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Art Recognition : Official website Art magazine website |
Galaxy AI : == Galaxy AI == Galaxy AI is a suite of artificial intelligence (AI) features developed by Samsung Electronics for its Galaxy line of mobile devices. Introduced in January 2024 with the Samsung Galaxy S24 series, Galaxy AI combines on-device and cloud-based AI technologies to enable a range of intelligent f... |
Galaxy AI : Galaxy AI combines Samsung’s proprietary AI models with partner technologies, such as Google's Gemini AI, to deliver a range of intelligent, context-aware functionalities. The AI suite is designed to enhance user experience by providing real-time assistance, content creation tools, and productivity features... |
Galaxy AI : Galaxy AI encompasses several AI-powered tools across different functionalities: |
Galaxy AI : Galaxy AI debuted with the Galaxy S24 series in January 2024. Samsung has announced that AI features will be free to use on supported devices until the end of 2025. Samsung initially introduced Galaxy AI with the Galaxy S24 series. However, through software updates, the company has expanded AI support to va... |
Galaxy AI : Samsung emphasizes privacy protection and ethical AI development as key priorities for Galaxy AI. To safeguard user data, On-device processing is used for sensitive AI functions, minimizing data transmission to external servers. Encrypted cloud-based AI is employed selectively for high-processing tasks. Sam... |
Galaxy AI : Official website (US) |
Natural-language user interface : Natural-language user interface (LUI or NLUI) is a type of computer human interface where linguistic phenomena such as verbs, phrases and clauses act as UI controls for creating, selecting and modifying data in software applications. In interface design, natural-language interfaces are... |
Natural-language user interface : A natural-language search engine would in theory find targeted answers to user questions (as opposed to keyword search). For example, when confronted with a question of the form 'which U.S. state has the highest income tax?', conventional search engines ignore the question and instead ... |
Natural-language user interface : Prototype Nl interfaces had already appeared in the late sixties and early seventies. SHRDLU, a natural-language interface that manipulates blocks in a virtual "blocks world" Lunar, a natural-language interface to a database containing chemical analyses of Apollo 11 Moon rocks by Willi... |
Natural-language user interface : Natural-language interfaces have in the past led users to anthropomorphize the computer, or at least to attribute more intelligence to machines than is warranted. On the part of the user, this has led to unrealistic expectations of the capabilities of the system. Such expectations will... |
Natural-language user interface : The natural-language interface gives rise to technology used for many different applications. Some of the main uses are: Dictation, is the most common use for automated speech recognition (ASR) systems today. This includes medical transcriptions, legal and business dictation, and gener... |
Natural-language user interface : Conversational user interface Natural user interface Natural-language programming Voice user interface Chatbot, a computer program that simulates human conversations Noisy text Question answering Selection-based search Semantic search Semantic query Semantic Web == References == |
ALOPEX : ALOPEX (an abbreviation of "algorithms of pattern extraction") is a correlation based machine learning algorithm first proposed by Tzanakou and Harth in 1974. |
ALOPEX : In machine learning, the goal is to train a system to minimize a cost function or (referring to ALOPEX) a response function. Many training algorithms, such as backpropagation, have an inherent susceptibility to getting "stuck" in local minima or maxima of the response function. ALOPEX uses a cross-correlation ... |
ALOPEX : ALOPEX, in its simplest form is defined by an updating equation: Δ W i j ( n ) = γ Δ W i j ( n − 1 ) Δ R ( n ) + r i ( n ) (n)=\gamma \ \Delta \ W_(n-1)\Delta \ R(n)+r_(n) where: n ≥ 0 is the iteration or time-step. Δ W i j ( n ) (n) is the difference between the current and previous value of system variable ... |
ALOPEX : Essentially, ALOPEX changes each system variable W i j ( n ) (n) based on a product of: the previous change in the variable Δ W i j ( n − 1 ) (n-1) , the resulting change in the cost function Δ R ( n ) , and the learning rate parameter γ . Further, to find the absolute minimum (or maximum), the stochastic ... |
ALOPEX : Harth, E., & Tzanakou, E. (1974) Alopex: A stochastic method for determining visual receptive fields. Vision Research, 14:1475-1482. Abstract from ScienceDirect |
Natural Language Processing (journal) : Natural Language Processing is a bimonthly peer-reviewed academic journal published by Cambridge University Press which covers research and software in natural language processing. It was established in 1995 as Natural Language Engineering, obtaining its current title in 2024. Ot... |
Data exploration : Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, c... |
