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Feature (machine learning) : In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression task... |
Feature (machine learning) : In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning al... |
Feature (machine learning) : A numeric feature can be conveniently described by a feature vector. One way to achieve binary classification is using a linear predictor function (related to the perceptron) with a feature vector as input. The method consists of calculating the scalar product between the feature vector and... |
Feature (machine learning) : In character recognition, features may include histograms counting the number of black pixels along horizontal and vertical directions, number of internal holes, stroke detection and many others. In speech recognition, features for recognizing phonemes can include noise ratios, length of so... |
Feature (machine learning) : In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical anal... |
Feature (machine learning) : The initial set of raw features can be redundant and large enough that estimation and optimization is made difficult or ineffective. Therefore, a preliminary step in many applications of machine learning and pattern recognition consists of selecting a subset of features, or constructing a n... |
Feature (machine learning) : Covariate Dimensionality reduction Feature engineering Hashing trick Statistical classification Explainable artificial intelligence == References == |
Prompt engineering : Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model. A prompt is natural language text describing the task that an AI should perform. A prompt for a text-to-text language model ... |
Prompt engineering : In 2018, researchers first proposed that all previously separate tasks in natural language processing (NLP) could be cast as a question-answering problem over a context. In addition, they trained a first single, joint, multi-task model that would answer any task-related question like "What is the s... |
Prompt engineering : Multiple distinct prompt engineering techniques have been published. |
Prompt engineering : In 2022, text-to-image models like DALL-E 2, Stable Diffusion, and Midjourney were released to the public. These models take text prompts as input and use them to generate AI-generated images. Text-to-image models typically do not understand grammar and sentence structure in the same way as large l... |
Prompt engineering : Some approaches augment or replace natural language text prompts with non-text input. |
Prompt engineering : Prompt injection is a cybersecurity exploit in which adversaries craft inputs that appear legitimate but are designed to cause unintended behavior in machine learning models, particularly large language models (LLMs). This attack takes advantage of the model's inability to distinguish between devel... |
Prompt engineering : Social engineering (security) == References == |
DATR : DATR is a language for lexical knowledge representation. The lexical knowledge is encoded in a network of nodes. Each node has a set of attributes encoded with it. A node can represent a word or a word form. DATR was developed in the late 1980s by Roger Evans, Gerald Gazdar and Bill Keller, and used extensively ... |
DATR : DATR at the University of Sussex DATR repository and RFC compliant ZDATR implementation at Universität Bielefeld |
Artificial intelligence in hiring : Artificial intelligence can be used to automate aspects of the job recruitment process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants. Proponen... |
Artificial intelligence in hiring : Artificial intelligence has fascinated researchers since the term was coined in the mid-1950s. Researchers have identified four main forms of intelligence that AI would need to possess to truly replace humans in the workplace: mechanical, analytical, intuitive, and empathetic. Automa... |
Artificial intelligence in hiring : Artificial intelligence in hiring confers many benefits, but it also has some challenges which have concerned experts. AI is only as good as the data it is using. Biases can inadvertently be baked into the data used in AI. Often companies will use data from their employees to decide ... |
Artificial intelligence in hiring : Artificial intelligence is changing the recruiting process by gradually replacing routine tasks performed by human recruiters. AI can reduce human involvement in hiring and reduce the human biases that hinder effective hiring decisions. And some platforms such as TalAiro go further T... |
Artificial intelligence in hiring : The Artificial Intelligence Video Interview Act, effective in Illinois since 2020, regulates the use of AI to analyze and evaluate job applicants’ video interviews. This law requires employers to follow guidelines to avoid any issues regarding using AI in the hiring process. == Refer... |
Attensity : Attensity provides social analytics and engagement applications for social customer relationship management (social CRM). Attensity's text analytics software applications extract facts, relationships and sentiment from unstructured data, which comprise approximately 85% of the information companies store el... |
