AI Detector Meme

AI Detector Meme — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Podium (company)

  Podium is a private technology company headquartered in Lehi, Utah that develops cloud-based software related to messaging, customer feedback, online reviews, selling products, and requesting payments. == History == Podium was founded in 2014 by Eric Rea and Dennis Steele, who developed a tool to help small businesses "build their online reputation" through online reviews. Podium was initially known as RepDrive before rebranding as Podium in 2015. In 2015, Podium moved from a spare bedroom to a new location above a Provo bike shop. In March 2020, Podium added payments technology to its product suite. In November 2021, Podium raised $201 million in Series D funding and was valued at $3 billion. == Product == Podium is a software-as-a-service platform designed to improve business online reputation. It helps users manage business interactions in one tool. Users can communicate reviews, texts, chats, and post payment directly within the app.

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  Information Coding Classification

  The Information Coding Classification (ICC) is a classification system covering almost all extant 6500 knowledge fields (knowledge domains). Its conceptualization goes beyond the scope of the well known library classification systems, such as Dewey Decimal Classification (DDC), Universal Decimal Classification (UDC), and Library of Congress Classification (LCC), by extending also to knowledge systems that so far have not afforded to classify literature. ICC actually presents a flexible universal ordering system for both literature and other kinds of information, set out as knowledge fields. From a methodological point of view, ICC differs from the above-mentioned systems along the following three lines: Its main classes are not based on disciplines but on nine live stages of development, so-called ontical levels. It breaks them roughly down into hierarchical steps by further nine categories which makes decimal number coding possible. The contents of a knowledge field is earmarked via a digital position scheme, which makes the first hierarchical step refer to the nine ontical levels (object areas as subject categories), and the second hierarchical step refer to nine functionally ordered form categories. Respective knowledge fields permit to step down by the same principle to a third and forth level, and even further to a fifth and sixth level. Finally, knowledge field subdivisions will have to conform to said digital position scheme. Hence, for a given knowledge field identical codes will mark identical categories under respective numbers of the coding system. This mnemotechnical aspect of the system helps memorizing and straightaway retrieving the whereabouts of respective interdisciplinary and transdisciplinary fields. The first two hierarchical levels may be regarded as a top- or upper ontology for ontologies and other applications. The terms of the first three hierarchical levels were set out in German and English in Wissensorganisation. Entwicklung, Aufgabe, Anwendung, Zukunft, on pp. 82 to 100. It was published in 2014 and available so far only in German. In the meantime, also the French terms of the knowledge fields have been collected. Competence for maintenance and further development rests with the German Chapter of the International Society for Knowledge Organization (ISKO) e.V. == Historical development == At the end of 1970, Prof. Alwin Diemer, Univ.of Düsseldorf proposed to Ingetraut Dahlberg to undertake a philosophical dissertation on The universal classification system of knowledge, its ontological, epistemological, and information theoretical foundations. Diemer had in mind an innovating ontological approach for such a system based on the whole spectrum of kinds of being and complying with epistemological requirements. The third requirement had already been taken up somehow in the Indian Colon Classification, yet it still called for explanations and additions. In 1974, the dissertation was published in German entitled Grundlagen universaler Wissensordnung. It started with conceptual clarifications, and why and how the term „universal“ was linked to knowledge, including knowledge fields, such as commodity science, artefacts, statistics, patents, standardization, communication, utility services et al. In chapter 3, six universal classification systems (DDC, UDC, LCC, BC, CC and BBK) were presented, analyzed and compared. While preparing the dissertation, Dahlberg started with elaborating the new universal system by first gleaning a lot of extant designations of knowledge fields from whatever available reference works. This was funded by the German Documentation Society (DGD) (1971-2) under the title of Order system of knowledge fields. In addition, the syllabuses of German universities and polytechniques were explored for relevant terms and documented (1975). Thereafter, it seemed necessary to add definitions from special dictionaries and encyclopediae; it soon appeared that the 12.500 terms included numerous synonyms, so that the whole collection boiled down to about 6.500 concept designations (Project Logstruktur, supported by the German Science Foundation (DFG) 1976-78). The outcome of this work was the formulation of 30 theses which ended up in 12 principles for the new system, published 40 years later under. These principles refer not only to theoretical foundations but also to structure and other organizational aspects of the whole array of knowledge fields. In 1974, the digital position scheme for field subdivision had already been developed to allow for classifying classification literature in the bibliographical section of the first issue of the Journal International Classification. In 1977, the entire ICC was ready for presentation at a seminar in Bangalore, India. A publication of the first three hierarchical levels appeared however only in 1982. It was applied to the bibliography of classification systems and thesauri in vol.1 of the International Classification and Indexing Bibliography; it has been updated. == Governing principles == These were published in full length in the book Wissensorganisation. Entwicklung, Aufgabe, Anwendung, Zukunft and the article Information Coding Classification. Geschichtliches, Prinzipien, Inhaltliches, hence it suffices to just mention their topics with some necessary additions. Principle 1: Concept theoretical approaches. Concepts are the contents of ICC, they are understood as being units of knowledge. The „birth“ of a concept. Where do the characteristics, the knowledge elements come from? How do conceptual relations arise? Principle 2: The four kinds of concept relations and their applications. Principle 3: Decimal numbers form the ICC codes as its universal language. Principle 4: The nine ontical levels of ICC. They were grouped under three captions: Prolegomena (1-3), life sciences (4-6) and human output (7-9): Structure and form Matter and energy Cosmos and earth Biosphere Anthroposphere Sociosphere Material products (economics and technology) Intellectual products (knowledge and information) Spiritual products (products of mind and culture) Principle 5: Knowledge fields are structured by categories, based on the Aristotelian form-categories, under a digital position scheme, a kind of scaling rule for subdividing a given field as follows: General area: problems, theories, principles (axiom and structure) Object area: objects, kinds, parts, properties of objects Activity area: methods, processes, activities Field properties or first characterization Persons or secondary characterization Societies or tertiary characterization Influences from outside Applications of the field to other fields Field information and synthesizing tasks The digital position scheme, called Systematifier, has also been used for structuring the entire system via the categories figuring on the upper zero level. An example of its application is the structure of the classification system for knowledge organization literature Gliederung der Klassifikationsliteratur. (A simplified version with an additional introduction is given in, p. 71) Principle 6: The ontical levels outlined under principle 4 conform to the „integrative level theory“ which means that every level is integrated in the following one. In addition, each knowledge area presumes the following one. Principle 7: The combination potential of knowledge fields (interdisciplinarity and transdisciplinarity)is determined by the digital position scheme. (Examples are given in, p. 103-4) Principle 8: The categories of the zero-level are general concepts, their possible subdivisions could once be used for classificatory statements. (These subdivisions still need elaboration) Principle 9 and 10: These relate to the combination potential of classificatory statements with space and time concepts. (Still to be elaborated) Principle 11: The system's mnemotechnical aspect relies on the fixed system position codes and on the 3x3 form- and subject-categories. Principle 12: The combination potential of system position 1, 8 and 9 make ICC to a self-networking system which complies with the present scientific development. == In matrix form == The first two levels of ICC can be represented by following matrix. The first hierarchical level of the 9 subject categories results from the first vertical array under codes 1-9. The second hierarchical level of subject categories is structured by the 9 functionally ordered form categories, listed in the first horizontal line under codes 01-09. Some exceptions are mentioned in principle 7. == Research == === Exploration of automatic classification === For classifying web documents as conceived by Jens Hartmann, University of Karlsruhe, Prof.Walter Koch, University of Graz, has explored in his Institute for Applied Information Technology Research Society (AIT) the application of ICC to automatically classifying metadata of some 350.000 documents. This was facilitated by data generated within the framework of an E

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  Generative AI Copyright Disclosure Act

  The Generative AI Copyright Disclosure Act is a piece of legislation introduced by California Representative Adam Schiff in the United States Congress on April 9, 2024. It concerns the transparency of companies regarding their use of copyrighted work to train their generative artificial intelligence (AI) models. The legislation requires the submission of a notice regarding the identity and the uniform resource locator (URL) address of the copyrighted works used in the training data to the Register of Copyrights at least 30 days before the public release of the new or updated version of the AI model; it does not ban the use of copyrighted works for AI training. The bill's requirements would apply retroactively to prior AI models. Violation penalties would start at US$5,000. The legislation does not have a maximum penalty assessment that can be charged. The bill by Schiff was introduced a few days after The New York Times published an article regarding the business activities of major tech firms, including Google and Meta, in the training of their generative AI platforms on April 6, 2024. The legislation is supported by the Professional Photographers of America (PPA), SAG-AFTRA, the Writers Guild of America, the International Alliance of Theatrical Stage Employees (IATSE), the Recording Industry Association of America (RIAA), and others.

