AI Analytics Ranking

AI Analytics Ranking — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Metigo

    Metigo

    metigo is a software application that performs image-based modelling and close range photogrammetry. It produces rectified imagery plans, true ortho-projections on planar, cylindric and conic surfaces, 3D photorealistic models, measurements from photography and mappings on a photographic base for uses in the cultural heritage sector, mainly conservation. == Products == The metigo product line currently consists of the mapping software metigo MAP, the stereo-photogrammetry modeling software metigo 3D, the free viewer metigo VIEW. These products are all standalone and are not depending on other software, such as AutoCAD. === metigo MAP === metigo MAP is mainly used to map findings and conservation measured on a uniform metric photographic base. Therefore, photos of planar surfaces can be rectified based on geometrical informations, e.g. height and width of a rectangle, or cartesian coordinates measured by total station. Beside rectified imagery several other metric mapping bases can be imported and used: true ortho-projections; scaled scans of plans and plots; CAD-files; 3D models, such as digital surface models (DSM) produced by stereo-photogrammetry, SfM or 3D scanning. metigo MAP 's strong point is that rectified imagery taken with different techniques (visual light, sided light, IR, UV, UV-fluorescence, X-ray), historic images and photos taken at various stages of the conservation process can be superimposed and evaluated mutually. The user can allocate several attributes, such as different conservation measures and damage classes, to the mapped geometries. The mappings can be analysed by geometries as well as by user-defined attributes at any stage of the project. metigo MAP targets mainly conservators in different cultural heritage fields. Using it no specialist knowledge of surveying and photogrammetric techniques are needed. === metigo 3D === metigo 3D is a stereo-photogrammetric kit that allows to calculate bundle adjustments (axios3D), create high-quality 3D point clouds using multiple stereo photo pairs combined with metric survey data, mesh these point clouds, texture the meshes with high-resolution image data to create photo-realistic models, ortho-project orientated images on digital surface models (DSM) on planes and best-fit cylinders and cones, create unwrappings and developed views of curved surfaces, analyse deformations of 3D surfaces. metigo 3D targets metric survey specialists working in the cultural heritage sector. == Supported file formats == metigo has the ability to read the following formats: images: JPEG (.jpg), Tiff (.tif), Bitmaps (.bmp), CompuServ (.gif), Encapsualated Postscript (.eps), PCX (.pcx), Photo-CD (.pcd), PICT (.pcd), PNG (.png), Targa (.tga), RAW-format of several camera brands. CAD: DBX, DXF, DWG. 3D: many ASCII-formats (.stl, .wrl, etc.) point data: format editor for ASCII files. == Supported languages == Currently, an English and German version of the software is supported. For metigo MAP beside these a French and Polish GUI is offered for sale. == Applications == The main applications of metigo are: conservation in the cultural heritage context, e.g. stone conservation paintings tapestry etc. architecture, archaeology, many other are possible, e.g. forensics. == History == The first public release of metigo was in 2000.

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  • MemoQ

    MemoQ

    memoQ is a computer-assisted translation software suite which runs on Microsoft Windows operating systems. It is developed by the Hungarian software company memoQ Fordítástechnológiai Zrt. (memoQ Translation Technologies), formerly Kilgray, a provider of translation management software established in 2004 and cited as one of the fastest-growing companies in the translation technology sector in 2012, and 2013. memoQ provides translation memory, terminology, machine translation integration and reference information management in desktop, client/server and web application environments. == History == memoQ, a translation environment tool first released in 2006, was the first product created by memoQ Translation Technologies, a company founded in Hungary by the three language technologists Balázs Kis, István Lengyel and Gábor Ugray. In the years since the software was first presented, it has grown in popularity and is now among the most frequent TEnT applications used for translation (it was rated as the third most used CAT tool in a Proz.com study in 2013 and as the second most widely used tool in a June 2010 survey of 458 working translators), after SDL Trados, Wordfast, Déjà Vu, OmegaT and others. Today it is available in desktop versions for translators (Translator Pro edition), and project managers (Project Manager edition), as well as site-installed and hosted server applications offering integration with the desktop versions and a web browser interface. There are currently several active online forums in which users provide each other with independent advice and support on the software's functions, as well as many online tutorials created by professional trainers and active users. Before its commercial debut, a version of memoQ (2.0) was distributed as postcardware.

