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Pattern recognition : A modern definition of pattern recognition is: The field of pattern recognition is 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 into different categories. Pa... |
Pattern recognition : The problem of pattern recognition can be stated as follows: Given an unknown function g : X → Y \rightarrow (the ground truth) that maps input instances x ∈ X \in to output labels y ∈ Y , along with training data D = =\_,y_),\dots ,(_,y_)\ assumed to represent accurate examples of the mappin... |
Pattern recognition : Within medical science, pattern recognition is the basis for computer-aided diagnosis (CAD) systems. CAD describes a procedure that supports the doctor's interpretations and findings. Other typical applications of pattern recognition techniques are automatic speech recognition, speaker identificat... |
Pattern recognition : Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature. Statistical algorithms can further be categorized as generative or discriminative. |
Pattern recognition : Fukunaga, Keinosuke (1990). Introduction to Statistical Pattern Recognition (2nd ed.). Boston: Academic Press. ISBN 978-0-12-269851-4. Hornegger, Joachim; Paulus, Dietrich W. R. (1999). Applied Pattern Recognition: A Practical Introduction to Image and Speech Processing in C++ (2nd ed.). San Franc... |
Pattern recognition : The International Association for Pattern Recognition List of Pattern Recognition web sites Journal of Pattern Recognition Research Archived 2008-09-08 at the Wayback Machine Pattern Recognition Info Pattern Recognition (Journal of the Pattern Recognition Society) International Journal of Pattern ... |
Grossberg network : Grossberg network is an artificial neural network introduced by Stephen Grossberg. It is a self organizing, competitive network based on continuous time. Grossberg, a neuroscientist and a biomedical engineer, designed this network based on the human visual system. |
Grossberg network : The shunting model is one of Grossberg's neural network models, based on a Leaky integrator, described by the differential equation d n d t = − A n + ( B − n ) E − ( C + n ) I \;=\;-An\;+(B-n)E\;-(C+n)I , where n = n ( t ) represents the activation level of a neuron, E = E ( t ) and I = I ( t ) r... |
Generative literature : Generative literature is poetry or fiction that is automatically generated, often using computers. It is a genre of electronic literature, and also related to generative art. John Clark's Latin Verse Machine (1830–1843) is probably the first example of mechanised generative literature, while Chr... |
Generative literature : Hannes Bajohr defines generative literature as literature involving "the automatic production of text according to predetermined parameters, usually following a combinatory, sometimes aleatory logic, and it emphasizes the production rather than the reception of the work (unlike, say, hypertext).... |
Generative literature : Bajohr describes two main paradigms of generative literature: the sequential paradigm, where the text generation is "executed as a sequence of rule-steps" and employs linear algorithms, and the connectionist paradigm, which is based on neural nets. The latter leads to what Bajohr calls a algorit... |
Generative literature : The first examples of automated generative literature are poetry: John Clark's mechanical Latin Verse Machine (1830–1843) produced lines of hexameter verse in Latin, and Christopher Strachey's love letter generator (1952), programmed on the Manchester Mark 1 computer, generated short, satirical ... |
Generative literature : Story generators have often followed specific narratological theories of how stories are constructed. An early example is Grimes' Fairy Tales, the "first to take a grammar-based approach and the first to operationalize Propp's famous model." Mike Sharples and Rafael Peréz y Peréz's book Story Ma... |
Glossary of artificial intelligence : This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence (AI), its subdisciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of mach... |
Glossary of artificial intelligence : A* search Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be use... |
Glossary of artificial intelligence : backpropagation A method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network. Backpropagation is shorthand for "the backward propagation of errors", since an error is computed at the output and distri... |
Glossary of artificial intelligence : capsule neural network (CapsNet) A machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization. case-based reasoning (CBR) Broa... |
Glossary of artificial intelligence : Darkforest A computer go program developed by Facebook, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its predecessor with Monte Carlo tree search. The MCTS effectively takes tree search methods com... |
