AI Coding Quality

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

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  Viewport

  A viewport is a polygon viewing region in computer graphics. In computer graphics theory, there are two region-like notions of relevance when rendering some objects to an image. In textbook terminology, the world coordinate window is the area of interest (meaning what the user wants to visualize) in some application-specific coordinates, e.g. miles, centimeters etc. The word window as used here should not be confused with the GUI window, i.e. the notion used in window managers. Rather it is an analogy with how a window limits what one can see outside a room. In contrast, the viewport is an area (typically rectangular) expressed in rendering-device-specific coordinates, e.g. pixels for screen coordinates, in which the objects of interest are going to be rendered. Clipping to the world-coordinates window is usually applied to the objects before they are passed through the window-to-viewport transformation. For a 2D object, the latter transformation is simply a combination of translation and scaling, the latter not necessarily uniform. An analogy of this transformation process based on traditional photography notions is to equate the world-clipping window with the camera settings and the variously sized prints that can be obtained from the resulting film image as possible viewports. Because the physical-device-based coordinates may not be portable from one device to another, a software abstraction layer known as normalized device coordinates is typically introduced for expressing viewports; it appears for example in the Graphical Kernel System (GKS) and later systems inspired from it. In 3D computer graphics, the viewport refers to the 2D rectangle used to project the 3D scene to the position of a virtual camera. A viewport is a region of the screen used to display a portion of the total image to be shown. In virtual desktops, the viewport is the visible portion of a 2D area which is larger than the visualization device. When viewing a document in a web browser, the viewport is the region of the browser window which contains the visible portion of the document. If the size of the viewport changes, for example as a result of the user resizing the browser window, then the browser may reflow the document (recalculate the locations and sizes of elements of the document). If the document is larger than the viewport, the user can control the portion of the document which is visible by scrolling in the viewport.

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  HiLog

  HiLog is a programming logic with higher-order syntax, which allows arbitrary terms to appear in predicate and function positions. However, the model theory of HiLog is first-order. Although syntactically HiLog strictly extends first order logic, HiLog can be embedded into this logic. HiLog was first described in 1989. It was later extended in the direction of many-sorted logic. The XSB system parses HiLog syntax, but the integration of HiLog into XSB is only partial. In particular, HiLog is not integrated with the XSB module system. A full implementation of HiLog is available in the Flora-2 system. It has been shown that HiLog can be embedded into first-order logic through a fairly simple transformation. For instance, p(X)(Y,Z(V)(W)) gets embedded as the following first-order term: apply(p(X),Y,apply(apply(Z,V),W)). The Framework for Logic-Based Dialects (RIF-FLD) of the Rule Interchange Format (RIF) is largely based on the ideas underlying HiLog and F-logic. == Examples == In all the examples below, capitalized symbols denote variables and the comma denotes logical conjunction, as in most logic programming languages. The first and the second examples show that variables can appear in predicate positions. Predicates can even be complex terms, such as closure(P) or maplist(F) below. The third example shows that variables can also appear in place of atomic formulas, while the fourth example illustrates the use of variables in place of function symbols. The first example defines a generic transitive closure operator, which can be applied to an arbitrary binary predicate. The second example is similar. It defines a LISP-like mapping operator, which applies to an arbitrary binary predicate. The third example shows that the Prolog meta-predicate call/1 can be expressed in HiLog in a natural way and without the use of extra-logical features. The last example defines a predicate that traverses arbitrary binary trees represented as first-order terms.