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  • Büchi automaton

    Büchi automaton

    In computer science and automata theory, a deterministic Büchi automaton is a theoretical machine which either accepts or rejects infinite inputs. Such a machine has a set of states and a transition function, which determines which state the machine should move to from its current state when it reads the next input character. Some states are accepting states and one state is the start state. The machine accepts an input if and only if it will pass through an accepting state infinitely many times as it reads the input. A non-deterministic Büchi automaton, later referred to just as a Büchi automaton, has a transition function which may have multiple outputs, leading to many possible paths for the same input; it accepts an infinite input if and only if some possible path is accepting. Deterministic and non-deterministic Büchi automata generalize deterministic finite automata and nondeterministic finite automata to infinite inputs. Each are types of ω-automata. Büchi automata recognize the ω-regular languages, the infinite word version of regular languages. They are named after the Swiss mathematician Julius Richard Büchi, who invented them in 1962. Büchi automata are often used in model checking as an automata-theoretic version of a formula in linear temporal logic. == Formal definition == Formally, a deterministic Büchi automaton is a tuple A = ( Q , Σ , δ , q 0 , F ) {\textstyle A=(Q,\Sigma ,\delta ,q_{0},\mathbf {F} )} that consists of the following components: Q {\textstyle Q} is a finite set. The elements of Q {\textstyle Q} are called the states of A {\textstyle A} . Σ {\textstyle \Sigma } is a finite set called the alphabet of A {\textstyle A} . δ : Q × Σ → Q {\textstyle \delta \colon Q\times \Sigma \to Q} is a function, called the transition function of A {\textstyle A} . q 0 {\textstyle q_{0}} is an element of Q {\textstyle Q} , called the initial state of A {\textstyle A} . F ⊆ Q {\textstyle \mathbf {F} \subseteq Q} is the acceptance condition. A run i _ = i 0 i 1 i 2 ⋯ ∈ Σ ω {\displaystyle {\underline {i}}=i_{0}i_{1}i_{2}\cdots \in \Sigma ^{\omega }} is an infinite string of inputs of A {\displaystyle A} . By calling δ {\displaystyle \delta } recursively, we can extend it to a function δ ω : Σ ω → Q ω {\displaystyle \delta ^{\omega }:\Sigma ^{\omega }\to Q^{\omega }} . A state q ∈ Q {\displaystyle q\in Q} is said to occur infinitely often for a run i _ {\displaystyle {\underline {i}}} when the set { n ∈ N ∣ δ ω ( i _ ) n = q } {\displaystyle \{n\in \mathbb {N} \mid \delta ^{\omega }({\underline {i}})_{n}=q\}} is infinite. Let I n f ( i _ ) {\displaystyle \mathrm {Inf} ({\underline {i}})} be the set of states occurring infinitely often for i _ {\displaystyle {\underline {i}}} . The language of A {\displaystyle A} is then the set of runs of A {\displaystyle A} in which at least one of the infinitely-often occurring states is in F {\textstyle \mathbf {F} } ; in symbols: L ( A ) = { i _ ∈ Σ ω ∣ I n f ( i _ ) ∩ F ≠ ∅ } . {\displaystyle L(A)=\{{\underline {i}}\in \Sigma ^{\omega }\mid \mathrm {Inf} ({\underline {i}})\cap \mathbf {F} \neq \varnothing \}.