Glossary of artificial intelligence : 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 stopp... |
Glossary of artificial intelligence : 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 chara... |
Glossary of artificial intelligence : 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 c... |
Glossary of artificial intelligence : 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 an... |
Glossary of artificial intelligence : 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 netw... |
Glossary of artificial intelligence : junction tree algorithm Also Clique Tree. A method used in machine learning to extract marginalization in general graphs. In essence, it entails performing belief propagation on a modified graph called a junction tree. The graph is called a tree because it branches into different s... |
Glossary of artificial intelligence : 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, pri... |
Glossary of artificial intelligence : 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 capabilit... |
Glossary of artificial intelligence : 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 technologi... |
Glossary of artificial intelligence : 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 represen... |
Glossary of artificial intelligence : 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 hypothese... |
Glossary of artificial intelligence : 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 differ... |
Glossary of artificial intelligence : 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... |
Glossary of artificial intelligence : 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... |
Glossary of artificial intelligence : 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 opp... |
Glossary of artificial intelligence : 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 lea... |
Glossary of artificial intelligence : unsupervised learning A type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels. It is also known as self-organization and allows modeling probability densities of given inputs. It is one of the three basic paradig... |
Glossary of artificial intelligence : vision processing unit (VPU) A type of microprocessor designed to accelerate machine vision tasks. Value-alignment complete Analogous to an AI-complete problem, a value-alignment complete problem is a problem where the AI control problem needs to be fully solved to solve it. |
Glossary of artificial intelligence : Watson A question-answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO, industrialist Thomas J. Watson. weak AI A... |
Glossary of artificial intelligence : XGBoost Short for eXtreme Gradient Boosting, XGBoost is an open-source software library which provides a regularizing gradient boosting framework for multiple programming languages. |
Semantic neural network : Semantic neural network (SNN) is based on John von Neumann's neural network [von Neumann, 1966] and Nikolai Amosov M-Network. There are limitations to a link topology for the von Neumann’s network but SNN accept a case without these limitations. Only logical values can be processed, but SNN ac... |
Semantic neural network : Computational creativity Semantic hashing Semantic Pointer Architecture Sparse distributed memory |
Semantic neural network : Neumann, J., 1966. Theory of self-reproducing automata, edited and completed by Arthur W. Burks. - University of Illinois press, Urbana and London Dudar Z.V., Shuklin D.E., 2000. Implementation of neurons for semantic neural nets that’s understanding texts in natural language. In Radio-electro... |
Kleene star : In mathematical logic and computer science, the Kleene star (or Kleene operator or Kleene closure) is a unary operation, either on sets of strings or on sets of symbols or characters. In mathematics, it is more commonly known as the free monoid construction. The application of the Kleene star to a set V ... |
Kleene star : Given a set V , define V 0 = =\ (the language consisting only of the empty string), V 1 = V , =V, and define recursively the set V i + 1 = =\v\in V\ for each i > 0. If V is a formal language, then V i , the i -th power of the set V , is a shorthand for the concatenation of set V with itself i ti... |
Kleene star : In some formal language studies, (e.g. AFL theory) a variation on the Kleene star operation called the Kleene plus is used. The Kleene plus omits the V 0 term in the above union. In other words, the Kleene plus on V is V + = ⋃ i ≥ 1 V i = V 1 ∪ V 2 ∪ V 3 ∪ ⋯ , =\bigcup _V^=V^\cup V^\cup V^\cup \cdots , ... |
Kleene star : Example of Kleene star applied to set of strings: * = . Example of Kleene plus applied to set of characters: + = . Kleene star applied to the same character set: * = . Example of Kleene star applied to the empty set: ∅* = . Example of Kleene plus applied to the empty set: ∅+ = ∅ ∅* = = ∅, where concatena... |
