AI Data Quality Tools

AI Data Quality Tools — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  LRE Map

  The LRE Map (Language Resources and Evaluation) is a freely accessible large database on resources dedicated to Natural language processing. The original feature of LRE Map is that the records are collected during the submission of different major Natural language processing conferences. The records are then cleaned and gathered into a global database called "LRE Map". The LRE Map is intended to be an instrument for collecting information about language resources and to become, at the same time, a community for users, a place to share and discover resources, discuss opinions, provide feedback, discover new trends, etc. It is an instrument for discovering, searching and documenting language resources, here intended in a broad sense, as both data and tools. The large amount of information contained in the Map can be analyzed in many different ways. For instance, the LRE Map can provide information about the most frequent type of resource, the most represented language, the applications for which resources are used or are being developed, the proportion of new resources vs. already existing ones, or the way in which resources are distributed to the community. == Context == Several institutions worldwide maintain catalogues of language resources (ELRA, LDC, NICT Universal Catalogue, ACL Data and Code Repository, OLAC, LT World, etc.) However, it has been estimated that only 10% of existing resources are known, either through distribution catalogues or via direct publicity by providers (web sites and the like). The rest remains hidden, the only occasions where it briefly emerges being when a resource is presented in the context of a research paper or report at some conference. Even in this case, nevertheless, it might be that a resource remains in the background simply because the focus of the research is not on the resource per se. == History == The LRE Map originated under the name "LREC Map" during the preparation of LREC 2010 conference. More specifically, the idea was discussed within the FlaReNet project, and in collaboration with ELRA and the Institute of Computational Linguistics of CNR in Pisa, the Map was put in place at LREC 2010. The LREC organizers asked the authors to provide some basic information about all the resources (in a broad sense, i.e. including tools, standards and evaluation packages), either used or created, described in their papers. All these descriptors were then gathered in a global matrix called the LREC Map. The same methodology and requirements from the authors has been then applied and extended to other conferences, namely COLING-2010, EMNLP-2010, RANLP-2011, LREC 2012, LREC 2014 and LREC 2016. After this generalization to other conferences, the LREC Map has been renamed as the LRE Map. == Size and content == The size of the database increases over time. The data collected amount to 4776 entries. Each resource is described according to the following attributes: Resource type, e.g. lexicon, annotation tool, tagger/parser. Resource production status, e.g. newly created finished, existing-updated. Resource availability, e.g. freely available, from data center. Resource modality, e.g. speech, written, sign language. Resource use, e.g. named entity recognition, language identification, machine translation. Resource language, e.g. English, 23 European Union languages, official languages of India. == Uses == The LRE map is a very important tool to chart the NLP field. Compared to other studied based on subjective scorings, the LRE map is made of real facts. The map has a great potential for many uses, in addition to being an information gathering tool: It is a great instrument for monitoring the evolution of the field (useful for funders), if applied in different contexts and times. It can be seen as a huge joint effort, the beginning of an even larger cooperative action not just among few leaders but among all the researchers. It is also an "educational" means towards the broad acknowledgment of the need of meta-research activities with the active involvement of many. It is also instrumental in introducing the new notion of "citation of resources" that could provide an award and a means of scholarly recognition for researchers engaged in resource creation. It is used to help the organization of the conferences of the field like LREC. == Derived matrices == The data were then cleaned and sorted by Joseph Mariani (CNRS-LIMSI IMMI) and Gil Francopoulo (CNRS-LIMSI IMMI + Tagmatica) in order to compute the various matrices of the final FLaReNet reports. One of them, the matrix for written data at LREC 2010 is as follows: English is the most studied language. Secondly, come French and German languages and then Italian and Spanish. == Future == The LRE Map has been extended to Language Resources and Evaluation Journal and other conferences.

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  AI Pair Programmers: Free vs Paid (2026)

  Trying to pick the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI pair programmer slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  Klaus-Robert Müller