Attensity : Attensity was founded in 2000. An early investor in Attensity was In-Q-Tel, which funds technology to support the missions of the US Government and the broader DOD. InTTENSITY, an independent company that has combined Inxight with Attensity Software (the only joint development project that combines two InQT... |
Attensity : Text mining |
Attensity : Official website Archive of official website |
Algorithmic party platforms in the United States : Algorithmic party platforms are a recent development in political campaigning where artificial intelligence (AI) and machine learning are used to shape and adjust party messaging dynamically. Unlike traditional platforms that are drafted well before an election, these ... |
Algorithmic party platforms in the United States : The integration of artificial intelligence (AI) into political campaigns has introduced a significant shift in how party platforms are shaped and communicated. Traditionally, platforms were drafted months before elections and remained static throughout the campaign. Ho... |
Algorithmic party platforms in the United States : Artificial intelligence (AI) has become instrumental in enabling political campaigns to adapt their platforms in real time, responding swiftly to evolving voter sentiments and emerging issues. By analyzing extensive datasets—including polling results, social media acti... |
Algorithmic party platforms in the United States : While AI-driven platforms offer significant advantages, they also introduce ethical and transparency challenges. One primary concern is the potential for AI to manipulate voter perception. The ability to adjust messaging dynamically raises questions about the authentic... |
Algorithmic party platforms in the United States : Despite the challenges, AI-driven platforms offer numerous benefits that can enhance the democratic process. By tailoring messaging to specific voter concerns, AI helps campaigns address diverse needs more effectively. This targeted approach ensures that underrepresent... |
Hopfield network : A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory. The Hopfield network, named for John Hopfield, consists of a single layer of neurons, where each neuron is connected to every other neuron except i... |
Hopfield network : One origin of associative memory is human cognitive psychology, specifically the associative memory. Frank Rosenblatt studied "close-loop cross-coupled perceptrons", which are 3-layered perceptron networks whose middle layer contains recurrent connections that change by a Hebbian learning rule.: 73–7... |
Hopfield network : The units in Hopfield nets are binary threshold units, i.e. the units only take on two different values for their states, and the value is determined by whether or not the unit's input exceeds its threshold U i . Discrete Hopfield nets describe relationships between binary (firing or not-firing) neu... |
Hopfield network : Updating one unit (node in the graph simulating the artificial neuron) in the Hopfield network is performed using the following rule: s i ← \leftarrow \left\+1&\sum _s_\geq \theta _,\\-1&\end\right. where: w i j is the strength of the connection weight from unit j to unit i (the weight of the connec... |
Hopfield network : Bruck in his paper in 1990 studied discrete Hopfield networks and proved a generalized convergence theorem that is based on the connection between the network's dynamics and cuts in the associated graph. This generalization covered both asynchronous as well as synchronous dynamics and presented eleme... |
Hopfield network : Hopfield nets have a scalar value associated with each state of the network, referred to as the "energy", E, of the network, where: E = − 1 2 ∑ i , j w i j s i s j − ∑ i θ i s i \sum _w_s_s_-\sum _\theta _s_ This quantity is called "energy" because it either decreases or stays the same upon network u... |
Hopfield network : Hopfield and Tank presented the Hopfield network application in solving the classical traveling-salesman problem in 1985. Since then, the Hopfield network has been widely used for optimization. The idea of using the Hopfield network in optimization problems is straightforward: If a constrained/uncons... |
Hopfield network : Initialization of the Hopfield networks is done by setting the values of the units to the desired start pattern. Repeated updates are then performed until the network converges to an attractor pattern. Convergence is generally assured, as Hopfield proved that the attractors of this nonlinear dynamica... |
Hopfield network : Training a Hopfield net involves lowering the energy of states that the net should "remember". This allows the net to serve as a content addressable memory system, that is to say, the network will converge to a "remembered" state if it is given only part of the state. The net can be used to recover f... |
Hopfield network : Patterns that the network uses for training (called retrieval states) become attractors of the system. Repeated updates would eventually lead to convergence to one of the retrieval states. However, sometimes the network will converge to spurious patterns (different from the training patterns). In fac... |
