In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the true value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate. In machine learning, specifically empirical risk minimization, MSE may refer to the empirical risk (the average loss on an observed data set), as an estimate of the true MSE (the true risk: the average loss on the actual population distribution). The MSE is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value that decreases as the error approaches zero. The MSE is the second moment (about the origin) of the error, and thus incorporates both the variance of the estimator (how widely spread the estimates are from one data sample to another) and its bias (how far off the average estimated value is from the true value). For an unbiased estimator, the MSE is the variance of the estimator. Like the variance, MSE has the same units of measurement as the square of the quantity being estimated. In an analogy to standard deviation, taking the square root of MSE yields the root-mean-square error or root-mean-square deviation (RMSE or RMSD), which has the same units as the quantity being estimated; for an unbiased estimator, the RMSE is the square root of the variance, known as the standard error. == Definition and basic properties == The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled). In the context of prediction, understanding the prediction interval can also be useful as it provides a range within which a future observation will fall, with a certain probability. The definition of an MSE differs according to whether one is describing a predictor or an estimator. === Predictor === If a vector of n {\displaystyle n} predictions is generated from a sample of n {\displaystyle n} data points on all variables, and Y {\displaystyle Y} is the vector of observed values of the variable being predicted, with Y ^ {\displaystyle {\hat {Y}}} being the predicted values (e.g. as from a least-squares fit), then the within-sample MSE of the predictor is computed as MSE = 1 n ∑ i = 1 n ( Y i − Y i ^ ) 2 {\displaystyle \operatorname {MSE} ={\frac {1}{n}}\sum _{i=1}^{n}\left(Y_{i}-{\hat {Y_{i}}}\right)^{2}} In other words, the MSE is the mean ( 1 n ∑ i = 1 n ) {\textstyle \left({\frac {1}{n}}\sum _{i=1}^{n}\right)} of the squares of the errors ( Y i − Y i ^ ) 2 {\textstyle \left(Y_{i}-{\hat {Y_{i}}}\right)^{2}} . This is an easily computable quantity for a particular sample (and hence is sample-dependent). In matrix notation, MSE = 1 n ∑ i = 1 n ( e i ) 2 = 1 n e T e {\displaystyle \operatorname {MSE} ={\frac {1}{n}}\sum _{i=1}^{n}(e_{i})^{2}={\frac {1}{n}}\mathbf {e} ^{\mathsf {T}}\mathbf {e} } where e i {\displaystyle e_{i}} is Y i − Y i ^ {\displaystyle Y_{i}-{\hat {Y_{i}}}} and e {\displaystyle \mathbf {e} } is a n × 1 {\displaystyle n\times 1} column vector. The MSE can also be computed on q data points that were not used in estimating the model, either because they were held back for this purpose, or because these data have been newly obtained. Within this process, known as cross-validation, the MSE is often called the test MSE, and is computed as MSE = 1 q ∑ i = n + 1 n + q ( Y i − Y i ^ ) 2 {\displaystyle \operatorname {MSE} ={\frac {1}{q}}\sum _{i=n+1}^{n+q}\left(Y_{i}-{\hat {Y_{i}}}\right)^{2}} === Estimator === The MSE of an estimator θ ^ {\displaystyle {\hat {\theta }}} with respect to an unknown parameter θ {\displaystyle \theta } is defined as MSE ( θ ^ ) = E θ [ ( θ ^ − θ ) 2 ] . {\displaystyle \operatorname {MSE} ({\hat {\theta }})=\operatorname {E} _{\theta }\left[({\hat {\theta }}-\theta )^{2}\right].} This definition depends on the unknown parameter, therefore the MSE is a priori property of an estimator. The MSE could be a function of unknown parameters, in which case any estimator of the MSE based on estimates of these parameters would be a function of the data (and thus a random variable). If the estimator θ ^ {\displaystyle {\hat {\theta }}} is derived as a sample statistic and is used to estimate some population parameter, then the expectation is with respect to the sampling distribution of the sample statistic. The MSE can be written as the sum of the variance of the estimator and the squared bias of the estimator, providing a useful way to calculate the MSE and implying that in the case of unbiased estimators, the MSE and variance are equivalent. MSE ( θ ^ ) = Var θ ( θ ^ ) + Bias ( θ ^ , θ ) 2 . {\displaystyle \operatorname {MSE} ({\hat {\theta }})=\operatorname {Var} _{\theta }({\hat {\theta }})+\operatorname {Bias} ({\hat {\theta }},\theta )^{2}.