Types of granulation. As mentioned above, granular computing is not an algorithm or process; there is no particular method that is called "granular computing". It is rather an approach to looking at data that recognizes how different and interesting regularities in the data can appear at different levels of gr... | Wikipedia - Granular computing - Types of granulation | 218 | 1,175 | null |
Section: Types of granulation > Value granulation (discretization/quantization). One type of granulation is the quantization of variables. It is very common that in data mining or machine-learning applications the resolution of variables needs to be decreased in order to extract meaningful regularities. An example of t... | Wikipedia - Granular computing - Types of granulation > Value granulation (discretization/quantization) | 153 | 744 | null |
Section: Types of granulation > Value granulation (discretization/quantization) > Motivations. There are several interrelated reasons for granulating variables in this fashion: Based on prior domain knowledge, there is no expectation that minute variations in temperature (e.g., the difference between 80–80.7 °F (26.7–2... | Wikipedia - Granular computing - Types of granulation > Value granulation (discretization/quantization) > Motivations | 335 | 1,654 | null |
Independent of prior domain knowledge, it is often the case that meaningful regularities (i.e., which can be detected by a given learning methodology, representational language, etc.) may exist at one level of resolution and not at another. For example, a simple learner or pattern recognition system may seek to extract... | Wikipedia - Granular computing - Types of granulation > Value granulation (discretization/quantization) > Motivations | 289 | 1,112 | null |
The variables may each be regarded at two different resolutions. Variable X {\displaystyle X} may be regarded at a high (quaternary) resolution wherein it takes on the four values { x 1 , x 2 , x 3 , x 4 } {\displaystyle \{x_{1},x_{2},x_{3},x_{4}\}} or at a lower (binary) resolution wherein it takes on the two values {... | Wikipedia - Granular computing - Types of granulation > Value granulation (discretization/quantization) > Motivations | 241 | 657 | null |
{\displaystyle \{X_{1},X_{2}\}.} Similarly, variable Y {\displaystyle Y} may be regarded at a high (quaternary) resolution or at a lower (binary) resolution, where it takes on the values { y 1 , y 2 , y 3 , y 4 } {\displaystyle \{y_{1},y_{2},y_{3},y_{4}\}} or { Y 1 , Y 2 } , {\displaystyle \{Y_{1},Y_{2}\},} respectivel... | Wikipedia - Granular computing - Types of granulation > Value granulation (discretization/quantization) > Motivations | 251 | 635 | null |
At the high resolution, there are no detectable implications of the form X = x i → Y = y j , {\displaystyle X=x_{i}\rightarrow Y=y_{j},} since every x i {\displaystyle x_{i}} is associated with more than one y j , {\displaystyle y_{j},} and thus, for all x i , {\displaystyle x_{i},} p ( Y = y j | X = x i ) < 1. {\displ... | Wikipedia - Granular computing - Types of granulation > Value granulation (discretization/quantization) > Motivations | 323 | 926 | null |
Section: Types of granulation > Value granulation (discretization/quantization) > Issues and methods. It is not feasible to exhaustively test all possible discretization resolutions on all variables in order to see which combination of resolutions yields interesting or significant results. Instead, the feature space mu... | Wikipedia - Granular computing - Types of granulation > Value granulation (discretization/quantization) > Issues and methods | 179 | 869 | null |
A sample of papers that address the problem of variable discretization in general, and multiple-variable discretization in particular, is as follows: Chiu, Wong & Cheung (1991), Bay (2001), Liu et al. (2002), Wang & Liu (1998), Zighed, Rabaséda & Rakotomalala (1998), Catlett (1991), Dougherty, Kohavi & Sahami (1995), M... | Wikipedia - Granular computing - Types of granulation > Value granulation (discretization/quantization) > Issues and methods | 223 | 650 | null |
Section: Types of granulation > Variable granulation (clustering/aggregation/transformation) > Variable transformation. A number of classical methods, such as principal component analysis, multidimensional scaling, factor analysis, and structural equation modeling, and their relatives, fall under the genus of "variable... | Wikipedia - Granular computing - Types of granulation > Variable granulation (clustering/aggregation/transformation) > Variable transformation | 209 | 1,077 | null |