Kleene star : Strings form a monoid with concatenation as the binary operation and ε the identity element. The Kleene star is defined for any monoid, not just strings. More precisely, let (M, ⋅) be a monoid, and S ⊆ M. Then S* is the smallest submonoid of M containing S; that is, S* contains the neutral element of M, t... |
Kleene star : Wildcard character Glob (programming) |
Kleene star : Hopcroft, John E.; Ullman, Jeffrey D. (1979). Introduction to Automata Theory, Languages, and Computation (1st ed.). Addison-Wesley. |
Federated learning : Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized, rather than centrally stored. A defining characteristic of federated learning i... |
Federated learning : Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets contained in local nodes without explicitly exchanging data samples. The general principle consists in training local models on local data samples and exchanging parameter... |
Federated learning : Federated learning requires frequent communication between nodes during the learning process. Thus, it requires not only enough local computing power and memory, but also high bandwidth connections to be able to exchange parameters of the machine learning model. However, the technology also avoids ... |
Federated learning : A number of different algorithms for federated optimization have been proposed. |
Federated learning : Federated learning has started to emerge as an important research topic in 2015 and 2016, with the first publications on federated averaging in telecommunication settings. Before that, in a thesis work titled "A Framework for Multi-source Prefetching Through Adaptive Weight", an approach to aggrega... |
Federated learning : Federated learning typically applies when individual actors need to train models on larger datasets than their own, but cannot afford to share the data in itself with others (e.g., for legal, strategic or economic reasons). The technology yet requires good connections between local servers and mini... |
Federated learning : "Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016" at eur-lex.europa.eu. Retrieved October 18, 2019. "Data minimisation and privacy-preserving techniques in AI systems" Archived 2020-07-23 at the Wayback Machine at UK Information Commissioners Office. Retrieve... |
AIXI : AIXI is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book Universal Artificial Intelligence. AIXI is a rei... |
AIXI : According to Hutter, the word "AIXI" can have several interpretations. AIXI can stand for AI based on Solomonoff's distribution, denoted by ξ (which is the Greek letter xi), or e.g. it can stand for AI "crossed" (X) with induction (I). There are other interpretations. |
AIXI : AIXI is a reinforcement learning agent that interacts with some stochastic and unknown but computable environment μ . The interaction proceeds in time steps, from t = 1 to t = m , where m ∈ N is the lifespan of the AIXI agent. At time step t, the agent chooses an action a t ∈ A \in (e.g. a limb movement) a... |
AIXI : AIXI's performance is measured by the expected total number of rewards it receives. AIXI has been proven to be optimal in the following ways. Pareto optimality: there is no other agent that performs at least as well as AIXI in all environments while performing strictly better in at least one environment. Balance... |
AIXI : Like Solomonoff induction, AIXI is incomputable. However, there are computable approximations of it. One such approximation is AIXItl, which performs at least as well as the provably best time t and space l limited agent. Another approximation to AIXI with a restricted environment class is MC-AIXI (FAC-CTW) (whi... |
AIXI : Gödel machine |
AIXI : "Universal Algorithmic Intelligence: A mathematical top->down approach", Marcus Hutter, arXiv:cs/0701125; also in Artificial General Intelligence, eds. B. Goertzel and C. Pennachin, Springer, 2007, ISBN 9783540237334, pp. 227–290, doi:10.1007/978-3-540-68677-4_8. |
Cognitive philology : Cognitive philology is the science that studies written and oral texts as the product of human mental processes. Studies in cognitive philology compare documentary evidence emerging from textual investigations with results of experimental research, especially in the fields of cognitive and ecologi... |
Cognitive philology : Artificial intelligence Cognitive archaeology Cognitive linguistics Cognitive poetics Cognitive psychology Cognitive rhetoric Information theory Philology |
Cognitive philology : (in Italian) Rivista di Filologia Cognitiva CogLit: Literature and Cognitive Linguistics Cognitive Philology Institute for Psychological Study of the Arts |