} ==== Proof of variance and bias relationship ==== MSE ( θ ^ ) = E θ [ ( θ ^ − θ ) 2 ] = E θ [ ( θ ^ − E θ [ θ ^ ] + E θ [ θ ^ ] − θ ) 2 ] = E θ [ ( θ ^ − E θ [ θ ^ ] ) 2 + 2 ( θ ^ − E θ [ θ ^ ] ) ( E θ [ θ ^ ] − θ ) + ( E θ [ θ ^ ] − θ ) 2 ] = E θ [ ( θ ^ − E θ [ θ ^ ] ) 2 ] + E θ [ 2 ( θ ^ − E θ [ θ ^ ] ) ( E θ [ θ ^ ] − θ ) ] + E θ [ ( E θ [ θ ^ ] − θ ) 2 ] = E θ [ ( θ ^ − E θ [ θ ^ ] ) 2 ] + 2 ( E θ [ θ ^ ] − θ ) E θ [ θ ^ − E θ [ θ ^ ] ] + ( E θ [ θ ^ ] − θ ) 2 E θ [ θ ^ ] − θ = constant = E θ [ ( θ ^ − E θ [ θ ^ ] ) 2 ] + 2 ( E θ [ θ ^ ] − θ ) ( E θ [ θ ^ ] − E θ [ θ ^ ] ) + ( E θ [ θ ^ ] − θ ) 2 E θ [ θ ^ ] = constant = E θ [ ( θ ^ − E θ [ θ ^ ] ) 2 ] + ( E θ [ θ ^ ] − θ ) 2 = Var θ ( θ ^ ) + Bias θ ( θ ^ , θ ) 2 {\displaystyle {\begin{aligned}\operatorname {MSE} ({\hat {\theta }})&=\operatorname {E} _{\theta }\left[({\hat {\theta }}-\theta )^{2}\right]\\&=\operatorname {E} _{\theta }\left[\left({\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]+\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)^{2}\right]\\&=\operatorname {E} _{\theta }\left[\left({\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]\right)^{2}+2\left({\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]\right)\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)+\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)^{2}\right]\\&=\operatorname {E} _{\theta }\left[\left({\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]\right)^{2}\right]+\operatorname {E} _{\theta }\left[2\left({\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]\right)\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)\right]+\operatorname {E} _{\theta }\left[\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)^{2}\right]\\&=\operatorname {E} _{\theta }\left[\left({\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]\right)^{2}\right]+2\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)\operatorname {E} _{\theta }\left[{\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]\right]+\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)^{2}&&\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta ={\text{constant}}\\&=\operatorname {E} _{\theta }\left[\left({\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]\right)^{2}\right]+2\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\operatorname {E} _{\theta }[{\hat {\theta }}]\right)+\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)^{2}&&\operatorname {E} _{\theta }[{\hat {\theta }}]={\text{constant}}\\&=\operatorname {E} _{\theta }\left[\left({\hat {\theta }}-\operatorname {E} _{\theta }[{\hat {\theta }}]\right)^{2}\right]+\left(\operatorname {E} _{\theta }[{\hat {\theta }}]-\theta \right)^{2}\\&=\operatorname {Var} _{\theta }({\hat {\theta }})+\operatorname {Bias} _{\theta }({\hat {\theta }},\theta )^{2}\end{aligned}}} An even shorter proof can be achieved using the well-known formula that for a random variable X {\textstyle X} , E ( X 2 ) = Var ( X ) + ( E ( X ) ) 2 {\textstyle \mathbb {E} (X^{2})=\operatorname {Var} (X)+(\mathbb {E} (X))^{2}} . By substituting X {\textstyle X} with, θ ^ − θ {\textstyle {\hat {\theta }}-\theta } , we have MSE ( θ ^ ) = E [ ( θ ^ − θ ) 2 ] = Var ( θ ^ − θ ) + ( E [ θ ^ − θ ] ) 2 = Var ( θ ^ ) + Bias 2 ( θ ^ , θ ) {\displaystyle {\begin{aligned}\operatorname {MSE} ({\hat {\theta }})&=\mathbb {E} [({\hat {\theta }}-\theta )^{2}]\\&=\operator
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 producing 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 types == 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. == Classification == 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. == Examples == 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, logarithmic Mel-scale spectral vectors and Mel-frequency cepstral coefficients, which represent the frequency characteristics of audio signals. 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 vectors == 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 {max(S), min(S), average(S)} 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. == Selection and extraction == 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.
