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Feature (machine learning) : In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding. The concept of "features" is related to that of explanatory variables used in statistical techniques such as linear regression. |
Feature (machine learning) : In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly. Categorical features are discrete values that can be grouped into categories. Examples of categorical features include gender, color, and zip code. Categorical features typically need to be converted to numerical features before they can be used in machine learning algorithms. This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding. The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features. Other machine learning algorithms, such as linear regression, can only handle numerical features. |
Feature (machine learning) : A numeric feature can be conveniently described by a feature vector. One way to achieve binary classification is using a linear predictor function (related to the perceptron) with a feature vector as input. The method consists of calculating the scalar product between the feature vector and a vector of weights, qualifying those observations whose result exceeds a threshold. Algorithms for classification from a feature vector include nearest neighbor classification, neural networks, and statistical techniques such as Bayesian approaches. |
Feature (machine learning) : In character recognition, features may include histograms counting the number of black pixels along horizontal and vertical directions, number of internal holes, stroke detection and many others. In speech recognition, features for recognizing phonemes can include noise ratios, length of sounds, relative power, filter matches and many others. In spam detection algorithms, features may include the presence or absence of certain email headers, the email structure, the language, the frequency of specific terms, the grammatical correctness of the text. In computer vision, there are a large number of possible features, such as edges and objects. |
Feature (machine learning) : In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values might correspond to the pixels of an image, while when representing texts the features might be the frequencies of occurrence of textual terms. Feature vectors are equivalent to the vectors of explanatory variables used in statistical procedures such as linear regression. Feature vectors are often combined with weights using a dot product in order to construct a linear predictor function that is used to determine a score for making a prediction. The vector space associated with these vectors is often called the feature space. In order to reduce the dimensionality of the feature space, a number of dimensionality reduction techniques can be employed. Higher-level features can be obtained from already available features and added to the feature vector; for example, for the study of diseases the feature 'Age' is useful and is defined as Age = 'Year of death' minus 'Year of birth' . This process is referred to as feature construction. Feature construction is the application of a set of constructive operators to a set of existing features resulting in construction of new features. Examples of such constructive operators include checking for the equality conditions , the arithmetic operators , the array operators as well as other more sophisticated operators, for example count(S,C) that counts the number of features in the feature vector S satisfying some condition C or, for example, distances to other recognition classes generalized by some accepting device. Feature construction has long been considered a powerful tool for increasing both accuracy and understanding of structure, particularly in high-dimensional problems. Applications include studies of disease and emotion recognition from speech. |
Feature (machine learning) : The initial set of raw features can be redundant and large enough that estimation and optimization is made difficult or ineffective. Therefore, a preliminary step in many applications of machine learning and pattern recognition consists of selecting a subset of features, or constructing a new and reduced set of features to facilitate learning, and to improve generalization and interpretability. Extracting or selecting features is a combination of art and science; developing systems to do so is known as feature engineering. It requires the experimentation of multiple possibilities and the combination of automated techniques with the intuition and knowledge of the domain expert. Automating this process is feature learning, where a machine not only uses features for learning, but learns the features itself. |
Feature (machine learning) : Covariate Dimensionality reduction Feature engineering Hashing trick Statistical classification Explainable artificial intelligence == References == |
Prompt engineering : Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model. A prompt is natural language text describing the task that an AI should perform. A prompt for a text-to-text language model can be a query, a command, or a longer statement including context, instructions, and conversation history. Prompt engineering may involve phrasing a query, specifying a style, choice of words and grammar, providing relevant context, or describing a character for the AI to mimic. When communicating with a text-to-image or a text-to-audio model, a typical prompt is a description of a desired output such as "a high-quality photo of an astronaut riding a horse" or "Lo-fi slow BPM electro chill with organic samples". Prompting a text-to-image model may involve adding, removing, emphasizing, and re-ordering words to achieve a desired subject, style, layout, lighting, and aesthetic. |
Prompt engineering : In 2018, researchers first proposed that all previously separate tasks in natural language processing (NLP) could be cast as a question-answering problem over a context. In addition, they trained a first single, joint, multi-task model that would answer any task-related question like "What is the sentiment" or "Translate this sentence to German" or "Who is the president?" The AI boom saw an increase in the amount of "prompting technique" to get the model to output the desired outcome and avoid nonsensical output, a process characterized by trial-and-error. After the release of ChatGPT in 2022, prompt engineering was soon seen as an important business skill, albeit one with an uncertain economic future. A repository for prompts reported that over 2,000 public prompts for around 170 datasets were available in February 2022. In 2022, the chain-of-thought prompting technique was proposed by Google researchers. In 2023, several text-to-text and text-to-image prompt databases were made publicly available. The Personalized Image-Prompt (PIP) dataset, a generated image-text dataset that has been categorized by 3,115 users, has also been made available publicly in 2024. |
Prompt engineering : Multiple distinct prompt engineering techniques have been published. |
Prompt engineering : In 2022, text-to-image models like DALL-E 2, Stable Diffusion, and Midjourney were released to the public. These models take text prompts as input and use them to generate AI-generated images. Text-to-image models typically do not understand grammar and sentence structure in the same way as large language models, thus may require a different set of prompting techniques. Text-to-image models do not natively understand negation. The prompt "a party with no cake" is likely to produce an image including a cake. As an alternative, negative prompts allow a user to indicate, in a separate prompt, which terms should not appear in the resulting image. Techniques such as framing the normal prompt into a sequence-to-sequence language modeling problem can be used to automatically generate an output for the negative prompt. |
Prompt engineering : Some approaches augment or replace natural language text prompts with non-text input. |
Prompt engineering : Prompt injection is a cybersecurity exploit in which adversaries craft inputs that appear legitimate but are designed to cause unintended behavior in machine learning models, particularly large language models (LLMs). This attack takes advantage of the model's inability to distinguish between developer-defined prompts and user inputs, allowing adversaries to bypass safeguards and influence model behaviour. While LLMs are designed to follow trusted instructions, they can be manipulated into carrying out unintended responses through carefully crafted inputs. |
Prompt engineering : Social engineering (security) == References == |
DATR : DATR is a language for lexical knowledge representation. The lexical knowledge is encoded in a network of nodes. Each node has a set of attributes encoded with it. A node can represent a word or a word form. DATR was developed in the late 1980s by Roger Evans, Gerald Gazdar and Bill Keller, and used extensively in the 1990s; the standard specification is contained in the Evans and Gazdar RFC, available on the Sussex website (below). DATR has been implemented in a variety of programming languages, and several implementations are available on the internet, including an RFC compliant implementation at the Bielefeld website (below). DATR is still used for encoding inheritance networks in various linguistic and non-linguistic domains and is under discussion as a standard notation for the representation of lexical information. |
DATR : DATR at the University of Sussex DATR repository and RFC compliant ZDATR implementation at Universität Bielefeld |
Artificial intelligence in hiring : Artificial intelligence can be used to automate aspects of the job recruitment process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants. Proponents of artificial intelligence in hiring claim it reduces bias, assists with finding qualified candidates, and frees up human resource workers' time for other tasks, while opponents worry that AI perpetuates inequalities in the workplace and will eliminate jobs. Despite the potential benefits, the ethical implications of AI in hiring remain a subject of debate, with concerns about algorithmic transparency, accountability, and the need for ongoing oversight to ensure fair and unbiased decision-making throughout the recruitment process. |
Artificial intelligence in hiring : Artificial intelligence has fascinated researchers since the term was coined in the mid-1950s. Researchers have identified four main forms of intelligence that AI would need to possess to truly replace humans in the workplace: mechanical, analytical, intuitive, and empathetic. Automation follows a predictable progression in which it will first be able to replace the mechanical tasks, then analytical tasks, then intuitive tasks, and finally empathy based tasks. However, full automation is not the only potential outcome of AI advancements. Humans may instead work alongside machines, enhancing the effectiveness of both. In the hiring context, this means that AI has already replaced many basic human resource tasks in recruitment and screening, while freeing up time for human resource workers to do other more creative tasks that can not yet be automated or do not make fiscal sense to automate. It also means that the type of jobs companies are recruiting and hiring form will continue to shift as the skillsets that are most valuable change. Human resources has been identified as one of the ten industries most affected by AI. It is increasingly common for companies to use AI to automate aspects of their hiring process. The hospitality, finance, and tech industries in particular have incorporated AI into their hiring processes to significant extents. Human resources is fundamentally an industry based around making predictions. Human resource specialists must predict which people would make quality candidates for a job, which marketing strategies would get those people to apply, which applicants would make the best employees, what kinds of compensation would get them to accept an offer, what is needed to retain an employee, which employees should be promoted, what a companies staffing needs, among others. AI is particularly adept at prediction because it can analyze huge amounts of data. This enables AI to make insights many humans would miss and find connections between seemingly unrelated data points. This provides value to a company and has made it advantageous to use AI to automate or augment many human resource tasks. |
