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Retrieval-augmented generation : RAG is not a complete solution to the problem of hallucinations in LLMs. According to Ars Technica, "It is not a direct solution because the LLM can still hallucinate around the source material in its response." While RAG improves the accuracy of large language models (LLMs), it does no... |
Three-factor learning : In neuroscience and machine learning, three-factor learning is the combinaison of Hebbian plasticity with a third modulatory factor to stabilise and enhance synaptic learning. This third factor can represent various signals such as reward, punishment, error, surprise, or novelty, often implement... |
Three-factor learning : Three-factor learning introduces the concept of eligibility traces, which flag synapses for potential modification pending the arrival of the third factor, and helps temporal credit assignement by bridging the gap between rapid neuronal firing and slower behavioral timescales, from which learnin... |
Artisto : Artisto is a video processing application with art and movie effects filters based on neural network algorithms created in 2016 by Mail.ru Group machine learning specialists. At the moment the application can process videos up to 10 seconds long and offers users 21 filters, including those based on the works ... |
Artisto : Information on the application first appeared on Mail.ru Group Vice President Anna Artamonova's FB page on July 29, 2016. At the moment of posting there was only an Android version available. According to Anna, the application's first version only took eight days to develop. On July 31, the application was ad... |
Artisto : The idea of transferring styles from works of famous artists to images was first mentioned in September 2015 after the publication of Leon Gatys's article "A Neural Algorithm of Artistic Style", where he described the algorithm in detail. The major shortcoming of this algorithm is its slow performance, which ... |
Artisto : Official website |
Hybrid Kohonen self-organizing map : In artificial neural networks, a hybrid Kohonen self-organizing map is a type of self-organizing map (SOM) named for the Finnish professor Teuvo Kohonen, where the network architecture consists of an input layer fully connected to a 2–D SOM or Kohonen layer. The output from the Koho... |
Language identification : In natural language processing, language identification or language guessing is the problem of determining which natural language given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods. |
Language identification : There are several statistical approaches to language identification using different techniques to classify the data. One technique is to compare the compressibility of the text to the compressibility of texts in a set of known languages. This approach is known as mutual information based dista... |
Language identification : One of the great bottlenecks of language identification systems is to distinguish between closely related languages. Similar languages like Bulgarian and Macedonian or Indonesian and Malay present significant lexical and structural overlap, making it challenging for systems to discriminate bet... |
Language identification : Apache OpenNLP includes char n-gram based statistical detector and comes with a model that can distinguish 103 languages Apache Tika contains a language detector for 18 languages |
Language identification : Native Language Identification Algorithmic information theory Artificial grammar learning Family name affixes Kolmogorov complexity Language Analysis for the Determination of Origin Machine translation Translation |
Language identification : Benedetto, D., E. Caglioti and V. Loreto. Language trees and zipping. Physical Review Letters, 88:4 (2002), Complexity theory. Cavnar, William B. and John M. Trenkle. "N-Gram-Based Text Categorization". Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieva... |
And–or tree : An and–or tree is a graphical representation of the reduction of problems (or goals) to conjunctions and disjunctions of subproblems (or subgoals). |
And–or tree : The and–or tree: represents the search space for solving the problem P, using the goal-reduction methods: P if Q and R P if S Q if T Q if U |
And–or tree : Given an initial problem P0 and set of problem solving methods of the form: P if P1 and … and Pn the associated and–or tree is a set of labelled nodes such that: The root of the tree is a node labelled by P0. For every node N labelled by a problem or sub-problem P and for every method of the form P if P1 ... |
And–or tree : An and–or tree specifies only the search space for solving a problem. Different search strategies for searching the space are possible. These include searching the tree depth-first, breadth-first, or best-first using some measure of desirability of solutions. The search strategy can be sequential, searchi... |
And–or tree : The methods used for generating and–or trees are propositional logic programs (without variables). In the case of logic programs containing variables, the solutions of conjoint sub-problems must be compatible. Subject to this complication, sequential and parallel search strategies for and–or trees provide... |