  Klaus-Robert Müller (born 1964 in Karlsruhe, West Germany) is a German computer scientist and physicist, most noted for his work in machine learning and brain–computer interfaces. == Career == Klaus-Robert Müller received his Diplom in mathematical physics and PhD in theoretical computer science from the University of Karlsruhe. Following his Ph.D. he went to Berlin as a postdoctoral fellow at GMD (German National Research Center for Computer Science) Berlin (now part of Fraunhofer Institute for Open Communication Systems), where he started building up the Intelligent Data Analysis (IDA) group. From 1994 to 1995 he was a research fellow at Shun'ichi Amari's lab at the University of Tokyo. 1999 Müller became an associate professor for neuroinformatics at the University of Potsdam, transitioning to the full professorship for Neural Networks and Time Series Analysis in 2003. Since 2006 he holds the chair for Machine Learning at Technische Universität Berlin. Since 2012 he holds a distinguished professorship at Korea University in Seoul. He co-founded and is co-director of the Berlin Big Data Center (BBDC) of TU Berlin. As of 2017, 29 former doctoral or postdoctoral researchers of Klaus-Robert Müller have become full professors themselves. Bernhard Schölkopf and Alexander J. Smola were supervised by him as members of his research group. Since 2020 he is director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), a German National AI Competence Center, and director of the European Laboratory for Learning and Intelligent Systems (ELLIS) unit Berlin. In 2020/2021 he spent his sabbatical at Google Brain as a principal scientist. == Research == Müller has contributed extensively to several major interests of machine learning, including support vector machines (SVMs) and kernel methods, and artificial neural networks. He pioneered applying new methods of pattern recognition in domains like brain–computer interfaces, using them for patients with Locked-in syndrome. He is one of the leading computer scientists affiliated with Germany. His current research interests include: Statistical learning theory (Support Vector Machines, Deep Neural Networks, Boosting) Learning of non-stationarity data Fusion of structured heterogeneous multi-modal data, co-adaptation Applications: MEG, EEG, NIRS, ECoG, EMG, Brain Computer Interfaces, computational neuroscience, computer vision, genomic data analysis, computational chemistry and atomistic simulations, digital pathology == Honours and awards == Klaus-Robert Müller was elected a fellow of the German National Academy of Sciences Leopoldina in 2012. In 2017 he was elected member of the Berlin-Brandenburg Academy of Sciences and Humanities and also external scientific member of the Max Planck Society. In 2021 he was elected member of the German Academy of Science and Engineering. His work was honoured with several awards, including: 2026 Gottfried Wilhelm Leibniz Prize 2025 IEEE Neural Network Pioneer Award 2024 Feynman Prize in Nanotechnology 2023 Hector Fellow 2025, 2024, 2023, 2022, 2021, 2020, and 2019 Clarivate Highly Cited Researcher 2017 Vodafone Innovations Award 2017 2014 Science Prize of Berlin 2014 by the Governing Mayor of Berlin 2014 European Research Council Panel Consolidator Grants 2009 Best Paper award by IEEE Engineering in Medicine and Biology Society EMBS 2006 SEL-ALCATEL Research Prize for Technical Communication 1999 Olympus Award for Pattern Recognition == Books == with Holzinger, Andreas; et al., eds. (2022). xxAI – Beyond Explainable Artificial Intelligence. Lecture Notes in Computer Science. Vol. 13200. Springer Cham. doi:10.1007/978-3-031-04083-2. ISBN 978-3-031-04082-5. with Schütt, Kristof T.; et al., eds. (2020). Machine Learning Meets Quantum Physics. Lecture Notes in Physics. Vol. 968. Springer Cham. doi:10.1007/978-3-030-40245-7. ISBN 978-3-030-40244-0. S2CID 242406994. with Samek, Wojciech; et al., eds. (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science. Vol. 11700. Springer Cham. doi:10.1007/978-3-030-28954-6. ISBN 978-3-030-28953-9. with Montavon, Grégoire; et al., eds. (2012). Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science. Vol. 7700 (2nd ed.). Springer Berlin, Heidelberg. doi:10.1007/978-3-642-35289-8. ISBN 978-3-642-35288-1. S2CID 39578794.

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  How to Choose an AI Marketing Tool

  Curious about the best AI marketing tool? An AI marketing tool is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI marketing tool slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  Latent semantic analysis

  Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text (the distributional hypothesis). A matrix containing word counts per document (rows represent unique words and columns represent each document) is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of rows while preserving the similarity structure among columns. Documents are then compared by cosine similarity between any two columns. Values close to 1 represent very similar documents while values close to 0 represent very dissimilar documents. An information retrieval technique using latent semantic structure was patented in 1988 by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI). == Overview == === Occurrence matrix === LSA can use a document-term matrix which describes the occurrences of terms in documents; it is a sparse matrix whose rows correspond to terms and whose columns correspond to documents. A typical example of the weighting of the elements of the matrix is tf-idf (term frequency–inverse document frequency): the weight of an element of the matrix is proportional to the number of times the terms appear in each document, where rare terms are upweighted to reflect their relative importance. This matrix is also common to standard semantic models, though it is not necessarily explicitly expressed as a matrix, since the mathematical properties of matrices are not always used. === Rank lowering === After the construction of the occurrence matrix, LSA finds a low-rank approximation to the term-document matrix. There could be various reasons for these approximations: The original term-document matrix is presumed too large for the computing resources; in this case, the approximated low rank matrix is interpreted as an approximation (a "least and necessary evil"). The original term-document matrix is presumed noisy: for example, anecdotal instances of terms are to be eliminated. From this point of view, the approximated matrix is interpreted as a de-noisified matrix (a better matrix than the original). The original term-document matrix is presumed overly sparse relative to the "true" term-document matrix. That is, the original matrix lists only the words actually in each document, whereas we might be interested in all words related to each document—generally a much larger set due to synonymy. The consequence of the rank lowering is that some dimensions are combined and depend on more than one term: {(car), (truck), (flower)} → {(1.3452 car + 0.2828 truck), (flower)} This mitigates the problem of identifying synonymy, as the rank lowering is expected to merge the dimensions associated with terms that have similar meanings. It also partially mitigates the problem with polysemy, since components of polysemous words that point in the "right" direction are added to the components of words that share a similar meaning. Conversely, components that point in other directions tend to either simply cancel out, or, at worst, to be smaller than components in the directions corresponding to the intended sense. === Derivation === Let X {\displaystyle X} be a matrix where element ( i , j ) {\displaystyle (i,j)} describes the occurrence of term i {\displaystyle i} in document j {\displaystyle j} (this can be, for example, the frequency). X {\displaystyle X} will look like this: d j ↓ t i T → [ x 1 , 1 … x 1 , j … x 1 , n ⋮ ⋱ ⋮ ⋱ ⋮ x i , 1 … x i , j … x i , n ⋮ ⋱ ⋮ ⋱ ⋮ x m , 1 … x m , j … x m , n ] {\displaystyle {\begin{matrix}&{\textbf {d}}_{j}\\&\downarrow \\{\textbf {t}}_{i}^{T}\rightarrow &{\begin{bmatrix}x_{1,1}&\dots &x_{1,j}&\dots &x_{1,n}\\\vdots &\ddots &\vdots &\ddots &\vdots \\x_{i,1}&\dots &x_{i,j}&\dots &x_{i,n}\\\vdots &\ddots &\vdots &\ddots &\vdots \\x_{m,1}&\dots &x_{m,j}&\dots &x_{m,n}\\\end{bmatrix}}\end{matrix}}} Now a row in this matrix will be a vector corresponding to a term, giving its relation to each document: t i T = [ x i , 1 … x i , j … x i , n ] {\displaystyle {\textbf {t}}_{i}^{T}={\begin{bmatrix}x_{i,1}&\dots &x_{i,j}&\dots &x_{i,n}\end{bmatrix}}} Likewise, a column in this matrix will be a vector corresponding to a document, giving its relation to each term: d j = [ x 1 , j ⋮ x i , j ⋮ x m , j ] {\displaystyle {\textbf {d}}_{j}={\begin{bmatrix}x_{1,j}\\\vdots \\x_{i,j}\\\vdots \\x_{m,j}\\\end{bmatrix}}} Now the dot product t i T t p {\displaystyle {\textbf {t}}_{i}^{T}{\textbf {t}}_{p}} between two term vectors gives the correlation between the terms over the set of documents. The matrix product X X T {\displaystyle XX^{T}} contains all these dot products. Element ( i , p ) {\displaystyle (i,p)} (which is equal to element ( p , i ) {\displaystyle (p,i)} ) contains the dot product t i T t p {\displaystyle {\textbf {t}}_{i}^{T}{\textbf {t}}_{p}} ( = t p T t i {\displaystyle ={\textbf {t}}_{p}^{T}{\textbf {t}}_{i}} ). Likewise, the matrix X T X {\displaystyle X^{T}X} contains the dot products between all the document vectors, giving their correlation over the terms: d j T d q = d q T d j {\displaystyle {\textbf {d}}_{j}^{T}{\textbf {d}}_{q}={\textbf {d}}_{q}^{T}{\textbf {d}}_{j}} . Now, from the theory of linear algebra, there exists a decomposition of X {\displaystyle X} such that U {\displaystyle U} and V {\displaystyle V} are orthogonal matrices and Σ {\displaystyle \Sigma } is a diagonal matrix. This is called a singular value decomposition (SVD): X = U Σ V T {\displaystyle {\begin{matrix}X=U\Sigma V^{T}\end{matrix}}} The matrix products giving us the term and document correlations then become X X T = ( U Σ V T ) ( U Σ V T ) T = ( U Σ V T ) ( V T T Σ T U T ) = U Σ V T V Σ T U T = U Σ Σ T U T X T X = ( U Σ V T ) T ( U Σ V T ) = ( V T T Σ T U T ) ( U Σ V T ) = V Σ T U T U Σ V T = V Σ T Σ V T {\displaystyle {\begin{matrix}XX^{T}&=&(U\Sigma V^{T})(U\Sigma V^{T})^{T}=(U\Sigma V^{T})(V^{T^{T}}\Sigma ^{T}U^{T})=U\Sigma V^{T}V\Sigma ^{T}U^{T}=U\Sigma \Sigma ^{T}U^{T}\\X^{T}X&=&(U\Sigma V^{T})^{T}(U\Sigma V^{T})=(V^{T^{T}}\Sigma ^{T}U^{T})(U\Sigma V^{T})=V\Sigma ^{T}U^{T}U\Sigma V^{T}=V\Sigma ^{T}\Sigma V^{T}\end{matrix}}} Since Σ Σ T {\displaystyle \Sigma \Sigma ^{T}} and Σ T Σ {\displaystyle \Sigma ^{T}\Sigma } are diagonal we see that U {\displaystyle U} must contain the eigenvectors of X X T {\displaystyle XX^{T}} , while V {\displaystyle V} must be the eigenvectors of X T X {\displaystyle X^{T}X} . Both products have the same non-zero eigenvalues, given by the non-zero entries of Σ Σ T {\displaystyle \Sigma \Sigma ^{T}} , or equally, by the non-zero entries of Σ T Σ {\displaystyle \Sigma ^{T}\Sigma } . Now the decomposition looks like this: X U Σ V T ( d j ) ( d ^ j ) ↓ ↓ ( t i T ) → [ x 1 , 1 … x 1 , j … x 1 , n ⋮ ⋱ ⋮ ⋱ ⋮ x i , 1 … x i , j … x i , n ⋮ ⋱ ⋮ ⋱ ⋮ x m , 1 … x m , j … x m , n ] = ( t ^ i T ) → [ [ u 1 ] … [ u l ] ] ⋅ [ σ 1 … 0 ⋮ ⋱ ⋮ 0 … σ l ] ⋅ [ [ v 1 ] ⋮ [ v l ] ] {\displaystyle {\begin{matrix}&X&&&U&&\Sigma &&V^{T}\\&({\textbf {d}}_{j})&&&&&&&({\hat {\textbf {d}}}_{j})\\&\downarrow &&&&&&&\downarrow \\({\textbf {t}}_{i}^{T})\rightarrow &{\begin{bmatrix}x_{1,1}&\dots &x_{1,j}&\dots &x_{1,n}\\\vdots &\ddots &\vdots &\ddots &\vdots \\x_{i,1}&\dots &x_{i,j}&\dots &x_{i,n}\\\vdots &\ddots &\vdots &\ddots &\vdots \\x_{m,1}&\dots &x_{m,j}&\dots &x_{m,n}\\\end{bmatrix}}&=&({\hat {\textbf {t}}}_{i}^{T})\rightarrow &{\begin{bmatrix}{\begin{bmatrix}\,\\\,\\{\textbf {u}}_{1}\\\,\\\,\end{bmatrix}}\dots {\begin{bmatrix}\,\\\,\\{\textbf {u}}_{l}\\\,\\\,\end{bmatrix}}\end{bmatrix}}&\cdot &{\begin{bmatrix}\sigma _{1}&\dots &0\\\vdots &\ddots &\vdots \\0&\dots &\sigma _{l}\\\end{bmatrix}}&\cdot &{\begin{bmatrix}{\begin{bmatrix}&&{\textbf {v}}_{1}&&\end{bmatrix}}\\\vdots \\{\begin{bmatrix}&&{\textbf {v}}_{l}&&\end{bmatrix}}\end{bmatrix}}\end{matrix}}} The values σ 1 , … , σ l {\displaystyle \sigma _{1},\dots ,\sigma _{l}} are called the singular values, and u 1 , … , u l {\displaystyle u_{1},\dots ,u_{l}} and v 1 , … , v l {\displaystyle v_{1},\dots ,v_{l}} the left and right singular vectors. Notice the only part of U {\displaystyle U} that contributes to t i {\displaystyle {\textbf {t}}_{i}} is the i 'th {\displaystyle i{\textrm {'th}}} row. Let this row vector be called t ^ i T {\displaystyle {\hat {\textrm {t}}}_{i}^{T}} . Likewise, the only part of V T {\displaystyle V^{T}} that contributes to d j {\displaystyle {\textbf {d}}_{j}} is the j 'th {\displaystyle j{\textrm {'th}}} column, d ^ j {\displaystyle {\hat {\textrm {d}}}_{j}} . These are not the eigenvectors, but depend on all the eigenvectors. I