Hopfield network : The Network capacity of the Hopfield network model is determined by neuron amounts and connections within a given network. Therefore, the number of memories that are able to be stored is dependent on neurons and connections. Furthermore, it was shown that the recall accuracy between vectors and nodes... |
Hopfield network : The Hopfield network is a model for human associative learning and recall. It accounts for associative memory through the incorporation of memory vectors. Memory vectors can be slightly used, and this would spark the retrieval of the most similar vector in the network. However, we will find out that ... |
Hopfield network : Hopfield networks are recurrent neural networks with dynamical trajectories converging to fixed point attractor states and described by an energy function. The state of each model neuron i is defined by a time-dependent variable V i , which can be chosen to be either discrete or continuous. A compl... |
Hopfield network : Associative memory (disambiguation) Autoassociative memory Boltzmann machine – like a Hopfield net but uses annealed Gibbs sampling instead of gradient descent Dynamical systems model of cognition Ising model Hebbian theory |
Hopfield network : Rojas, Raul (12 July 1996). "13. The Hopfield model" (PDF). Neural Networks – A Systematic Introduction. Springer. ISBN 978-3-540-60505-8. Hopfield Network Javascript The Travelling Salesman Problem Archived 2015-05-30 at the Wayback Machine – Hopfield Neural Network JAVA Applet Hopfield, John (2007)... |
Open Syllabus Project : The Open Syllabus Project (OSP) is an online open-source platform that catalogs and analyzes millions of college syllabi. Founded by researchers from the American Assembly at Columbia University, the OSP has amassed the most extensive collection of searchable syllabi. Since its beta launch in 20... |
Open Syllabus Project : The OSP was formed by a group of data scientists, sociologists, and digital-humanities researchers at the American Assembly, a public-policy institute based at Columbia University. The OSP was partly funded by the Sloan Foundation and the Arcadia Fund. Joe Karaganis, former vice-president of the... |
Open Syllabus Project : The OSP has collected syllabi data from over 80 countries dating to 2000. The syllabi stem from over 4,000 worldwide institutions. Most of the OSP's data originates from the United States. Canada, Australia, and the U.K also have large datasets. The OSP primarily collects syllabi by scraping pub... |
Open Syllabus Project : According to William Germano et al., the OSP is a "fascinating resource but is also prone to misrepresenting or at least distracting us from the most important business of a syllabus: communicating with students." Historian William Caferro remarks that the OSP is a "tacit experience of sharing, ... |
Open Syllabus Project : Digital preservation List of Web archiving initiatives |
Open Syllabus Project : Karaganis, Joe, ed. (2018). Shadow Libraries: Access to Knowledge in Global Higher Education. MIT Press. doi:10.7551/mitpress/11339.001.0001. ISBN 9780262535014. OCLC 1052851639. |
Open Syllabus Project : Official website Open Syllabus Galaxy |
YandexGPT : YandexGPT is a neural network of the GPT family developed by the Russian company Yandex LLC. YandexGPT can create and revise texts, generate new ideas and capture the context of the conversation with the user. YandexGPT is trained using a dataset which includes information from books, magazines, newspapers ... |
YandexGPT : YandexGPT is integrated into virtual assistant Alice (an analog of Siri and Alexa) and is available in Yandex services and applications. The company gives businesses access to the neural network’s API through the public cloud platform Yandex Cloud and develops its own B2B solutions on its basis. Since July ... |
YandexGPT : In February 2023, Yandex announced that it was working on its own version of the ChatGPT generative neural network while developing a language model from the YaLM (Yet another Language Model) family. The project was tentatively named YaLM 2.0, which was later changed to YandexGPT. On May 17, the company unv... |
YandexGPT : Official website |
Language engineering : Language engineering involves the creation of natural language processing systems, whose cost and outputs are measurable and predictable. It is a distinct field contrasted to natural language processing and computational linguistics. A recent trend of language engineering is the use of Semantic W... |
CMU Pronouncing Dictionary : The CMU Pronouncing Dictionary (also known as CMUdict) is an open-source pronouncing dictionary originally created by the Speech Group at Carnegie Mellon University (CMU) for use in speech recognition research. CMUdict provides a mapping orthographic/phonetic for English words in their Nort... |
CMU Pronouncing Dictionary : The database is distributed as a plain text file with one entry to a line in the format "WORD <pronunciation>" with a two-space separator between the parts. If multiple pronunciations are available for a word, variants are identified using numbered versions (e.g. WORD(1)). The pronunciation... |