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  Capsule neural network

  A capsule neural network (CapsNet) is 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. The idea is to add structures called "capsules" to a convolutional neural network (CNN), and to reuse output from several of those capsules to form more stable (with respect to various perturbations) representations for higher capsules. The output is a vector consisting of the probability of an observation, and a pose for that observation. This vector is similar to what is done for example when doing classification with localization in CNNs. Among other benefits, capsnets address the "Picasso problem" in image recognition: images that have all the right parts but that are not in the correct spatial relationship (e.g., in a "face", the positions of the mouth and one eye are switched). For image recognition, capsnets exploit the fact that while viewpoint changes have nonlinear effects at the pixel level, they have linear effects at the part/object level. This can be compared to inverting the rendering of an object of multiple parts. == History == In 2000, Geoffrey Hinton et al. described an imaging system that combined segmentation and recognition into a single inference process using parse trees. So-called credibility networks described the joint distribution over the latent variables and over the possible parse trees. That system proved useful on the MNIST handwritten digit database. A dynamic routing mechanism for capsule networks was introduced by Hinton and his team in 2017. The approach was claimed to reduce error rates on MNIST and to reduce training set sizes. Results were claimed to be considerably better than a CNN on highly overlapped digits. In Hinton's original idea one minicolumn would represent and detect one multidimensional entity. == Transformations == An invariant is an object property that does not change as a result of some transformation. For example, the area of a circle does not change if the circle is shifted to the left. Informally, an equivariant is a property that changes predictably under transformation. For example, the center of a circle moves by the same amount as the circle when shifted. A nonequivariant is a property whose value does not change predictably under a transformation. For example, transforming a circle into an ellipse means that its perimeter can no longer be computed as π times the diameter. In computer vision, the class of an object is expected to be an invariant over many transformations. I.e., a cat is still a cat if it is shifted, turned upside down or shrunken in size. However, many other properties are instead equivariant. The volume of a cat changes when it is scaled. Equivariant properties such as a spatial relationship are captured in a pose, data that describes an object's translation, rotation, scale and reflection. Translation is a change in location in one or more dimensions. Rotation is a change in orientation. Scale is a change in size. Reflection is a mirror image. Unsupervised capsnets learn a global linear manifold between an object and its pose as a matrix of weights. In other words, capsnets can identify an object independent of its pose, rather than having to learn to recognize the object while including its spatial relationships as part of the object. In capsnets, the pose can incorporate properties other than spatial relationships, e.g., color (cats can be of various colors). Multiplying the object by the manifold poses the object (for an object, in space). == Pooling == Capsnets reject the pooling layer strategy of conventional CNNs that reduces the amount of detail to be processed at the next higher layer. Pooling allows a degree of translational invariance (it can recognize the same object in a somewhat different location) and allows a larger number of feature types to be represented. Capsnet proponents argue that pooling: violates biological shape perception in that it has no intrinsic coordinate frame; provides invariance (discarding positional information) instead of equivariance (disentangling that information); ignores the linear manifold that underlies many variations among images; routes statically instead of communicating a potential "find" to the feature that can appreciate it; damages nearby feature detectors, by deleting the information they rely upon. == Capsules == A capsule is a set of neurons that individually activate for various properties of a type of object, such as position, size and hue. Formally, a capsule is a set of neurons that collectively produce an activity vector with one element for each neuron to hold that neuron's instantiation value (e.g., hue). Graphics programs use instantiation value to draw an object. Capsnets attempt to derive these from their input. The probability of the entity's presence in a specific input is the vector's length, while the vector's orientation quantifies the capsule's properties. Artificial neurons traditionally output a scalar, real-valued activation that loosely represents the probability of an observation. Capsnets replace scalar-output feature detectors with vector-output capsules and max-pooling with routing-by-agreement. Because capsules are independent, when multiple capsules agree, the probability of correct detection is much higher. A minimal cluster of two capsules considering a six-dimensional entity would agree within 10% by chance only once in a million trials. As the number of dimensions increase, the likelihood of a chance agreement across a larger cluster with higher dimensions decreases exponentially. Capsules in higher layers take outputs from capsules at lower layers, and accept those whose outputs cluster. A cluster causes the higher capsule to output a high probability of observation that an entity is present and also output a high-dimensional (20-50+) pose. Higher-level capsules ignore outliers, concentrating on clusters. This is similar to the Hough transform, the RHT and RANSAC from classic digital image processing. == Routing by agreement == The outputs from one capsule (child) are routed to capsules in the next layer (parent) according to the child's ability to predict the parents' outputs. Over the course of a few iterations, each parents' outputs may converge with the predictions of some children and diverge from those of others, meaning that that parent is present or absent from the scene. For each possible parent, each child computes a prediction vector by multiplying its output by a weight matrix (trained by backpropagation). Next the output of the parent is computed as the scalar product of a prediction with a coefficient representing the probability that this child belongs to that parent. A child whose predictions are relatively close to the resulting output successively increases the coefficient between that parent and child and decreases it for parents that it matches less well. This increases the contribution that that child makes to that parent, thus increasing the scalar product of the capsule's prediction with the parent's output. After a few iterations, the coefficients strongly connect a parent to its most likely children, indicating that the presence of the children imply the presence of the parent in the scene. The more children whose predictions are close to a parent's output, the more quickly the coefficients grow, driving convergence. The pose of the parent (reflected in its output) progressively becomes compatible with that of its children. The coefficients' initial logits are the log prior probabilities that a child belongs to a parent. The priors can be trained discriminatively along with the weights. The priors depend on the location and type of the child and parent capsules, but not on the current input. At each iteration, the coefficients are adjusted via a "routing" softmax so that they continue to sum to 1 (to express the probability that a given capsule is the parent of a given child.) Softmax amplifies larger values and diminishes smaller values beyond their proportion of the total. Similarly, the probability that a feature is present in the input is exaggerated by a nonlinear "squashing" function that reduces values (smaller ones drastically and larger ones such that they are less than 1). This dynamic routing mechanism provides the necessary deprecation of alternatives ("explaining away") that is needed for segmenting overlapped objects. This learned routing of signals has no clear biological equivalent. Some operations can be found in cortical layers, but they do not seem to relate this technique. === Math/code === The pose vector u i {\textstyle \mathbf {u} _{i}} is rotated and translated by a matrix W i j {\textstyle \mathbf {W} _{ij}} into a vector u ^ j | i {\textstyle \mathbf {\hat {u}} _{j|i}} that predicts the output of the parent capsule. u ^ j | i = W i j u i {\displaystyle \mathbf {