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  Ziad Obermeyer

  Ziad Obermeyer (Arabic: زياد أوبرماير) is a Lebanese American physician and researcher whose work focuses on machine learning, health policy, and clinical decision-making in medicine. He is the Blue Cross of California Distinguished Associate Professor at the UC Berkeley School of Public Health, a Chan Zuckerberg Biohub investigator, and a research associate at the National Bureau of Economic Research. He is known for his research on racial bias in health care algorithms and the use of artificial intelligence in health care. == Early life and education == Obermeyer was born in Beirut, Lebanon, and raised in Cambridge, Massachusetts. He earned a Bachelor of Arts degree from Harvard College and a Master of Philosophy (M.Phil.) in History and Science from the University of Cambridge. He received his Doctor of Medicine (M.D.) from Harvard Medical School in 2008. Before pursuing medicine, Obermeyer worked as a consultant at McKinsey & Company, advising pharmaceutical and global health clients in New Jersey, Geneva, and Tokyo. After completing his medical degree, he trained as an emergency physician at Mass General Brigham (MGB) in Boston, Massachusetts. He later continued practicing emergency medicine at the Fort Defiance Indian Hospital on the Navajo Nation in Arizona. == Academic career == Obermeyer served as an Assistant Professor at Harvard Medical School from 2014 to 2020. In 2020, he joined the University of California, Berkeley as an Associate Professor and the Blue Cross of California Distinguished Professor at the School of Public Health. == Research focus == === Algorithmic racial bias in healthcare === In 2019, Obermeyer and economist Sendhil Mullainathan examined a commercial healthcare algorithm by UnitedHealth Group, used in hospitals and by insurers to identify patients with complex health needs. The study found that the algorithm underestimated the health needs of Black patients compared to white patients with similar conditions and that reformulating it would reduce racial bias. In 2020, Obermeyer analyzed an algorithm used to allocate CARE Act relief funding to hospitals. The study identified allocation patterns that favored hospitals with higher revenues over hospitals serving larger numbers of COVID-19 patients who are predominantly Black. === Clinical decision-making === In 2021, Obermeyer and colleagues examined physician decision-making in cardiac care using machine learning models. The study found that physicians misdiagnose cases when they rely on symptoms representative of a heart attack, such as chest pain, over other symptoms. === Pain assessment === Obermeyer developed a deep learning approach to investigate the severity of osteoarthritis in underserved communities. == Policy and regulatory work == Following the publication of the 2019 algorithmic racial bias study, the New York Department of Financial Services and Department of Health launched an investigation into UnitedHealth Group's algorithm, requesting that the company cease using it, citing discriminatory business practices. Also related to this study, in December 2019, Democratic Senators Cory Booker and Ron Wyden released letters to the Federal Trade Commission and Centers for Medicare and Medicaid Services asking to investigate potential discrimination in decision-making algorithms against marginalized communities in healthcare. The senators also wrote to major healthcare companies, including Aetna and Blue Cross Blue Shield, about their internal safeguards against racial bias in their technology. In 2021, Obermeyer and colleagues at the University of Chicago Booth School of Business released the Algorithmic Bias Playbook, a resource for policymakers and technical teams working in healthcare on how to measure and mitigate algorithmic racial bias. Obermeyer testified before the U.S. Senate Financial Committee in February 2024 on artificial intelligence in healthcare, recommending transparency requirements for AI developers and independent algorithm evaluations. In December 2025, he testified before the United States House Committee on Oversight and Government Reform on the role of AI in affordable healthcare and the impact of its integration on the workforce. == Organizations == In 2021, Obermeyer cofounded Nightingale Open Science, a non-profit that creates new medical imaging datasets available for research, and Dandelion Health, a health data analytics company. In June 2023, the company launched a program to audit and evaluate the performance of algorithms to identify potential racial, ethnic, and geographic bias, funded by the Gordon and Betty Moore Foundation and the SCAN Foundation. Dandelion Health partnered with the American Heart Association in 2025 to power an AI assessment lab for cardiovascular algorithms. Obermeyer is a founding faculty member of the University of California, Berkeley–University of California, San Francisco joint program in computational precision health. == Recognition == TIME magazine named Obermeyer one of the 100 most influential people in artificial intelligence in 2023. He has served as a Chan Zuckerberg Biohub Investigator since 2022, and as a Research Associate at the National Bureau of Economic Research since 2023. He was designated an Emerging Leader by the National Academy of Medicine in 2020. Obermeyer's racial bias study received the Willard G. Manning Memorial Award for the Best Research in Health Econometrics from the American Society of Health Economists (ASHEcon) in 2021 and the Responsible Business Education Award from the Financial Times in 2022.