} In a (non-deterministic) Büchi automaton, the transition function δ {\textstyle \delta } is replaced with a transition relation Δ {\textstyle \Delta } that returns a set of states, and the single initial state q 0 {\textstyle q_{0}} is replaced by a set I {\textstyle I} of initial states. Generally, the term Büchi automaton without qualifier refers to non-deterministic Büchi automata. For more comprehensive formalism see also ω-automaton. == Closure properties == The set of Büchi automata is closed under the following operations. Let A = ( Q A , Σ , Δ A , I A , F A ) {\displaystyle A=(Q_{A},\Sigma ,\Delta _{A},I_{A},{F}_{A})} and B = ( Q B , Σ , Δ B , I B , F B ) {\displaystyle B=(Q_{B},\Sigma ,\Delta _{B},I_{B},{F}_{B})} be Büchi automata and C = ( Q C , Σ , Δ C , I C , F C ) {\displaystyle C=(Q_{C},\Sigma ,\Delta _{C},I_{C},{F}_{C})} be a finite automaton. Union: There is a Büchi automaton that recognizes the language L ( A ) ∪ L ( B ) . {\displaystyle L(A)\cup L(B).} Proof: If we assume, w.l.o.g., Q A ∩ Q B {\displaystyle Q_{A}\cap Q_{B}} is empty then L ( A ) ∪ L ( B ) {\displaystyle L(A)\cup L(B)} is recognized by the Büchi automaton ( Q A ∪ Q B , Σ ∪ Σ , Δ A ∪ Δ B , I A ∪ I B , F A ∪ F B ) . {\displaystyle (Q_{A}\cup Q_{B},\Sigma \cup \Sigma ,\Delta _{A}\cup \Delta _{B},I_{A}\cup I_{B},{F}_{A}\cup {F}_{B}).} Intersection: There is a Büchi automaton that recognizes the language L ( A ) ∩ L ( B ) . {\displaystyle L(A)\cap L(B).} Proof: The Büchi automaton A ′ = ( Q ′ , Σ , Δ ′ , I ′ , F ′ ) {\displaystyle A'=(Q',\Sigma ,\Delta ',I',F')} recognizes L ( A ) ∩ L ( B ) , {\displaystyle L(A)\cap L(B),} where Q ′ = Q A × Q B × { 1 , 2 } {\displaystyle Q'=Q_{A}\times Q_{B}\times \{1,2\}} Δ ′ = Δ 1 ∪ Δ 2 {\displaystyle \Delta '=\Delta _{1}\cup \Delta _{2}} Δ 1 = { ( ( q A , q B , 1 ) , a , ( q A ′ , q B ′ , i ) ) | ( q A , a , q A ′ ) ∈ Δ A and ( q B , a , q B ′ ) ∈ Δ B and if q A ∈ F A then i = 2 else i = 1 } {\displaystyle \Delta _{1}=\{((q_{A},q_{B},1),a,(q'_{A},q'_{B},i))|(q_{A},a,q'_{A})\in \Delta _{A}{\text{ and }}(q_{B},a,q'_{B})\in \Delta _{B}{\text{ and if }}q_{A}\in F_{A}{\text{ then }}i=2{\text{ else }}i=1\}} Δ 2 = { ( ( q A , q B , 2 ) , a , ( q A ′ , q B ′ , i ) ) | ( q A , a , q A ′ ) ∈ Δ A and ( q B , a , q B ′ ) ∈ Δ B and if q B ∈ F B then i = 1 else i = 2 } {\displaystyle \Delta _{2}=\{((q_{A},q_{B},2),a,(q'_{A},q'_{B},i))|(q_{A},a,q'_{A})\in \Delta _{A}{\text{ and }}(q_{B},a,q'_{B})\in \Delta _{B}{\text{ and if }}q_{B}\in F_{B}{\text{ then }}i=1{\text{ else }}i=2\}} I ′ = I A × I B × { 1 } {\displaystyle I'=I_{A}\times I_{B}\times \{1\}} F ′ = { ( q A , q B , 2 ) | q B ∈ F B } {\displaystyle F'=\{(q_{A},q_{B},2)|q_{B}\in F_{B}\}} By construction, r ′ = ( q A 0 , q B 0 , i 0 ) , ( q A 1 , q B 1 , i 1 ) , … {\displaystyle r'=(q_{A}^{0},q_{B}^{0},i^{0}),(q_{A}^{1},q_{B}^{1},i^{1}),\dots } is a