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  Cross-entropy method

  The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous problems, with either a static or noisy objective. The method approximates the optimal importance sampling estimator by repeating two phases: Draw a sample from a probability distribution. Minimize the cross-entropy between this distribution and a target distribution to produce a better sample in the next iteration. Reuven Rubinstein developed the method in the context of rare-event simulation, where tiny probabilities must be estimated, for example in network reliability analysis, queueing models, or performance analysis of telecommunication systems. The method has also been applied to the traveling salesman, quadratic assignment, DNA sequence alignment, max-cut and buffer allocation problems. == Estimation via importance sampling == Consider the general problem of estimating the quantity ℓ = E u [ H ( X ) ] = ∫ H ( x ) f ( x ; u ) d x {\displaystyle \ell =\mathbb {E} _{\mathbf {u} }[H(\mathbf {X} )]=\int H(\mathbf {x} )\,f(\mathbf {x} ;\mathbf {u} )\,{\textrm {d}}\mathbf {x} } , where H {\displaystyle H} is some performance function and f ( x ; u ) {\displaystyle f(\mathbf {x} ;\mathbf {u} )} is a member of some parametric family of distributions. Using importance sampling this quantity can be estimated as ℓ ^ = 1 N ∑ i = 1 N H ( X i ) f ( X i ; u ) g ( X i ) {\displaystyle {\hat {\ell }}={\frac {1}{N}}\sum _{i=1}^{N}H(\mathbf {X} _{i}){\frac {f(\mathbf {X} _{i};\mathbf {u} )}{g(\mathbf {X} _{i})}}} , where X 1 , … , X N {\displaystyle \mathbf {X} _{1},\dots ,\mathbf {X} _{N}} is a random sample from g {\displaystyle g\,} . For positive H {\displaystyle H} , the theoretically optimal importance sampling density (PDF) is given by g ∗ ( x ) = H ( x ) f ( x ; u ) / ℓ {\displaystyle g^{}(\mathbf {x} )=H(\mathbf {x} )f(\mathbf {x} ;\mathbf {u} )/\ell } . This, however, depends on the unknown ℓ {\displaystyle \ell } . The CE method aims to approximate the optimal PDF by adaptively selecting members of the parametric family that are closest (in the Kullback–Leibler sense) to the optimal PDF g ∗ {\displaystyle g^{}} . == Generic CE algorithm == Choose initial parameter vector v ( 0 ) {\displaystyle \mathbf {v} ^{(0)}} ; set t = 1. Generate a random sample X 1 , … , X N {\displaystyle \mathbf {X} _{1},\dots ,\mathbf {X} _{N}} from f ( ⋅ ; v ( t − 1 ) ) {\displaystyle f(\cdot ;\mathbf {v} ^{(t-1)})} Solve for v ( t ) {\displaystyle \mathbf {v} ^{(t)}} , where v ( t ) = argmax v ⁡ 1 N ∑ i = 1 N H ( X i ) f ( X i ; u ) f ( X i ; v ( t − 1 ) ) log ⁡ f ( X i ; v ) {\displaystyle \mathbf {v} ^{(t)}=\mathop {\textrm {argmax}} _{\mathbf {v} }{\frac {1}{N}}\sum _{i=1}^{N}H(\mathbf {X} _{i}){\frac {f(\mathbf {X} _{i};\mathbf {u} )}{f(\mathbf {X} _{i};\mathbf {v} ^{(t-1)})}}\log f(\mathbf {X} _{i};\mathbf {v} )} If convergence is reached then stop; otherwise, increase t by 1 and reiterate from step 2. In several cases, the solution to step 3 can be found analytically. Situations in which this occurs are When f {\displaystyle f\,} belongs to the natural exponential family When f {\displaystyle f\,} is discrete with finite support When H ( X ) = I { x ∈ A } {\displaystyle H(\mathbf {X} )=\mathrm {I} _{\{\mathbf {x} \in A\}}} and f ( X i ; u ) = f ( X i ; v ( t − 1 ) ) {\displaystyle f(\mathbf {X} _{i};\mathbf {u} )=f(\mathbf {X} _{i};\mathbf {v} ^{(t-1)})} , then v ( t ) {\displaystyle \mathbf {v} ^{(t)}} corresponds to the maximum likelihood estimator based on those X k ∈ A {\displaystyle \mathbf {X} _{k}\in A} . == Continuous optimization—example == The same CE algorithm can be used for optimization, rather than estimation. Suppose the problem is to maximize some function S {\displaystyle S} , for example, S ( x ) = e − ( x − 2 ) 2 + 0.8 e − ( x + 2 ) 2 {\displaystyle S(x)={\textrm {e}}^{-(x-2)^{2}}+0.8\,{\textrm {e}}^{-(x+2)^{2}}} . To apply CE, one considers first the associated stochastic problem of estimating P θ ( S ( X ) ≥ γ ) {\displaystyle \mathbb {P} _{\boldsymbol {\theta }}(S(X)\geq \gamma )} for a given level γ {\displaystyle \gamma \,} , and parametric family { f ( ⋅ ; θ ) } {\displaystyle \left\{f(\cdot ;{\boldsymbol {\theta }})\right\}} , for example the 1-dimensional Gaussian distribution, parameterized by its mean μ t {\displaystyle \mu _{t}\,} and variance σ t 2 {\displaystyle \sigma _{t}^{2}} (so θ = ( μ , σ 2 ) {\displaystyle {\boldsymbol {\theta }}=(\mu ,\sigma ^{2})} here). Hence, for a given γ {\displaystyle \gamma \,} , the goal is to find θ {\displaystyle {\boldsymbol {\theta }}} so that D K L ( I { S ( x ) ≥ γ } ‖ f θ ) {\displaystyle D_{\mathrm {KL} }({\textrm {I}}_{\{S(x)\geq \gamma \}}\|f_{\boldsymbol {\theta }})} is minimized. This is done by solving the sample version (stochastic counterpart) of the KL divergence minimization problem, as in step 3 above. It turns out that parameters that minimize the stochastic counterpart for this choice of target distribution and parametric family are the sample mean and sample variance corresponding to the elite samples, which are those samples that have objective function value ≥ γ {\displaystyle \geq \gamma } . The worst of the elite samples is then used as the level parameter for the next iteration. This yields the following randomized algorithm that happens to coincide with the so-called Estimation of Multivariate Normal Algorithm (EMNA), an estimation of distribution algorithm. === Pseudocode === // Initialize parameters μ := −6 σ2 := 100 t := 0 maxits := 100 N := 100 Ne := 10 // While maxits not exceeded and not converged while t < maxits and σ2 > ε do // Obtain N samples from current sampling distribution X := SampleGaussian(μ, σ2, N) // Evaluate objective function at sampled points S := exp(−(X − 2) ^ 2) + 0.8 exp(−(X + 2) ^ 2) // Sort X by objective function values in descending order X := sort(X, S) // Update parameters of sampling distribution via elite samples μ := mean(X(1:Ne)) σ2 := var(X(1:Ne)) t := t + 1 // Return mean of final sampling distribution as solution return μ == Related methods == Simulated annealing Genetic algorithms Harmony search Estimation of distribution algorithm Tabu search Natural Evolution Strategy Ant colony optimization algorithms