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  Writer invariant

  Writer invariant, also called authorial invariant or author's invariant, is a property of a text which is invariant of its author, that is, it will be similar in all texts of a given author and different in texts of different authors. It can be used to find plagiarism or discover who is real author of anonymously published text. Writer invariant is also an author's pattern of writing a letter in handwritten text recognition. While it is generally recognised that writer invariants exist, it is not agreed what properties of a text should be used. Among the first ones used was distribution of word lengths; other proposed invariants include average sentence length, average word length, noun, verb or adjective usage frequency, vocabulary richness, and frequency of function words, or specific function words. Of these, average sentence lengths can be very similar in works of different authors or vary significantly even within a single work; average word lengths likewise turn out to be very similar in works of different authors. Analysis of function words shows promise because they are used by authors unconsciously.

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  Best AI Code-review Tools in 2026

  Looking for the best AI code-review tool? An AI code-review tool is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI code-review tool slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.

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  Per-pixel lighting

  In computer graphics, per-pixel lighting refers to any technique for lighting an image or scene that calculates illumination for each pixel on a rendered image. This is in contrast to other popular methods of lighting such as vertex lighting, which calculates illumination at each vertex of a 3D model and then interpolates the resulting values over the model's faces to calculate the final per-pixel color values. Per-pixel lighting is commonly used with techniques, such as blending, alpha blending, alpha to coverage, anti-aliasing, texture filtering, clipping, hidden-surface determination, Z-buffering, stencil buffering, shading, mipmapping, normal mapping, bump mapping, displacement mapping, parallax mapping, shadow mapping, specular mapping, shadow volumes, high-dynamic-range rendering, ambient occlusion (screen space ambient occlusion, screen space directional occlusion, ray-traced ambient occlusion), ray tracing, global illumination, and tessellation. Each of these techniques provides some additional data about the surface being lit or the scene and light sources that contributes to the final look and feel of the surface. Most modern video game engines implement lighting using per-pixel techniques instead of vertex lighting to achieve increased detail and realism. The id Tech 4 engine, used to develop such games as Brink and Doom 3, was one of the first game engines to implement a completely per-pixel shading engine. All versions of the CryENGINE, Frostbite Engine, and Unreal Engine, among others, also implement per-pixel shading techniques. Deferred shading is a recent development in per-pixel lighting notable for its use in the Frostbite Engine and Battlefield 3. Deferred shading techniques are capable of rendering potentially large numbers of small lights inexpensively (other per-pixel lighting approaches require full-screen calculations for each light in a scene, regardless of size). == History == While only recently have personal computers and video hardware become powerful enough to perform full per-pixel shading in real-time applications such as games, many of the core concepts used in per-pixel lighting models have existed for decades. Frank Crow published a paper describing the theory of shadow volumes in 1977. This technique uses the stencil buffer to specify areas of the screen that correspond to surfaces that lie in a "shadow volume", or a shape representing a volume of space eclipsed from a light source by some object. These shadowed areas are typically shaded after the scene is rendered to buffers by storing shadowed areas with the stencil buffer. Jim Blinn first introduced the idea of normal mapping in a 1978 SIGGRAPH paper. Blinn pointed out that the earlier idea of unlit texture mapping proposed by Edwin Catmull was unrealistic for simulating rough surfaces. Instead of mapping a texture onto an object to simulate roughness, Blinn proposed a method of calculating the degree of lighting a point on a surface should receive based on an established "perturbation" of the normals across the surface. == Hardware rendering == Real-time applications, such as video games, usually implement per-pixel lighting through the use of pixel shaders, allowing the GPU hardware to process the effect. The scene to be rendered is first rasterized onto a number of buffers storing different types of data to be used in rendering the scene, such as depth, normal direction, and diffuse color. Then, the data is passed into a shader and used to compute the final appearance of the scene, pixel-by-pixel. Deferred shading is a per-pixel shading technique that has recently become feasible for games. With deferred shading, a "g-buffer" is used to store all terms needed to shade a final scene on the pixel level. The format of this data varies from application to application depending on the desired effect, and can include normal data, positional data, specular data, diffuse data, emissive maps and albedo, among others. Using multiple render targets, all of this data can be rendered to the g-buffer with a single pass, and a shader can calculate the final color of each pixel based on the data from the g-buffer in a final "deferred pass". Because deferred shading assumes only one visible fragment per pixel sample, transparent objects are generally handled in a separate forward pass. == Software rendering == Per-pixel lighting is also performed in software on many high-end commercial rendering applications which typically do not render at interactive framerates. This is called offline rendering or software rendering. NVidia's mental ray rendering software, which is integrated with such suites as Autodesk's Softimage is a well-known example.