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  User profile

  A user profile is a collection of settings and information associated with a user. It contains critical information that is used to identify an individual, such as their name, age, portrait photograph and individual characteristics such as knowledge or expertise. User profiles are most commonly present on social media websites such as Facebook, Instagram, and LinkedIn; and serve as voluntary digital identity of an individual, highlighting their key features and traits. In personal computing and operating systems, user profiles serve to categorise files, settings, and documents by individual user environments, known as 'accounts', allowing the operating system to be more friendly and catered to the user. Physical user profiles serve as identity documents such as passports, driving licenses and legal documents that are used to identify an individual under the legal system. A user profile can also be considered as the computer representation of a user model. A user model is a (data) structure that is used to capture certain characteristics about an individual user, and the process of obtaining the user profile is called user modeling or profiling. == Origin == The origin of user profiles can be traced to the origin of the passport, an identity document (ID) made mandatory in 1920, after World War I following negotiations at the League of Nations. The passport served as an official government record of an individual. Consequently, Immigration Act of 1924 was established to identify an individual's country of origin. In the 21st century, passports have now become a highly sought-after commodity as it is widely accepted as a source of verifying an individual's identity under the legal system. With the advent of digital revolution and social media websites, user profiles have transitioned to an organised group of data describing the interaction between a user and a system. Social media sites like Instagram allow individuals to create profiles that are representative of their desired personality and image. Filling all fields of profile information may not be necessary to create a meaningful self-presentation, which grants individual more control over of the identity they wish to present by displaying the most meaningful attributes. A personal user profile is a key aspect of an individual's social networking experience, around which his/her public identity is built. == Types of user profiles == A user profile can be of any format if it contains information, settings and/or characteristics specific to an individual. Most popular user profiles include those on photo and video sharing websites such as Facebook and Instagram, accounts on operating systems, such as those on Windows and MacOS and physical documents such as passports and driving licenses. === Social media === Effectively structured user profiles on social media channels such as Instagram and Facebook offer a way for people to form impressions about someone that is predictive or similarly meeting them offline. The condensed format of social media profiles allows for quick filtering of millions of profiles by matching individuals by similar characteristics and interests; information provided upon sign up. A research conducted highlights that only a "thin slice" of information is required to form an impression about an individual online (Stecher and Counts 2008). Online user profiles eliminate the complexity of interaction that is present in 'face-to-face' meetings such as behavioural, facial, and environmental information, resulting in increased predictiveness of user personality. Dating apps and websites solely rely on an individual's user profile and the information provided to form interactions and communication with others on the platform. Despite having control over presented information, lying is minimal in online dating contexts (Hancock, Toma and Ellison, 2007). Apps such as Bumble allow users to 'match' with other individuals based on their characteristics and selected filters that allow users to narrow the spectrum of search to their preference. Information for a user's profile is voluntarily specified by the user and includes information such as height, interests, photographs, gender or education. The requirement of information varies respective to each platform, and there surrounds little consensus to an appropriate amount of information for a condensed user profile. Universally, all social networking platforms display an individual's profile picture and an "about me" page that allows for self-expression. === Influencers === Influencer user profiles are third party endorsers who shape audience attitudes and decisions through social media content such as photos, blogs and tweets. Social Media Influencers (SMI) often hold a significant following on a social media platform which enables them to be recognised as opinion leaders to shape an information influence to their audience. 'Influencer marketing' industry gained prominence in 2018, when the photo sharing app Instagram crossed 1 billion users, subsequently with approximately 60,000 google search queries for 'influencer marketing' the same year. Influencer user profiles hold a unique selling point, or public personality that is unique and charismatic to the needs and wants of their target audience. SMI profiles advertise product information, latest promotions and regularly engage with their followers to maintain their online persona. Messages endorsed by social media influencers are often perceived as reliable and compelling, as a study conducted found 82% of followers were more inclined to follow the suggestions of their favorite influencer. This allows advertisers to leverage online user profiles and their audience rapport to target younger and niche audiences. According to a market survey, influencer marketing through social media profiles yields a return 11 times higher than traditional marketing, as they are more capable of communicating to a niche segment. Most popular influencers include sport starts such as Cristiano Ronaldo and Hollywood personalities such as Dwayne Johnson and Kylie Jenner each with over 200 million followers respectively. === Ecommerce === Online shopping or Ecommerce websites such as Amazon use information from a customer's user profile and interests to generate a list of recommended items to shop. Recommendation algorithms analyse user demographic data, history, and favourite artists to compile suggestions. The store rapidly adapts to changing user needs and preferences, with generation of real time results required within half of a second. New profiles naturally have limited information for algorithms to analyse, and customer data of each interaction provides valuable information which is stored as a database linked with each individual profile. User profiles on ecommerce websites also serve to improve sales of sellers as individuals are recommend products that other "customers who bought this item also bought" to widen the selection of the buyer. A study conducted found that user profiles and recommendation algorithms have significant impact on related product sales and overall spending of an individual. A process known as "collaborative filtering" tries to analyse common products of interest for an individual on the basis of views expressed by other similar behaving profiles. Features such as product ratings, seller ratings and comments allow individual user profiles to contribute to recommendation algorithms, eliminate adverse selection and contribute to shaping an online marketplace adhering to Amazons zero tolerance policy for misleading products. == Digital user profiles == Modern software and applications account for user profiles as a foundation on which a usable application is built. The structure and layout of an application such as its menus, features and controls are often derived from user's selected settings and preferences. The origin of digital user profiles in computer systems was first initiated by Windows NT that held user settings and information in a separate environment variable named %USERPROFILE% and held the framework to a user's profile root. Consequently, operating systems such as MacOS further accelerated prominence of user profiles in Mac OS X 10.0. Iterations since have been made with each operating system release with the aim to maximise user friendliness with the system. Features such as keyboard layouts, time zones, measurement units, synchronisation of different services and privacy preferences are made available during the setup of a user account on the computer === Types of accounts === ==== Administrator ==== Administrator user profiles have complete access to the system and its permissions. It is often the first user profile on a system by design, and is what allows other accounts to be created. However, since the administrator account has no restrictions, they are highly vulnerable to malware and viruses, with potential to impact all other accounts.