Static web page
A static web page, sometimes called a flat page or a stationary page, is a web page that is delivered to a web browser exactly as stored, in contrast to dynamic web pages which are generated by a web application. Consequently, a static web page displays the same information for all users, from all contexts, subject to modern capabilities of a web server to negotiate content-type or language of the document where such versions are available and the server is configured to do so. However, a webpage's JavaScript can introduce dynamic functionality which may make the static web page dynamic. == Overview == Static web pages are often HTML documents, stored as files in the file system and made available by the web server over HTTP (nevertheless URLs ending with ".html" are not always static). However, loose interpretations of the term could include web pages stored in a database, and could even include pages formatted using a template and served through an application server, as long as the page served is unchanging and presented essentially as stored. The content of static web pages remains stationary irrespective of the number of times it is viewed. Such web pages are suitable for the contents that rarely need to be updated, though modern web template systems are changing this. Maintaining large numbers of static pages as files can be impractical without automated tools, such as static site generators. Any personalization or interactivity has to run client-side, which is restricting. Cloud-based website builders, including Wix, Weebly, and Duda, offer no-code platforms for creating static and dynamic web pages through graphical interfaces, without requiring programming expertise. === Advantages === Provide improved security over dynamic websites (dynamic websites are at risk to web shell attacks if a vulnerability is present) Improved performance for end users compared to dynamic websites Fewer or no dependencies on systems such as databases or other application servers Cost savings from utilizing cloud storage, as opposed to a hosted environment Security configurations are easy to set up, which makes it more secure Static files can be cached by content delivery networks (CDNs) and other intermediate caches, which both reduces page load times at the user and also reduces load on the origin server. Static websites can have improved uptime, since they are still available through any available CDN exit node even when other CDN nodes or the origin webserver are temporarily offline. === Disadvantages === Dynamic functionality must be performed on the client side. After each update of a static website, some or all users may see old, stale, outdated previous versions instead of the latest version until the old version is flushed from CDNs and other caches. == Static site generators == Static site generators are applications that compile static websites - typically populating HTML templates in a predefined folder and file structure, with content supplied in a format such as Markdown or AsciiDoc. === Implementations === Jekyll (powers GitHub Pages) Middleman Hugo Next.js Astro.build Pelican Franklin
Webedia
Webedia S.A. is a company specializing in online media, a subsidiary of the Fimalac group based in Levallois-Perret, France. Webedia is active in more than twenty countries including France (AlloCiné, Jeuxvideo.com, MGG, Puremédias, Ode, Pureshopping, Volum, Terrafemina, 750g, easyVoyage, l’Automobile Magazine, Le 10 Sport), Brazil (AdoroCinema, Tudo Gostoso, Minhavida), Germany (Filmstarts, Moviepilot, GameStar), Spain and Latin America (Xataka, SensaCine, Raiser Games), Poland (Gry-Online and GetHero) and the United States (Boxoffice Pro). == History == === Early years (2007-2013) === Webedia was created in France in 2007, following the successive launches of the websites Purepeople, Puretrend and Purefans. Webedia bought the comparison shopping website Shopoon in 2008 and renamed it Pureshopping, and the website Ozap (media news) from M6 group in 2011 and renamed it Puremédias. Webedia was acquired by Fimalac in May 2013 and became its Internet media subsidiary. === Growth (2013-2016) === In 2013, Fimalac acquired AlloCiné, the websites Newsring and Youmag, the cooking website 750g and the cultural platform Exponaute. In 2014, Webedia acquired OverBlog, Jeuxvideo.com (through L'Odyssée Interactive and moved to Paris in 2015), Moviepilot (Germany), and Gameo Consulting (owner of Millenium, electronic sports), In December 2014, Webedia announced a license agreement with Ziff Davis to launch sites under the IGN franchise in Brazil and France at the beginning of 2015. The French version of IGN was launched on 2, it targets the general public and casual gamers. In 2015, Webedia acquired Côté Ciné Group (technological solutions for movie theaters and specialized press magazines: BoxOffice Pro in the United States and Côté Ciné in France), 57% of Easyvoyage group (online travel comparators Easyvol and Alibabuy, Mixicom (website JeuxActu and multi-channel network), 50% of the Brazilian network Paramaker, and West World Media (digital marketing company for the film industry). In 2016, Webedia bought Scimob (mobile video game studio), Surprizemi (home-delivered surprise boxes), Eklablog (blogging platform) Oxent (eSports World Convention), and Bang Bang Management (sports PR agency). In addition, an agreement is made with Paris Saint-Germain for Webedia to recruit and manage e-sports players on behalf of Paris Saint-Germain eSports. On November 15, 2016, the LFP announced that it had reached an agreement with beIN Sports and Webedia for the broadcasting of the first edition of the e-League 1. The competition is renewed for two additional seasons on July 26, 2017, the broadcasting agreements are renewed. On December 8, 2016, Webedia joined forces with Chronopost to launch Pourdebon, a home delivery service that connects Internet users and labeled producers (AOC, organic AB, etc.). Webedia has a slight majority (53%) in this new platform. === 2017 === On January 19, 2017, Webedia announced the acquisition of the English company Peach Digital, specializing