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  Top 10 AI Chatbots Compared (2026)

  Shopping for the best AI chatbot? An AI chatbot is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI chatbot slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.

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  Best AI Essay Writers in 2026

  Comparing the best AI essay writer? An AI essay writer is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI essay writer slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  How to Choose an AI Video Editor

  In search of the best AI video editor? An AI video editor is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI video editor slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  Trustworthy computing

  The term trustworthy computing (TwC) has been applied to computing systems that are inherently secure, available, and reliable. It is particularly associated with the Microsoft initiative of the same name, launched in 2002. == History == Until 1995, there were restrictions on commercial traffic over the Internet. On, May 26, 1995, Bill Gates sent the "Internet Tidal Wave" memorandum to Microsoft executives assigning "...the Internet this highest level of importance..." but Microsoft's Windows 95 was released without a web browser as Microsoft had not yet developed one. The success of the web had caught them by surprise but by mid 1995, they were testing their own web server, and on August 24, 1995, launched a major online service, The Microsoft Network (MSN). The National Research Council recognized that the rise of the Internet simultaneously increased societal reliance on computer systems while increasing the vulnerability of such systems to failure and produced an important report in 1999, "Trust in Cyberspace". This report reviews the cost of un-trustworthy systems and identifies actions required for improvement. == Microsoft and Trustworthy Computing == Bill Gates launched Microsoft's "Trustworthy Computing" initiative with a January 15, 2002 memo, referencing an internal whitepaper by Microsoft CTO and Senior Vice President Craig Mundie. The move was reportedly prompted by the fact that they "...had been under fire from some of its larger customers–government agencies, financial companies and others–about the security problems in Windows, issues that were being brought front and center by a series of self-replicating worms and embarrassing attacks." such as Code Red, Nimda, Klez and Slammer. Four areas were identified as the initiative's key areas: Security, Privacy, Reliability, and Business Integrity, and despite some initial scepticism, at its 10-year anniversary it was generally accepted as having "...made a positive impact on the industry...". The Trustworthy Computing campaign was the main reason why Easter eggs disappeared from Windows, Office and other Microsoft products.

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  International Computer Archive of Modern and Medieval English