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  HTK (software)

  HTK (Hidden Markov Model Toolkit) is a proprietary software toolkit for handling HMMs. It is mainly intended for speech recognition, but has been used in many other pattern recognition applications that employ HMMs, including speech synthesis, character recognition and DNA sequencing. Originally developed at the Machine Intelligence Laboratory (formerly known as the Speech Vision and Robotics Group) of the Cambridge University Engineering Department (CUED), HTK is now being widely used among researchers who are working on HMMs.

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  MobileNet

  MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision tasks. They are designed for small size, low latency, and low power consumption, making them suitable for on-device inference and edge computing on resource-constrained devices like mobile phones and embedded systems. They were originally designed to be run efficiently on mobile devices with TensorFlow Lite. The need for efficient deep learning models on mobile devices led researchers at Google to develop MobileNet. As of June 2025, the family has five versions, each improving upon the previous one in terms of performance and efficiency. == Features == === V1 === MobileNetV1 was published in April 2017. Its main architectural innovation was incorporation of depthwise separable convolutions. It was first developed by Laurent Sifre during an internship at Google Brain in 2013 as an architectural variation on AlexNet to improve convergence speed and model size. The depthwise separable convolution decomposes a single standard convolution into two convolutions: a depthwise convolution that filters each input channel independently and a pointwise convolution ( 1 × 1 {\displaystyle 1\times 1} convolution) that combines the outputs of the depthwise convolution. This factorization significantly reduces computational cost. The MobileNetV1 has two hyperparameters: a width multiplier α {\displaystyle \alpha } that controls the number of channels in each layer. Smaller values of α {\displaystyle \alpha } lead to smaller and faster models, but at the cost of reduced accuracy, and a resolution multiplier ρ {\displaystyle \rho } , which controls the input resolution of the images. Lower resolutions result in faster processing but potentially lower accuracy. === V2 === MobileNetV2 was published in March 2019. It uses inverted residual layers and linear bottlenecks. Inverted residuals modify the traditional residual block structure. Instead of compressing the input channels before the depthwise convolution, they expand them. This expansion is followed by a 1 × 1 {\displaystyle 1\times 1} depthwise convolution and then a 1 × 1 {\displaystyle 1\times 1} projection layer that reduces the number of channels back down. This inverted structure helps to maintain representational capacity by allowing the depthwise convolution to operate on a higher-dimensional feature space, thus preserving more information flow during the convolutional process. Linear bottlenecks removes the typical ReLU activation function in the projection layers. This was rationalized by arguing that that nonlinear activation loses information in lower-dimensional spaces, which is problematic when the number of channels is already small. === V3 === MobileNetV3 was published in 2019. The publication included MobileNetV3-Small, MobileNetV3-Large, and MobileNetEdgeTPU (optimized for Pixel 4). They were found by a form of neural architecture search (NAS) that takes mobile latency into account, to achieve good trade-off between accuracy and latency. It used piecewise-linear approximations of swish and sigmoid activation functions (which they called "h-swish" and "h-sigmoid"), squeeze-and-excitation modules, and the inverted bottlenecks of MobileNetV2. === V4 === MobileNetV4 was published in September 2024. The publication included a large number of architectures found by NAS. Inspired by Vision Transformers, the V4 series included multi-query attention. It also unified both inverted residual and inverted bottleneck from the V3 series with the "universal inverted bottleneck", which includes these two as special cases. === V5 === MobileNetV5's architecture was published shortly after the release of Gemma 3n in June 2025. While the announcement stated a technical report on MobileNetV5 would be available soon, this has not yet materialised. The network is 10 times larger than the largest V4 variant.