run of automaton A' on input word w {\textstyle w} if r A = q A 0 , q A 1 , … {\displaystyle r_{A}=q_{A}^{0},q_{A}^{1},\dots } is run of A {\textstyle A} on w {\textstyle w} and r B = q B 0 , q B 1 , … {\displaystyle r_{B}=q_{B}^{0},q_{B}^{1},\dots } is run of B {\textstyle B} on w {\textstyle w} . r A {\textstyle r_{A}} is accepting and r B {\textstyle r_{B}} is accepting if r ′ {\textstyle r'} is concatenation of an infinite series of finite segments of 1-states (states with third component 1) and 2-states (states with third component 2) alternatively. There is such a series of segments of r ′ {\textstyle r'} if r ′ {\textstyle r'} is accepted by A ′ {\textstyle A'} . Concatenation: There is a Büchi automaton that recognizes the language L ( C ) ⋅ L ( A ) . {\displaystyle L(C)\cdot L(A).} Proof: If we assume, w.l.o.g., Q C ∩ Q A {\displaystyle Q_{C}\cap Q_{A}} is empty then the Büchi automaton A ′ = ( Q C ∪ Q A , Σ , Δ ′ , I ′ , F A ) {\displaystyle A'=(Q_{C}\cup Q_{A},\Sigma ,\Delta ',I',F_{A})} recognizes L ( C ) ⋅ L ( A ) {\displaystyle L(C)\cdot L(A)} , where Δ ′ = Δ A ∪ Δ C ∪ { ( q , a , q ′ ) | q ′ ∈ I A and ∃ f ∈ F C . ( q , a , f ) ∈ Δ C } {\displaystyle \Delta '=\Delta _{A}\cup \Delta _{C}\cup \{(q,a,q')|q'\in I_{A}{\text{ and }}\exists f\in F_{C}.(q,a,f)\in \Delta _{C}\}} if I C ∩ F C is empty then I ′ = I C otherwise I ′ = I C ∪ I A {\displaystyle {\text{ if }}I_{C}\cap F_{C}{\text{ is empty then }}I'=I_{C}{\text{ otherwise }}I'=I_{C}\cup I_{A}} ω-closure: If L ( C ) {\displaystyle L(C)} does not contain the empty word then there is a Büchi automaton that recognizes the language L ( C ) ω . {\displaystyle L(C)^{\omega }.} Proof: The Büchi automaton that recognizes L ( C ) ω {\displaystyle L(C)^{\omega }} is constructed in two stages. First, we construct a finite automaton A ′ {\textstyle A'} such that A ′ {\textstyle A'} also recognizes L ( C ) {\displaystyle L(C)} but there are no incoming transitions to initial states of A ′ {\textstyle A'} . So, A ′ = ( Q C ∪ { q new } , Σ , Δ ′ , { q new } , F C ) , {\displaystyle A'=(Q_{C}\cup \{q_{\text{new}}\},\Sigma ,\Delta ',\{q_{\text{new}}\},F_{C}),} where Δ ′ = Δ C ∪ { ( q new , a , q ′ ) | ∃ q ∈ I C . ( q , a , q ′ ) ∈ Δ C } . {\displaystyle \Delta '=\Delta _{C}\cup \{(q_{\text{new}},a,q')|\exists q\in I_{C}.(q,a,q')\in \Delta _{C}\}.} Note that L ( C ) = L ( A ′ ) {\displaystyle L(C)=L(A')} because L ( C ) {\displaystyle L(C)} does not contain the empty string. Second, we will construct the Büchi automaton A ″ {\textstyle A''} that recognize L ( C ) ω {\displaystyle L(C)^{\omega }} by adding a loop back to the initial state of A ′ {\textstyle A'} . So, A ″ = ( Q C ∪ { q new } , Σ , Δ ″ , { q new } , { q new } ) {\displaystyle A''=(Q_{C}\cup \{q_{\text{new}}\},\Sigma ,\Delta '',\{q_{\text{new}}\},\{q_{\text{new}}\})} , where Δ ″ = Δ ′ ∪ { ( q , a , q new ) | ∃ q ′ ∈ F C . ( q , a , q ′ ) ∈ Δ ′ } . {\displaystyle \Delta ''=\Delta '\cup \{(q,a,q_{\text{new}})|\exists q'\in F_{C}.(q,a,q')\in \Delta '\}.} Complementation:

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  • Heikki Mannila

    Heikki Mannila

    Heikki Olavi Mannila (born 4 January 1960 in Espoo) is a Finnish computer scientist, the president of the Academy of Finland. Mannila earned his Ph.D. in 1985 from the University of Helsinki under the supervision of Esko Ukkonen and for many years he was a professor at the University of Helsinki himself. From 2004 to 2008 he was Academy Professor at the Academy of Finland. He became Vice President for Academic Affairs at Aalto University in 2009, and was appointed by the Finnish government as president of the Academy of Finland for a term lasting from 2012 to 2017. The appointment was renewed for the period 2017–2022. Mannila is known for his research in data mining, and has published highly cited papers on association rule learning and sequence mining. With David Hand and Padhraic Smyth, he is the co-author of the book Principles of Data Mining (MIT Press, 2001). Heikki Mannila is son to the professor Elina Haavio-Mannila.

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  • Data Science and Predictive Analytics

    Data Science and Predictive Analytics

    The first edition of the textbook Data Science and Predictive Analytics: Biomedical and Health Applications using R, authored by Ivo D. Dinov, was published in August 2018 by Springer. The second edition of the book was printed in 2023. This textbook covers some of the core mathematical foundations, computational techniques, and artificial intelligence approaches used in data science research and applications. By using the statistical computing platform R and a broad range of biomedical case-studies, the 23 chapters of the book first edition provide explicit examples of importing, exporting, processing, modeling, visualizing, and interpreting large, multivariate, incomplete, heterogeneous, longitudinal, and incomplete datasets (big data). == Structure == === First edition table of contents === The first edition of the Data Science and Predictive Analytics (DSPA) textbook is divided into the following 23 chapters, each progressively building on the previous content. === Second edition table of contents === The significantly reorganized revised edition of the book (2023) expands and modernizes the presented mathematical principles, computational methods, data science techniques, model-based machine learning and model-free artificial intelligence algorithms. The 14 chapters of the new edition start with an introduction and progressively build foundational skills to naturally reach biomedical applications of deep learning. Introduction Basic Visualization and Exploratory Data Analytics Linear Algebra, Matrix Computing, and Regression Modeling Linear and Nonlinear Dimensionality Reduction Supervised Classification Black Box Machine Learning Methods Qualitative Learning Methods—Text Mining, Natural Language Processing, and Apriori Association Rules Learning Unsupervised Clustering Model Performance Assessment, Validation, and Improvement Specialized Machine Learning Topics Variable Importance and Feature Selection Big Longitudinal Data Analysis Function Optimization Deep Learning, Neural Networks == Reception == The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the Journal of the American Statistical Association, International Statistical Institute’s ISI Review Journal, and the Journal of the American Library Association. Many scholarly publications reference the DSPA textbook. As of January 17, 2021, the electronic version of the book first edition (ISBN 978-3-319-72347-1) is freely available on SpringerLink and has been downloaded over 6 million times. The textbook is globally available in print (hardcover and softcover) and electronic formats (PDF and EPub) in many college and university libraries and has been used for data science, computational statistics, and analytics classes at various institutions.

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  • Markov chain geostatistics

    Markov chain geostatistics

    Markov chain geostatistics uses Markov chain spatial models, simulation algorithms and associated spatial correlation measures (e.g., transiogram) based on the Markov chain random field theory, which extends a single Markov chain into a multi-dimensional random field for geostatistical modeling. A Markov chain random field is still a single spatial Markov chain. The spatial Markov chain moves or jumps in a space and decides its state at any unobserved location through interactions with its nearest known neighbors in different directions. The data interaction process can be well explained as a local sequential Bayesian updating process within a neighborhood. Because single-step transition probability matrices are difficult to estimate from sparse sample data and are impractical in representing the complex spatial heterogeneity of states, the transiogram, which is defined as a transition probability function over the distance lag, is proposed as the accompanying spatial measure of Markov chain random fields.

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  • AI Paraphrasing Tools Reviews: What Actually Works in 2026

    AI Paraphrasing Tools Reviews: What Actually Works in 2026

    Comparing the best AI paraphrasing tool? An AI paraphrasing tool 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 paraphrasing tool 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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  • Erkki Oja