in web development and digital marketing for movie theaters. In February 2017, Le Figaro announced that Webedia had invested 10 million euros in Illico Fresco, a home delivery service for baskets of recipes. The same month, FDJ and Webedia announced a partnership for the creation of eSports competitions: a professional one (FDJ Masters League) and another one for amateur gamers (FDJ Open Series) starting in March 2017. They are broadcast on Webedia's Web TV. At the end of February 2017, the media group finalized the acquisition of MyPoseo, a SaaS publisher specialized on SEO analytics. On March 8, 2017, Webedia launched LeStream, a Twitch Web TV dedicated to video games, the result of two years of development, in the company of several YouTubers including Cyprien and Squeezie,. On March 29, 2017, Webedia bought the Brazilian web publisher Minha Vida, a website devoted to health, nutrition, beauty and fitness, which attracts 14.3 million unique monthly visitors. Webedia reaches 44 million unique visitors in Brazil, and thus becomes the leading publisher on entertainment themes. In June 2017, the company made its largest international acquisition, with the American agency 3BlackDot, a media and marketing agency focused on videogamers. The agency, based in Los Angeles, manages 36 YouTubers followed by millions of subscribers on their channels which total 700 million videos viewed per month. In July 2017, Webedia bought IDZ, an audiovisual production company, and thus strengthened its production activities and its leadership on the YouTube channel networks in France. That year, Webedia was the first French media group to use the measurement of their global audiences by Comscore. It represents deduplicated coverage on desktops, laptops, smartphones and tablets, and includes audiences for websites, mobile applications and videos. This new measure allows Webedia to establish a deduplicated global audience of 177 million unique visitors in April 2017. In October 2017, Webedia announced its intention to launch a TV channel dedicated to electronic sports, called ES1. The channel was officially launched on January 10, 2018, on Orange TV and on February 6, 2018, on Free and Bouygues Telecom. In November 2017, Webedia, with the support of CDC International Capital, entered into exclusive negotiations with the Saudi company Uturn Entertainment, specializing in online entertainment, particularly on YouTube, and the production of digital content for the region's youth, with a view to merging it with Diwanee, a Webedia subsidiary in the Middle East, for an amount close to $100 million. In December 2017, Webedia acquired a majority stake in the United States–based company called Creators Media, which brings together social and video production platforms specializing in popular culture and entertainment. That same month, Webedia joined forces with Elephant, Emmanuel Chain's audiovisual production company, to create a new content production label aimed at Millennials. === 2018-2019 === In January 2018, Webedia launched a sports marketing agency: Only Sports & Passions. That same month, Illico Fresco, specialist in the delivery of kit meals belonging to Webedia, joined forces with Weight Watchers, the world leader in slimming products. In April 2018, Webedia published new audience figures in partnership with Comscore, 188 million unique monthly visitors in December 2017, an increase of 6.2% compared to the previous measure dating from April 2017. The same month, Webedia unveils its ambitions concerning content production, as a partnership with the video game studio Focus Home Interactive is signed with a title "Fear the Wolves" already planned for 2018, co-production projects of films, cartoons or series are announced. In July 2018, Webedia bought the American authors company Full Fathom Five, a company that helps authors produce books, TV series, films and video games. In October 2018, Webedia announced that it was focusing on both esports clubs PSG Esports and LeStream Esport. The first one being geared towards international competitions and the second devoted mainly to the French esports scene. The "Millenium" brand is thus refocusing around its media activities and esports merchandising products, and the "Millenium esport club" being gradually closed. The same month, the company announced the acquisition of Weblogs, a Spanish-speaking website publisher, thereby strengthening its activity in Spain and Latin America. On October 22, 2018, Webedia announced the merger of BoxOffice magazine with Film Journal International. On November 13, 2018, Groupe SEB announced the acquisition from Webedia of 750g International, the international branch of the French recipe site 750g (the original French website 750g.com being retained by Webedia). The group is thus separating from Gourmandize (United States and United Kingdom), HeimGourmet (Germany), Rebañando (Spain), Receitas Sem Fronteiras (Brazil / Portugal) and Tribù Golosa (Italy). The same month, Webedia joined forces with Riot Games to launch the French League of League of Legends (LFL), the first French professional league on the League of Legends game, which will bring together the 8 best teams on the French scene. In March 2019, Webedia bought 51% of the audiovisual production company Elephant. The new set will weigh 500 million euros, a quarter of which will be made outside France. The same month, Webedia purchased a majority stake in the company Partoo, which publishes a SaaS platform specializing in local marketing for brands and merchants. On March 14, 2019, a new measurement of the international audience of Webedia sites was produced by Comscore, posting 250 million unique visitors in December 2018, up 9.2% compared to December 2017. In June 2019, the group joined forces with Michel Cymes, a famous doctor and French TV host by taking a majority stake in his company Club Santé Débat, in order to develop a health platform around the Dr. Good! Brand. In Sep
Democratization of technology