Artificial intelligence in hiring : Artificial intelligence in hiring confers many benefits, but it also has some challenges which have concerned experts. AI is only as good as the data it is using. Biases can inadvertently be baked into the data used in AI. Often companies will use data from their employees to decide what people to recruit or hire. This can perpetuate bias and lead to more homogenous workforces. Facebook Ads was an example of a platform that created such controversy for allowing business owners to specify what type of employee they are looking for. For example, job advertisements for nursing and teach could be set such that only women of a specific age group would see the advertisements. Facebook Ads has since then removed this function from its platform, citing the potential problems with the function in perpetuating biases and stereotypes against minorities. The growing use of Artificial Intelligence-enabled hiring systems has become an important component of modern talent hiring, particularly through social networks such as LinkedIn and Facebook. However, data overflow embedded in the hiring systems, based on Natural Language Processing (NLP) methods, may result in unconscious gender bias. Utilizing data driven methods may mitigate some bias generated from these systems It can also be hard to quantify what makes a good employee. This poses a challenge for training AI to predict which employees will be best. Commonly used metrics like performance reviews can be subjective and have been shown to favor white employees over black employees and men over women. Another challenge is the limited amount of available data. Employers only collect certain details about candidates during the initial stages of the hiring process. This requires AI to make determinations about candidates with very limited information to go off of. Additionally, many employers do not hire employees frequently and so have limited firm specific data to go off. To combat this, many firms will use algorithms and data from other firms in their industry. AI's reliance on applicant and current employees personal data raises privacy issues. These issues effect both the applicants and current employees, but also may have implications for third parties who are linked through social media to applicants or current employees. For example, a sweep of someone's social media will also show their friends and people they have tagged in photos or posts. AI makes it easier for companies to search applicants social media accounts. A study conducted by Monash University found that 45% of hiring managers use social media to gain insight on applicants. Seventy percent of those surveyed said they had rejected an applicant because of things discovered on their applicant's social media, yet only 17% of hiring managers saw using social media in the hiring process as a violation of applicants privacy. Using social media in the hiring process is appealing to hiring managers because it offers them a less curated view of applicants lives. The privacy trade-off is significant. Social media profiles often reveal information about applicants that human resource departments are legally not allowed to require applicants to divulge like race, ability status, and sexual orientation. |
Artificial intelligence in hiring : Artificial intelligence is changing the recruiting process by gradually replacing routine tasks performed by human recruiters. AI can reduce human involvement in hiring and reduce the human biases that hinder effective hiring decisions. And some platforms such as TalAiro go further Talairo is an AI-powered Talent Impact Platform designed to optimize hiring for agencies and enterprises. It leverages patented AI models to match job descriptions with candidates, automate administrative tasks, and provide deep hiring insights, all in an effort to maximize business outcomes. AI is changing the way work is done. Artificial intelligence along with other technological advances such as improvements in robotics have placed 47% of jobs at risk of being eliminated in the near future. Some classify the shifts in labor brought about by AI as a 4th industrial revolution, which they call Industrial Revolution 4.0. According to some scholars, however, the transformative impact of AI on labor has been overstated. The "no-real-change" theory holds that an IT revolution has already occurred, but that the benefits of implementing new technologies does not outweigh the costs associated with adopting them. This theory claims that the result of the IT revolution is thus much less impactful than had originally been forecasted. Other scholars refute this theory claiming that AI has already led to significant job loss for unskilled labor and that it will eliminate middle skill and high skill jobs in the future. This position is based around the idea that AI is not yet a technology of general use and that any potential 4th industrial revolution has not fully occurred. A third theory holds that the effect of AI and other technological advances is too complicated to yet be understood. This theory is centered around the idea that while AI will likely eliminate jobs in the short term it will also likely increase the demand for other jobs. The question then becomes will the new jobs be accessible to people and will they emerge near when jobs are eliminated. Although robots can replace people to complete some tasks, there are still many tasks that cannot be done alone by robots that master artificial intelligence. A study analyzed 2,000 work tasks in 800 different occupations globally, and concluded that half (totaling US$15 trillion in salaries) could be automated by adapting already existing technologies. Less than 5% of occupations could be fully automated and 60% have at least 30% automatable tasks. In other words, in most cases, artificial intelligence is a tool rather than a substitute for labor. As artificial intelligence enters the field of human work, people have gradually discovered that artificial intelligence is incapable of unique tasks, and the advantage of human beings is to understand uniqueness and use tools rationally. At this time, human-machine reciprocal work came into being. Brandão discovers that people can form organic partnerships with machines. “Humans enable machines to do what they do best: doing repetitive tasks, analyzing significant volumes of data, and dealing with routine cases. Due to reciprocity, machines enable humans to have their potentialities "strengthened" for tasks such as resolving ambiguous information, exercising the judgment of difficult cases, and contacting dissatisfied clients.” Daugherty and Wilson have observed successful new types of human-computer interaction in occupations and tasks in various fields. In other words, even in activities and capabilities that are considered simpler, new technologies will not pose an imminent danger to workers. As far as General Electric is concerned, buyers of it and its equipment will always need maintenance workers. Entrepreneurs need these workers to work well with new systems that can integrate their skills with advanced technologies in novel ways. Artificial intelligence has sped up the hiring process considerably, dramatically reducing costs. For example, Unilever has reviewed over 250,000 applications using AI and reduced its hiring process from 4 months to 4 weeks. This saved the company 50,000 hours of labor. The increased efficiency AI promises has sped up its adoption by human resource departments globally. |
Artificial intelligence in hiring : The Artificial Intelligence Video Interview Act, effective in Illinois since 2020, regulates the use of AI to analyze and evaluate job applicants’ video interviews. This law requires employers to follow guidelines to avoid any issues regarding using AI in the hiring process. == References == |
Attensity : Attensity provides social analytics and engagement applications for social customer relationship management (social CRM). Attensity's text analytics software applications extract facts, relationships and sentiment from unstructured data, which comprise approximately 85% of the information companies store electronically. |
Attensity : Attensity was founded in 2000. An early investor in Attensity was In-Q-Tel, which funds technology to support the missions of the US Government and the broader DOD. InTTENSITY, an independent company that has combined Inxight with Attensity Software (the only joint development project that combines two InQTel funded software packages), is the exclusive distributor and outlet for Attensity in the Federal Market. In 2009, Attensity Corp., then based in Palo Alto, merged with Germany's Empolis and Living-e AG to form Attensity Group. In 2010, Attensity Group acquired Biz360, Inc., a provider of social media monitoring and market intelligence solutions. In early 2012, Attensity Group divested itself of the Empolis business unit via a management buyout; that unit currently conducts business under its pre-merger name. Attensity Group is a closely held private company. Its majority shareholder is Aeris Capital, a private Swiss investment office advising a high-net-worth individual and his charitable foundation. Foundation Capital, Granite Ventures, and Scale Venture Partners were among Biz360's investors and thus became shareholders in Attensity Group. In February, 2016, Attensity's IP assets were acquired by InContact, and Attensity closed. |
Attensity : Text mining |
Attensity : Official website Archive of official website |
Algorithmic party platforms in the United States : Algorithmic party platforms are a recent development in political campaigning where artificial intelligence (AI) and machine learning are used to shape and adjust party messaging dynamically. Unlike traditional platforms that are drafted well before an election, these platforms adapt based on real-time data such as polling results, voter sentiment, and trends on social media. This allows campaigns to remain responsive to emerging issues throughout the election cycle. These platforms rely on predictive analytics to segment voters into smaller, highly specific groups. AI analyzes demographic data, behavioral patterns, and online activities to identify which issues resonate most with each group. Campaigns then tailor their messages accordingly, ensuring that different voter segments receive targeted communication. This approach optimizes resources and enhances voter engagement by focusing on relevant issues. During the 2024 U.S. election, campaigns utilized these tools to adjust messaging on-the-fly. For example, the AI firm Resonate identified a voter segment labeled "Cyber Crusaders," consisting of socially conservative yet fiscally liberal individuals. Campaigns used this insight to quickly focus outreach and policy discussions around the concerns of this group, demonstrating how AI-driven platforms can influence strategy as events unfold. |