    Erkki Oja

    Erkki Oja (born 22 March 1948) is a Finnish computer scientist and Aalto Distinguished Professor in the Department of Information and Computer Science at Aalto University School of Science. He is recognized for developing Oja's rule, which is a model of how neurons in the brain or in artificial neural networks learn over time. == Early life and education == Oja was born in Helsinki and studied at Helsinki University of Technology, where he received his diploma engineer in 1972, licentiate in technology in 1975 and Doctor of Technology in 1977. == Career == Oja was a research associate at the Center for Cognitive Science at Brown University between 1977 and 1978 and a research fellow at the Academy of Finland from 1976 to 1981. Since 1981, he took up a professorship in applied mathematics at Kuopio University (now University of Eastern Finland). He was a visiting research scholar at Tokyo Institute of Technology from 1983 to 1984. From 1987 to 1993, he was a professor in computer science at the Lappeenranta University of Technology. He moved back to the Helsinki University of Technology (now Aalto University) from 1993 as a professor in computer science. He retired in 2015. == Honors and awards == Oja is a Fellow of the International Association for Pattern Recognition and the IEEE, and a member of the Finnish Academy of Sciences. He served as chairman of the European Neural Network Society between 2000 and 2005, and as the chairman of the Academy of Finland’s Research Council for Natural Sciences and Engineering between 2007 and 2012. He was awarded the Frank Rosenblatt Award for his contributions to artificial intelligence research in 2019. Oja was a member of the Board of Governors for the International Neural Network Society (INNIS) in 2003. He received honorary doctorates from Uppsala University and Lappeenranta University of Technology in 2008.

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  • The Outliner of Giants

    The Outliner of Giants

    The Outliner of Giants was commercial outlining software. Like other outliners, it allowed the user to create a document consisting of a series of nested lists. It was one of a number of browser-based outliners that are delivered as a web application, used through a web browser, rather than being installed as a stand-alone application. The Outliner of Giants was released in 2009. The service was shut down on December 31, 2017 and only exports are allowed at this time. == Feature set == Unlike most other browser-based outliners - which often focus on providing a minimum viable product - the Outliner of Giants had much of the functionality typically associated with a desktop outliner, such as the ability to use of columns to structure information. However, The Outliner of Giants did not support offline editing, requiring an active internet connection in order to make changes to an outline document. === Outlining === Like all outliners, The Outliner of Giants supported the creation of a hierarchy of items, with users modifying the parent-child relationship between items in order to structure a document. This included the ability to promote or demote items up or down the hierarchy, or move an item up or down a list of siblings on the same level. The Outliner of Giants did not support the true cloning of items (where an item can appear to be in multiple places within the hierarchy at the same time), although it did support the copying of single or multiple nodes. === Import === The Outliner of Giants could import both plain text and the OPML XML format, which is commonly used to transfer data between outlining applications. === Editing === Outline documents could be edited using a WYSIWYG editor, as well as the Markdown, and Textile markup languages. === Annotation === The Outliner of Giants supported functions to annotate an outline, such as the ability to add colored labels, highlights and text, as well as tags and hashtags. === Collaboration === The Outliner of Giants supported real-time collaboration, where multiple users could edit the same document, and can see the changes made by another user as they happened. === Publication === Outlines created through The Outliner of Giants could be published directly online through the service, either as outlines, pages or in a blog format. === Export === The Outliner of Giants can export outline data as plain text, HTML, as well as directly to the Google Docs word processor.

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  • Eduard Hovy

    Eduard Hovy

    Eduard Hovy is a Research Professor in the Language Technologies Institute at Carnegie Mellon University. He is one of the original 17 Fellows of the Association for Computational Linguistics. == Biography == Eduard Hovy received M.S. (December 1982) and Ph.D. (May 1987) degrees in Computer Science from Yale University. He was awarded honorary doctorates from the National University of Distance Education (UNED) in Madrid in 2013 and the University of Antwerp in 2015.

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  • NFA minimization

    NFA minimization

    In automata theory (a branch of theoretical computer science), NFA minimization is the task of transforming a given nondeterministic finite automaton (NFA) into an equivalent NFA that has a minimum number of states, transitions, or both. While efficient algorithms exist for DFA minimization, NFA minimization is PSPACE-complete. No efficient (polynomial time) algorithms are known, and under the standard assumption that P ≠ PSPACE, none exist. The most efficient known algorithm is the Kameda–Weiner algorithm. == Non-uniqueness of minimal NFA == Unlike deterministic finite automata, minimal NFAs may not be unique. There may be multiple NFAs with the same number of states that accept the same regular language, but for which there is no equivalent NFA or DFA with fewer states.