Section: Types of granulation > Variable granulation (clustering/aggregation/transformation) > Variable aggregation. A different class of variable granulation methods derive more from data clustering methodologies than from the linear systems theory informing the above methods. It was noted fairly early that one may co... | Wikipedia - Granular computing - Types of granulation > Variable granulation (clustering/aggregation/transformation) > Variable aggregation | 305 | 1,567 | null |
Although variable clustering methods based on linear correlation have been proposed (Duda, Hart & Stork 2001;Rencher 2002), more powerful methods of variable clustering are based on the mutual information between variables. Watanabe has shown (Watanabe 1960;Watanabe 1969) that for any set of variables one can construct... | Wikipedia - Granular computing - Types of granulation > Variable granulation (clustering/aggregation/transformation) > Variable aggregation | 332 | 1,522 | null |
Section: Types of granulation > System granulation (aggregation). In database systems, aggregations (see e.g. OLAP aggregation and Business intelligence systems) result in transforming original data tables (often called information systems) into the tables with different semantics of rows and columns, wherein the rows ... | Wikipedia - Granular computing - Types of granulation > System granulation (aggregation) | 273 | 1,444 | null |
Section: Types of granulation > Concept granulation (component analysis). The origins of the granular computing ideology are to be found in the rough sets and fuzzy sets literatures. One of the key insights of rough set research—although by no means unique to it—is that, in general, the selection of different sets of f... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) | 211 | 954 | null |
Consider the attribute-value system below: When the full set of attributes P = { P 1 , P 2 , P 3 , P 4 , P 5 } {\displaystyle P=\{P_{1},P_{2},P_{3},P_{4},P_{5}\}} is considered, we see that we have the following seven equivalence classes or primitive (simple) concepts: { { O 1 , O 2 } { O 3 , O 7 , O 10 } { O 4 } { O 5... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 350 | 852 | null |
attributes. The remaining five objects are each discernible from all other objects. Now, let us imagine a projection of the attribute value system onto attribute P 1 {\displaystyle P_{1}} alone, which would represent, for example, the view from an observer which is only capable of detecting this single attribute. Then ... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 303 | 1,045 | null |
As an example of this, we can consider the effect of concept granulation on the measure known as attribute dependency (a simpler relative of the mutual information). To establish this notion of dependency (see also rough sets), let [ x ] Q = { Q 1 , Q 2 , Q 3 , … , Q N } {\displaystyle [x]_{Q}=\{Q_{1},Q_{2},Q_{3},\dots... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 241 | 761 | null |
For example, if the attribute set Q consists of attribute P 1 {\displaystyle P_{1}} alone, as above, then the concept structure [ x ] Q {\displaystyle [x]_{Q}} will be composed of Q 1 = { O 1 , O 2 } , Q 2 = { O 3 , O 5 , O 7 , O 9 , O 10 } , Q 3 = { O 4 , O 6 , O 8 } . {\displaystyle {\begin{aligned}Q_{1}&=\{O_{1},O_{... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 410 | 906 | null |
{\displaystyle {\begin{aligned}Q_{1}&=\{O_{1},O_{2}\},\\Q_{2}&=\{O_{3},O_{5},O_{7},O_{9},O_{10}\},\\Q_{3}&=\{O_{4},O_{6},O_{8}\}.\end{aligned}}} The dependency of attribute set Q on another attribute set P, γ P ( Q ) , {\displaystyle \gamma _{P}(Q),} is given by γ P ( Q ) = | ∑ i = 1 N P _ Q i | | U | ≤ 1 {\displaystyl... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 359 | 818 | null |
{\displaystyle {\underline {P}}Q_{i}.} More simply, this approximation is the number of objects which on attribute set P can be positively identified as belonging to target set Q i . {\displaystyle Q_{i}.} Added across all equivalence classes in [ x ] Q , {\displaystyle [x]_{Q},} the numerator above represents the tota... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 294 | 1,184 | null |
Having gotten definitions now out of the way, we can make the simple observation that the choice of concept granularity (i.e., choice of attributes) will influence the detected dependencies among attributes. Consider again the attribute value table from above: Consider the dependency of attribute set Q = { P 4 , P 5 } ... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 222 | 725 | null |