Algorithmic party platforms in the United States : The integration of artificial intelligence (AI) into political campaigns has introduced a significant shift in how party platforms are shaped and communicated. Traditionally, platforms were drafted months before elections and remained static throughout the campaign. However, algorithmic platforms now rely on continuous data streams to adjust messaging and policy priorities in real time. This allows campaigns to adapt to emerging voter concerns, ensuring their strategies remain relevant throughout the election cycle. AI systems analyze large volumes of data, including polling results, social media interactions, and voter behavior patterns. Predictive analytics tools segment voters into specific micro-groups based on demographic and behavioral data. Campaigns can then customize their messaging to align with the priorities of these smaller segments, adjusting their stances as trends develop during the campaign. This level of segmentation and customization ensures that outreach resonates with voters and maximizes engagement. Beyond messaging, AI also optimizes resource allocation by helping campaigns target specific efforts more effectively. With predictive analytics, campaigns can identify which areas or demographics are most likely to benefit from increased outreach, such as canvassing or targeted advertisements. AI tools monitor shifts in voter sentiment in real time, allowing campaigns to quickly pivot their strategies in response to developing events and voter priorities. This capability ensures that campaign resources are used efficiently, minimizing waste while maximizing impact throughout the election cycle. AI's use extends beyond national campaigns, with local and grassroots campaigns also leveraging these technologies to compete more effectively. By automating communication processes and generating customized voter outreach, smaller campaigns can now utilize AI to a degree previously available only to well-funded candidates. However, this growing reliance on AI raises concerns around transparency and the ethical implications of automated content creation, such as AI-generated ads and responses. AI technology, which was previously accessible only to large, well-funded campaigns, has become increasingly available to smaller, local campaigns. With declining costs and easier access, grassroots campaigns now have the ability to implement predictive analytics, automate communications, and generate targeted ads. This democratization of technology allows smaller campaigns to compete more effectively by dynamically adjusting to the concerns of their constituents. However, the growing use of AI in political campaigns raises concerns about transparency and the potential manipulation of voters. The ability to adjust messaging in real time introduces ethical questions about the authenticity of platforms and voter trust. Additionally, the use of synthetic media, including AI-generated ads and deepfakes, presents challenges in maintaining accountability and preventing disinformation in political discourse. |
Algorithmic party platforms in the United States : Artificial intelligence (AI) has become instrumental in enabling political campaigns to adapt their platforms in real time, responding swiftly to evolving voter sentiments and emerging issues. By analyzing extensive datasets—including polling results, social media activity, and demographic information—AI systems provide campaigns with actionable insights that inform dynamic strategy adjustments. A study by Sanders, Ulinich, and Schneier (2023) demonstrated the potential of AI-based political issue polling, where AI chatbots simulated public opinion on various policy issues. The findings indicated that AI could effectively anticipate both the mean level and distribution of public opinion, particularly in ideological breakdowns, with correlations typically exceeding 85%. This suggests that AI can serve as a valuable tool for campaigns to gauge voter sentiment accurately and promptly. Moreover, AI facilitates the segmentation of voters into micro-groups based on demographic and behavioral data, allowing for tailored messaging that resonates with specific audiences. This targeted approach enhances voter engagement and optimizes resource allocation, as campaigns can focus their efforts on demographics most receptive to their messages. The dynamic nature of AI-driven platforms ensures that campaign strategies remain relevant and responsive throughout the election cycle. However, the integration of AI in political platforms also raises ethical and transparency concerns, particularly regarding the authenticity of dynamically adjusted messaging and the potential for voter manipulation. Addressing these challenges is crucial to maintaining voter trust and the integrity of the democratic process. In summary, AI significantly shapes political platforms in real time by providing campaigns with the tools to analyze voter sentiment, segment audiences, and adjust strategies dynamically. While offering substantial benefits in responsiveness and engagement, it is imperative to navigate the accompanying ethical considerations to ensure the responsible use of AI in political campaigning. |
Algorithmic party platforms in the United States : While AI-driven platforms offer significant advantages, they also introduce ethical and transparency challenges. One primary concern is the potential for AI to manipulate voter perception. The ability to adjust messaging dynamically raises questions about the authenticity of political platforms, as voters may feel deceived if they perceive platforms as opportunistic or insincere. The use of synthetic media, including AI-generated advertisements and deepfakes, exacerbates these challenges. These tools have the potential to blur the line between reality and fiction, making it difficult for voters to discern genuine content from fabricated material. This has led to concerns about misinformation, voter manipulation, and the erosion of trust in democratic processes. Additionally, the lack of transparency in how AI systems operate poses significant risks. Many algorithms function as "black boxes," with their decision-making processes opaque even to their developers. This opacity makes it challenging to ensure accountability, particularly when AI-generated strategies lead to controversial or unintended outcomes. Efforts to address these challenges include calls for greater transparency in AI usage within campaigns. Policymakers and advocacy groups have proposed regulations requiring campaigns to disclose when AI is used in content creation or voter outreach. These measures aim to balance the benefits of AI with the need for ethical integrity and accountability. |
Algorithmic party platforms in the United States : Despite the challenges, AI-driven platforms offer numerous benefits that can enhance the democratic process. By tailoring messaging to specific voter concerns, AI helps campaigns address diverse needs more effectively. This targeted approach ensures that underrepresented groups receive attention, fostering a more inclusive political discourse. AI also democratizes access to advanced campaign tools. Smaller campaigns, which previously lacked the resources to compete with well-funded opponents, can now utilize AI to level the playing field. Predictive analytics, automated communications, and targeted advertisements empower grassroots movements to amplify their voices and engage constituents more effectively. Moreover, AI's ability to process vast amounts of data provides valuable insights into voter sentiment. By identifying trends and patterns, campaigns can address pressing issues proactively, fostering a more informed and responsive political environment. These capabilities also extend to crisis management, as AI enables campaigns to adjust swiftly in response to unforeseen events, ensuring stability and resilience. == References == |
Hopfield network : A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory. The Hopfield network, named for John Hopfield, consists of a single layer of neurons, where each neuron is connected to every other neuron except itself. These connections are bidirectional and symmetric, meaning the weight of the connection from neuron i to neuron j is the same as the weight from neuron j to neuron i. Patterns are associatively recalled by fixing certain inputs, and dynamically evolve the network to minimize an energy function, towards local energy minimum states that correspond to stored patterns. Patterns are associatively learned (or "stored") by a Hebbian learning algorithm. One of the key features of Hopfield networks is their ability to recover complete patterns from partial or noisy inputs, making them robust in the face of incomplete or corrupted data. Their connection to statistical mechanics, recurrent networks, and human cognitive psychology has led to their application in various fields, including physics, psychology, neuroscience, and machine learning theory and practice. |
Hopfield network : One origin of associative memory is human cognitive psychology, specifically the associative memory. Frank Rosenblatt studied "close-loop cross-coupled perceptrons", which are 3-layered perceptron networks whose middle layer contains recurrent connections that change by a Hebbian learning rule.: 73–75 : Chapter 19, 21 Another model of associative memory is where the output does not loop back to the input. W. K. Taylor proposed such a model trained by Hebbian learning in 1956. Karl Steinbuch, who wanted to understand learning, and inspired by watching his children learn, published the Lernmatrix in 1961. It was translated to English in 1963. Similar research was done with the correlogram of D. J. Willshaw et al. in 1969. Teuvo Kohonen trained an associative memory by gradient descent in 1974. Another origin of associative memory was statistical mechanics. The Ising model was published in 1920s as a model of magnetism, however it studied the thermal equilibrium, which does not change with time. Roy J. Glauber in 1963 studied the Ising model evolving in time, as a process towards thermal equilibrium (Glauber dynamics), adding in the component of time. The second component to be added was adaptation to stimulus. Described independently by Kaoru Nakano in 1971 and Shun'ichi Amari in 1972, they proposed to modify the weights of an Ising model by Hebbian learning rule as a model of associative memory. The same idea was published by William A. Little in 1974, who was acknowledged by Hopfield in his 1982 paper. See Carpenter (1989) and Cowan (1990) for a technical description of some of these early works in associative memory. The Sherrington–Kirkpatrick model of spin glass, published in 1975, is the Hopfield network with random initialization. Sherrington and Kirkpatrick found that it is highly likely for the energy function of the SK model to have many local minima. In the 1982 paper, Hopfield applied this recently developed theory to study the Hopfield network with binary activation functions. In a 1984 paper he extended this to continuous activation functions. It became a standard model for the study of neural networks through statistical mechanics. A major advance in memory storage capacity was developed by Dimitry Krotov and Hopfield in 2016 through a change in network dynamics and energy function. This idea was further extended by Demircigil and collaborators in 2017. The continuous dynamics of large memory capacity models was developed in a series of papers between 2016 and 2020. Large memory storage capacity Hopfield Networks are now called Dense Associative Memories or modern Hopfield networks. In 2024, John J. Hopfield and Geoffrey E. Hinton were awarded the Nobel Prize in Physics for their foundational contributions to machine learning, such as the Hopfield network. |