And–or tree : And–or trees can also be used to represent the search spaces for two-person games. The root node of such a tree represents the problem of one of the players winning the game, starting from the initial state of the game. Given a node N, labelled by the problem P of the player winning the game from a partic... |
And–or tree : Luger, George F.; Stubblefield, William A. (1993). Artificial intelligence: structures and strategies for complex problem solving (2 ed.). The Benjamin/Cummings. ISBN 978-0-8053-4785-2. Retrieved 28 February 2013. Nilsson, Nils J. (1998). Artificial Intelligence: A New Synthesis. Morgan Kaufmann. ISBN 978... |
Artificial intelligence in the 2024 United States presidential election : Artificial intelligence (AI) has been developed rapidly in recent years, and has been used by groups in the 2024 United States presidential election, as well as foreign groups such as China, Russia and Iran. There have also been efforts to contro... |
Artificial intelligence in the 2024 United States presidential election : Artificial intelligence has been used as a tool for data science, polling groups and data analysts have used artificial intelligence to analyze election data and make predictions |
Artificial intelligence in the 2024 United States presidential election : 2024 United States presidential election Artificial Intelligence == References == |
Hyperparameter (machine learning) : In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparamet... |
Hyperparameter (machine learning) : The time required to train and test a model can depend upon the choice of its hyperparameters. A hyperparameter is usually of continuous or integer type, leading to mixed-type optimization problems. The existence of some hyperparameters is conditional upon the value of others, e.g. t... |
Hyperparameter (machine learning) : Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model which minimizes a predefined loss function on given test data. The objective function takes a tuple of hyperparameters and returns the associated loss. Typically these methods are not gradient b... |
Hyperparameter (machine learning) : Apart from tuning hyperparameters, machine learning involves storing and organizing the parameters and results, and making sure they are reproducible. In the absence of a robust infrastructure for this purpose, research code often evolves quickly and compromises essential aspects lik... |
Hyperparameter (machine learning) : Hyper-heuristic Replication crisis == References == |
Syntactic parsing (computational linguistics) : Syntactic parsing is the automatic analysis of syntactic structure of natural language, especially syntactic relations (in dependency grammar) and labelling spans of constituents (in constituency grammar). It is motivated by the problem of structural ambiguity in natural ... |
Syntactic parsing (computational linguistics) : Constituency parsing involves parsing in accordance with constituency grammar formalisms, such as Minimalism or the formalism of the Penn Treebank. This, at the very least, means telling which spans are constituents (e.g. [The man] is here.) and what kind of constituent i... |
Syntactic parsing (computational linguistics) : Dependency parsing is parsing according to a dependency grammar formalism, such as Universal Dependencies (which is also a project that produces multilingual dependency treebanks). This means assigning a head (or multiple heads in some formalisms like Enhanced Dependencie... |
Syntactic parsing (computational linguistics) : The performance of syntactic parsers is measured using standard evaluation metrics. Both constituency and dependency parsing approaches can be evaluated for the ratio of exact matches (percentage of sentences that were perfectly parsed), and precision, recall, and F1-scor... |
Syntactic parsing (computational linguistics) : Given that much work on English syntactic parsing depended on the Penn Treebank, which used a constituency formalism, many works on dependency parsing developed ways to deterministically convert the Penn formalism to a dependency syntax, in order to use it as training dat... |
Syntactic parsing (computational linguistics) : Jurafsky, Dan; Martin, James H. (2021). Speech and Language Processing (3 ed.). Retrieved 22 October 2021. Dependency parsing Kübler, Sandra; McDonald, Ryan; Nivre, Joakim (2009). Graeme Hirst (ed.). Dependency Parsing. Synthesis Lectures on Human Language Technologies. M... |
PropBank : PropBank is a corpus that is annotated with verbal propositions and their arguments—a "proposition bank". Although "PropBank" refers to a specific corpus produced by Martha Palmer et al., the term propbank is also coming to be used as a common noun referring to any corpus that has been annotated with proposi... |
PropBank : PropBank differs from FrameNet, the resource to which it is most frequently compared, in several ways. PropBank is a verb-oriented resource, while FrameNet is centered on the more abstract notion of frames, which generalizes descriptions across similar verbs (e.g. "describe" and "characterize") as well as no... |
PropBank : VerbNet FrameNet d:Wikidata:WikiProject Events and Role Frames |
PropBank : PropBank website GitHub repositories PropBank about (Martha Palmer) NomBank website SALSA website |
Bayesian optimization : Bayesian optimization is a sequential design strategy for global optimization of black-box functions, that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions. With the rise of artificial intelligence innovation in the 21st century, Bayesian o... |