Documenting Hate : Documenting Hate is a project of ProPublica, in collaboration with a number of journalistic, academic, and computing organizations, for systematic tracking of hate crimes and bias incidents. It uses an online form to facilitate reporting of incidents by the general public. Since August 2017, it has a... |
Documenting Hate : Unite the Right rally |
Documenting Hate : Documenting Hate on ProPublica (www.documentinghate.com redirects to this ProPublica page) Documenting Hate News Index Google News Lab Google Cloud Natural Language API Pitch Interactive |
Phi coefficient : In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or rφ) is a measure of association for two binary variables. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classificat... |
Phi coefficient : A Pearson correlation coefficient estimated for two binary variables will return the phi coefficient. Two binary variables are considered positively associated if most of the data falls along the diagonal cells. In contrast, two binary variables are considered negatively associated if most of the data... |
Phi coefficient : Although computationally the Pearson correlation coefficient reduces to the phi coefficient in the 2×2 case, they are not in general the same. The Pearson correlation coefficient ranges from −1 to +1, where ±1 indicates perfect agreement or disagreement, and 0 indicates no relationship. The phi coeffi... |
Phi coefficient : The MCC is defined identically to phi coefficient, introduced by Karl Pearson, also known as the Yule phi coefficient from its introduction by Udny Yule in 1912. Despite these antecedents which predate Matthews's use by several decades, the term MCC is widely used in the field of bioinformatics and ma... |
Phi coefficient : Given a sample of 12 pictures, 8 of cats and 4 of dogs, where cats belong to class 1 and dogs belong to class 0, actual = [1,1,1,1,1,1,1,1,0,0,0,0], assume that a classifier that distinguishes between cats and dogs is trained, and we take the 12 pictures and run them through the classifier, and the cl... |
Phi coefficient : Let us define an experiment from P positive instances and N negative instances for some condition. The four outcomes can be formulated in a 2×2 contingency table or confusion matrix, as follows: |
Phi coefficient : The Matthews correlation coefficient has been generalized to the multiclass case. The generalization called the R K statistic (for K different classes) was defined in terms of a K × K confusion matrix C . MCC = ∑ k ∑ l ∑ m C k k C l m − C k l C m k ∑ k ( ∑ l C k l ) ( ∑ k ′ | k ′ ≠ k ∑ l ′ C k ′ l ... |
Phi coefficient : As explained by Davide Chicco in his paper "Ten quick tips for machine learning in computational biology" (BioData Mining, 2017) and "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation" (BMC Genomics, 2020), the Matthews correlat... |
Phi coefficient : Cohen's kappa Contingency table Cramér's V, a similar measure of association between nominal variables. F1 score Fowlkes–Mallows index Polychoric correlation (subtype: Tetrachoric correlation), when variables are seen as dichotomized versions of (latent) continuous variables == References == |
Histogram of oriented displacements : Histogram of oriented displacements (HOD) is a 2D trajectory descriptor. The trajectory is described using a histogram of the directions between each two consecutive points. Given a trajectory T = , where Pt is the 2D position at time t. For each pair of positions Pt and Pt+1, calc... |
Environmental impact of artificial intelligence : The environmental impact of artificial intelligence includes substantial energy consumption for training and using deep learning models, and the related carbon footprint and water usage. Some scientists have suggested that artificial intelligence (AI) may also provide s... |
Environmental impact of artificial intelligence : AI has a significant carbon footprint due to growing energy usage, especially due to training and usage. Researchers have argued that the carbon footprint of AI models during training should be considered when attempting to understand the impact of AI. One study suggest... |
Environmental impact of artificial intelligence : Cooling AI servers can demand large amounts of fresh water which is evaporated in cooling towers. In fact, data centers housing AI are globally expected to consume six times more water than the country of Denmark. By 2027, AI may use up to 6.6 billion cubic meters of wa... |
Environmental impact of artificial intelligence : E-waste due to production of AI hardware may also contribute to emissions. The rapid growth of AI may also lead to faster deprecation of devices, resulting in hazardous e-waste. Some applications of AI, such as for robot recycling, may reduce e-waste. |