Hopfield network : The units in Hopfield nets are binary threshold units, i.e. the units only take on two different values for their states, and the value is determined by whether or not the unit's input exceeds its threshold U i . Discrete Hopfield nets describe relationships between binary (firing or not-firing) neurons 1 , 2 , … , i , j , … , N . At a certain time, the state of the neural net is described by a vector V , which records which neurons are firing in a binary word of N bits. The interactions w i j between neurons have units that usually take on values of 1 or −1, and this convention will be used throughout this article. However, other literature might use units that take values of 0 and 1. These interactions are "learned" via Hebb's law of association, such that, for a certain state V s and distinct nodes i , j w i j = V i s V j s =V_^V_^ but w i i = 0 =0 . (Note that the Hebbian learning rule takes the form w i j = ( 2 V i s − 1 ) ( 2 V j s − 1 ) =(2V_^-1)(2V_^-1) when the units assume values in .) Once the network is trained, w i j no longer evolve. If a new state of neurons V s ′ is introduced to the neural network, the net acts on neurons such that V i s ′ → 1 ^\rightarrow 1 if ∑ j w i j V j s ′ ≥ U i w_V_^\geq U_ V i s ′ → − 1 ^\rightarrow -1 if ∑ j w i j V j s ′ < U i w_V_^<U_ where U i is the threshold value of the i'th neuron (often taken to be 0). In this way, Hopfield networks have the ability to "remember" states stored in the interaction matrix, because if a new state V s ′ is subjected to the interaction matrix, each neuron will change until it matches the original state V s (see the Updates section below). The connections in a Hopfield net typically have the following restrictions: w i i = 0 , ∀ i =0,\forall i (no unit has a connection with itself) w i j = w j i , ∀ i , j =w_,\forall i,j (connections are symmetric) The constraint that weights are symmetric guarantees that the energy function decreases monotonically while following the activation rules. A network with asymmetric weights may exhibit some periodic or chaotic behaviour; however, Hopfield found that this behavior is confined to relatively small parts of the phase space and does not impair the network's ability to act as a content-addressable associative memory system. Hopfield also modeled neural nets for continuous values, in which the electric output of each neuron is not binary but some value between 0 and 1. He found that this type of network was also able to store and reproduce memorized states. Notice that every pair of units i and j in a Hopfield network has a connection that is described by the connectivity weight w i j . In this sense, the Hopfield network can be formally described as a complete undirected graph G = ⟨ V , f ⟩ , where V is a set of McCulloch–Pitts neurons and f : V 2 → R \rightarrow \mathbb is a function that links pairs of units to a real value, the connectivity weight. |
Hopfield network : Updating one unit (node in the graph simulating the artificial neuron) in the Hopfield network is performed using the following rule: s i ← \leftarrow \left\+1&\sum _s_\geq \theta _,\\-1&\end\right. where: w i j is the strength of the connection weight from unit j to unit i (the weight of the connection). s i is the state of unit i. θ i is the threshold of unit i. Updates in the Hopfield network can be performed in two different ways: Asynchronous: Only one unit is updated at a time. This unit can be picked at random, or a pre-defined order can be imposed from the very beginning. Synchronous: All units are updated at the same time. This requires a central clock to the system in order to maintain synchronization. This method is viewed by some as less realistic, based on an absence of observed global clock influencing analogous biological or physical systems of interest. |
Hopfield network : Bruck in his paper in 1990 studied discrete Hopfield networks and proved a generalized convergence theorem that is based on the connection between the network's dynamics and cuts in the associated graph. This generalization covered both asynchronous as well as synchronous dynamics and presented elementary proofs based on greedy algorithms for max-cut in graphs. A subsequent paper further investigated the behavior of any neuron in both discrete-time and continuous-time Hopfield networks when the corresponding energy function is minimized during an optimization process. Bruck showed that neuron j changes its state if and only if it further decreases the following biased pseudo-cut. The discrete Hopfield network minimizes the following biased pseudo-cut for the synaptic weight matrix of the Hopfield net. J p s e u d o − c u t ( k ) = ∑ i ∈ C 1 ( k ) ∑ j ∈ C 2 ( k ) w i j + ∑ j ∈ C 1 ( k ) θ j (k)=\sum _(k)\sum _(k)w_+\sum _(k) where C 1 ( k ) (k) and C 2 ( k ) (k) represents the set of neurons which are −1 and +1, respectively, at time k . For further details, see the recent paper. The discrete-time Hopfield Network always minimizes exactly the following pseudo-cut U ( k ) = ∑ i = 1 N ∑ j = 1 N w i j ( s i ( k ) − s j ( k ) ) 2 + 2 ∑ j = 1 N θ j s j ( k ) ^\sum _^w_(s_(k)-s_(k))^+2\sum _^\theta _s_(k) The continuous-time Hopfield network always minimizes an upper bound to the following weighted cut V ( t ) = ∑ i = 1 N ∑ j = 1 N w i j ( f ( s i ( t ) ) − f ( s j ( t ) ) 2 + 2 ∑ j = 1 N θ j f ( s j ( t ) ) ^\sum _^w_(f(s_(t))-f(s_(t))^+2\sum _^\theta _f(s_(t)) where f ( ⋅ ) is a zero-centered sigmoid function. The complex Hopfield network, on the other hand, generally tends to minimize the so-called shadow-cut of the complex weight matrix of the net. |
Hopfield network : Hopfield nets have a scalar value associated with each state of the network, referred to as the "energy", E, of the network, where: E = − 1 2 ∑ i , j w i j s i s j − ∑ i θ i s i \sum _w_s_s_-\sum _\theta _s_ This quantity is called "energy" because it either decreases or stays the same upon network units being updated. Furthermore, under repeated updating the network will eventually converge to a state which is a local minimum in the energy function (which is considered to be a Lyapunov function). Thus, if a state is a local minimum in the energy function it is a stable state for the network. Note that this energy function belongs to a general class of models in physics under the name of Ising models; these in turn are a special case of Markov networks, since the associated probability measure, the Gibbs measure, has the Markov property. |
Hopfield network : Hopfield and Tank presented the Hopfield network application in solving the classical traveling-salesman problem in 1985. Since then, the Hopfield network has been widely used for optimization. The idea of using the Hopfield network in optimization problems is straightforward: If a constrained/unconstrained cost function can be written in the form of the Hopfield energy function E, then there exists a Hopfield network whose equilibrium points represent solutions to the constrained/unconstrained optimization problem. Minimizing the Hopfield energy function both minimizes the objective function and satisfies the constraints also as the constraints are "embedded" into the synaptic weights of the network. Although including the optimization constraints into the synaptic weights in the best possible way is a challenging task, many difficult optimization problems with constraints in different disciplines have been converted to the Hopfield energy function: Associative memory systems, Analog-to-Digital conversion, job-shop scheduling problem, quadratic assignment and other related NP-complete problems, channel allocation problem in wireless networks, mobile ad-hoc network routing problem, image restoration, system identification, combinatorial optimization, etc, just to name a few. However, while it is possible to convert hard optimization problems to Hopfield energy functions, it does not guarantee convergence to a solution (even in exponential time). |
Hopfield network : Initialization of the Hopfield networks is done by setting the values of the units to the desired start pattern. Repeated updates are then performed until the network converges to an attractor pattern. Convergence is generally assured, as Hopfield proved that the attractors of this nonlinear dynamical system are stable, not periodic or chaotic as in some other systems. Therefore, in the context of Hopfield networks, an attractor pattern is a final stable state, a pattern that cannot change any value within it under updating. |
Hopfield network : Training a Hopfield net involves lowering the energy of states that the net should "remember". This allows the net to serve as a content addressable memory system, that is to say, the network will converge to a "remembered" state if it is given only part of the state. The net can be used to recover from a distorted input to the trained state that is most similar to that input. This is called associative memory because it recovers memories on the basis of similarity. For example, if we train a Hopfield net with five units so that the state (1, −1, 1, −1, 1) is an energy minimum, and we give the network the state (1, −1, −1, −1, 1) it will converge to (1, −1, 1, −1, 1). Thus, the network is properly trained when the energy of states which the network should remember are local minima. Note that, in contrast to Perceptron training, the thresholds of the neurons are never updated. |
Hopfield network : Patterns that the network uses for training (called retrieval states) become attractors of the system. Repeated updates would eventually lead to convergence to one of the retrieval states. However, sometimes the network will converge to spurious patterns (different from the training patterns). In fact, the number of spurious patterns can be exponential in the number of stored patterns, even if the stored patterns are orthogonal. The energy in these spurious patterns is also a local minimum. For each stored pattern x, the negation -x is also a spurious pattern. A spurious state can also be a linear combination of an odd number of retrieval states. For example, when using 3 patterns μ 1 , μ 2 , μ 3 ,\mu _,\mu _ , one can get the following spurious state: ϵ i m i x = ± sgn ( ± ϵ i μ 1 ± ϵ i μ 2 ± ϵ i μ 3 ) ^=\pm \operatorname (\pm \epsilon _^\pm \epsilon _^\pm \epsilon _^) Spurious patterns that have an even number of states cannot exist, since they might sum up to zero |
Hopfield network : The Network capacity of the Hopfield network model is determined by neuron amounts and connections within a given network. Therefore, the number of memories that are able to be stored is dependent on neurons and connections. Furthermore, it was shown that the recall accuracy between vectors and nodes was 0.138 (approximately 138 vectors can be recalled from storage for every 1000 nodes) (Hertz et al., 1991). Therefore, it is evident that many mistakes will occur if one tries to store a large number of vectors. When the Hopfield model does not recall the right pattern, it is possible that an intrusion has taken place, since semantically related items tend to confuse the individual, and recollection of the wrong pattern occurs. Therefore, the Hopfield network model is shown to confuse one stored item with that of another upon retrieval. Perfect recalls and high capacity, >0.14, can be loaded in the network by Storkey learning method; ETAM, ETAM experiments also in. Ulterior models inspired by the Hopfield network were later devised to raise the storage limit and reduce the retrieval error rate, with some being capable of one-shot learning. The storage capacity can be given as C ≅ n 2 log 2 n n where n is the number of neurons in the net. |