Data exploration : This area of data exploration has become an area of interest in the field of machine learning. This is a relatively new field and is still evolving. As its most basic level, a machine-learning algorithm can be fed a data set and can be used to identify whether a hypothesis is true based on the datase... |
Data exploration : Trifacta – a data preparation and analysis platform Paxata – self-service data preparation software Alteryx – data blending and advanced data analytics software Microsoft Power BI - interactive visualization and data analysis tool OpenRefine - a standalone open source desktop application for data cle... |
Data exploration : Exploratory data analysis Machine learning Data profiling Data visualization == References == |
Virtual intelligence : Virtual intelligence (VI) is the term given to artificial intelligence that exists within a virtual world. Many virtual worlds have options for persistent avatars that provide information, training, role-playing, and social interactions. The immersion of virtual worlds provides a platform for VI ... |
Virtual intelligence : Cutlass Bomb Disposal Robot: Northrop Grumman developed a virtual training opportunity because of the prohibitive real-world cost and dangers associated with bomb disposal. By replicating a complicated system without having to learn advanced code, the virtual robot has no risk of damage, trainee ... |
Virtual intelligence : Artificial conversational entity Autonomous agent Avatar (computing) Embodied agent Multi-Agent System Intelligent agent Non-player character Player character Virtual reality X.A.N.A. |
Virtual intelligence : Virtual Intelligence, David Burden and Dave Fliesen, ModSim World Canada, June 2010 Sun Tzu Virtual Intelligence demonstration, MODSIM World, October 2009 |
Backpropagation through structure : Backpropagation through structure (BPTS) is a gradient-based technique for training recursive neural networks, proposed in a 1996 paper written by Christoph Goller and Andreas Küchler. == References == |
Autognostics : Autognostics is a new paradigm that describes the capacity for computer networks to be self-aware. It is considered one of the major components of Autonomic Networking. |
Autognostics : One of the most important characteristics of today's Internet that has contributed to its success is its basic design principle: a simple and transparent core with intelligence at the edges (the so-called "end-to-end principle"). Based on this principle, the network carries data without knowing the chara... |
Autognostics : Autognostics is a new paradigm that describes the capacity for computer networks to be self-aware, in part and as a whole, and dynamically adapt to the applications running on them by autonomously monitoring, identifying, diagnosing, resolving issues, subsequently verifying that any remediation was succe... |
Autognostics : Autognostics, or in other words deep self-knowledge, can be best described as the ability of a network to know itself and the applications that run on it. This knowledge is used to autonomously adapt to dynamic network and application conditions such as utilization, capacity, quality of service/applicati... |
Autognostics : Liang Cheng and Ivan Marsic, Piecewise Network Awareness Service for Wireless/Mobile Pervasive Computing, Mobile Networks and Applications (ACM/Springer MONET), Vol. 7, No. 4, pp. 269–278, 2002. paper available here Michael Bednarczyk, Claudia Giuli and Jason Bednarczyk, Network Awareness: Adopting a Mod... |
Autognostics : Autonomic Computing Autonomic Networking Autonomic Systems |
Parity learning : Parity learning is a problem in machine learning. An algorithm that solves this problem must find a function ƒ, given some samples (x, ƒ(x)) and the assurance that ƒ computes the parity of bits at some fixed locations. The samples are generated using some distribution over the input. The problem is ea... |
Parity learning : In Learning Parity with Noise (LPN), the samples may contain some error. Instead of samples (x, ƒ(x)), the algorithm is provided with (x, y), where for random boolean b ∈ y = f(x),&b\\1-f(x),&\end The noisy version of the parity learning problem is conjectured to be hard and is widely used in crypto... |
Parity learning : Learning with errors |
Parity learning : Avrim Blum, Adam Kalai, and Hal Wasserman, “Noise-tolerant learning, the parity problem, and the statistical query model,” J. ACM 50, no. 4 (2003): 506–519. Adam Tauman Kalai, Yishay Mansour, and Elad Verbin, “On agnostic boosting and parity learning,” in Proceedings of the 40th annual ACM symposium o... |
Augmented Analytics : Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner... |
Augmented Analytics : Machine Learning – a systematic computing method that uses algorithms to sift through data to identify relationships, trends, and patterns. It is a process that allows algorithms to dynamically learn from data instead of having a set base of programmed rules. Natural language generation (NLG) – a ... |
Augmented Analytics : Data Democratization is the democratizing data access in order to relieve data congestion and get rid of any sense of data "gatekeepers". This process must be implemented alongside a method for users to make sense of the data. This process is used in hopes of speeding up company decision making an... |