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  BRFplus

  BRFplus (Business Rule Framework plus) is a business rule management system (BRMS) offered by SAP AG. BRFplus is part of the SAP NetWeaver ABAP stack. Therefore, all SAP applications that are based on SAP NetWeaver can access BRFplus within the boundaries of an SAP system. However, it is also possible to generate web services so that BRFplus rules can also be offered as a service in a SOA landscape, regardless of the software platform used by the service consumers. BRFplus development started as a supporting tool that was part of SAP Business ByDesign, an ERP solution targeted at small and medium size companies. By that time, the tool was called "Formula and Derivation Tool" (FDT). Later on, it was decided to maintain BRFplus on those codelines that serve as the basis for SAP Business Suite. With that, business rules that have been created for Business ByDesign can easily be taken over in a full-size SAP system where they are ready for use without any changes. == Overview == BRFplus offers a unified modeling and runtime environment for business rules that addresses both technical users (programmers, system administrators) as well as business users who take care of operational business processes (like procurement, bidding, tax form validation, etc.). The different requirements and usage scenarios of the different target groups can be covered with the help of the SAP authorization system and a user interface that can be individually customized. Being integrated into SAP NetWeaver, BRFplus-based applications can look at, and model, business rules from a strictly business-oriented perspective, rather than starting with the underlying technical artifacts. This is because the integration allows for direct access to the business objects available in the SAP dictionary (like customer, supplier, material, bill, etc.). In addition to the predefined expression types (decision table, decision tree, formula, database access, loops, etc.) and actions (sending e-mails, triggering a workflow, etc.), BRFplus can be extended by custom expression types. Also, direct calls of function modules as well as ABAP OO class methods are supported so that the entire range of the ABAP programming language is available for solving business tasks. BRFplus comes with an optional versioning mechanism. Versioning can be switched on and off for individual objects as well as for entire applications. Versioned business rules are needed in certain use cases for legal reasons, but they also allow for simulating the system behavior as it would have been at a particular point in time. Once the rule objects are in a consistent state and active, the system automatically generates ABAP OO classes that encapsulate the functional scope of the underlying rule object. This is done on an on-demand base and speeds up processing. The execution of functions as well as of single expressions can be simulated. The processing log of the simulation is useful for checking the implementation and for investigating problems. BRFplus applications can be exported and imported as an XML file. This is an easy way of creating a data backup. XML files can also be used for deploying rule applications throughout the company. == Main object types == === Application === The application object serves as a container for all the BRFplus objects that have been assembled to solve a particular business task. It is possible to define certain default settings on application level that are inherited by all objects that are created in the scope of that application. === Function === A function is used to connect a business application with the rule processing framework of BRFplus. The calling business application passes input values to the function which are then processed by the expressions and rulesets that are associated with the called function. The calculated result is then returned to the calling business application. === Expression types and action types === Boolean BRMS Connector Case Database Lookup Decision Table Decision Tree Formula Function Call Loop Procedure Call Random Number Search Tree Step Sequence Value Range1 XSL Transformation === Ruleset === A ruleset is a container for an arbitrary number of rule objects which in turn carry out the necessary calculations with the help of assigned expressions and actions. Instead of assigning an expression to a function, it is also possible to assign any number of rulesets to a function. When the function is called, all assigned rulesets are subsequently processed. === Data objects === BRFplus supports elementary data objects (text, number, boolean, time point, amount, quantity) as well as structures and tables. Structures can be nested. For all types of data objects it is possible to reference data objects that reside in the data dictionary of the backend system. With that, a BRFplus data object does not only inherit the type definition of the referenced object but can also access associated data like domain value lists or object documentation. === Other objects === With catalogs, it is possible to define business-specific subsets of the rule objects that reside in the system. This is helpful for hiding the complexity of a rule system, thus improving usability. Object filters are used by system administrators to ensure that for selected users, only a predefined subset of object types is visible. This is useful to enforce access rights as well as modeling policies. == Other BRM solutions offered by SAP == BRFplus is positioned as the successor product of an older business rule solution known as BRF (Business Rule Framework). For a longer transition phase, both solutions exist in parallel. However, an increasing number of SAP applications that used to be based on BRF are migrating to BRFplus. While BRFplus supports business rules for applications based on the SAP NetWeaver ABAP stack, SAP is offering another product named SAP NetWeaver Business Rules Management (BRM). BRM supports business rule modeling for the SAP NetWeaver Java stack. Both products do not compete. They are available in parallel and can be used in a collaborative approach to deal with use cases where both technology stacks are used in parallel. BRFplus comes with a special expression type that helps bridging the gap between the two different technologies. == Availability == BRFplus has been delivered to the public with SAP NetWeaver 7.0 Enhancement Package 1 for the first time. Being part of SAP NetWeaver, the usage of BRFplus is covered by the "SAP NetWeaver Foundation for Third Party Applications" license, with no additional costs. == Literature == Carsten Ziegler, Thomas Albrecht: BRFplus – Business Rule Management for ABAP Applications. Galileo Press 2011. ISBN 978-1-59229-293-6