Hopfield network : The Hopfield network is a model for human associative learning and recall. It accounts for associative memory through the incorporation of memory vectors. Memory vectors can be slightly used, and this would spark the retrieval of the most similar vector in the network. However, we will find out that due to this process, intrusions can occur. In associative memory for the Hopfield network, there are two types of operations: auto-association and hetero-association. The first being when a vector is associated with itself, and the latter being when two different vectors are associated in storage. Furthermore, both types of operations are possible to store within a single memory matrix, but only if that given representation matrix is not one or the other of the operations, but rather the combination (auto-associative and hetero-associative) of the two. Hopfield's network model utilizes the same learning rule as Hebb's (1949) learning rule, which characterised learning as being a result of the strengthening of the weights in cases of neuronal activity. Rizzuto and Kahana (2001) were able to show that the neural network model can account for repetition on recall accuracy by incorporating a probabilistic-learning algorithm. During the retrieval process, no learning occurs. As a result, the weights of the network remain fixed, showing that the model is able to switch from a learning stage to a recall stage. By adding contextual drift they were able to show the rapid forgetting that occurs in a Hopfield model during a cued-recall task. The entire network contributes to the change in the activation of any single node. McCulloch and Pitts' (1943) dynamical rule, which describes the behavior of neurons, does so in a way that shows how the activations of multiple neurons map onto the activation of a new neuron's firing rate, and how the weights of the neurons strengthen the synaptic connections between the new activated neuron (and those that activated it). Hopfield would use McCulloch–Pitts's dynamical rule in order to show how retrieval is possible in the Hopfield network. However, Hopfield would do so in a repetitious fashion. Hopfield would use a nonlinear activation function, instead of using a linear function. This would therefore create the Hopfield dynamical rule and with this, Hopfield was able to show that with the nonlinear activation function, the dynamical rule will always modify the values of the state vector in the direction of one of the stored patterns. |
Hopfield network : Hopfield networks are recurrent neural networks with dynamical trajectories converging to fixed point attractor states and described by an energy function. The state of each model neuron i is defined by a time-dependent variable V i , which can be chosen to be either discrete or continuous. A complete model describes the mathematics of how the future state of activity of each neuron depends on the known present or previous activity of all the neurons. In the original Hopfield model of associative memory, the variables were binary, and the dynamics were described by a one-at-a-time update of the state of the neurons. An energy function quadratic in the V i was defined, and the dynamics consisted of changing the activity of each single neuron i only if doing so would lower the total energy of the system. This same idea was extended to the case of V i being a continuous variable representing the output of neuron i , and V i being a monotonic function of an input current. The dynamics became expressed as a set of first-order differential equations for which the "energy" of the system always decreased. The energy in the continuous case has one term which is quadratic in the V i (as in the binary model), and a second term which depends on the gain function (neuron's activation function). While having many desirable properties of associative memory, both of these classical systems suffer from a small memory storage capacity, which scales linearly with the number of input features. In contrast, by increasing the number of parameters in the model so that there are not just pair-wise but also higher-order interactions between the neurons, one can increase the memory storage capacity. Dense Associative Memories (also known as the modern Hopfield networks) are generalizations of the classical Hopfield Networks that break the linear scaling relationship between the number of input features and the number of stored memories. This is achieved by introducing stronger non-linearities (either in the energy function or neurons' activation functions) leading to super-linear (even an exponential) memory storage capacity as a function of the number of feature neurons, in effect increasing the order of interactions between the neurons. The network still requires a sufficient number of hidden neurons. The key theoretical idea behind dense associative memory networks is to use an energy function and an update rule that is more sharply peaked around the stored memories in the space of neuron's configurations compared to the classical model, as demonstrated when the higher-order interactions and subsequent energy landscapes are explicitly modelled. |
Hopfield network : Associative memory (disambiguation) Autoassociative memory Boltzmann machine – like a Hopfield net but uses annealed Gibbs sampling instead of gradient descent Dynamical systems model of cognition Ising model Hebbian theory |
Hopfield network : Rojas, Raul (12 July 1996). "13. The Hopfield model" (PDF). Neural Networks – A Systematic Introduction. Springer. ISBN 978-3-540-60505-8. Hopfield Network Javascript The Travelling Salesman Problem Archived 2015-05-30 at the Wayback Machine – Hopfield Neural Network JAVA Applet Hopfield, John (2007). "Hopfield network". Scholarpedia. 2 (5): 1977. Bibcode:2007SchpJ...2.1977H. doi:10.4249/scholarpedia.1977. "Don't Forget About Associative Memories". The Gradient. November 7, 2020. Retrieved September 27, 2024. Fletcher, Tristan. "Hopfield Network Learning Using Deterministic Latent Variables" (PDF) (Tutorial). Archived from the original (PDF) on 2011-10-05. |
Open Syllabus Project : The Open Syllabus Project (OSP) is an online open-source platform that catalogs and analyzes millions of college syllabi. Founded by researchers from the American Assembly at Columbia University, the OSP has amassed the most extensive collection of searchable syllabi. Since its beta launch in 2016, the OSP has collected over 7 million course syllabi from over 80 countries, primarily by scraping publicly accessible university websites. The project is directed by Joe Karaganis. |
Open Syllabus Project : The OSP was formed by a group of data scientists, sociologists, and digital-humanities researchers at the American Assembly, a public-policy institute based at Columbia University. The OSP was partly funded by the Sloan Foundation and the Arcadia Fund. Joe Karaganis, former vice-president of the American Assembly, serves as the project director of the OSP. The project builds on prior attempts to archive syllabi, such as H-Net, MIT OpenCourseWare, and historian Dan Cohen's defunct Syllabus Finder website (Cohen now sits on the OSP's advisory board). The OSP became a non-profit and independent of the American Assembly in November 2019. In January 2016, the OSP launched a beta version of their "Syllabus Explorer," which they had collected data for since 2013. The Syllabus Explorer allows users to browse and search texts from over one million college course syllabi. The OSP launched a more comprehensive version 2.0 of the Syllabus Explorer in July 2019. The newer version includes an interactive visualization that displays texts as dots on a knowledge map. As of 2022, the OSP has collected over 7 million course syllabi. The Syllabus Explorer represents the "largest collection of searchable syllabi ever amassed." |
Open Syllabus Project : The OSP has collected syllabi data from over 80 countries dating to 2000. The syllabi stem from over 4,000 worldwide institutions. Most of the OSP's data originates from the United States. Canada, Australia, and the U.K also have large datasets. The OSP primarily collects syllabi by scraping publicly accessible university websites. The OSP also allows syllabi submissions from faculty, students, and administrators. The OSP developers use machine learning and natural language processing to extract metadata from such syllabi. Since only metadata is collected, no individual syllabus or personal identifying information is found in the OSP database. The OSP classifies the syllabi into 62 subject fields – corresponding to the U.S. Department of Education's Classification of Instructional Programs (CIP). Additionally, the OSP assigns each text a "teaching score" from 0–100. This score represents the text's percentile rank among citations in the total citation count and is a numerical indicator of the relative frequency of which a particular work is taught. The OSP also has data on which texts are most likely to be assigned together. The developers behind the OSP admit that the database is incomplete and likely contains "a fair number of errors." Karaganis estimates that 80–100 million syllabi exist in the United States alone. The OSP is unable to access syllabi behind private course-management software like Blackboard. |
Open Syllabus Project : According to William Germano et al., the OSP is a "fascinating resource but is also prone to misrepresenting or at least distracting us from the most important business of a syllabus: communicating with students." Historian William Caferro remarks that the OSP is a "tacit experience of sharing, but a useful one." English professor Bart Beaty writes that, "Despite the many reservations about the completeness of its data, the OSP provides a rare opportunity for scholars to move beyond the anecdotal in discussions of canon-formation in teaching." Media theorist Elizabeth Losh opines that "big data approaches", like the OSP, may "raise troubling questions for instructors about informed consent, pedagogical privacy, and quantified metrics." |
Open Syllabus Project : Digital preservation List of Web archiving initiatives |
Open Syllabus Project : Karaganis, Joe, ed. (2018). Shadow Libraries: Access to Knowledge in Global Higher Education. MIT Press. doi:10.7551/mitpress/11339.001.0001. ISBN 9780262535014. OCLC 1052851639. |
Open Syllabus Project : Official website Open Syllabus Galaxy |
YandexGPT : YandexGPT is a neural network of the GPT family developed by the Russian company Yandex LLC. YandexGPT can create and revise texts, generate new ideas and capture the context of the conversation with the user. YandexGPT is trained using a dataset which includes information from books, magazines, newspapers and other open sources available on the internet. The neural network may get facts wrong and fantasize, but as it learns, it will produce increasingly accurate answers. |
YandexGPT : YandexGPT is integrated into virtual assistant Alice (an analog of Siri and Alexa) and is available in Yandex services and applications. The company gives businesses access to the neural network’s API through the public cloud platform Yandex Cloud and develops its own B2B solutions on its basis. Since July 2023, 800 companies have participated in the closed testing of YandexGPT. IT developers, banks, retail businesses, and companies from other industries can use the technology in two modes — API and Playground (an interface in the Yandex Cloud console for testing models and hypotheses). Two model versions are available to businesses: one works in asynchronous mode and is better able to handle complex tasks, while the other is suitable for creating quick responses in real time. As a result, YandexGPT has been tested in dozens of scenarios such as content tasks, tech support, creating chatbots, virtual assistants, etc. |
YandexGPT : In February 2023, Yandex announced that it was working on its own version of the ChatGPT generative neural network while developing a language model from the YaLM (Yet another Language Model) family. The project was tentatively named YaLM 2.0, which was later changed to YandexGPT. On May 17, the company unveiled a neural network called YandexGPT (YaGPT) and enabled its virtual assistant Alice to interact with the new language model. On June 15, 2023, Yandex added the YandexGPT language model to the image generation application Shedevrum. This enabled its users to create fully-fledged posts complete with a title, text, and relevant illustration. In July 2023, YandexGPT launched new features enabling businesses to create virtual assistants and chatbots, as well as generate and structure texts. On September 7, 2023, Yandex presented a new version of the language model, YandexGPT 2, at the Practical ML Conf. Compared to the previous one, the new version is able to perform more types of tasks, and the quality of answers has improved. The developers claimed that YandexGPT 2 answered user questions better than the first version in 67% of cases. From October 6, 2023, YandexGPT can create short retellings of online Russian-language videos on the Internet. It can summarize videos that are from two minutes to four hours long and contain speech. |