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  • Best AI Copywriting Tools in 2026

    Best AI Copywriting Tools in 2026

    Looking for the best AI copywriting tool? An AI copywriting 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 copywriting 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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  • Flask (web framework)

    Flask (web framework)

    Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. However, Flask supports extensions that can add application features as if they were implemented in Flask itself. Extensions exist for object-relational mappers, form validation, upload handling, various open authentication technologies and several common framework related tools. Applications that use the Flask framework include Pinterest and LinkedIn. == History == Flask was created by Armin Ronacher of Pocoo, an international group of Python enthusiasts formed in 2004. According to Ronacher, the idea was originally an April Fool's joke that was popular enough to make into a serious application. The name is a play on the earlier Bottle framework. When Ronacher and Georg Brandl created a bulletin board system written in Python in 2004, the Pocoo projects Werkzeug and Jinja were developed. In April 2016, the Pocoo team was disbanded and development of Flask and related libraries passed to the newly formed Pallets project. Flask has become popular among Python enthusiasts. As of October 2020, it has the second-most number of stars on GitHub among Python web-development frameworks, only slightly behind Django, and was voted the most popular web framework in the Python Developers Survey for years between and including 2018 and 2022. == Components == The microframework Flask is part of the Pallets Projects (formerly Pocoo), and based on several others of them, all under a BSD license. === Werkzeug === Werkzeug (German for "tool") is a utility library for the Python programming language for Web Server Gateway Interface (WSGI) applications. Werkzeug can instantiate objects for request, response, and utility functions. It can be used as the basis for a custom software framework and supports Python 2.7 and 3.5 and later. === Jinja === Jinja, also by Ronacher, is a template engine for the Python programming language. Similar to the Django web framework, it handles templates in a sandbox. === MarkupSafe === MarkupSafe is a string handling library for the Python programming language. The eponymous MarkupSafe type extends the Python string type and marks its contents as "safe"; combining MarkupSafe with regular strings automatically escapes the unmarked strings, while avoiding double escaping of already marked strings. === ItsDangerous === ItsDangerous is a safe data serialization library for the Python programming language. It is used to store the session of a Flask application in a cookie without allowing users to tamper with the session contents. === Click === Click is a Python package used by Flask to create command-line interfaces (CLI) by providing a simple and composable way to define commands, arguments, and options. == Features == Development server and debugger Integrated support for unit testing RESTful request dispatching Uses Jinja templating Support for secure cookies (client side sessions) 100% WSGI 1.0 compliant Unicode-based Complete documentation Google App Engine compatibility Extensions available to extend functionality == Example == The following code shows a simple web application that displays "Hello World!" when visited: === Render Template with Flask === ==== Jinja in HTML for the Render Template ====

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  • Tamara Broderick

    Tamara Broderick

    Tamara Ann Broderick is an American computer scientist at the Massachusetts Institute of Technology. She works on machine learning and Bayesian inference. == Education and early career == Broderick is from Parma Heights, Ohio. She attended Laurel School and graduated in 2003. Whilst at high school she took part in the inaugural Massachusetts Institute of Technology Women's Technology Program. She studied mathematics at Princeton University, earning a bachelor's degree in 2007. She was a Marshall scholar, allowing her to pursue graduate research at the University of Cambridge. She was a runner-up in the Association for Women in Mathematics Alice T. Shafer Prize for Excellence in Mathematics. She was co-president of the Princeton Math Club and organised a competition for high school maths teams. She won the Phi Beta Kappa Prize for the highest academic average at Princeton University. During her undergraduate degree, Broderick worked on dark matter haloes with Rachel Mandelbaum. Broderick moved to the United Kingdom for her graduate studies, earning a Master of Advanced Studies for completing Part III of the Mathematical Tripos at the University of Cambridge in 2009. Her Master's thesis looked at the Nomon selection method, improving the efficiency of communications. She returned to America in 2009, joining University of California, Berkeley for her Master's and PhD. Her graduate research was supported by the Berkeley Fellowship and a National Science Foundation Fellowship. Her PhD thesis Clusters and features from combinatorial stochastic processes looked at clustering and speeding up the analysis of large, streaming data sets. In 2013 she was selected for the Berkeley EECS Rising Stars conference. == Research and career == Broderick joined Massachusetts Institute of Technology as an assistant professor in 2015. She is interested in Bayesian statistics and graphical models. She was the recipient of a Google Faculty Research Grant and International Society for Bayesian Analysis Lifetime Members Junior Researcher Award. She was awarded an Army Research Office young investigator program award to investigate machine-learning to quantify uncertainty in data analysis. Broderick is also Alfred P. Sloan Foundation scholar. === Academic service === In 2018, Broderick spoke at the Harvard University Institute for Applied Computational Science Women in Data Science conference. She spoke about Bayesian inference at the 2018 International Conference on Machine Learning. She led a three-day Masterclass on machine learning at University College London in June 2018. Broderick is a scientific advisor for AI.Reverie and WiML (Women in Machine Learning). She has developed a high-school level introduction to machine learning with the Women's Technology Program (WTP). Software she has developed is available on her website. === Awards and honors === Broderick was awarded the Evelyn Fix Memorial Medal and Citation and the International Society for Bayesian Analysis Savage Award for her doctoral thesis. She was awarded a National Science Foundation CAREER Award to scale her machine learning techniques. She was a 2021 Leadership Academy winner of the Committee of Presidents of Statistical Societies.