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  Jarosław Królewski

  Jarosław Królewski ([jaˈrɔswaf kruˈlɛfskʲi]; born September 26, 1986) is a Polish entrepreneur, programmer, sociologist, investor, and philanthropist from Hańczowa, Poland. He is a researcher and lecturer at the AGH University of Krakow. He was selected as a Young Global Leader by the World Economic Forum in 2025. Królewski is a cofounder and chief executive of the software development company Synerise that develops its namesake business intelligence software based on artificial intelligence and big data. He is also the president and a majority stakeholder of the Polish soccer club Wisła Kraków. == Biography == === Scientific activities === Królewski graduated from the AGH University of Kraków and the University of Banking and Management in Kraków. He completed two fields of study: a master's degree in sociology, and an engineer's degree in computer science. He co-created innovative study programs, including social informatics and electronic business, recognized as the most innovative field of study in Poland in 2012 by the Ministry of Science and Higher Education, which led to the AGH receiving a PLN 1 million award for the development of the program. Królewski is a research and teaching employee at AGH, where since 2010 he has been conducting classes and lectures on the Internet, mobile technologies, and UX/UI. He has been preparing a PhD thesis. He is the brand ambassador of the Academy. He is also a mentor of the Polish Development Fund network. In 2019, on the occasion of the AGH University's 100th anniversary, Królewski was honored the title of "AGH Graduate Junior 2018." Królewski is the co-originator of the "Data Science in Business and Administration" doctoral studies organized by the Faculty of Computer Science and Electronic Economy of the Poznań University of Economics. He is a co-author of a textbook E-marketing. Contemporary trends. Starter package (2013), and an Book on algorithmic governance Algocracy. How and why artificial intelligence changes everything (with Krzysztof Rybiński, 2023). === Business career === Throughout the 2000s, Królewski was responsible for issues of usability and user experience at the advertising agency Eskadra in Kraków. In 2012, along with programmer Miłosz Baluś and graphic designer Krzysztof Kochmański, he founded the software house Humanoit Group. The company created a project management software using machine learning and artificial intelligence. In 2013, HG Intelligence was established to create a platform for analytics and automation of business processes called "Synerise" that combined big data with artificial intelligence mechanisms. Królewski became the president of the company's management board. In 2016, the company rebranded itself after its own platform. It is one of the fastest growing enterprises in Poland – in 2019 it was valued at USD 85 million (PLN 323.5 million), and its value is still growing, in 2022 it announced an investment of USD 23 million. Królewski is a supporter of releasing some software in open-source form, an example of which is the open library Cleora.ai. Królewski has been described "one of the most promising young Polish businessmen in the technology industry." According to Forbes, he is a "visionary computer scientist who in many respects resembles the young Bill Gates." Królewski considers himself a “technological determinist and optimist.” He never wants to be a millionaire or billionaire, he spends 80 percent of his private income on education, sports and charities. === Sports === In his youth (2002–2006) he was a football player of the (then 4th-league) club Glinik Gorlice, and represented it at the then-highest level of junior competitions in Poland. He played there with Rafał Wisłocki, later president of Wisła Kraków and vice-president of Bruk-Bet Termalica Nieciecza. In early 2019, Królewski was the initiator of a rescue operation that saved Wisła Kraków from bankruptcy, as well as the originator of the crowdfunding issue of shares of Wisła Kraków, pioneering in Polish sports, during restructuring and searching for a strategic investor. The offered shares constituted 5.1 percent. all the company's shares, which meant that the club was valued at PLN 74.4 million. 40,000 shares were put up for sale, each worth PLN 100. Within 24 hours, they were purchased by 9,124 investors through an equity crowdfunding platform Beesfund, earning the club PLN 4 million. In March 2019, Królewski became vice-chairman of Wisła's supervisory board, a position he held until 2021. In April 2020, he became Wisła's co-owner, along with the footballer Jakub Błaszczykowski, and Tomasz Jażdżyński, president of Gremi Media (publisher of the news outlets Rzeczpospolita and Parkiet). The three granted a bridging loan to the club of PLN 4 million, each supporting PLN 1.33 million. The funds were used to repay the club's debts to players. In November 2022, the supervisory board of Wisła Kraków appointed Królewski as the president of the club's management board. In December 2022, Królewski took over a majority stake in the club. In January 2024, based on match statistics, he used AI tools to select Wisła's new coach, Albert Rudé. === Social activities === Królewski is the creator and originator of the nationwide educational project "AI Schools & Academy", the first artificial intelligence teaching program in Polish kindergartens, primary and secondary schools in Polish history. Launched in 2018, the project was financed by Synerise business partners: Carrefour, CCC, Ernst & Young, IDC, Media Expert, Microsoft, Orange Foundation, Oriflame, Bank Pekao, Photon, PZU, and Żabka. Physicists, mathematicians, and computer scientists conduct special classes in 1,500 kindergartens, primary and secondary schools. Outstanding students and teachers are awarded scholarships. The project was appreciated by experts. In the years 2018–2020, Królewski was the main sponsor of Glinik Gorlice. He also supported the women's football team Staszkówka Jelna (of Staszkówka). After taking over the shares of Wisła Kraków in 2020, he launched socially conscience initiatives along with other shareholders, including a women's football team, the amp football section, and the blind football section. He has privately sponsored social charities. == Accolades and awards == In 2017, Królewski along with the Synerise co-founders Baluś and Kochmański was included in the “New Europe 100” list of eastern Europe's brightest and best citizens changing the region's societies, politics, or business environments, according to Res Publica, along with the International Visegrad Fund, Google and the Financial Times. Królewski was included on Ernst & Young's list of the 30 most promising technology entrepreneurs in the world. In 2018, he was honored with the Special Jury Award in the Polish edition of the Ernst & Young Entrepreneur of the Year Award competition, for combining scientific activities with entrepreneurship. The same year, Królewski won an award in the competition Digital Shapers, distinguishing outstanding tech personalities by the Digital Poland Foundation. He was also selected to Ernst & Young startup program EY Accelerating Entrepreneurs for businesses that focus on disruptive fields. In 2019, as part of the AI Awards competition, Królewski received the title of AI Person of the Year. == Private life == Królewski comes from a Lemko family from Hańczowa in the Low Beskids. He is married to Aleksandra Królewska.