YandexGPT : Official website |
Language engineering : Language engineering involves the creation of natural language processing systems, whose cost and outputs are measurable and predictable. It is a distinct field contrasted to natural language processing and computational linguistics. A recent trend of language engineering is the use of Semantic Web technologies for the creation, archiving, processing, and retrieval of machine processable language data. == References == |
CMU Pronouncing Dictionary : The CMU Pronouncing Dictionary (also known as CMUdict) is an open-source pronouncing dictionary originally created by the Speech Group at Carnegie Mellon University (CMU) for use in speech recognition research. CMUdict provides a mapping orthographic/phonetic for English words in their North American pronunciations. It is commonly used to generate representations for speech recognition (ASR), e.g. the CMU Sphinx system, and speech synthesis (TTS), e.g. the Festival system. CMUdict can be used as a training corpus for building statistical grapheme-to-phoneme (g2p) models that will generate pronunciations for words not yet included in the dictionary. The most recent release is 0.7b; it contains over 134,000 entries. An interactive lookup version is available. |
CMU Pronouncing Dictionary : The database is distributed as a plain text file with one entry to a line in the format "WORD <pronunciation>" with a two-space separator between the parts. If multiple pronunciations are available for a word, variants are identified using numbered versions (e.g. WORD(1)). The pronunciation is encoded using a modified form of the ARPABET system, with the addition of stress marks on vowels of levels 0, 1, and 2. A line-initial ;;; token indicates a comment. A derived format, directly suitable for speech recognition engines is also available as part of the distribution; this format collapses stress distinctions (typically not used in ASR). The following is a table of phonemes used by CMU Pronouncing Dictionary. |
CMU Pronouncing Dictionary : The Unifon converter is based on the CMU Pronouncing Dictionary. The Natural Language Toolkit contains an interface to the CMU Pronouncing Dictionary. The Carnegie Mellon Logios tool incorporates the CMU Pronouncing Dictionary. PronunDict, a pronunciation dictionary of American English, uses the CMU Pronouncing Dictionary as its data source. Pronunciation is transcribed in IPA symbols. This dictionary also supports searching by pronunciation. Some singing voice synthesizer software like CeVIO Creative Studio and Synthesizer V uses modified version of CMU Pronouncing Dictionary for synthesizing English singing voices. Transcriber, a tool for the full text phonetic transcription, uses the CMU Pronouncing Dictionary 15.ai, a real-time text-to-speech tool using artificial intelligence, uses the CMU Pronouncing Dictionary |
CMU Pronouncing Dictionary : Moby Pronunciator, a similar project |
CMU Pronouncing Dictionary : The current version of the dictionary is at SourceForge, although there is also a version maintained on GitHub. Homepage – includes database search RDF converted to Resource Description Framework by the open source Texai project. |
Query understanding : Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but specifically focused on the understanding of search queries. |
Query understanding : Proceedings of ACM SIGIR 2011 Workshop on Query Representation and Understanding Query Understanding for Search Engines (Yi Chang and Hongbo Deng, Eds.) == References == |
Bayesian learning mechanisms : Bayesian learning mechanisms are probabilistic causal models used in computer science to research the fundamental underpinnings of machine learning, and in cognitive neuroscience, to model conceptual development. Bayesian learning mechanisms have also been used in economics and cognitive psychology to study social learning in theoretical models of herd behavior. |
Neocognitron : The neocognitron is a hierarchical, multilayered artificial neural network proposed by Kunihiko Fukushima in 1979. It has been used for Japanese handwritten character recognition and other pattern recognition tasks, and served as the inspiration for convolutional neural networks. Previously in 1969, he published a similar architecture, but with hand-designed kernels inspired by convolutions in mammalian vision. In 1975 he improved it to the Cognitron, and in 1979 he improved it to the neocognitron, which learns all convolutional kernels by unsupervised learning (in his terminology, "self-organized by 'learning without a teacher'"). The neocognitron was inspired by the model proposed by Hubel & Wiesel in 1959. They found two types of cells in the visual primary cortex called simple cell and complex cell, and also proposed a cascading model of these two types of cells for use in pattern recognition tasks. The neocognitron is a natural extension of these cascading models. The neocognitron consists of multiple types of cells, the most important of which are called S-cells and C-cells. The local features are extracted by S-cells, and these features' deformation, such as local shifts, are tolerated by C-cells. Local features in the input are integrated gradually and classified in the higher layers. The idea of local feature integration is found in several other models, such as the Convolutional Neural Network model, the SIFT method, and the HoG method. There are various kinds of neocognitron. For example, some types of neocognitron can detect multiple patterns in the same input by using backward signals to achieve selective attention. |
Neocognitron : Artificial neural network Deep learning Pattern recognition Receptive field Self-organizing map Unsupervised learning |
Neocognitron : Fukushima, Kunihiko (April 1980). "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position". Biological Cybernetics. 36 (4): 193–202. doi:10.1007/bf00344251. PMID 7370364. S2CID 206775608. Fukushima, Kunihiko; Miyake, S.; Ito, T. (1983). "Neocognitron: a neural network model for a mechanism of visual pattern recognition". IEEE Transactions on Systems, Man, and Cybernetics. SMC-13 (3): 826–834. doi:10.1109/TSMC.1983.6313076. S2CID 8235461. Fukushima, Kunihiko (1987). "A hierarchical neural network model for selective attention". In Eckmiller, R.; Von der Malsburg, C. (eds.). Neural computers. Springer-Verlag. pp. 81–90. Fukushima, Kunihiko (2007). "Neocognitron". Scholarpedia. 2 (1): 1717. Bibcode:2007SchpJ...2.1717F. doi:10.4249/scholarpedia.1717. Hubel, D.H.; Wiesel, T.N. (1959). "Receptive fields of single neurones in the cat's striate cortex". J Physiol. 148 (3): 574–591. doi:10.1113/jphysiol.1959.sp006308. PMC 1363130. PMID 14403679. |
Neocognitron : Neocognitron on Scholarpedia NeoCognitron by Ing. Gabriel Minarik - application (C#) and video Neocognitron resources at Visiome Platform - includes MATLAB environment Beholder - a Neocognitron simulator |
Mindpixel : Mindpixel was a web-based collaborative artificial intelligence project which aimed to create a knowledgebase of millions of human validated true/false statements, or probabilistic propositions. It ran from 2000 to 2005. |
Mindpixel : Participants in the project created one-line statements which aimed to be objectively true or false to 20 other anonymous participants. In order to submit their statement they had first to check the true/false validity of 20 such statements submitted by others. Participants whose replies were consistently out of step with the majority had their status downgraded and were eventually excluded. Likewise, participants who made contributions which others could not agree were objectively true or false had their status downgraded. A validated true/false statement is called a mindpixel. The project enlisted the efforts of thousands of participants and claimed to be "the planet's largest artificial intelligence effort". The project was conceived by Chris McKinstry, a computer scientist and former Very Large Telescope operator for the European Southern Observatory in Chile, as MISTIC (Minimum Intelligent Signal Test Item Corpus) in 1996. Mindpixel was developed out of this program, and started in 2000 and had 1.4 million mindpixels in January 2004. The database and its software is known as GAC, which stands for "Generic Artificial Consciousness" and is pronounced Jak. McKinstry believed that the Mindpixel database could be used in conjunction with a neural net to produce a body of human "common sense" knowledge which would have market value. Participants in the project were promised shares in any future value according to the number of mindpixels they had successfully created. On 20 September 2005 Mindpixel lost its free server and is no longer operational. It was being rewritten by Chris McKinstry as Mindpixel 2 and was intended to appear on a new server in France. Chris McKinstry died of suicide on 23 January 2006 and the future of the project and the integrity of the data is uncertain. Some Mindpixel data have been utilized by Michael Spivey of Cornell University and Rick Dale of The University of Memphis to study theories of high-level reasoning and continuous temporal dynamics of thought. McKinstry, along with Dale and Spivey, designed an experiment that has now been published in Psychological Science in its January, 2008 issue. In this paper, McKinstry (as posthumous first author), Dale, and Spivey use a very small and carefully selected set of Mindpixel statements to show that even high-level thought processes like decision making can be revealed in the nonlinear dynamics of bodily action. Other similar AI-driven knowledge acquisition projects are Never-Ending Language Learning and Open Mind Common Sense (run by MIT), the latter being also hampered when its director died of suicide. |
Mindpixel : Never-Ending Language Learning Cyc |
Mindpixel : Mindpixel Home page (Currently points to a "Mindpixel IQ test" using the Mindpixel Db of validated statements) |
Vector database : A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, so that one can search the database with a query vector to retrieve the closest matching database records. Vectors are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, with the number of dimensions ranging from a few hundred to tens of thousands, depending on the complexity of the data being represented. A vector's position in this space represents its characteristics. Words, phrases, or entire documents, as well as images, audio, and other types of data, can all be vectorized. These feature vectors may be computed from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning networks. The goal is that semantically similar data items receive feature vectors close to each other. Vector databases can be used for similarity search, semantic search, multi-modal search, recommendations engines, large language models (LLMs), object detection, etc. Vector databases are also often used to implement retrieval-augmented generation (RAG), a method to improve domain-specific responses of large language models. The retrieval component of a RAG can be any search system, but is most often implemented as a vector database. Text documents describing the domain of interest are collected, and for each document or document section, a feature vector (known as an "embedding") is computed, typically using a deep learning network, and stored in a vector database. Given a user prompt, the feature vector of the prompt is computed, and the database is queried to retrieve the most relevant documents. These are then automatically added into the context window of the large language model, and the large language model proceeds to create a response to the prompt given this context. |