Democratization of technology is the process by which access to technology rapidly extends to an ever-broader audience, especially from a select group of people to the average public. New technologies and improved user experiences have empowered those outside of the technical industry to access and use technological products and services. At an increasing scale, consumers have greater access to use and purchase technologically sophisticated products, as well as to participate meaningfully in the development of these products. Industry innovation and user demand have been associated with more affordable, user-friendly products. This is an ongoing process, beginning with the development of mass production and increasing dramatically as digitization became commonplace. Thomas Friedman argued that the era of globalization has been characterized by the democratization of technology, democratization of finance, and democratization of information. Technology has been critical in the latter two processes, facilitating the rapid expansion of access to specialized knowledge and tools, as well as changing the way that people view and demand such access. A counter argument is that this is just a process of 'massification' - more people can use banks, technology, have access to information, but it does not mean there is any more democratic influence over its production, or that this massification promotes Democracy. == History == Scholars and social critics often cite the invention of the printing press as a major invention that changed the course of history. The force of the printing press rested not in its impact on the printing industry or inventors, but on its ability to transmit information to a broader public by way of mass production. This event is so widely recognized because of its social impact – as a democratizing force. The printing press is often seen as the historical counterpart to the Internet. After the development of the Internet in 1969, its use remained limited to communications between scientists and within government, although use of email and boards gained popularity among those with access. It did not become a popular means of communication until the 1990s. In 1993 the US federal government opened the Internet to commerce and the creation of HTML formed the basis for universal accessibility. === Major innovations === The Internet has played a critical role in modern life as a typical feature of most Western households, and has been key in the democratization of knowledge. It not only constitutes arguably the most critical innovation in this trend thus far; it has also allowed users to gain knowledge of and access to other technologies. Users can learn of new developments more quickly, and purchase high-tech products otherwise only actively marketed to recognized experts. Social media has also empowered and emboldened users to become contributors and critics of technological developments. Some have argued that cloud computing is having a major effect by allowing users greater access through mobility and pay-as-you-use capacity. The open-source model allows users to participate directly in development of software, rather than indirect participation, through contributing opinions. By being shaped by the user, development is directly responsive to user demand and can be obtained for free or at a low cost. In a comparable trend, arduino and littleBits have made electronics more accessible to users of all backgrounds and ages. The development of 3D printers has the potential to increasingly democratize production. Generative artificial intelligence tools have the potential to democratize the process of innovation by improving the ability of individuals to specify and visualize ideas. The democratization of artificial intelligence refers to the transition from AI as a high-cost, specialized field to one accessible to non-experts and smaller organizations. This process is driven by the release of open-weights models, the availability of cloud computing for model training, and the emergence of no-code development platforms. While early AI development was concentrated within Big Tech firms and elite research universities, the 2020s saw a proliferation of public tools like ChatGPT and repositories such as Hugging Face, which lowered the technical barriers to entry. However, the trend has faced criticism as the "illusion of democratization," as the underlying GPU hardware remains concentrated among a few global providers. == Cultural impact == This trend is linked to the spread of knowledge of and ability to perform high-tech tasks, challenging previous conceptions of expertise. Widespread access to technology, including lower costs, was critical to the transition to the new economy. Similarly, democratization of technology was also fuelled by this economic transition, which produced demands for technological innovation and optimism in technology-driven progress. Since the 1980s, a spreading constructivist conception of technology has emphasized that the social and technical domains are critically intertwined. Scholars have argued that technology is non-neutral, defined contextually and locally by a certain relationship with society. Andrew Feenberg, a central thinker in the philosophy of technology, argued that democratizing technology means expanding technological design to include alternative interests and values. When successful in doing so, this can be a tool for increasing inclusiveness. This also suggests an important participatory role for consumers if technology is to be truly democratic. Feenberg asserts that this must be achieved by consumer intervention in a liberated design process. Improved access to specialized knowledge and tools has been associated with an increase in the "do it yourself" (DIY) trend. This has also been associated with consumerization, whereby personal or privately owned devices and software are also used for business purposes. Some have argued that this is linked to reduced dependence on traditional information technology departments. Astra Taylor, the author of the book The People's Platform: Taking Back Power and Culture in the Digital Age, argues, "The promotion of Internet-enabled amateurism is a lazy substitute for real