Bayesian optimization : The term is generally attributed to Jonas Mockus and is coined in his work from a series of publications on global optimization in the 1970s and 1980s. |
Bayesian optimization : Bayesian optimization is typically used on problems of the form max x ∈ A f ( x ) f(x) , where A is a set of points, x , which rely upon less (or equal to) than 20 dimensions ( R d , d ≤ 20 ^,d\leq 20 ), and whose membership can easily be evaluated. Bayesian optimization is particularly advan... |
Bayesian optimization : Examples of acquisition functions include probability of improvement expected improvement Bayesian expected losses upper confidence bounds (UCB) or lower confidence bounds Thompson sampling and hybrids of these. They all trade-off exploration and exploitation so as to minimize the number of func... |
Bayesian optimization : The maximum of the acquisition function is typically found by resorting to discretization or by means of an auxiliary optimizer. Acquisition functions are maximized using a numerical optimization technique, such as Newton's method or quasi-Newton methods like the Broyden–Fletcher–Goldfarb–Shanno... |
Bayesian optimization : The approach has been applied to solve a wide range of problems, including learning to rank, computer graphics and visual design, robotics, sensor networks, automatic algorithm configuration, automatic machine learning toolboxes, reinforcement learning, planning, visual attention, architecture c... |
Bayesian optimization : Multi-armed bandit Kriging Thompson sampling Global optimization Bayesian experimental design Probabilistic numerics Pareto optimum Active learning (machine learning) Multi-objective optimization == References == |
Teacher forcing : Teacher forcing is an algorithm for training the weights of recurrent neural networks (RNNs). It involves feeding observed sequence values (i.e. ground-truth samples) back into the RNN after each step, thus forcing the RNN to stay close to the ground-truth sequence. The term "teacher forcing" can be m... |
Teacher forcing : Online machine learning Reinforcement learning == References == |
Description logic : Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedur... |
Description logic : A description logic (DL) models concepts, roles and individuals, and their relationships. The fundamental modeling concept of a DL is the axiom—a logical statement relating roles and/or concepts. This is a key difference from the frames paradigm where a frame specification declares and completely de... |
Description logic : Description logic was given its current name in the 1980s. Previous to this it was called (chronologically): terminological systems, and concept languages. |
Description logic : In DL, a distinction is drawn between the so-called TBox (terminological box) and the ABox (assertional box). In general, the TBox contains sentences describing concept hierarchies (i.e., relations between concepts) while the ABox contains ground sentences stating where in the hierarchy, individuals... |
Description logic : Like first-order logic (FOL), a syntax defines which collections of symbols are legal expressions in a description logic, and semantics determine meaning. Unlike FOL, a DL may have several well known syntactic variants. |
Description logic : Formal concept analysis Lattice (order) Formal semantics (natural language) Semantic parameterization Semantic reasoner |
Description logic : F. Baader, D. Calvanese, D. L. McGuinness, D. Nardi, P. F. Patel-Schneider: The Description Logic Handbook: Theory, Implementation, Applications. Cambridge University Press, Cambridge, UK, 2003. ISBN 0-521-78176-0 Ian Horrocks, Ulrike Sattler: Ontology Reasoning in the SHOQ(D) Description Logic, in ... |
Description logic : Description Logic Complexity Navigator, maintained by Evgeny Zolin at the Department of Computer Science List of Reasoners, OWL research at the University of Manchester Description Logics Workshop, homepage of the collecting information about the community and archives of the workshop proceedings |
Concurrent MetateM : Concurrent MetateM is a multi-agent language in which each agent is programmed using a set of (augmented) temporal logic specifications of the behaviour it should exhibit. These specifications are executed directly to generate the behaviour of the agent. As a result, there is no risk of invalidatin... |
Concurrent MetateM : The Temporal Connectives of Concurrent MetateM can divided into two categories, as follows: Strict past time connectives: '●' (weak last), '◎' (strong last), '◆' (was), '■' (heretofore), 'S' (since), and 'Z' (zince, or weak since). Present and future time connectives: '◯' (next), '◇' (sometime), '□... |
Concurrent MetateM : A Java implementation of a MetateM interpreter can be downloaded from here |
Computational cybernetics : Computational cybernetics is the integration of cybernetics and computational intelligence techniques. Though the term Cybernetics entered the technical lexicon in the 1940s and 1950s, it was first used informally as a popular noun in the 1960s, when it became associated with computers, robo... |
Computational cybernetics : Cybercognition Computational Heuristic Intelligence == References == |