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  • Douwe Kiela

    Douwe Kiela

    Douwe Kiela is a Dutch-American research scientist and entrepreneur working in the field of artificial intelligence with a focus on machine learning and natural language processing. He is a research scientist director at Google DeepMind. He previously co-founded and served as CEO of Contextual AI, an enterprise software company that provides a platform for building grounded AI agents for enterprise knowledge bases. He previously led the research team at Meta AI that introduced the RAG approach in 2020, co-authoring the foundational paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." Kiela also served as Head of Research at Hugging Face and is an adjunct professor in Symbolic Systems at Stanford University. == Early life and education == Douwe Kiela was born in Amsterdam, Netherlands, in 1986. He earned a Bachelor of Science degree in Liberal Arts and Sciences from Utrecht University, with a double major in Cognitive Artificial Intelligence and Philosophy. He then obtained an MSc in logic (cum laude) from the University of Amsterdam's Institute for Logic, Language and Computation (ILLC). Kiela received an MPhil and PhD in Computer Science from the University of Cambridge, specializing in natural language processing and machine learning. == Career == === Facebook AI Research (Meta) === In 2016, Kiela joined Facebook AI Research (FAIR) as a postdoctoral researcher, later becoming a research scientist in New York. While at Meta, he co-authored papers in natural language processing, with a focus on multimodal and grounded language learning. His projects included creating a virtual assistant bot that could navigate tourists around a city and leading the development of Dynabench, an interactive benchmarking platform released in 2020 that used human feedback to test and improve language models. In 2020, Kiela led the Meta AI research team that introduced Retrieval-Augmented Generation (RAG), co-authoring the influential paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," alongside Patrick Lewis, Ethan Perez, and other researchers. The RAG framework transformed how large language models access and incorporate external information by allowing them to retrieve relevant context from external knowledge bases at query time, rather than relying solely on pre-trained data. This approach addressed key limitations such as hallucination, outdated information, and lack of source attribution. The RAG technique has since become widely adopted in enterprise AI applications and knowledge-intensive natural language processing tasks. === Hugging Face === After leaving Meta, Kiela served as Head of Research at Hugging Face. === Contextual AI === In 2023, Kiela co-founded Contextual AI with Amanpreet Singh, another former researcher at Facebook AI Research and Hugging Face. The Mountain View-based company develops a platform for building grounded AI agents for enterprises, focusing on applications in technology, semiconductor, logistics, finance, and media sectors. Contextual AI raised $20 million in seed funding in June 2023, led by Bain Capital Ventures. In August 2024, the company completed an $80 million Series A funding round led by Greycroft, with participation from Bezos Expeditions, NVentures (Nvidia), HSBC Ventures, and Snowflake Ventures, among others. In May 2026, Kiela joined Google DeepMind as part of a licensing agreement between Google and Contextual AI under which more than 20 Contextual AI researchers joined DeepMind. Following his departure, Jay Chen became interim CEO of Contextual AI. === Academic roles === Douwe Kiela serves as an adjunct professor in Symbolic Systems at Stanford University. In a 2023 interview with the Stanford Daily, he commented on the development of Alpaca, a low-cost instruction-finetuned model based on Meta's LLaMA, and emphasized the importance of open academic research in large language models.

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