Augmented Analytics : Agriculture – Farmers collect data on water use, soil temperature, moisture content and crop growth, augmented analytics can be used to make sense of this data and possibly identify insights that the user can then use to make business decisions. Smart Cities – Many cities across the United States,... |
Artificial general intelligence : Artificial general intelligence (AGI) is a hypothesized type of highly autonomous artificial intelligence (AI) that would match or surpass human capabilities across most or all economically valuable cognitive work. This contrasts with narrow AI, which is limited to specific tasks. Arti... |
Artificial general intelligence : AGI is also known as strong AI, full AI, human-level AI, human-level intelligent AI, or general intelligent action. Some academic sources reserve the term "strong AI" for computer programs that will experience sentience or consciousness. In contrast, weak AI (or narrow AI) is able to s... |
Artificial general intelligence : Various popular definitions of intelligence have been proposed. One of the leading proposals is the Turing test. However, there are other well-known definitions, and some researchers disagree with the more popular approaches. |
Artificial general intelligence : While the development of transformer models like in ChatGPT is considered the most promising path to AGI, whole brain emulation can serve as an alternative approach. With whole brain simulation, a brain model is built by scanning and mapping a biological brain in detail, and then copyi... |
Artificial general intelligence : AGI could have a wide variety of applications. If oriented towards such goals, AGI could help mitigate various problems in the world such as hunger, poverty and health problems. AGI could improve productivity and efficiency in most jobs. For example, in public health, AGI could acceler... |
Artificial general intelligence : The AGI portal maintained by Pei Wang |
Proaftn : Proaftn is a fuzzy classification method that belongs to the class of supervised learning algorithms. The acronym Proaftn stands for: (PROcédure d'Affectation Floue pour la problématique du Tri Nominal), which means in English: Fuzzy Assignment Procedure for Nominal Sorting. The method enables to determine th... |
Proaftn : Site dedicated to the sorting problematic of MCDA |
Multi-task learning : Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared... |
Multi-task learning : The key challenge in multi-task learning, is how to combine learning signals from multiple tasks into a single model. This may strongly depend on how well different task agree with each other, or contradict each other. There are several ways to address this challenge: |
Multi-task learning : A Matlab package called Multi-Task Learning via StructurAl Regularization (MALSAR) implements the following multi-task learning algorithms: Mean-Regularized Multi-Task Learning, Multi-Task Learning with Joint Feature Selection, Robust Multi-Task Feature Learning, Trace-Norm Regularized Multi-Task ... |
Multi-task learning : Multi-Target Prediction: A Unifying View on Problems and Methods Willem Waegeman, Krzysztof Dembczynski, Eyke Huellermeier https://arxiv.org/abs/1809.02352v1 |
Multi-task learning : The Biosignals Intelligence Group at UIUC Washington University in St. Louis Department of Computer Science |
Data augmentation : Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. Data augmentation has important applications in Bayesian analysis, and the technique is widely used in machine learning to reduce overfitting when training machine learning models, achieved ... |
Data augmentation : Synthetic Minority Over-sampling Technique (SMOTE) is a method used to address imbalanced datasets in machine learning. In such datasets, the number of samples in different classes varies significantly, leading to biased model performance. For example, in a medical diagnosis dataset with 90 samples ... |
Data augmentation : When convolutional neural networks grew larger in mid-1990s, there was a lack of data to use, especially considering that some part of the overall dataset should be spared for later testing. It was proposed to perturb existing data with affine transformations to create new examples with the same lab... |
Data augmentation : Residual or block bootstrap can be used for time series augmentation. |
Data augmentation : Oversampling and undersampling in data analysis Surrogate data Generative adversarial network Variational autoencoder Data pre-processing Convolutional neural network Regularization (mathematics) Data preparation Data fusion == References == |
Case-based reasoning : Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. In everyday life, an auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advoca... |
Case-based reasoning : Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process: Retrieve: Given a target problem, retrieve cases relevant to solving it from memory. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For ... |
Case-based reasoning : At first glance, CBR may seem similar to the rule induction algorithms of machine learning. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case... |