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  Visual hierarchy

  Visual hierarchy, in Gestalt psychology, describes how particular elements in a visual field stand out more than others in a pattern, creating a perceived order of importance. Although it can occur naturally, the term is most often used in design—especially graphic design and cartography—where elements are arranged to appear more important than others. This order is created by the visual contrast between forms in a field of perception. Objects with highest contrast to their surroundings are recognized first by the human mind. == Evidence == There is some scientific evidence for visual hierarchy using eye tracking. For example, one study found that when people agree that a graphic design is good, they exhibit more similar eye movements; measured by the Fréchet distance. == Theory == The concept of visual hierarchy is based in Gestalt psychological theory, an early 20th-century German theory that proposes that the human brain has innate organizing tendencies that “structure individual elements, shapes or forms into a coherent, organized whole,” especially when processing visual information. The German word Gestalt translates into “form,” “pattern,” or “shape” in English. When an element in a visual field disconnects from the ‘whole’ created by the brain's perceptual organization, it “stands out” to the viewer. The shapes that disconnect most severely from their surroundings stand out the most. This is commonly encapsulated as the Von Restorff effect, which states that isolation attracts attention. === Physical characteristics === The brain distinguishes objects based on differences in their physical appearances. These characteristics fall into four categories: color, size, alignment, and character. Each type of contrast can be used to construct a visual hierarchy. The same characteristics are also sometimes categorized (especially among cartographers) according to the visual variables of Jacques Bertin. Color encompasses the hue, saturation, value, and perceived texture of forms. Dark figures will stand out on a light background, light figures will stand out on a dark background, brightly colored figures will stand out on a muted background, and so on. The fluorescent colors used for tennis balls and other sports equipment is intended to make them instantly stand out against almost any natural visual field. Size has a strong influence on visual hierarchy. Large elements typically attract attention, provided that they can be recognized as figures. Alignment is the arrangement of forms relative to one another. For example, items in the upper left corner of a page are often seen first (at least for those readers accustomed to western languages), the center of the field has prominence. Negative space can also be employed: a figure isolated among large amounts of white space will stand out more than one amid other figures. Character includes several kinds of contrasts based on shape. For example, complex patterns attract more attention than simple or predictable patterns, intricate shapes attract more attention than generalized ones. Even large-scale patterns can attract attention if they contrast with the pattern in the remainder of the visual field. Camouflage is an example of eliminating contrast in character in color and/or character specifically to reduce visual hierarchy. The "squint test" is often suggested as a simple, if unscientific, method to evaluate the visual hierarchy of a graphical product like a map or web page. When viewed out of focus (or from a great distance), the viewer is not distracted by details, but can only see overall (gestalt) patterns such as visual hierarchy. All of the above patterns, except some aspects of character, are recognizable by this method. == Application == Visual hierarchy is an important concept in the field of graphic design, a field that specializes in visual organization. Designers attempt to control visual hierarchy to guide the eye to information in a specific order for a specific purpose. One could compare visual hierarchy in graphic design to grammatical structure in writing in terms of the importance of each principle to these fields. === Cartography === In cartographic design, visual hierarchy is used to emphasize certain important features on a map over less important features. Typically, a map has a purpose that dictates a conceptual hierarchy of what should be more or less important, so one of the goals of the choice of map symbols is to match the visual hierarchy to the conceptual hierarchy. The Visual hierarchy of a map may apply to individual geographic features (such as making a single country stand out), to map layers of related features (e.g., making lakes stand out more than roads), and to the entire layout of map and non-map elements (e.g., making the title look more important than the scale bar). Like the main map elements, such features have weight, and the properties that apply to visual hierarchy of map layers also apply to other elements on the page. Size and alignment are the two main determinants of the visual hierarchy for these features. Cartographers often utilize principles of negative space and figure-ground contrast to design an appropriate visual hierarchy by employing contrast between unused space and layout features. === User experience design and behavioral design === In user experience design and behavioural design, such as web design, visual hierarchy is used to prioritize navigational structures and content, so that audiences focus on elements that facilitate system usage, or increases the chance that they notice content that contains psychological nudges. Color is one of many factors used in the design of a visual hierarchy, and a key factor due to the high salience of color perception.

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  OpenVX

  OpenVX is an open, royalty-free standard for cross-platform acceleration of computer vision applications. It is designed by the Khronos Group to facilitate portable, optimized and power-efficient processing of methods for vision algorithms. This is aimed for embedded and real-time programs within computer vision and related scenarios. It uses a connected graph representation of operations. == Overview == OpenVX specifies a higher level of abstraction for programming computer vision use cases than compute frameworks such as OpenCL. The high level makes the programming easy and the underlying execution will be efficient on different computing architectures. This is done while having a consistent and portable vision acceleration API. OpenVX is based on a connected graph of vision nodes that can execute the preferred chain of operations. It uses an opaque memory model, allowing to move image data between the host (CPU) memory and accelerator, such as GPU memory. As a result, the OpenVX implementation can optimize the execution through various techniques, such as acceleration on various processing units or dedicated hardware. This architecture facilitates applications programmed in OpenVX on different systems with different power and performance, including battery-sensitive, vision-enabled, wearable displays. OpenVX is complementary to the open source vision library OpenCV. OpenVX in some applications offers a better optimized graph management than OpenCV. == History == OpenVX 1.0 specification was released in October 2014. OpenVX sample implementation was released in December 2014. OpenVX 1.1 specification was released on May 2, 2016. OpenVX 1.2 was released on May 1, 2017. Updated OpenVX adopters program and OpenVX 1.2 conformance test suite was released on November 21, 2017. OpenVX 1.2.1 was released on November 27, 2018. OpenVX 1.3 was released on October 22, 2019. == Implementations, frameworks and libraries == AMD MIVisionX Archived 2019-08-05 at the Wayback Machine - for AMD's CPUs and GPUs. Cadence - for Cadence Design Systems's Tensilica Vision DSPs. Imagination - for Imagination Technologies's PowerVR GPUs Synopsys - for Synopsys' DesignWare EV Vision Processors Texas Instruments’ OpenVX (TIOVX) - for Texas Instruments’ Jacinto™ ADAS SoCs. NVIDIA VisionWorks - for CUDA-capable Nvidia GPUs and SoCs. OpenVINO - for Intel's CPUs, GPUs, VPUs, and FPGAs.

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  CuckooChess

  CuckooChess is an advanced free and open-source chess engine under the GNU General Public License written in Java by Peter Österlund. CuckooChess provides an own GUI, and optionally supports the Universal Chess Interface protocol for the use with external GUIs such as Arena. An Android port is available, where its GUI is also based on Peter Österlund's Stockfish port dubbed DroidFish. The program uses the Chess Cases chess font, created by Matthieu Leschemelle. The name CuckooChess comes due that the transposition table is based on Cuckoo hashing. Android app based chess gaming app Droidfish employs both CuckooChess and Stockfish chess engines. Similarly, Kickstarter funded AI based virtual reality chess game Square Off also uses CuckooChess engine. It has an ELO rating of 2583 (as of July 2018) and a rank of 135‑137 in the Computer Chess Rating List.

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  Candid (app)

  Candid was a mobile app for anonymous discussions. It used machine learning to create personalized newsfeeds of opinions and real conversations, and also for moderation and filtering. Users posted under pseudonyms such as "HyperMantis", "SincereGiraffe", "GroundedTurtle" and "ExuberantRaptor", that are unique for each thread. Founder and CEO Bindu Reddy said that she needed "a place to express myself and engage in discussions where ideas can be debated on their own merits instead of being used to attack me as a person", which Candid tried to solve by redirecting off-topic comments to their appropriate groups, removing spam and flagging negative posts. They used natural language processing to identify hate speech, slander and threats, and removed them accordingly with human intervention. Candid software analyzed topics and tried to flag rumors and lies as such. Users could flag problematic posts and a team of ten contractors would review them individually. With time the system analyzed a user's interactions and give them labels, such as socializer, explorer, positive, influencer, hater, gossip, etc. In June 2017, Candid announced that it would be shut down because its parent company, Post Intelligence, was being acquired. The app was forecast to close on June 23, 2017, but didn't actually close until June 25, 2017.