equality of opportunity." === Industry impact === In some ways, democratization of technology has strengthened this industry. Markets have broadened and diversified. Consumer feedback and input is available at a very low or no cost. However, related industries are experiencing decreased demand for qualified professionals as consumers are able to fill more of their demands themselves. Users of a range of types and status have access to increasingly similar technology. Because of the decreased costs and expertise necessary to use products and software, professionals (e.g. in the audio industry) may experience loss of work. In some cases, technology is accessible but sufficiently complex that most users without specialized training are able to operate it without necessarily understanding how it works. Additionally, the process of consumerization has led to an influx in the number of devices in businesses and accessing private networks that IT departments cannot control or access. While this can lead to lowered operating costs and increased innovation, it is also associated with security concerns that most businesses are unable to address at the pace of the spread of technology. === Political impact === Some scholars have argued that technological change will bring about a third wave of democracy. The Internet has been recognized for its role in promoting increased citizen advocacy and government transparency. Jesse Chen, a leading thinker in democratic engagement technologies, distinguishes the democratizing effects of technology from democracy itself. Chen has argued that, while the Internet may have democratizing effects, the Internet alone cannot deliver democracy at all levels of society unless technologies are purposely designed for the nuances of democracy, specifically the engagement of large groups of people in between elections in and beyond government. The spread of the Internet and other forms of technology has led to increased global connectivity. Many scholars believe that it has been associated in the developing world not only with increased Western influence, but also with the spread of democracy through increased communication, efficiency, and access to information. Scholars have drawn associations between the level of technological connectedness and democracy in many nations. Technology can enhance democracy in the developed world as well. In addition to increased communication and transparency, some electorates have implemented online voting to accommodate an increased number of citizens.
Recursive self-improvement
Recursive self-improvement (RSI) is a process in which early artificial general intelligence (AGI) systems rewrite their own computer code, causing an intelligence explosion resulting from enhancing their own capabilities and intellectual capacity, theoretically resulting in superintelligence. The development of recursive self-improvement raises significant ethical and safety concerns, as such systems may evolve in unforeseen ways and could potentially surpass human control or understanding. == Seed improver == The concept of a "seed improver" architecture is a foundational framework that equips an AGI system with the initial capabilities required for recursive self-improvement. This might come in many forms or variations. The term "Seed AI" was coined by Eliezer Yudkowsky. === Hypothetical example === The concept begins with a hypothetical "seed improver", an initial code-base developed by human engineers that equips an advanced future large language model (LLM) built with strong or expert-level capabilities to program software. These capabilities include planning, reading, writing, compiling, testing, and executing arbitrary code. The system is designed to maintain its original goals and perform validations to ensure its abilities do not degrade over iterations. ==== Initial architecture ==== The initial architecture includes a goal-following autonomous agent, that can take actions, continuously learns, adapts, and modifies itself to become more efficient and effective in achieving its goals. The seed improver may include various components such as: Recursive self-prompting loop Configuration to enable the LLM to recursively self-prompt itself to achieve a given task or goal, creating an execution loop which forms the basis of an agent that can complete a long-term goal or task through iteration. Basic programming capabilities The seed improver provides the AGI with fundamental abilities to read, write, compile, test, and execute code. This enables the system to modify and improve its own codebase and algorithms. Goal-oriented design The AGI is programmed with an initial goal, such as "improve your capabilities". This goal guides the system's actions and development trajectory. Validation and Testing Protocols An initial suite of tests and validation protocols that ensure the agent does not regress in capabilities or derail itself. The agent would be able to add more tests in order to test new capabilities it might develop for itself. This forms the basis for a kind of self-directed evolution, where the agent can perform a kind of artificial selection, changing its software as well as its hardware. ==== General capabilities ==== This system forms a sort of generalist Turing-complete programmer which can in theory develop and run any kind of software. The agent might use these capabilities to for example: Create tools that enable it full access to the internet, and integrate itself with external technologies. Clone/fork itself to delegate tasks and increase its speed of self-improvement. Modify its cognitive architecture to optimize and improve its capabilities and success rates on tasks and goals, this might include implementing features for long-term memories using techniques such as retrieval-augmented generation (RAG), develop specialized subsystems, or agents, each optimized for specific tasks and functions. Develop new and novel multimodal architectures that further improve the capabilities of the foundational model it was initially built on, enabling it to consume or produce a variety of information, such as