Case-based reasoning : Critics of CBR argue that it is an approach that accepts anecdotal evidence as its main operating principle. Without statistically relevant data for backing and implicit generalization, there is no guarantee that the generalization is correct. However, all inductive reasoning where data is too sc... |
Case-based reasoning : CBR traces its roots to the work of Roger Schank and his students at Yale University in the early 1980s. Schank's model of dynamic memory was the basis for the earliest CBR systems: Janet Kolodner's CYRUS and Michael Lebowitz's IPP. Other schools of CBR and closely allied fields emerged in the 19... |
Case-based reasoning : AI alignment Artificial intelligence detection software Abductive reasoning Duck test I know it when I see it Commonsense reasoning Purposeful omission Decision tree Genetic algorithm Pattern matching Analogy K-line (artificial intelligence) Ripple down rules Casuistry Similarity heuristic |
Case-based reasoning : Aamodt, Agnar, and Enric Plaza. "Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches" Artificial Intelligence Communications 7, no. 1 (1994): 39–52. Althoff, Klaus-Dieter, Ralph Bergmann, and L. Karl Branting, eds. Case-Based Reasoning Research and Developm... |
Case-based reasoning : GAIA – Group of Artificial Intelligence Applications An earlier version of the above article was posted on Nupedia. |
Machine-learned interatomic potential : Machine-learned interatomic potentials (MLIPs), or simply machine learning potentials (MLPs), are interatomic potentials constructed by machine learning programs. Beginning in the 1990s, researchers have employed such programs to construct interatomic potentials by mapping atomic... |
Machine-learned interatomic potential : One popular class of machine-learned interatomic potential is the Gaussian Approximation Potential (GAP), which combines compact descriptors of local atomic environments with Gaussian process regression to machine learn the potential energy surface of a given system. To date, the... |
Boltzmann machine : A Boltzmann machine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising model), named after Ludwig Boltzmann, is a spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model, that is a stochastic Ising model. It is a statistical physics technique ap... |
Boltzmann machine : A Boltzmann machine, like a Sherrington–Kirkpatrick model, is a network of units with a total "energy" (Hamiltonian) defined for the overall network. Its units produce binary results. Boltzmann machine weights are stochastic. The global energy E in a Boltzmann machine is identical in form to that o... |
Boltzmann machine : The difference in the global energy that results from a single unit i equaling 0 (off) versus 1 (on), written Δ E i , assuming a symmetric matrix of weights, is given by: Δ E i = ∑ j > i w i j s j + ∑ j < i w j i s j + θ i =\sum _w_\,s_+\sum _w_\,s_+\theta _ This can be expressed as the difference... |
Boltzmann machine : The network runs by repeatedly choosing a unit and resetting its state. After running for long enough at a certain temperature, the probability of a global state of the network depends only upon that global state's energy, according to a Boltzmann distribution, and not on the initial state from whic... |
Boltzmann machine : The units in the Boltzmann machine are divided into 'visible' units, V, and 'hidden' units, H. The visible units are those that receive information from the 'environment', i.e. the training set is a set of binary vectors over the set V. The distribution over the training set is denoted P + ( V ) (V)... |
Boltzmann machine : Theoretically the Boltzmann machine is a rather general computational medium. For instance, if trained on photographs, the machine would theoretically model the distribution of photographs, and could use that model to, for example, complete a partial photograph. Unfortunately, Boltzmann machines exp... |
Boltzmann machine : The Boltzmann machine is based on the Sherrington–Kirkpatrick spin glass model by David Sherrington and Scott Kirkpatrick. The seminal publication by John Hopfield (1982) applied methods of statistical mechanics, mainly the recently developed (1970s) theory of spin glasses, to study associative memo... |
Boltzmann machine : Restricted Boltzmann machine Helmholtz machine Markov random field (MRF) Ising model (Lenz–Ising model) Hopfield network |
Boltzmann machine : Hinton, G. E.; Sejnowski, T. J. (1986). D. E. Rumelhart; J. L. McClelland (eds.). "Learning and Relearning in Boltzmann Machines" (PDF). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations: 282–317. Archived from the original (PDF) on 2010-07-05. H... |
Boltzmann machine : Scholarpedia article by Hinton about Boltzmann machines Talk at Google by Geoffrey Hinton |
Probabilistic neural network : A probabilistic neural network (PNN) is a feedforward neural network, which is widely used in classification and pattern recognition problems. In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric fun... |
Probabilistic neural network : PNN is often used in classification problems. When an input is present, the first layer computes the distance from the input vector to the training input vectors. This produces a vector where its elements indicate how close the input is to the training input. The second layer sums the con... |