Vector database : The most important techniques for similarity search on high-dimensional vectors include: Hierarchical Navigable Small World (HNSW) graphs Locality-sensitive Hashing (LSH) and Sketching Product Quantization (PQ) Inverted Files and combinations of these techniques. In recent benchmarks, HNSW-based implementations have been among the best performers. Conferences such as the International Conference on Similarity Search and Applications, SISAP and the Conference on Neural Information Processing Systems (NeurIPS) host competitions on vector search in large databases. |
Vector database : Curse of dimensionality – Difficulties arising when analyzing data with many aspects ("dimensions") Machine learning – Study of algorithms that improve automatically through experience Nearest neighbor search – Optimization problem in computer science Recommender system – System to predict users' preferences |
Vector database : Sawers, Paul (2024-04-20). "Why vector databases are having a moment as the AI hype cycle peaks". TechCrunch. Retrieved 2024-04-23. |
Reservoir computing : Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output. The first key benefit of this framework is that training is performed only at the readout stage, as the reservoir dynamics are fixed. The second is that the computational power of naturally available systems, both classical and quantum mechanical, can be used to reduce the effective computational cost. |
Reservoir computing : The first examples of reservoir neural networks demonstrated that randomly connected recurrent neural networks could be used for sensorimotor sequence learning, and simple forms of interval and speech discrimination. In these early models the memory in the network took the form of both short-term synaptic plasticity and activity mediated by recurrent connections. In other early reservoir neural network models the memory of the recent stimulus history was provided solely by the recurrent activity. Overall, the general concept of reservoir computing stems from the use of recursive connections within neural networks to create a complex dynamical system. It is a generalisation of earlier neural network architectures such as recurrent neural networks, liquid-state machines and echo-state networks. Reservoir computing also extends to physical systems that are not networks in the classical sense, but rather continuous systems in space and/or time: e.g. a literal "bucket of water" can serve as a reservoir that performs computations on inputs given as perturbations of the surface. The resultant complexity of such recurrent neural networks was found to be useful in solving a variety of problems including language processing and dynamic system modeling. However, training of recurrent neural networks is challenging and computationally expensive. Reservoir computing reduces those training-related challenges by fixing the dynamics of the reservoir and only training the linear output layer. A large variety of nonlinear dynamical systems can serve as a reservoir that performs computations. In recent years semiconductor lasers have attracted considerable interest as computation can be fast and energy efficient compared to electrical components. Recent advances in both AI and quantum information theory have given rise to the concept of quantum neural networks. These hold promise in quantum information processing, which is challenging to classical networks, but can also find application in solving classical problems. In 2018, a physical realization of a quantum reservoir computing architecture was demonstrated in the form of nuclear spins within a molecular solid. However, the nuclear spin experiments in did not demonstrate quantum reservoir computing per se as they did not involve processing of sequential data. Rather the data were vector inputs, which makes this more accurately a demonstration of quantum implementation of a random kitchen sink algorithm (also going by the name of extreme learning machines in some communities). In 2019, another possible implementation of quantum reservoir processors was proposed in the form of two-dimensional fermionic lattices. In 2020, realization of reservoir computing on gate-based quantum computers was proposed and demonstrated on cloud-based IBM superconducting near-term quantum computers. Reservoir computers have been used for time-series analysis purposes. In particular, some of their usages involve chaotic time-series prediction, separation of chaotic signals, and link inference of networks from their dynamics. |
Reservoir computing : Quantum reservoir computing may use the nonlinear nature of quantum mechanical interactions or processes to form the characteristic nonlinear reservoirs but may also be done with linear reservoirs when the injection of the input to the reservoir creates the nonlinearity. The marriage of machine learning and quantum devices is leading to the emergence of quantum neuromorphic computing as a new research area. |
Reservoir computing : Deep learning Extreme learning machines Unconventional computing |
Reservoir computing : Reservoir Computing using delay systems, Nature Communications 2011 Optoelectronic Reservoir Computing, Scientific Reports February 2012 Optoelectronic Reservoir Computing, Optics Express 2012 All-optical Reservoir Computing, Nature Communications 2013 Memristor Models for Machine learning, Neural Computation 2014 arxiv |
Sketch Engine : Sketch Engine is a corpus manager and text analysis software developed by Lexical Computing since 2003. Its purpose is to enable people studying language behaviour (lexicographers, researchers in corpus linguistics, translators or language learners) to search large text collections according to complex and linguistically motivated queries. Sketch Engine gained its name after one of the key features, word sketches: one-page, automatic, corpus-derived summaries of a word's grammatical and collocational behaviour. Currently, it supports and provides corpora in over 90 languages. |
Sketch Engine : Sketch Engine is a product of Lexical Computing, a company founded in 2003 by the lexicographer and research scientist Adam Kilgarriff. He started a collaboration with Pavel Rychlý, a computer scientist working at the Natural Language Processing Centre, Masaryk University, and the developer of Manatee and Bonito (two major parts of the software suite). Kilgarriff also introduced the concept of word sketches. Since then, Sketch Engine has been commercial software, however, all the core features of Manatee and Bonito that were developed by 2003 (and extended since then) are freely available under the GPL license within the NoSketch Engine suite. |
Sketch Engine : A list of tools available in Sketch Engine: Word sketches – a one-page automatic derived summary of a word's grammatical and collocational behaviour Word sketch difference – compares and contrasts two words by analysing their collocations Distributional thesaurus – automated thesaurus for finding words with similar meaning or appearing in the same/similar context Concordance search – finds occurrences of a word form, lemma, phrase, tag or complex structure Collocation search – word co-occurrence analysis displaying the most frequent words (for a search word) which can be regarded as collocation candidates Word lists – generates frequency lists which can be filtered with complex criteria n-grams – generates frequency lists of multi-word expressions Terminology / Keyword extraction (both monolingual and bilingual) – automatic extraction of key words and multi-word terms from texts (based on frequency count and linguistic criteria) Diachronic analysis (Trends) – detecting words which undergo changes in the frequency of use in time (show trending words) Corpus building and management – create corpora from the Web or uploaded texts including part-of-speech tagging and lemmatization which can be used as data mining software Parallel corpus (bilingual) facilities – looking up translation examples (EUR-Lex corpus, Europarl corpus, OPUS corpus, etc.) or building a parallel corpus from own aligned texts Text type analysis – statistics of metadata in the corpus |
Sketch Engine : Sketch Engine provides access to more than 700 text corpora. There are monolingual as well as multilingual corpora of different sizes (from thousand of words up to 60 billions of words) and various sources (e.g. web, books, subtitles, legal documents). The list of corpora includes British National Corpus, Brown Corpus, Cambridge Academic English Corpus and Cambridge Learner Corpus, CHILDES corpora of child language, OpenSubtitles (a set of 60 parallel corpora), 24 multilingual corpora of EUR-Lex documents, the TenTen Corpus Family (multi-billion web corpora), and Trends corpora (monitor corpora with daily updates). |
Sketch Engine : Sketch Engine consists of three main components: an underlying database management system called Manatee, a web interface search front-end called Bonito, and a web interface for corpus building and management called Corpus Architect. |
Sketch Engine : Sketch Engine has been used by major British and other publishing houses for producing dictionaries such as Macmillan English Dictionary, Dictionnaires Le Robert, Oxford University Press or Shogakukan. Four of United Kingdom's five biggest dictionary publishers use Sketch Engine. |
Sketch Engine : Thomas, James (March 2016). Discovering English with Sketch Engine : a corpus-based approach to language exploration. Workbook and glossary. Brno: Versatile. ISBN 9788026095798. |
Sketch Engine : Sketch Engine website List of corpora available in Sketch Engine OneClick terms – online term extractor with term extraction technology from Sketch Engine SKELL – Sketch Engine for language learning |
Instance selection : Instance selection (or dataset reduction, or dataset condensation) is an important data pre-processing step that can be applied in many machine learning (or data mining) tasks. Approaches for instance selection can be applied for reducing the original dataset to a manageable volume, leading to a reduction of the computational resources that are necessary for performing the learning process. Algorithms of instance selection can also be applied for removing noisy instances, before applying learning algorithms. This step can improve the accuracy in classification problems. Algorithm for instance selection should identify a subset of the total available data to achieve the original purpose of the data mining (or machine learning) application as if the whole data had been used. Considering this, the optimal outcome of IS would be the minimum data subset that can accomplish the same task with no performance loss, in comparison with the performance achieved when the task is performed using the whole available data. Therefore, every instance selection strategy should deal with a trade-off between the reduction rate of the dataset and the classification quality. |
Instance selection : The literature provides several different algorithms for instance selection. They can be distinguished from each other according to several different criteria. Considering this, instance selection algorithms can be grouped in two main classes, according to what instances they select: algorithms that preserve the instances at the boundaries of classes and algorithms that preserve the internal instances of the classes. Within the category of algorithms that select instances at the boundaries it is possible to cite DROP3, ICF and LSBo. On the other hand, within the category of algorithms that select internal instances, it is possible to mention ENN and LSSm. In general, algorithm such as ENN and LSSm are used for removing harmful (noisy) instances from the dataset. They do not reduce the data as the algorithms that select border instances, but they remove instances at the boundaries that have a negative impact on the data mining task. They can be used by other instance selection algorithms, as a filtering step. For example, the ENN algorithm is used by DROP3 as the first step, and the LSSm algorithm is used by LSBo. There is also another group of algorithms that adopt different selection criteria. For example, the algorithms LDIS, CDIS and XLDIS select the densest instances in a given arbitrary neighborhood. The selected instances can include both, border and internal instances. The LDIS and CDIS algorithms are very simple and select subsets that are very representative of the original dataset. Besides that, since they search by the representative instances in each class separately, they are faster (in terms of time complexity and effective running time) than other algorithms, such as DROP3 and ICF. Besides that, there is a third category of algorithms that, instead of selecting actual instances of the dataset, select prototypes (that can be synthetic instances). In this category it is possible to include PSSA, PSDSP and PSSP. The three algorithms adopt the notion of spatial partition (a hyperrectangle) for identifying similar instances and extract prototypes for each set of similar instances. In general, these approaches can also be modified for selecting actual instances of the datasets. The algorithm ISDSP adopts a similar approach for selecting actual instances (instead of prototypes). == References == |