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  Lethal autonomous weapon

  A lethal autonomous weapon (LAW), also known as a lethal autonomous weapon system (LAWS), autonomous weapon system (AWS), robotic weapon, or killer robot, is a type of military drone or military robot, which is autonomous in that it can independently search for and engage targets based on programmed constraints and descriptions. As of 2025, most military drones (including unmanned aerial vehicles and unmanned combat aerial vehicles) and military robots are not truly autonomous. LAWs may engage in drone warfare in the air, on land, on water, underwater, or in space. == Definitions == In weapons development, the term "autonomous" is somewhat ambiguous and can vary hugely between different scholars, nations and organizations. There is no definition of lethal autonomous weapon systems that is generally agreed upon among different countries. The official United States Department of Defense Policy on Autonomy in Weapon Systems (Department of Defense Directive 3000.09) defines an Autonomous Weapon System as one that "...once activated, can select and engage targets without further intervention by a human operator." Heather Roff, a writer for Case Western Reserve University School of Law, describes autonomous weapon systems as "... capable of learning and adapting their 'functioning in response to changing circumstances in the environment in which [they are] deployed,' as well as capable of making firing decisions on their own." The British Ministry of Defence states "Whilst definitions can vary, the key difference is that an automated system is capable of carrying out complicated tasks but is incapable of complex decision-making, whereas an autonomous system is capable of deciding a course of action without depending on human oversight and control." Scholars such as Peter Asaro and Mark Gubrud believe that any weapon system that is capable of releasing a lethal force without the operation, decision, or confirmation of a human supervisor can be deemed autonomous. == Automatic defensive systems == Some definitions of autonomous weapon systems are broad enough to include land mines and naval mines, simple automatically-triggered lethal weapons that have been in use for centuries. Some current examples of LAWs are automated "hardkill" active protection systems, such as a radar-guided close-in weapon systems (CIWS) used to defend ships that have been in use since the 1970s (e.g., the US Phalanx CIWS). Such systems can autonomously identify and attack oncoming missiles, rockets, artillery fire, aircraft, and surface vessels according to criteria set by the human operator. Similar systems exist for tanks, such as the Russian Arena, the Israeli Trophy, and the German AMAP-ADS. Several types of stationary sentry guns, which can fire at humans and vehicles, are used in South Korea and Israel. Many missile defence systems, such as Iron Dome, also have autonomous targeting capabilities. The main reason for not having a "human in the loop" in these systems is the need for rapid response. They have generally been used to protect personnel and installations against incoming projectiles. == Autonomous offensive systems == According to The Economist in 2018, as technology advances, applications of uncrewed undersea vehicles could include mine clearance, mine-laying, anti-submarine sensor networking in contested waters, patrolling with active sonar, resupplying manned submarines, and becoming low-cost missile platforms. In 2017 the Russian Federation was developing artificially intelligent missiles, drones, unmanned vehicles, military robots and medic robots. In 2018, the U.S. Nuclear Posture Review alleged that Russia was developing a "new intercontinental, nuclear-armed, nuclear-powered, undersea autonomous torpedo" named "Status 6". Israeli Minister Ayoob Kara stated in 2017 that Israel is developing military robots, including ones as small as flies. In October 2018, Zeng Yi, a senior executive at the Chinese defense firm Norinco, gave a speech in which he said that "In future battlegrounds, there will be no people fighting", and that the use of lethal autonomous weapons in warfare is "inevitable". In 2019, US Defense Secretary Mark Esper lashed out at China for selling drones capable of taking life with no human oversight. As of 2020, DARPA was working on making swarms of 250 autonomous lethal drones available to the American military. The US Navy is developing unmanned surface vehicles, also called sea drones, including Ghost Fleet Overlord, with plans to equip them with weapons and with the potential to use them semi-autonomously. In 2020 a Kargu 2 drone hunted down and attacked a human target in Libya, according to a report from the UN Security Council's Panel of Experts on Libya, published in March 2021. This may have been the first time an autonomous killer robot armed with lethal weaponry attacked human beings. In May 2021 Israel conducted an AI-guided combat drone swarm attack in Gaza. In the Russo-Ukrainian war, Ukraine has developed advanced drones with integrated artificial intelligence for a range of drone warfare purposes, including to attack infrastructure in Russia, although as of May 2026, Al Jazeera reported that humans remain in control of operation. == Ethical and legal issues == === Degree of human control === Three classifications of the degree of human control of autonomous weapon systems were laid out by Bonnie Docherty in a 2012 Human Rights Watch report. human-in-the-loop: a human must instigate the action of the weapon (in other words not fully autonomous). human-on-the-loop: a human may abort an action. human-out-of-the-loop: no human action is involved. === Standard used in US policy === Department of Defense Directive 3000.09 states that "Autonomous … weapons systems shall be designed to allow commanders and operators to exercise appropriate levels of human judgment over the use of force." However, as noted in the Bulletin of the Atomic Scientists, the policy requires that autonomous weapon systems that kill people or use kinetic force, selecting and engaging targets without further human intervention, be certified as compliant with "appropriate levels" and other standards, not that such weapon systems cannot meet these standards and are therefore forbidden. "Semi-autonomous" hunter-killers that autonomously identify and attack targets do not even require certification. Deputy Defense Secretary Robert O. Work said in 2016 that the Defense Department would "not delegate lethal authority to a machine to make a decision", but might need to reconsider this since "authoritarian regimes" may do so. In October 2016 President Barack Obama stated that early in his career he was wary of a future in which a US president making use of drone warfare could "carry on perpetual wars all over the world, and a lot of them covert, without any accountability or democratic debate". In the US, security-related AI has fallen under the purview of the National Security Commission on Artificial Intelligence since 2018. On October 31, 2019, the United States Department of Defense's Defense Innovation Board published the draft of a report outlining five principles for weaponized AI and making 12 recommendations for the ethical use of artificial intelligence by the Department of Defense that would ensure a human operator would always be able to look into the 'black box' and understand the kill-chain process. A major concern is how the report will be implemented. === Possible violations of ethics and international acts === Stuart Russell, professor of computer science from University of California, Berkeley stated the concern he has with LAWs is that his view is that it is unethical and inhumane. The main issue with this system is it is hard to distinguish between combatants and non-combatants. There is concern by some economists and legal scholars about whether LAWs would violate International Humanitarian Law, especially the principle of distinction, which requires the ability to discriminate combatants from non-combatants, and the principle of proportionality, which requires that damage to civilians be proportional to the military aim. This concern is often invoked as a reason to ban "killer robots" altogether - but it is doubtful that this concern can be an argument against LAWs that do not violate International Humanitarian Law. A 2021 report by the American Congressional Research Service states that "there are no domestic or international legal prohibitions on the development of use of LAWs," although it acknowledges ongoing talks at the UN Convention on Certain Conventional Weapons (CCW). LAWs are said by some to blur the boundaries of who is responsible for a particular killing. Philosopher Robert Sparrow argues that autonomous weapons are causally but not morally responsible, similar to child soldiers. In each case, he argues there is a risk of atrocities occurring without an appropriate subject to hold responsible, which violates jus in bell

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  ICAD (software)