CMU Pronouncing Dictionary : The Unifon converter is based on the CMU Pronouncing Dictionary. The Natural Language Toolkit contains an interface to the CMU Pronouncing Dictionary. The Carnegie Mellon Logios tool incorporates the CMU Pronouncing Dictionary. PronunDict, a pronunciation dictionary of American English, use... |
CMU Pronouncing Dictionary : Moby Pronunciator, a similar project |
CMU Pronouncing Dictionary : The current version of the dictionary is at SourceForge, although there is also a version maintained on GitHub. Homepage – includes database search RDF converted to Resource Description Framework by the open source Texai project. |
Query understanding : Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but sp... |
Query understanding : Proceedings of ACM SIGIR 2011 Workshop on Query Representation and Understanding Query Understanding for Search Engines (Yi Chang and Hongbo Deng, Eds.) == References == |
Bayesian learning mechanisms : Bayesian learning mechanisms are probabilistic causal models used in computer science to research the fundamental underpinnings of machine learning, and in cognitive neuroscience, to model conceptual development. Bayesian learning mechanisms have also been used in economics and cognitive ... |
Neocognitron : The neocognitron is a hierarchical, multilayered artificial neural network proposed by Kunihiko Fukushima in 1979. It has been used for Japanese handwritten character recognition and other pattern recognition tasks, and served as the inspiration for convolutional neural networks. Previously in 1969, he p... |
Neocognitron : Artificial neural network Deep learning Pattern recognition Receptive field Self-organizing map Unsupervised learning |
Neocognitron : Fukushima, Kunihiko (April 1980). "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position". Biological Cybernetics. 36 (4): 193–202. doi:10.1007/bf00344251. PMID 7370364. S2CID 206775608. Fukushima, Kunihiko; Miyake, S.; Ito, T. (1983).... |
Neocognitron : Neocognitron on Scholarpedia NeoCognitron by Ing. Gabriel Minarik - application (C#) and video Neocognitron resources at Visiome Platform - includes MATLAB environment Beholder - a Neocognitron simulator |
Mindpixel : Mindpixel was a web-based collaborative artificial intelligence project which aimed to create a knowledgebase of millions of human validated true/false statements, or probabilistic propositions. It ran from 2000 to 2005. |
Mindpixel : Participants in the project created one-line statements which aimed to be objectively true or false to 20 other anonymous participants. In order to submit their statement they had first to check the true/false validity of 20 such statements submitted by others. Participants whose replies were consistently o... |
Mindpixel : Never-Ending Language Learning Cyc |
Mindpixel : Mindpixel Home page (Currently points to a "Mindpixel IQ test" using the Mindpixel Db of validated statements) |
Vector database : A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, so that one can search the database with a query vector to... |
Vector database : The most important techniques for similarity search on high-dimensional vectors include: Hierarchical Navigable Small World (HNSW) graphs Locality-sensitive Hashing (LSH) and Sketching Product Quantization (PQ) Inverted Files and combinations of these techniques. In recent benchmarks, HNSW-based imple... |
Vector database : Curse of dimensionality – Difficulties arising when analyzing data with many aspects ("dimensions") Machine learning – Study of algorithms that improve automatically through experience Nearest neighbor search – Optimization problem in computer science Recommender system – System to predict users' pref... |
Vector database : Sawers, Paul (2024-04-20). "Why vector databases are having a moment as the AI hype cycle peaks". TechCrunch. Retrieved 2024-04-23. |
Reservoir computing : Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. After the input signal is fed into the reservoir, which is tre... |
Reservoir computing : The first examples of reservoir neural networks demonstrated that randomly connected recurrent neural networks could be used for sensorimotor sequence learning, and simple forms of interval and speech discrimination. In these early models the memory in the network took the form of both short-term ... |
Reservoir computing : Quantum reservoir computing may use the nonlinear nature of quantum mechanical interactions or processes to form the characteristic nonlinear reservoirs but may also be done with linear reservoirs when the injection of the input to the reservoir creates the nonlinearity. The marriage of machine le... |
Reservoir computing : Deep learning Extreme learning machines Unconventional computing |
Reservoir computing : Reservoir Computing using delay systems, Nature Communications 2011 Optoelectronic Reservoir Computing, Scientific Reports February 2012 Optoelectronic Reservoir Computing, Optics Express 2012 All-optical Reservoir Computing, Nature Communications 2013 Memristor Models for Machine learning, Neural... |
Sketch Engine : Sketch Engine is a corpus manager and text analysis software developed by Lexical Computing since 2003. Its purpose is to enable people studying language behaviour (lexicographers, researchers in corpus linguistics, translators or language learners) to search large text collections according to complex ... |