{\displaystyle [x]_{P}.} The equivalence classes of [ x ] Q {\displaystyle [x]_{Q}} and of [ x ] P {\displaystyle [x]_{P}} are shown below. The objects that can be definitively categorized according to concept structure [ x ] Q {\displaystyle [x]_{Q}} based on [ x ] P {\displaystyle [x]_{P}} are those in the set { O 1 ... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 289 | 838 | null |
We might then consider the dependency of the smaller attribute set Q = { P 4 } {\displaystyle Q=\{P_{4}\}} on the attribute set P = { P 2 , P 3 } . {\displaystyle P=\{P_{2},P_{3}\}.} The move from Q = { P 4 , P 5 } {\displaystyle Q=\{P_{4},P_{5}\}} to Q = { P 4 } {\displaystyle Q=\{P_{4}\}} induces a coarsening of the ... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 293 | 805 | null |
Clearly, [ x ] Q {\displaystyle [x]_{Q}} has a coarser granularity than it did earlier. The objects that can now be definitively categorized according to the concept structure [ x ] Q {\displaystyle [x]_{Q}} based on [ x ] P {\displaystyle [x]_{P}} constitute the complete universe { O 1 , O 2 , … , O 10 } {\displaystyl... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 341 | 1,184 | null |
However, we also note that the classes induced in [ x ] Q {\displaystyle [x]_{Q}} from the reduction in resolution necessary to obtain this deterministic dependency are now themselves large and few in number; as a result, the dependency we found, while strong, may be less valuable to us than the weaker dependency found... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Equivalence class granulation | 168 | 761 | null |
Section: Types of granulation > Concept granulation (component analysis) > Component granulation. Another perspective on concept granulation may be obtained from work on parametric models of categories. In mixture model learning, for example, a set of data is explained as a mixture of distinct Gaussian (or other) distr... | Wikipedia - Granular computing - Types of granulation > Concept granulation (component analysis) > Component granulation | 209 | 996 | null |
Section: Different interpretations of granular computing. Granular computing can be conceived as a framework of theories, methodologies, techniques, and tools that make use of information granules in the process of problem solving. In this sense, granular computing is used as an umbrella term to cover topics that have ... | Wikipedia - Granular computing - Different interpretations of granular computing | 226 | 1,217 | null |
Section: Transition. Grokking was introduced in January 2022 by OpenAI researchers investigating how neural network perform calculations. It is derived from the word grok coined by Robert Heinlein in his novel Stranger in a Strange Land. Grokking can be understood as a phase transition during the training process. Whil... | Wikipedia - Grokking (machine learning) - Transition | 295 | 1,533 | null |
Article: Hallucination (artificial intelligence). In the field of artificial intelligence (AI), a hallucination or artificial hallucination (also called bullshitting, confabulation or delusion) is a response generated by AI that contains false or misleading information presented as fact. This term draws a loose analogy... | Wikipedia - Hallucination (artificial intelligence) - Summary | 239 | 1,140 | null |
Section: Term > Origin. In 1995, Stephen Thaler demonstrated how hallucinations and phantom experiences emerge from artificial neural networks through random perturbation of their connection weights. In the early 2000s, the term "hallucination" was used in computer vision with a positive connotation to describe the pro... | Wikipedia - Hallucination (artificial intelligence) - Term > Origin | 341 | 1,708 | null |
Section: Term > Definitions and alternatives. Uses, definitions and characterizations of the term "hallucination" in the context of LLMs include: "a tendency to invent facts in moments of uncertainty" (OpenAI, May 2023) "a model's logical mistakes" (OpenAI, May 2023) "fabricating information entirely, but behaving as i... | Wikipedia - Hallucination (artificial intelligence) - Term > Definitions and alternatives | 231 | 1,037 | null |
Section: Term > Criticism. In the scientific community, some researchers avoid the term "hallucination", seeing it as potentially misleading. It has been criticized by Usama Fayyad, executive director of the Institute for Experimental Artificial Intelligence at Northeastern University, on the grounds that it misleading... | Wikipedia - Hallucination (artificial intelligence) - Term > Criticism | 224 | 1,048 | null |
Section: In natural language generation > Causes > Hallucination from modeling. Hallucination was shown to be a statistically inevitable byproduct of any imperfect generative model that is trained to maximize training likelihood, such as GPT-3, and requires active learning to be avoided. The pre-training of generative ... | Wikipedia - Hallucination (artificial intelligence) - In natural language generation > Causes > Hallucination from modeling | 335 | 1,621 | null |