  The International Computer Archive of Modern and Medieval English (ICAME) is an international group of linguists and data scientists working in corpus linguistics to digitise English texts. The organisation was founded in Oslo, Norway in 1977 as the International Computer Archive of Modern English, before being renamed to its current title. Its primary objectives were: collecting and distributing information on English language material available for computer processing; and linguistic research completed or in progress on this material; compiling an archive of corpora to be located at the University of Bergen, from where copies of the material can be obtained at cost. The portal to their materials is hosted at the University of Bergen, where they have set out the aim of the organization to "collect and distribute information on English language material available for computer processing and on linguistic research to compile an archive of English text corpora in machine-readable form, and to make material available to research institutions." Creating computer corpora, i.e. collections of texts in machine-readable form, is the most accessible way to study both transcribed spoken language and various genres of written texts for modern scholars, including both "descriptive and more theoretically-minded linguists". The ICAME group hosts academic conferences that focus on corpus linguistic studies of historical changes and contemporary grammatical descriptions of English, and makes corpora of different varieties of English available to scholars, starting with editions of the 1960s Brown Corpus. Their first academic conference was held in Bergen, Norway in 1979, and scholars who were interested in corpus linguistics continued to meet each spring in different European and English-speaking countries. At these meetings, the compilation and distribution of corpora they enabled played a key role in the creation of the field of corpus linguistics in the 20th century, a precursor to current big data analytics. In summarizing the field, Kennedy's Introduction to Corpus Linguistics notes that "for corpus linguists with an interest in the description of English, the International Computer Archive of Modern and Medieval English has been the major resource". The influence of ICAME on the field has also be laid out in Facchinetti's history, Corpus Linguistics Twenty-five Years On. One influential resource that ICAME made available was a CD of 20 different corpora, including those covering different regional Englishes (such as the Australian Corpus of English, the Wellington Corpus of Spoken New Zealand English, the Kolhapur Corpus of Indian English, the Bergen Corpus of London Teenage Language (COLT), the Helsinki Corpus of Older Scots, and the International Corpus of English—East-African component), as well as versions of the Brown Corpus and the Lancaster-Bergen-Oslo (LOB) corpus tagged for part of speech. ICAME also published an annual journal, the ICAME Journal, formerly ICAME News, that contains articles, conference reports, reviews and notices related to corpus linguistics. The current editors of the ICAME Journal are Merja Kytö and Anna-Brita Stenström.I am wearing a tie clip in the shape of a monkey wrench... The story behind this peculiar piece of jewelry goes back to the early 60s when I was assembling the notorious Brown Corpus and others were using computers to make concordances of William Butler Yeats and other poets. One of my colleagues, a specialist in modem Irish literature, was heard to remark that anyone who would use a computer on good literature was nothing but a plumber. Some of my students responded by forming a linguistic plumber's union, the symbol of which was, of course, a monkey wrench.

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  Abeba Birhane

  Abeba Birhane is an Ethiopian-born cognitive scientist who works at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Birhane's work with Vinay Prabhu uncovered that large-scale image datasets commonly used to develop AI systems, including ImageNet and 80 Million Tiny Images, carried racist and misogynistic labels and offensive images. She has been recognized by VentureBeat as a top innovator in computer vision and named as one of the 100 most influential persons in AI 2023 by TIME magazine. == Early life and education == Birhane was born in Ethiopia. She received her Bachelors of Science in Psychology and a Bachelors of Arts in Philosophy from The Open University. In 2015, she completed her Master of Science in Cognitive Science and, in 2021, her Ph.D. at the Complex Software Lab in the School of Computer Science at University College Dublin. == Career and research == Birhane studied the impacts of emerging AI technologies and how they shape individuals and local communities. She found that AI algorithms tend to disproportionately impact vulnerable groups such as older workers, trans people, immigrants, and children. Her research on relational ethics won the best paper award at NeurIPS’s Black in AI workshop in 2019. She has also studied and written about algorithmic colonization driven by corporate agendas. Her work in decolonizing computational sciences addressed the inherited oppressions in current systems especially towards women of color. In 2020, Birhane and Vinay Prabhu, principal machine learning scientist at UnifyID, published a paper examining the problematic data collection, labelling, classification, and consequences of large image datasets. These datasets, including ImageNet and MIT's 80 Million Tiny Images, have been used to develop thousands of AI algorithms and systems. Birhane and Prabhu found that they contained many racist and misogynistic labels and slurs as well as offensive images. This resulted in MIT voluntarily and formally taking down the 80 Million Tiny Images dataset. More recently, Birhane has worked with Rediet Abebe, George Obaido, and Sekou Remy on researching the barriers to data sharing in Africa. They found that power imbalances are significant in the data sharing process, even when the data comes from Africa. Their research was published at the ACM Conference on Fairness, Accountability, and Transparency. In 2024, Birhane established the AI Accountability Lab research group at Trinity College Dublin. == Selected awards == 2019 NeurIPS Black in AI Workshop Best Paper Award 2020 Venture Beat AI Innovations Award in the category Computer Vision Innovation (received with Vinay Prabhu) 2021 100 Brilliant Women in AI Ethics Hall of Fame Honoree 2022 Lero Director’s Prize for PhD/PostDoctoral Contribution. 2023 100 Most Influential People in AI by TIME magazine

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