  ICAD (Corporate history: ICAD, Inc., Concentra (name change at IPO in 1995), KTI (name change in 1998), Dassault Systèmes (purchase in 2001) () is a knowledge-based engineering (KBE) system that enables users to encode design knowledge using a semantic representation that can be evaluated for Parasolid output. ICAD has an open architecture that can utilize all the power and flexibility of the underlying language. KBE, as implemented via ICAD, received a lot of attention due to the remarkable results that appeared to take little effort. ICAD allowed one example of end-user computing that in a sense is unparalleled. Most ICAD developers were degreed engineers. Systems developed by ICAD users were non-trivial and consisted of highly complicated code. In the sense of end-user computing, ICAD was the first to allow the power of a domain tool to be in the hands of the user, at the same time being open to allow extensions as identified and defined by the domain expert or subject-matter expert (SME). A COE article looked at the resulting explosion of expectations (see AI winter), which were not sustainable. However, such a bubble burst does not diminish the existence of ability that would exist were expectations and use reasonable or properly managed. == History == The original implementation of ICAD was on a Lisp machine (Symbolics). Some of the principals involved with the development were Larry Rosenfeld, Avrum Belzer, Patrick M. O'Keefe, Philip Greenspun, and David F. Place. The time frame was 1984–85. ICAD started on special-purpose Symbolics Lisp hardware and was then ported to Unix when Common Lisp became portable to general-purpose workstations. The original domain for ICAD was mechanical design with many application successes. However, ICAD has found use in other domains, such as electrical design, shape modeling, etc. An example project could be wind tunnel design or the development of a support tool for aircraft multidisciplinary design. Further examples can be found in the presentations at the annual IIUG (International ICAD Users Group) that have been published in the KTI Vault (1999 through 2002). Boeing and Airbus used ICAD extensively to develop various components in the 1990s and early 21st century. As of 2003, ICAD was featured strongly in several areas as evidenced by the Vision & Strategy Product Vision and Strategy presentation. After 2003, ICAD use diminished. At the end of 2001, the KTI Company faced financial difficulties and laid off most of its best staff. They were eventually bought out by Dassault who effectively scuppered the ICAD product. See IIUG at COE, 2003 (first meeting due to Dassault by KTI) The ICAD system was very expensive, relatively, and was in the price range of high-end systems. Market dynamics couldn't support this as there may not have been sufficient differentiating factors between ICAD and the lower-end systems (or the promises from Dassault). KTI was absorbed by Dassault Systèmes and ICAD is no longer considered the go-forward tool for knowledge-based engineering (KBE) applications by that company. Dassault Systèmes is promoting a suite of tools oriented around version 5 of their popular CATIA CAD application, with Knowledgeware the replacement for ICAD. As of 2005, things were still a bit unclear. ICAD 8.3 was delivered. The recent COE Aerospace Conference had a discussion about the futures of KBE. One issue involves the stacking of 'meta' issues within a computer model. How this is resolved, whether by more icons or the availability of an external language, remains to be seen. The Genworks GDL product (including kernel technology from the Gendl Project) is the nearest functional equivalent to ICAD currently available. == Particulars == ICAD provided a declarative language (IDL) using New Flavors (never converted to Common Lisp Object System (CLOS)) that supported a mechanism for relating parts (defpart) via a hierarchical set of relationships. Technically, the ICAD Defpart was a Lisp macro; the ICAD defpart list was a set of generic classes that can be instantiated with specific properties depending upon what was represented. This defpart list was extendible via composited parts that represented domain entities. Along with the part-subpart relations, ICAD supported generic relations via the object modeling abilities of Lisp. Example applications of ICAD range from a small collection of defparts that represents a part or component to a larger collection that represents an assembly. In terms of power, an ICAD system, when fully specified, can generate thousands of instances of parts on a major assembly design. One example of an application driving thousands of instances of parts is that of an aircraft wing – where fastener type and placement may number in the thousands, each instance requiring evaluation of several factors driving the design parameters. == Futures (KBE, etc.) == One role for ICAD may be serving as the defining prototype for KBE which would require that we know more about what occurred the past 15 years (much information is tied up behind corporate firewalls and under proprietary walls). With the rise of functional programming languages (an example is Haskell) in the markets, perhaps some of the power attributable to Lisp may be replicated.

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  User modeling

  User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing but should not replace user testing. == Background == A user model is the collection and categorization of personal data associated with a specific user. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and a user profile is the actual representation in a given user model. The process of obtaining the user profile is called user modeling. Therefore, it is the basis for any adaptive changes to the system's behavior. Which data is included in the model depends on the purpose of the application. It can include personal information such as users' names and ages, their interests, their skills and knowledge, their goals and plans, their preferences and their dislikes or data about their behavior and their interactions with the system. There are different design patterns for user models, though often a mixture of them is used. Static user models Static user models are the most basic kinds of user models. Once the main data is gathered they are normally not changed again, they are static. Shifts in users' preferences are not registered and no learning algorithms are used to alter the model. Dynamic user models Dynamic user models allow a more up to date representation of users. Changes in their interests, their learning progress or interactions with the system are noticed and influence the user models. The models can thus be updated and take the current needs and goals of the users into account. Stereotype based user models Stereotype based user models are based on demographic statistics. Based on the gathered information users are classified into common stereotypes. The system then adapts to this stereotype. The application therefore can make assumptions about a user even though there might be no data about that specific area, because demographic studies have shown that other users in this stereotype have the same characteristics. Thus, stereotype based user models mainly rely on statistics and do not take into account that personal attributes might not match the stereotype. However, they allow predictions about a user even if there is rather little information about him or her. Highly adaptive user models Highly adaptive user models try to represent one particular user and therefore allow a very high adaptivity of the system. In contrast to stereotype based user models they do not rely on demographic statistics but aim to find a specific solution for each user. Although users can take great benefit from this high adaptivity, this kind of model needs to gather a lot of information first. == Data gathering == Information about users can be gathered in several ways. There are three main methods: Asking for specific facts while (first) interacting with the system Mostly this kind of data gathering is linked with the registration process. While registering users are asked for specific facts, their likes and dislikes and their needs. Often the given answers can be altered afterwards. Learning users' preferences by observing and interpreting their interactions with the system In this case users are not asked directly for their personal data and preferences, but this information is derived from their behavior while interacting with the system. The ways they choose to accomplish a tasks, the combination of things they takes interest in, these observations allow inferences about a specific user. The application dynamically learns from observing these interactions. Different machine learning algorithms may be used to accomplish this task. A hybrid approach which asks for explicit feedback and alters the user model by adaptive learning This approach is a mixture of the ones above. Users have to answer specific questions and give explicit feedback. Furthermore, their interactions with the system are observed and the derived information are used to automatically adjust the user models. Though the first method is a good way to quickly collect main data it lacks the ability to automatically adapt to shifts in users' interests. It depends on the users' readiness to give information and it is unlikely that they are going to edit their answers once the registration process is finished. Therefore, there is a high likelihood that the user models are not up to date. However, this first method allows the users to have full control over the collected data about them. It is their decision which information they are willing to provide. This possibility is missing in the second method. Adaptive changes in a system that learns users' preferences and needs only by interpreting their behavior might appear a bit opaque to the users, because they cannot fully understand and reconstruct why the system behaves the way it does. Moreover, the system is forced to collect a certain amount of data before it is able to predict the users' needs with the required accuracy. Therefore, it takes a certain learning time before a user can benefit from adaptive changes. However, afterwards these automatically adjusted user models allow a quite accurate adaptivity of the system. The hybrid approach tries to combine the advantages of both methods. Through collecting data by directly asking its users it gathers a first stock of information which can be used for adaptive changes. By learning from the users' interactions it can adjust the user models and reach more accuracy. Yet, the designer of the system has to decide, which of these information should have which amount of influence and what to do with learned data that contradicts some of the information given by a user. == System adaptation == Once a system has gathered information about a user it can evaluate that data by preset analytical algorithm and then start to adapt to the user's needs. These adaptations may concern every aspect of the system's behavior and depend on the system's purpose. Information and functions can be presented according to the user's interests, knowledge or goals by displaying only relevant features, hiding information the user does not need, making proposals what to do next and so on. One has to distinguish between adaptive and adaptable systems. In an adaptable system the user can manually change the system's appearance, behavior or functionality by actively selecting the corresponding options. Afterwards the system will stick to these choices. In an adaptive system a dynamic adaption to the user is automatically performed by the system itself, based on the built user model. Thus, an adaptive system needs ways to interpret information about the user in order to make these adaptations. One way to accomplish this task is implementing rule-based filtering. In this case a set of IF... THEN... rules is established that covers the knowledge base of the system. The IF-conditions can check for specific user-information and if they match the THEN-branch is performed which is responsible for the adaptive changes. Another approach is based on collaborative filtering. In this case information about a user is compared to that of other users of the same systems. Thus, if characteristics of the current user match those of another, the system can make assumptions about the current user by presuming that he or she is likely to have similar characteristics in areas where the model of the current user is lacking data. Based on these assumption the system then can perform adaptive changes. == Usages == Adaptive hypermedia: In an adaptive hypermedia system the displayed content and the offered hyperlinks are chosen on basis of users' specific characteristics, taking their goals, interests, knowledge and abilities into account. Thus, an adaptive hypermedia system aims to reduce the "lost in hyperspace" syndrome by presenting only relevant information. Adaptive educational hypermedia: Being a subdivision of adaptive hypermedia the main focus of adaptive educational hypermedia lies on education, displaying content and hyperlinks corresponding to the user's knowledge on the field of study. Intelligent tutoring system: Unlike adaptive educational hypermedia systems intelligent tutoring systems are stand-alone systems. Their aim is to help students in a specific field of study. To do so, they build up a user model where they store information about abilities, knowledge and needs of the user. The system can now adapt to this user by presenting approp