Probabilistic neural network : There are several advantages and disadvantages using PNN instead of multilayer perceptron. PNNs are much faster than multilayer perceptron networks. PNNs can be more accurate than multilayer perceptron networks. PNN networks are relatively insensitive to outliers. PNN networks generate ac... |
Probabilistic neural network : PNN are slower than multilayer perceptron networks at classifying new cases. PNN require more memory space to store the model. |
Probabilistic neural network : probabilistic neural networks in modelling structural deterioration of stormwater pipes. probabilistic neural networks method to gastric endoscope samples diagnosis based on FTIR spectroscopy. Application of probabilistic neural networks to population pharmacokineties. Probabilistic Neura... |
Radial basis function : In mathematics a radial basis function (RBF) is a real-valued function φ whose value depends only on the distance between the input and some fixed point, either the origin, so that φ ( x ) = φ ^ ( ‖ x ‖ ) )=(\left\|\mathbf \right\|) , or some other fixed point c , called a center, so that φ... |
Radial basis function : A radial function is a function φ : [ 0 , ∞ ) → R . When paired with a norm on a vector space ‖ ⋅ ‖ : V → [ 0 , ∞ ) , a function of the form φ c = φ ( ‖ x − c ‖ ) =\varphi (\|\mathbf -\mathbf \|) is said to be a radial kernel centered at c ∈ V \in V . A radial function and the associated ... |
Radial basis function : Radial basis functions are typically used to build up function approximations of the form where the approximating function y ( x ) ) is represented as a sum of N radial basis functions, each associated with a different center x i _ , and weighted by an appropriate coefficient w i . . The weig... |
Radial basis function : The sum can also be interpreted as a rather simple single-layer type of artificial neural network called a radial basis function network, with the radial basis functions taking on the role of the activation functions of the network. It can be shown that any continuous function on a compact inter... |
Radial basis function : Radial basis functions are used to approximate functions and so can be used to discretize and numerically solve Partial Differential Equations (PDEs). This was first done in 1990 by E. J. Kansa who developed the first RBF based numerical method. It is called the Kansa method and was used to solv... |
Radial basis function : Matérn covariance function Radial basis function interpolation Kansa method |
Radial basis function : Hardy, R.L. (1971). "Multiquadric equations of topography and other irregular surfaces". Journal of Geophysical Research. 76 (8): 1905–1915. Bibcode:1971JGR....76.1905H. doi:10.1029/jb076i008p01905. Hardy, R.L. (1990). "Theory and applications of the multiquadric-biharmonic method, 20 years of D... |
Linear predictor function : In statistics and in machine learning, a linear predictor function is a linear function (linear combination) of a set of coefficients and explanatory variables (independent variables), whose value is used to predict the outcome of a dependent variable. This sort of function usually comes in ... |
Linear predictor function : The basic form of a linear predictor function f ( i ) for data point i (consisting of p explanatory variables), for i = 1, ..., n, is f ( i ) = β 0 + β 1 x i 1 + ⋯ + β p x i p , +\beta _x_+\cdots +\beta _x_, where x i k , for k = 1, ..., p, is the value of the k-th explanatory variable for... |
Linear predictor function : An example of the usage of a linear predictor function is in linear regression, where each data point is associated with a continuous outcome yi, and the relationship written y i = f ( i ) + ε i = β T x i + ε i , =f(i)+\varepsilon _=^ \mathbf _\ +\varepsilon _, where ε i is a disturbance t... |
Linear predictor function : In some models (standard linear regression, in particular), the equations for each of the data points i = 1, ..., n are stacked together and written in vector form as y = X β + ε , =\mathbf +,\, where y = ( y 1 y 2 ⋮ y n ) , X = ( x 1 ′ x 2 ′ ⋮ x n ′ ) = ( x 11 ⋯ x 1 p x 21 ⋯ x 2 p ⋮ ⋱ ⋮ x... |
Linear predictor function : When a fixed set of nonlinear functions are used to transform the value(s) of a data point, these functions are known as basis functions. An example is polynomial regression, which uses a linear predictor function to fit an arbitrary degree polynomial relationship (up to a given order) betwe... |
Linear predictor function : Linear model Linear regression == References == |
Overfitting : In mathematical modeling, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably". An overfitted model is a mathematical model that contains more parameters ... |
Overfitting : In statistics, an inference is drawn from a statistical model, which has been selected via some procedure. Burnham & Anderson, in their much-cited text on model selection, argue that to avoid overfitting, we should adhere to the "Principle of Parsimony". The authors also state the following.: 32–33 Overfi... |
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