Sketch Engine : Sketch Engine is a product of Lexical Computing, a company founded in 2003 by the lexicographer and research scientist Adam Kilgarriff. He started a collaboration with Pavel Rychlý, a computer scientist working at the Natural Language Processing Centre, Masaryk University, and the developer of Manatee a... |
Sketch Engine : A list of tools available in Sketch Engine: Word sketches – a one-page automatic derived summary of a word's grammatical and collocational behaviour Word sketch difference – compares and contrasts two words by analysing their collocations Distributional thesaurus – automated thesaurus for finding words ... |
Sketch Engine : Sketch Engine provides access to more than 700 text corpora. There are monolingual as well as multilingual corpora of different sizes (from thousand of words up to 60 billions of words) and various sources (e.g. web, books, subtitles, legal documents). The list of corpora includes British National Corpu... |
Sketch Engine : Sketch Engine consists of three main components: an underlying database management system called Manatee, a web interface search front-end called Bonito, and a web interface for corpus building and management called Corpus Architect. |
Sketch Engine : Sketch Engine has been used by major British and other publishing houses for producing dictionaries such as Macmillan English Dictionary, Dictionnaires Le Robert, Oxford University Press or Shogakukan. Four of United Kingdom's five biggest dictionary publishers use Sketch Engine. |
Sketch Engine : Thomas, James (March 2016). Discovering English with Sketch Engine : a corpus-based approach to language exploration. Workbook and glossary. Brno: Versatile. ISBN 9788026095798. |
Sketch Engine : Sketch Engine website List of corpora available in Sketch Engine OneClick terms – online term extractor with term extraction technology from Sketch Engine SKELL – Sketch Engine for language learning |
Instance selection : Instance selection (or dataset reduction, or dataset condensation) is an important data pre-processing step that can be applied in many machine learning (or data mining) tasks. Approaches for instance selection can be applied for reducing the original dataset to a manageable volume, leading to a re... |
Instance selection : The literature provides several different algorithms for instance selection. They can be distinguished from each other according to several different criteria. Considering this, instance selection algorithms can be grouped in two main classes, according to what instances they select: algorithms tha... |
Art Recognition : Art Recognition is a Swiss technology company headquartered in Adliswil, within the Zurich metropolitan area, Switzerland. Specializing in the application of artificial intelligence (AI) for the purposes of art authentication and the detection of art forgeries, Art Recognition integrates advanced algo... |
Art Recognition : Art Recognition was established in 2019 by Dr. Carina Popovici and Christiane Hoppe-Oehl. The foundation of the company was driven by the long-standing challenge in the art world of authenticating paintings, a process traditionally reliant on expert judgment, historical research, and scientific analys... |
Art Recognition : Art Recognition employs a combination of machine learning techniques, computer vision algorithms, and deep neural networks to assess the authenticity of artworks. The AI algorithm analyzes various visual characteristics, such as brushstrokes, color palette, texture, and composition, to identify patter... |
Art Recognition : Art Recognition's collaboration with Tilburg University in the Netherlands has resulted in the acquisition of a research grant from Eurostars, Eureka (organisation) the Eureka's flagship small and medium-sized enterprises (SME) funding program. In addition, the company has formed a partnership with th... |
Art Recognition : In May 2024, Art Recognition played a key role in identifying counterfeit artworks, including alleged Monets and Renoirs, being sold on eBay. The findings contributed to a broader discussion on the role of AI in preventing art fraud, particularly in online marketplaces where traditional expertise is o... |
Art Recognition : Art Recognition's AI algorithm has received attention from various media outlets and industry events. The company was featured on the front page of The Wall Street Journal for its involvement in the authentication case of the Flaget Madonna, believed to have been partly painted by Raphael. A broadcast... |
Art Recognition : The technology developed by Art Recognition has been recognized for its role in providing a technology-based art authentication solution, compared to traditional methods. This advancement is seen as significant in the field of art verification, offering a modern approach to a historically complex proc... |
Art Recognition : Art Recognition's AI algorithm has been applied to several high-profile and controversial artworks, sparking significant interest and debate in the art world. Samson and Delilah at the National Gallery in London: The National Gallery's "Samson and Delilah", traditionally attributed to the artist Ruben... |
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