A Logical Calculus of the Ideas Immanent in Nervous Activity : "A Logical Calculus of the Ideas Immanent to Nervous Activity" is a 1943 article written by Warren McCulloch and Walter Pitts. The paper, published in the journal The Bulletin of Mathematical Biophysics, proposed a mathematical model of the nervous system a... |
A Logical Calculus of the Ideas Immanent in Nervous Activity : The artificial neuron used in the original paper is slightly different from the modern version. They considered neural networks that operate in discrete steps of time t = 0 , 1 , … . The neural network contains a number of neurons. Let the state of a neuro... |
A Logical Calculus of the Ideas Immanent in Nervous Activity : Artificial neural network Perceptron Connectionism Principia Mathematica History of artificial neural networks == References == |
Multi-surface method : The multi-surface method (MSM) is a form of decision making using the concept of piecewise-linear separability of datasets to categorize data. |
Multi-surface method : Two datasets are linearly separable if their convex hulls do not intersect. The method may be formulated as a feedforward neural network with weights that are trained via linear programming. Comparisons between neural networks trained with the MSM versus backpropagation show MSM is better able to... |
Multi-surface method : Given two finite disjoint point sets A , B ∈ R n \in \mathbb ^ , find a discriminant, f : R n → R ^\to \mathbb such that f ( A ) > 0 , f ( B ) ≤ 0 )>0,f()\leq 0 . If the intersection of convex hulls of the two sets is the empty set, then it is possible to use a single linear program to obtain... |
Multi-surface method : Backpropagation Linear Programming == References == |
Filtered-popping recursive transition network : A filtered-popping recursive transition network (FPRTN), or simply filtered-popping network (FPN), is a recursive transition network (RTN) extended with a map of states to keys where returning from a subroutine jump requires the acceptor and return states to be mapped to ... |
Filtered-popping recursive transition network : A FPN is a structure ( Q , K , Σ , δ , κ , Q I , F ) ,F) where Q is a finite set of states, K is a finite set of keys, Σ is a finite input alphabet, δ : Q × ( Σ ∪ ∪ Q ) → Q \cup Q)\to Q is a partial transition function, ε being the empty symbol, κ : Q → K is a map o... |
Filtered-popping recursive transition network : Transitions represent the possibility of bringing the FPN from a source state q s to a target state q t by possibly performing an additional action. Depending on this action, we distinguish the following types of explicitly-defined transitions: ε -transitions are trans... |
Filtered-popping recursive transition network : A (natural language) text can be enriched with meta-information by the application of a RTN with output; for instance, a RTN inserting XML tags can be used for transforming a plain text into a structured XML document. A RTN with output representing a natural language gram... |
Semantic compression : In natural language processing, semantic compression is a process of compacting a lexicon used to build a textual document (or a set of documents) by reducing language heterogeneity, while maintaining text semantics. As a result, the same ideas can be represented using a smaller set of words. In ... |
Semantic compression : Semantic compression is basically achieved in two steps, using frequency dictionaries and semantic network: determining cumulated term frequencies to identify target lexicon, replacing less frequent terms with their hypernyms (generalization) from target lexicon. Step 1 requires assembling word f... |
Semantic compression : A natural tendency to keep natural language expressions concise can be perceived as a form of implicit semantic compression, by omitting unmeaningful words or redundant meaningful words (especially to avoid pleonasms). |
Semantic compression : In the vector space model, compacting a lexicon leads to a reduction of dimensionality, which results in less computational complexity and a positive influence on efficiency. Semantic compression is advantageous in information retrieval tasks, improving their effectiveness (in terms of both preci... |
Semantic compression : Controlled natural language Information theory Lexical substitution Quantities of information Text simplification |
Semantic compression : Semantic compression on Project SENECA (Semantic Networks and Categorization) website |
Xiao-i : Xiao-i (or Xiao-I Corporation; Chinese: 小i机器人) is a Chinese cognitive artificial intelligence enterprise founded in 2001. On June 29, 2023, Xiao-i launched its generative model Hua Zang Universal Large Language Model. In the same year on October 26, Xiao-i launched the Hua Zang Ecosystem and showcased co-creat... |
Xiao-i : Xiao-i (Shanghai Xiao-i Robot Technology Co., Ltd) was founded in Shanghai, China, in 2001. In 2004, it released the world's first chatbot on MSN and Tencent QQ. In the same year, the company applied for an invention patent for this technology, and it was officially granted authorization in 2009, titled "A Cha... |
Xiao-i : Xiao-i has developed the first international standard in affective computing (ISO/IEC JTC1/SC35 WD30150). The standard defines a universal model for affective computing and outlines the definitions and application methods of affective computing user interface standards in specific contexts. The standard was pu... |