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  2024 National Public Data breach

  In August 2024, three class-action lawsuits were filed against National Public Data along with over 14 complaints filed in federal court, claiming that the company permitted hackers to steal sensitive private information covering millions of individuals. The theft was alleged to have occurred in April 2024. One of the lawsuits specifically claims that in April, a hacker going by the moniker "USDoD" posted a notice on the dark web, offering the data for sale at the price of US$3.5 million. The information stolen is alleged to include 2.9 billion records containing full names, current and past addresses, Social Security numbers, dates of birth, and telephone numbers. The stolen data contains records for people in the US, UK, and Canada. National Public Data confirmed on August 16, 2024, there was a breach originating from someone trying to breach their systems since December 2023, with the breach occurring from April 2024 and over the next few months. The company also confirmed that 2.9 billion records were obtained, though they were still working to determine how many people were affected by the breach, and were working with law enforcement to identify the hacker. == Jerico Pictures == Jerico Pictures, Inc., doing business as National Public Data, was a data broker company that performed employee background checks. Their primary service was collecting information from public data sources, including criminal records, addresses, and employment history, and offering that information for sale. On October 2, 2024, Jerico Pictures filed for Chapter 11 bankruptcy as it currently faces over a dozen lawsuits over the breach, and is potentially liable "for credit monitoring for hundreds of millions of potentially impacted individuals." In December 2024, National Public Data shut down, showing a closure notice on its website.

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  Representational harm

  Systems cause representational harm when they misrepresent a group of people in a negative manner. Representational harms include perpetuating harmful stereotypes about or minimizing the existence of a social group, such as a racial, ethnic, gender, or religious group. Machine learning algorithms often commit representational harm when they learn patterns from data that have algorithmic bias, and this has been shown to be the case with large language models. While preventing representational harm in models is essential to prevent harmful biases, researchers often lack precise definitions of representational harm and conflate it with allocative harm, an unequal distribution of resources among social groups, which is more widely studied and easier to measure. However, recognition of representational harms is growing and preventing them has become an active research area. Researchers have recently developed methods to effectively quantify representational harm in algorithms, making progress on preventing this harm in the future. == Types == Three prominent types of representational harm include stereotyping, denigration, and misrecognition. These subcategories present many dangers to individuals and groups. Stereotypes are oversimplified and usually undesirable representations of a specific group of people, usually by race and gender. This often leads to the denial of educational, employment, housing, and other opportunities. For example, the model minority stereotype of Asian Americans as highly intelligent and good at mathematics can be damaging professionally and academically. Representational harm happens when the representation of details teams improves damaging stereotypes, developing social exclusion and prejudice. This experience is particularly noticeable in the depiction of marginalised groups, containing people of color, women, LGBTQ+ people, and people with handicaps. Media depictions of these groups generally stop working to catch their array and intricacy. Instead, they are typically reduced to one-dimensional caricatures, which ultimately continue social prejudices. These organised depictions contribute to the help of hazardous stereotypes and the marginalisation of these locations. Denigration is the action of unfairly criticizing individuals. This frequently happens when the demeaning of social groups occurs. For example, when searching for "Black-sounding" names versus "white-sounding" ones, some retrieval systems bolster the false perception of criminality by displaying ads for bail-bonding businesses. A system may shift the representation of a group to be of lower social status, often resulting in a disregard from society. Research shows that hazardous depictions in the media can have substantial emotional and social impacts on both individuals and areas. Lawrence Bobo examined the issue of Ethnic stereotype in film, tv, and marketing. African Americans are commonly received duties specified by features such as "violent tendencies," "laziness," or being "merely for contentment features." While these representations might appear varied externally, they stay to boost underlying frameworks of white prominence and racial inequality. As a circumstances, Black individuals are frequently represented as law offenders or in secondary roles, which adds to the support of Ethnic stereotype and Institutional racism. Misrecognition, or incorrect recognition, can display in many forms, including, but not limited to, erasing and alienating social groups, and denying people the right to self-identify. Erasing and alienating social groups involves the unequal visibility of certain social groups; specifically, systematic ineligibility in algorithmic systems perpetuates inequality by contributing to the underrepresentation of social groups. Not allowing people to self-identify is closely related as people's identities can be 'erased' or 'alienated' in these algorithms. Misrecognition causes more than surface-level harm to individuals: psychological harm, social isolation, and emotional insecurity can emerge from this subcategory of representational harm. == Quantification == As the dangers of representational harm have become better understood, some researchers have developed methods to measure representational harm in algorithms. Modeling stereotyping is one way to identify representational harm. Representational stereotyping can be quantified by comparing the predicted outcomes for one social group with the ground-truth outcomes for that group observed in real data. For example, if individuals from group A achieve an outcome with a probability of 60%, stereotyping would be observed if it predicted individuals to achieve that outcome with a probability greater than 60%. The group modeled stereotyping in the context of classification, regression, and clustering problems, and developed a set of rules to quantitatively determine if the model predictions exhibit stereotyping in each of these cases. Other attempts to measure representational harms have focused on applications of algorithms in specific domains such as image captioning, the act of an algorithm generating a short description of an image. In a study on image captioning, researchers measured five types of representational harm. To quantify stereotyping, they measured the number of incorrect words included in the model-generated image caption when compared to a gold-standard caption. They manually reviewed each of the incorrectly included words, determining whether the incorrect word reflected a stereotype associated with the image or whether it was an unrelated error, which allowed them to have a proxy measure of the amount of stereotyping occurring in this caption generation. These researchers also attempted to measure demeaning representational harm. To measure this, they analyzed the frequency with which humans in the image were mentioned in the generated caption. It was hypothesized that if the individuals were not mentioned in the caption, then this was a form of dehumanization. == Examples == One of the most notorious examples of representational harm was committed by Google in 2015 when an algorithm in Google Photos classified Black people as gorillas. Developers at Google said that the problem was caused because there were not enough faces of Black people in the training dataset for the algorithm to learn the difference between Black people and gorillas. Google issued an apology and fixed the issue by blocking its algorithms from classifying anything as a primate. In 2023, Google's photos algorithm was still blocked from identifying gorillas in photos. Another prevalent example of representational harm is the possibility of stereotypes being encoded in word embeddings, which are trained using a wide range of text. These word embeddings are the representation of a word as an array of numbers in vector space, which allows an individual to calculate the relationships and similarities between words. However, recent studies have shown that these word embeddings may commonly encode harmful stereotypes, such as the common example that the phrase "computer programmer" is oftentimes more closely related to "man" than it is to "women" in vector space. This could be interpreted as a misrepresentation of computer programming as a profession that is better performed by men, which would be an example of representational harm. == Addressing representational harm == Initiatives to minimise representational harm include advertising for even more inclusive and accurate portrayals of marginalised teams in the media. Scholars and protestors recommend that the method to reducing representational injury depends on raising the selection of voices both behind and before the digital video camera. When marginalized groups are provided the chance to represent themselves, they can check traditional stereotypes and present their experiences additional authentically. Over the last few years, efforts to increase representation of people of color, women, and LGBTQ+ people in conventional media have made some progression. Films such as Selma, routed by Ava DuVernay, and tv series like Pose, developed by Ryan Murphy, have actually been extensively applauded for their nuanced and respectful representations of marginalised communities. These tasks existing complex individualities and stories that move past streamlined stereotypes. Self-representation is one more crucial method to addressing representational harm. By equipping marginalised locations to create their really own tales, media designers can effectively reduce the perpetuation of hazardous stereotypes. This procedure consists of both the manufacturing of media product by participants of these communities and proactively difficult typical media structures that have actually historically omitted them.