Environmental impact of artificial intelligence : A deal was approved on September 20th, 2024 between Microsoft and Constellation energy to re-open the Three Mile Island nuclear plant. Bill Gates has stated Microsoft intends to use the power to power its usage of Open AI's services within its systems as well as peoples... |
Environmental impact of artificial intelligence : AI has significant potential to help mitigate effects of climate change, such as through better weather predictions, disaster prevention and weather tracking. Some climate scientists have suggested that AI could be used to improve efficiencies of systems, such as renewa... |
Environmental impact of artificial intelligence : Environmental effects of bitcoin Environmental impact of computers == References == |
Empirical risk minimization : In statistical learning theory, the principle of empirical risk minimization defines a family of learning algorithms based on evaluating performance over a known and fixed dataset. The core idea is based on an application of the law of large numbers; more specifically, we cannot know exact... |
Empirical risk minimization : The following situation is a general setting of many supervised learning problems. There are two spaces of objects X and Y and we would like to learn a function h : X → Y (often called hypothesis) which outputs an object y ∈ Y , given x ∈ X . To do so, there is a training set of n ex... |
Empirical risk minimization : In general, the risk R ( h ) cannot be computed because the distribution P ( x , y ) is unknown to the learning algorithm. However, given a sample of iid training data points, we can compute an estimate, called the empirical risk, by computing the average of the loss function over the tr... |
Empirical risk minimization : Guarantees for the performance of empirical risk minimization depend strongly on the function class selected as well as the distributional assumptions made. In general, distribution-free methods are too coarse, and do not lead to practical bounds. However, they are still useful in deriving... |
Empirical risk minimization : Tilted empirical risk minimization is a machine learning technique used to modify standard loss functions like squared error, by introducing a tilt parameter. This parameter dynamically adjusts the weight of data points during training, allowing the algorithm to focus on specific regions o... |
Empirical risk minimization : M-estimator Maximum likelihood estimation |
Empirical risk minimization : Vapnik, V. (2000). The Nature of Statistical Learning Theory. Information Science and Statistics. Springer-Verlag. ISBN 978-0-387-98780-4. |
15.ai : 15.ai was a free non-commercial web application that used artificial intelligence to generate text-to-speech voices of fictional characters from popular media. Created by an anonymous artificial intelligence researcher known as 15, who began developing the technology as a freshman during their undergraduate res... |
15.ai : The platform was non-commercial, and operated without requiring user registration or accounts. Users generated speech by inputting text and selecting a character voice, with optional parameters for emotional contextualizers and phonetic transcriptions. Each request produced three audio variations with distinct ... |
15.ai : 15.ai was an early pioneer of audio deepfakes, leading to the emergence of AI speech synthesis-based memes during the initial stages of the AI boom in 2020. 15.ai is credited as the first mainstream platform to popularize AI voice cloning in Internet memes and content creation, particularly through its ability ... |
15.ai : AI boom Character.ai Deepfake Ethics of artificial intelligence WaveNet My Little Pony: Friendship Is Magic fandom |
15.ai : Archived frontend Official website Spy's Confession Pony Zone The Tax Breaks Pootis Hardbass |
ChatScript : ChatScript is a combination Natural Language engine and dialog management system designed initially for creating chatbots, but is currently also used for various forms of NL processing. It is written in C++. The engine is an open source project at SourceForge. and GitHub. ChatScript was written by Bruce Wi... |
ChatScript : In general ChatScript aims to author extremely concisely, since the limiting scalability of hand-authored chatbots is how much/fast one can write the script. Because ChatScript is designed for interactive conversation, it automatically maintains user state across volleys. A volley is any number of sentence... |
ChatScript : Words starting with ~ are concept sets. For example, ~fruit is the list of all known fruits. The simple pattern (~fruit) reacts if any fruit is mentioned immediately after the chatbot asks for favorite food. The slightly more complex pattern for the rule labelled WHATMUSIC requires all the words what, musi... |
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