Art Recognition : Art Recognition is a Swiss technology company headquartered in Adliswil, within the Zurich metropolitan area, Switzerland. Specializing in the application of artificial intelligence (AI) for the purposes of art authentication and the detection of art forgeries, Art Recognition integrates advanced algorithms and computer vision technology. The company's operations extend globally, with a primary aim to increase transparency and security in the art market. |
Art Recognition : Art Recognition was established in 2019 by Dr. Carina Popovici and Christiane Hoppe-Oehl. The foundation of the company was driven by the long-standing challenge in the art world of authenticating paintings, a process traditionally reliant on expert judgment, historical research, and scientific analysis. Recognizing the limitations of existing methods, the co-founders were motivated by technological advancements in digital imaging and pattern recognition algorithms in the field of art. These technological advancements, particularly in the realm of high-resolution digital imagery, enable a more detailed examination of artworks. By analyzing brushstrokes, signature patterns, and other distinct characteristics, and comparing them with known works by the same artist, digital tools offer a new dimension in authentication. Popovici and Hoppe-Oehl aimed to develop an advanced algorithm that could further assist experts by identifying stylistic elements and patterns unique to individual artists, thus aiding in the art authentication process. |
Art Recognition : Art Recognition employs a combination of machine learning techniques, computer vision algorithms, and deep neural networks to assess the authenticity of artworks. The AI algorithm analyzes various visual characteristics, such as brushstrokes, color palette, texture, and composition, to identify patterns and similarities with known authentic artworks. The company's technology undergoes a process of data collection, dataset preparation, and training. In the initial phase, datasets are compiled, and data selection is supervised by art historians to ensure the inclusion of genuine artworks by specific artists. This approach aims to avoid including artworks that may have been partially completed by apprentices or contain mixed authorship. Upon the preparation of datasets, a segment of the image set is used for training the AI algorithm, while the remaining images are set aside for testing. This phase aims to ensure the algorithm's proficiency in distinguishing authentic artworks from forgeries. Post-training, the algorithm undergoes evaluation with the test data, assessing its accuracy and efficacy in authenticating artworks. After the testing phase, the AI algorithm is applied to analyze new images, including submissions from clients. Additionally, the algorithm is designed to identify artworks generated by generative AI, mimicking the style of renowned artists. This capability equips the algorithm to withstand adversarial attacks, enhancing its reliability in differentiating between authentic and artificially generated fake art pieces. |
Art Recognition : Art Recognition's collaboration with Tilburg University in the Netherlands has resulted in the acquisition of a research grant from Eurostars, Eureka (organisation) the Eureka's flagship small and medium-sized enterprises (SME) funding program. In addition, the company has formed a partnership with the University of Liverpool in the United Kingdom, which has been supported by the Science and Technology Facilities Council (STFC) Impact Acceleration Award. Furthermore, Art Recognition has established a relationship with Innosuisse, a Swiss innovation agency, to expand its research and development initiatives. Art Recognition has formed a strategic collaboration with Nils Büttner, an art historian and professor at the State Academy of Fine Arts Stuttgart (ABK Stuttgart). By fostering dialogue between academic researchers and market professionals, the collaboration aims to refine existing authentication practices and introduce scientifically robust methodologies into the art sector. |
Art Recognition : In May 2024, Art Recognition played a key role in identifying counterfeit artworks, including alleged Monets and Renoirs, being sold on eBay. The findings contributed to a broader discussion on the role of AI in preventing art fraud, particularly in online marketplaces where traditional expertise is often lacking. The case underscored the increasing importance of AI as a fraud detection tool.[1] Germann Auction made history in November 2024 by becoming the first auction house to successfully conduct a sale of artwork authenticated entirely by artificial intelligence. This milestone reflects the increasing reliance on technological solutions in the art market, where AI-driven authentication processes provide data-backed evaluations of artwork authenticity.[[2]] The NHK WORLD-JAPAN news report examined the vulnerabilities of the Japanese art market to forgery, particularly in light of the Beltracchi scandal of the 1990s. During this period, Wolfgang Beltracchi successfully sold numerous counterfeit paintings to Japanese collectors, who only later discovered the deception. This case highlighted the challenges of authentication in the Asian art market, exposing weaknesses in provenance verification and expert assessment. The report explores how advancements in AI-based authentication, including the work of Art Recognition, are now being used to prevent similar fraud. Artificial intelligence is being integrated as a supplementary tool to traditional connoisseurship, aiming to enhance security in the global art trade.[3] As of January 2025, Art Recognition has appointed art crime expert and Pulitzer Prize finalist Noah Charney as an advisor. Charney, the founder of the Association for Research into Crimes against Art (ARCA), is a leading authority on art forgery, provenance studies, and cultural heritage crimes. |
Art Recognition : Art Recognition's AI algorithm has received attention from various media outlets and industry events. The company was featured on the front page of The Wall Street Journal for its involvement in the authentication case of the Flaget Madonna, believed to have been partly painted by Raphael. A broadcast by the Swiss public television SRF showcased how the algorithm can be used to detect art forgeries with high accuracy. Additionally, the company's work was featured in a TEDx talk discussing the use of AI in art authentication. |
Art Recognition : The technology developed by Art Recognition has been recognized for its role in providing a technology-based art authentication solution, compared to traditional methods. This advancement is seen as significant in the field of art verification, offering a modern approach to a historically complex process. The use of AI in art authentication, as pioneered by Art Recognition, has become a topic of professional discourse. Notably, this subject was the focus of a debate on Radio Télévision Suisse, where experts deliberated over the capabilities and limitations of AI in identifying art forgeries. Such discussions highlight the evolving landscape of art authentication in the age of digital technology. Despite the advancements in AI-driven art authentication, the field continues to face unique challenges, particularly regarding the acceptance of such technologies. Experts in the field stress the necessity of using AI as a complementary tool alongside traditional methods, rather than as a stand-alone or definitive solution for authenticating art. |
Art Recognition : Art Recognition's AI algorithm has been applied to several high-profile and controversial artworks, sparking significant interest and debate in the art world. Samson and Delilah at the National Gallery in London: The National Gallery's "Samson and Delilah", traditionally attributed to the artist Rubens, has also been examined using Art Recognition's AI, which has assessed the painting as non-authentic. This analysis contributed to ongoing scholarly discussions regarding the work's authenticity. De Brecy Tondo Madonna. A research team from Bradford University and Nottingham University initially attributed the painting to Raphael, employing an AI face recognition software, while the AI developed at Art Recognition returned a negative result. As face recognition methods have proven to be less appropriate for art authentication, the Bradford group developed an alternative AI-based approach similar to that used by Art Recognition. A key distinction between the two systems lies in their training datasets: the Bradford group's AI was trained on 49 images, whereas Art Recognition employed a larger dataset of over 100 images. This difference highlights the role of dataset size and composition in the effectiveness of AI-driven art analysis. Lucian Freud Painting Controversy: Featured in The New Yorker, a painting attributed to Lucian Freud became a subject of dispute. Art Recognition's AI analysis played a pivotal role in examining the painting's authenticity, contributing to the broader discussion about the challenges in verifying modern artworks. Titian at Kunsthaus Zürich: A painting attributed to Titian, housed at Kunsthaus Zürich, has been a topic of debate among art experts. The application of Art Recognition's technology offered a new perspective, utilizing AI to analyze the painting's stylistic elements in comparison with authenticated works of Titian. Following this debate, Kunsthaus Zürich has announced plans to initiate a comprehensive project aimed at resolving the authenticity questions surrounding the painting. This project is set to involve collaboration with scientists and technology companies, leveraging a multidisciplinary approach to authenticate the artwork. Art Recognition has contributed to the authentication debate surrounding The Polish Rider, a painting traditionally attributed to Rembrandt but subject to scholarly debate. Utilizing AI-driven analysis, the study examined stylistic features, brushstroke patterns, and compositional details to assess the painting’s authenticity. The findings provided quantitative insights that supported its attribution, reinforcing the potential of AI in resolving long-standing art historical disputes. In each of these instances, Art Recognition's involvement has provided additional perspectives through AI analysis while contributing to broader conversations about the role of technology in art authentication. These cases demonstrate the evolving nature of art verification, where traditional methods are being supplemented, and sometimes challenged, by new technological approaches. However, they also underline the ongoing debates about the acceptance of AI in the field of art history, especially in the authentication of works by renowned artists. |
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