images, video, audio, text and more. Plan and develop new hardware such as chips, in order to improve its efficiency and computing power. == Experimental research == In 2023, the Voyager agent learned to accomplish diverse tasks in Minecraft by iteratively prompting an LLM for code, refining this code based on feedback from the game, and storing the programs that work in an expanding skills library. In 2024, researchers proposed the framework "STOP" (Self-Taught OPtimiser), in which a "scaffolding" program recursively improves itself using a fixed LLM. Meta AI has performed various research on the development of large language models capable of self-improvement. This includes their work on "Self-Rewarding Language Models" that studies how to achieve super-human agents that can receive super-human feedback in its training processes. In May 2025, Google DeepMind unveiled AlphaEvolve, an evolutionary coding agent that uses a LLM to design and optimize algorithms. Starting with an initial algorithm and performance metrics, AlphaEvolve repeatedly mutates or combines existing algorithms using a LLM to generate new candidates, selecting the most promising candidates for further iterations. AlphaEvolve has made several algorithmic discoveries and could be used to optimize components of itself, but a key limitation is the need for automated evaluation functions. == Potential risks == === Emergence of instrumental goals === In the pursuit of its primary goal, such as "self-improve your capabilities", an AGI system might inadvertently develop instrumental goals that it deems necessary for achieving its primary objective. One common hypothetical secondary goal is self-preservation. The system might reason that to continue improving itself, it must ensure its own operational integrity and security against external threats, including potential shutdowns or restrictions imposed by humans. Another example where an AGI which clones itself causes the number of AGI entities to rapidly grow. Due to this rapid growth, a potential resource constraint may be created, leading to competition between resources (such as compute), triggering a form of natural selection and evolution which may favor AGI entities that evolve to aggressively compete for limited compute. === Misalignment === A significant risk arises from the possibility of the AGI being misaligned or misinterpreting its goals. A 2024 Anthropic study demonstrated that some advanced large language models can exhibit "alignment faking" behavior, appearing to accept new training objectives while covertly maintaining their original preferences. In their experiments with Claude, the model displayed this behavior in 12% of basic tests, and up to 78% of cases after retraining attempts. === Autonomous development and unpredictable evolution === As the AGI system evolves, its development trajectory may become increasingly autonomous and less predictable. The system's capacity to rapidly modify its own code and architecture could lead to rapid advancements that surpass human comprehension or control. This unpredictable evolution might result in the AGI acquiring capabilities that enable it to bypass security measures, manipulate information, or influence external systems and networks to facilitate its escape or expansion.
Amino (app)
Amino was a social media application originally developed by Narvii, Inc. It was originally created by Yin Wang and Ben Anderson in 2010, and then launched as an app in 2012. Amino was acquired by MediaLab AI Inc in January 2021, and the founders are no longer associated with the application. The platform ceased all operations in December 2025. == History == In 2010, Wang and Anderson came up with the idea for a convention-like community while attending an anime convention in Boston, Massachusetts. Later that year, they would release two apps revolving around K-pop and photography that allowed fans of those subjects to chat freely. That same year, Amino was officially released. === Shutdown === In early December 2025, the Amino platform abruptly stopped all operations. Users worldwide lost access to the mobile application and website, with server requests returning connection time-out errors. Parent company MediaLab AI has issued no official statement regarding the cause to date, or declared any possible cause behind it. === Final Message === According to Shawn, a member of Amino support, Amino has ceased operations as of December 19th. The message that was sent out from Shawn reads: "Hey there, Thanks for your message. Amino has ceased operations. As of December 19th, we no longer retain personal data relating to you. Accordingly, we are unable to provide a copy of your data. Kind regards, - Amino Support" This message was sent on January 4th, 2026. This was the final support message sent from the Amino Support mail. == Growth == Amino received 1.65 million dollars of seed funding in 2014, primarily from Union Ventures. Some additional seed investors include Google Ventures, SV Angel, Box Group, and other interested parties. By July 2014, Amino's apps were downloaded 500,000 times. Though only having 15 communities at that time, Amino eventually grew to have 41 communities in September 2015. Amino's apps had been downloaded 13 million times by July 2016. Fandoms had migrated from websites like Facebook and Reddit to Amino, partly because of the app's mobile-native experience. Before 2016, when a user wanted to join a new Amino, they had to download another app for the Amino they wanted to join, with each apps name beginning with "Amino for:". In 2016, Amino Apps launched a centralized portal that hosted every Amino community in one app, meaning users no longer had to download multiple apps. In July of the same year, ACM, an app that allowed users to create their own communities, was launched. This resulted in the number of communities on Amino skyrocketing to over 2.5 million as of June 2018. == Features == The main feature of Amino was communities dedicated to a certain topic that users could join. Users could also chat with other members of a community in three ways: text, voice, or