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  Computational creativity

  Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts (e.g., computational art as part of computational culture). Is the application of computer systems to emulate human-like creative processes, facilitating the generation of artistic and design outputs that mimic innovation and originality. The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends: To construct a program or computer capable of human-level creativity. To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans. To design programs that can enhance human creativity without necessarily being creative themselves. The field of computational creativity concerns itself with theoretical and practical issues in the study of creativity. Theoretical work on the nature and proper definition of creativity is performed in parallel with practical work on the implementation of systems that exhibit creativity, with one strand of work informing the other. The applied form of computational creativity is known as media synthesis. == Theoretical issues == Theoretical approaches concern the essence of creativity. Especially, under what circumstances it is possible to call the model a "creative" if eminent creativity is about rule-breaking or the disavowal of convention. This is a variant of Ada Lovelace's objection to machine intelligence, as recapitulated by modern theorists such as Teresa Amabile. If a machine can do only what it was programmed to do, how can its behavior ever be called creative? Indeed, not all computer theorists would agree with the premise that computers can only do what they are programmed to do—a key point in favor of computational creativity. == Defining creativity in computational terms == Because no single perspective or definition seems to offer a complete picture of creativity, the AI researchers Newell, Shaw and Simon developed the combination of novelty and usefulness into the cornerstone of a multi-pronged view of creativity, one that uses the following four criteria to categorize a given answer or solution as creative: The answer is novel and useful (either for the individual or for society) The answer demands that we reject ideas we had previously accepted The answer results from intense motivation and persistence The answer comes from clarifying a problem that was originally vague Margaret Boden focused on the first two of these criteria, arguing instead that creativity (at least when asking whether computers could be creative) should be defined as "the ability to come up with ideas or artifacts that are new, surprising, and valuable". Mihaly Csikszentmihalyi argued that creativity had to be considered instead in a social context, and his DIFI (Domain-Individual-Field Interaction) framework has since strongly influenced the field. In DIFI, an individual produces works whose novelty and value are assessed by the field—other people in society—providing feedback and ultimately adding the work, now deemed creative, to the domain of societal works from which an individual might be later influenced. Whereas the above reflects a top-down approach to computational creativity, an alternative thread has developed among bottom-up computational psychologists involved in artificial neural network research. During the late 1980s and early 1990s, for example, such generative neural systems were driven by genetic algorithms. Experiments involving recurrent nets were successful in hybridizing simple musical melodies and predicting listener expectations. == Historical evolution of computational creativity == The use computational processes to generate creative artifacts has been present from early times in history. During the late 1800's, methods for composing music combinatorily were explored, involving prominent figures like Mozart, Bach, Haydn, and Kiernberger. This approach extended to analytical endeavors as early as 1934, where simple mechanical models were built to explore mathematical problem solving. Professional interest in the creative aspect of computation also was commonly addressed in early discussions of artificial intelligence. The 1956 Dartmouth Conference, listed creativity, invention, and discovery as key goals for artificial intelligence. As the development of computers allowed systems of greater complexity, the 1970's and 1980's saw invention of early systems that modelled creativity using symbolic or rule-based approaches. The field of creative storytelling investigated several such models. Meehan's TALE-SPIN (1977) generated narratives through simulation of character goals and decision trees. Dehn's AUTHOR (1981) approached generation by simulating an author's process for crafting a story. Beyond narrative generation, computational creativity expanded into artistic and scientific domains. Artistic image generation was one of the disciplines that saw early potential in generated artifacts through computational creativity. One of the most prominent examples was Harold Cohen's AARON, which produced art through composition and adaptation of figures based on a large set of symbolic rules and heuristics for visual composition. Some systems also tackled creativity in scientific endeavors. BACON was said to rediscover natural laws like Boyle's Law and Kepler's law through hypothesis testing in constrained spaces. By the 1990's the modeling techniques became more adaptive, attempting to implement cognitive creative rules for generation. Turner's MINSTREL (1993) introduced TRAMs (Transform Recall Adapt Methods) to simulate creative re-use of prior material for generative storytelling. Meanwhile, Pérez y Pérez's MEXICA (1999) modeled the creative writing process using cycles of engagement and reflection. As systems increasingly incorporated models of internal evaluation, another approach that emerged was that of combining symbolic generation with domain-specific evaluation metrics, modeling generative and selective steps to creativity In the field of generational humor, the JAPE system (1994) generated pun-based riddles using Prolog and WordNet, applying symbolic pattern-matching rules and a large lexical database (WordNet) to compose riddles involving wordplay. WordNet is a system developed by George Miller and his team at Princeton, its platform and inspired word-mapping structures have been used as the backbone of several syntactic and semantic AI programs. A notable system for music generation was David Cope's EMI (Experiments in Musical Intelligence) or Emmy, which was trained in the styles of artists like Bach, Beethoven, or Chopin and generated novel pieces in their style through pattern abstraction and recomposition. In the 2000s and beyond, machine learning began influencing creative system design. Researchers such as Mihalcea and Strapparava trained classifiers to distinguish humorous from non-humorous text, using stylistic and semantic features. Meanwhile custom computational approaches led to chess systems like Deep Blue generating quasi-creative gameplay strategies through search algorithms and parallel processing constrained by specific rules and patterns for evaluation. The institutional development of computational creativity grew along its technical advances. Dedicated workshops such as the IJWCC emerged in the 1990s, growing out of interdisciplinary conferences focused on AI and creativity. By the early 2000s, the field coalesced around annual conferences like the International Conference on Computational Creativity (ICCC). Recently, with the advent of Deep Learning, Transformers, and further refinement in Machine Learning structures, computational creativity's implementation space has new tools for development. == Machine learning for computational creativity == While traditional computational approaches to creativity rely on the explicit formulation of prescriptions by developers and a certain degree of randomness in computer programs, machine learning methods allow computer programs to learn on heuristics from input data enabling creative capacities within the computer programs. Especially, deep artificial neural networks allow to learn patterns from input data that allow for the non-linear generation of creative artefacts. Before 1989, artificial neural networks have been used to model certain aspects of creativity. Peter Todd (1989) first trained a neural network to reproduce musical melodies from a training set of musical pieces. Then he used a change algorithm to modify the network's input parameters. The network was able to randomly generate new music in a highly uncontrolled manner. In 1992, Todd extended this work, using the so-called distal teacher approach that had been d

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