First, decoders can attend to the wrong part of the encoded input source, leading to erroneous generation. Second, the design of the decoding strategy itself can contribute to hallucinations. A decoding strategy that improves generation diversity, such as top-k sampling, is positively correlated with increased hallucin... | Wikipedia - Hallucination (artificial intelligence) - In natural language generation > Causes > Hallucination from modeling | 203 | 990 | null |
Section: In natural language generation > Examples. On 15 November 2022, researchers from Meta AI published Galactica, designed to "store, combine and reason about scientific knowledge". Content generated by Galactica came with the warning: "Outputs may be unreliable! Language Models are prone to hallucinate text." In ... | Wikipedia - Hallucination (artificial intelligence) - In natural language generation > Examples | 335 | 1,531 | null |
Other scholars such as Oren Etzioni have joined Kubacka in assessing that such software can often give "a very impressive-sounding answer that's just dead wrong". When CNBC asked ChatGPT for the lyrics to "Ballad of Dwight Fry", ChatGPT supplied invented lyrics rather than the actual lyrics. Asked questions about the C... | Wikipedia - Hallucination (artificial intelligence) - In natural language generation > Examples | 337 | 1,638 | null |
Asked for proof that dinosaurs built a civilization, ChatGPT claimed there were fossil remains of dinosaur tools and stated, "Some species of dinosaurs even developed primitive forms of art, such as engravings on stones". When prompted that "Scientists have recently discovered churros, the delicious fried-dough pastrie... | Wikipedia - Hallucination (artificial intelligence) - In natural language generation > Examples | 302 | 1,431 | null |
Schwartz said that he had never previously used ChatGPT, that he did not recognize the possibility that ChatGPT's output could have been fabricated, and that ChatGPT continued to assert the authenticity of the precedents after their nonexistence was discovered. In response, Brantley Starr of the Northern District of Te... | Wikipedia - Hallucination (artificial intelligence) - In natural language generation > Examples | 341 | 1,635 | null |
Castel characterized numerous errors and inconsistencies in the opinion summaries, describing one of the cited opinions as "gibberish" and "[bordering] on nonsensical". In June 2023, Mark Walters, a gun rights activist and radio personality, sued OpenAI in a Georgia state court after ChatGPT mischaracterized a legal co... | Wikipedia - Hallucination (artificial intelligence) - In natural language generation > Examples | 316 | 1,538 | null |
Section: In other modalities > Object detection. Various researchers cited by Wired have classified adversarial hallucinations as a high-dimensional statistical phenomenon, or have attributed hallucinations to insufficient training data. Some researchers believe that some "incorrect" AI responses classified by humans a... | Wikipedia - Hallucination (artificial intelligence) - In other modalities > Object detection | 337 | 1,602 | null |
Section: In scientific research > Problems. AI models can cause problems in the world of academic and scientific research due to their hallucinations. Specifically, models like ChatGPT have been recorded in multiple cases to cite sources for information that are either not correct or do not exist. A study conducted in ... | Wikipedia - Hallucination (artificial intelligence) - In scientific research > Problems | 349 | 1,763 | null |
On top of providing incorrect or missing reference material, ChatGPT also has issues with hallucinating the contents of some reference material. A study that analyzed a total of 115 references provided by ChatGPT documented that 47% of them were fabricated. Another 46% cited real references but extracted incorrect info... | Wikipedia - Hallucination (artificial intelligence) - In scientific research > Problems | 265 | 1,425 | null |
Research scientists had a similar rate of human error, identifying these abstracts at a rate of 68%. From this information, the authors of this study concluded, "[t]he ethical and acceptable boundaries of ChatGPT's use in scientific writing remain unclear, although some publishers are beginning to lay down policies." B... | Wikipedia - Hallucination (artificial intelligence) - In scientific research > Problems | 296 | 1,563 | null |
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