Xiao-i : Xiao-i has offices located in Delaware, Abu Dhabi, and Hong Kong. In 2018, Xiao-i established its APAC headquarters in Hong Kong. In June 2023, Xiao-i established its subsidiary, Xiao-I Plus Inc., in Delaware and held a launch ceremony at the University of Maryland. |
Xiao-i : Xiao-i has launched the Hua Zang Universal Large Language Model, and building on its foundation, Xiao-i has also launched the Hua Zang Ecosystem. Xiao-i's industry-specific solutions include "Hua Zang+Customer Service Center," "Hua Zang+Finance," "Hua Zang+Urban Public Service," "Hua Zang+Enterprise," "Hua Zan... |
Xiao-i : In 2004, Xiao-i filed an application for an invention patent titled "A Chatbot System." The patent was officially granted authorization in 2009, numbered ZL200410053749.9. In June 2012, Xiao-i contended that Apple Inc.'s integration of Siri in its iPhone 4S has infringed on its patented technology, falling wit... |
Generative AI pornography : Generative AI pornography or simply AI pornography is a digitally created pornography produced through generative artificial intelligence (AI) technologies. Unlike traditional pornography, which involves real actors and cameras, this content is synthesized entirely by AI algorithms. These al... |
Generative AI pornography : The use of generative AI in the adult industry began in the late 2010s, initially focusing on AI-generated art, music, and visual content. This trend accelerated in 2022 with Stability AI's release of Stable Diffusion (SD), an open-source text-to-image model that enables users to generate im... |
Generative AI pornography : The growth of generative AI pornography has also attracted some cause for criticism. AI technology can be exploited to create non-consensual pornographic material, posing risks similar to those seen with deepfake revenge porn and AI-generated NCII (Non-Consensual Intimate Image). A 2023 anal... |
Rule induction : Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data. Data mining in general and rule induction in detail are trying to create a... |
Rule induction : Some major rule induction paradigms are: Association rule learning algorithms (e.g., Agrawal) Decision rule algorithms (e.g., Quinlan 1987) Hypothesis testing algorithms (e.g., RULEX) Horn clause induction Version spaces Rough set rules Inductive Logic Programming Boolean decomposition (Feldman) |
Rule induction : Some rule induction algorithms are: Charade Rulex Progol CN2 |
Rule induction : Quinlan, J. R. (1987). "Generating production rules from decision trees" (PDF). In McDermott, John (ed.). Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87). Milan, Italy. pp. 304–307. |
Conversational user interface : A conversational user interface (CUI) is a user interface for computers that emulates a conversation with a real human. Historically, computers have relied on text-based user interfaces and graphical user interfaces (GUIs) (such as the user pressing a "back" button) to translate the user... |
Conversational user interface : A voice user interface allows a user to complete an action by speaking a command. Introduced in October 2011, Apple's Siri was one of the first voice assistants widely adopted. Siri allowed users of iPhone to get information and complete actions on their device simply by asking Siri. In ... |
Conversational user interface : A chatbot is a web- or mobile-based interface that allows the user to ask questions and retrieve information. This information can be generic in nature such as the Google Assistant chat window that allows for internet searches, or it can be a specific brand or service which allows the us... |
Conversational user interface : User interface User interface design Artificial conversational entity Natural-language user interface Voice user interface == References == |
Google Search AI Overviews : Google Search AI Overviews is a feature integrated into Google's search engine that produces AI-generated summaries of the search results. |
Google Search AI Overviews : AI Overviews were first introduced as part of Google's Search Generative Experience (SGE), which was unveiled at the Google I/O conference in May 2023. In May 2024, the feature was rebranded as AI Overviews and launched in the United States. The introduction of AI Overviews was seen as a st... |
Google Search AI Overviews : The AI Overviews feature uses advanced machine learning algorithms to generate summaries based on diverse web content. The overviews are designed to be concise, providing a snapshot of relevant information on the queried topic. To enhance user interaction, Google allows users to adjust the ... |
Google Search AI Overviews : AI Overviews received mixed feedback upon its introduction. Many users appreciated the convenience of obtaining immediate and relevant information without navigating through multiple search results. However, early iterations of the feature faced criticism for inaccuracies, including instanc... |
Google Search AI Overviews : Despite its potential, the feature has faced ongoing scrutiny. Critics argue that relying on AI-generated summaries may perpetuate inaccuracies or oversimplify complex topics. Furthermore, there is apprehension about the ethical implications of AI-driven content aggregation, including its i... |
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