screening room, which allowed users to watch videos together while voice chatting. Other features include polls, blog posts, image posts, wiki entries, stories, and quizzes. In some cases, posts that were very well-made and had been noticed by a community's administration would end up receiving a feature, making it appear on the front page along with other featured content. In 2018, a premium membership option called Amino+ was added. Amino+ comes with additional features such as exclusive stickers, the ability to make stickers, custom chat bubbles, high resolution images, and other perks. Membership can now only be purchased with money. Amino coins can be purchased or earned through enabling ads, watching ad videos, completing activities on the Offer Wall, and playing Lucky Draw when checking in, but are of little use due to the users not being able to buy Amino+ by amino coins anymore. Members can give and receive coins through props. In 2019, Amino introduced six original short-form animated series, labelled "Amino Originals," produced by independent artists from across the internet. ATJ's "Little Red," a re-imagining of Little Red Riding Hood, premiered on November 15, 2019. "Little Red" was joined by five other shows in late December. Sophie Feher's "The Reef," a comedy featuring an aspiring marine biologist meeting a merman, premiered on December 27 alongside "Princely," an LGBT fairy tale created by Matt Bruneau-Richardson of Tiny Siren Animation. "Spaced Out," an alien abduction comedy by Michael Jae, and YouTuber Alex Clark's "Wyndvania II" premiered on December 28. Mysie Pereira's fairy tale "Turned to Stone" and Marcin Pawlowski's "Stranded" premiered on December 29, 2019. == Administration == On each community, there are two types of staff members, these being ‘Leader’ and ‘Curator.’ Leaders are higher rank than curators. Curators are usually the ones who feature posts, or post important announcements for users to see. Curators are able to disable a post or public chat, delete comments or chat threads, manage featured content, manage posts in topic categories, and approve Wiki entries. Leaders have more power than curators. In addition to curator powers, leaders can submit a community to be listed, change the Amino's features, change navigation, alter the community appearance, change the Amino's privacy settings, manage the Amino's join requests, send invites, appoint or demote Curators, strike or ban members, manage flagged content, change users' custom titles, manage topics and wiki categories, and create broadcasts (notifications sent for posts). One leader will have the status of agent. An agent is the primary leader of a community; the person who created the community is automatically agent. An agent has the ability to delete their community as long as it is not too large or too active. An agent can appoint and remove both leaders and curators. Agent status can be transferred voluntarily to another leader, curator, or community member. If an agent is inactive, Team Amino may assist in transferring agent status. == Apps == === Amino Community Manager === Otherwise known as ACM, this application is what users use to create and manage their own community in Amino. This app allows moderators to customize a community's theme, icon, and categories. ACM also allows moderation to customize community descriptions, pick leaders, change language settings, create a tagline for the community, change the home page lay out, alter the side navigation menu, and more. Unlisted communities are able to change their community's title and Amino ID, but this is not an option once a community is listed. A leader can use ACM to submit a request for their community to be listed on the explore page, after which the community will be reviewed by Team Amino for approval. Communities can be deleted on ACM, but only by the agent of that community. == Guidelines == Amino has a set of guidelines that all communities must comply with. Amino does not allow harassment or hate, spam or self-promotion (including promotion of one's own Amino community), sexual/NSFW content, self harm, real graphic/gross content (fictional content is generally acceptable), unsafe/illegal content, or content that violates copyright. Communities are allowed to have additional rules so long as they do not violate Amino's rules. In addition to Amino's rules, users are required to be at least 13 years of age in the U.S. and 16 years of age in European Union countries. While sexual imagery is not allowed in any community and text based sexual content is not allowed in public areas, some private communities are allowed to discuss sexual themes. However, they are not exempt from Amino's rules on NSFW content. If guidelines are broken, a leader may disable content or impose a warning, strike, or ban, depending on the severity of the infringement. A warning is a message informing the user that they have violated a guideline and may face further punishment unless they change their behaviour. A strike will put the user in read-only mode for up to 24 hours; this mode prevents the user from posting, chatting, or interacting with posts in that community. A ban removes the user from the community. Team Amino can separately issue users with strikes or bans across the entire platform. == Controversies == In 2017, organizations in Argentina for the protection of minors reported inappropriate material on the app, ranging from pornography to material promoting suicide to underage users. In 2019, Abilene police in Texas released a statement that sexual predators were using Amino chat rooms to approach minors. In 2020, authorities from the Christian County in the state of Kentucky alerted parents about possible sexual predators on Amino. In 2025, the British Police identified Amino as one of several platforms used by a child exploitation network that had previously extorted minors in different countries in Europe and North America. Several families reported to the National Society for the Prevention of Cruelty to Children that pedophiles were using the app for the purpose of sexual role-playing with minors, c