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  LIFER/LADDER

  LIFER/LADDER was one of the first database natural language processing systems. It was designed as a natural language interface to a database of information about US Navy ships. This system, as described in a paper by Hendrix (1978), used a semantic grammar to parse questions and query a distributed database. It was implemented in Interlisp. The LIFER/LADDER system could only support simple one-table queries or multiple table queries with easy join conditions. Some examples of queries it could accept: What are the length, width, and draft of the Kitty Hawk? When will Reeves achieve readiness rating C2? What is the nearest ship to Naples with a doctor on board? What ships are carrying cargo for the United States? Where are they going? Print the American cruisers’ current positions and states of readiness?

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  Noom

  Noom is an American privately held digital health company that provides weight management and behavioral health services through a subscription-based mobile application. Founded in 2008, the company combines behavior change psychology with access to weight loss medications and dietary supplements. The platform incorporates elements of cognitive behavioral therapy (CBT) and goal-setting strategies, and its programs are designed to support users in developing healthier habits. In addition to its weight management services, Noom has expanded to offer products related to stress management and general wellness. Noom has received both praise and criticism. Supporters cite its focus on mental and behavioral aspects of health, while critics have raised concerns about the accuracy of its calorie goals, the use of algorithmically determined weight loss targets, and questions about the qualifications of some of its coaching staff. == History == Noom was founded in 2008 by friends Artem Petakov and Saeju Jeong. The company's mobile app officially launched in 2016. In 2025, Noom relocated its headquarters from New York City to Princeton, New Jersey. Petakov, a former software engineer at Google, currently leads Noom Ventures, while Jeong serves as Noom's Chairman. In 2023, Geoff Cook was appointed CEO of Noom. In 2019, Noom partnered with Novo Nordisk to offer patients prescribed the diabetes medication Saxenda one year of free access to the Noom platform. In 2020, Noom reported $400 million in revenue. As of April 2021, the company stated it employed approximately 3,000 people, including 2,700 coaches. == Services == === Noom App === The Noom app is the primary platform through which users engage with the company's services. Upon creating an account, users are prompted to provide physical information such as weight, height, and age, along with experiential data including lifestyle habits, personal goals, and perceived obstacles. Users log their meals and physical activity, and in return, the app delivers feedback through multiple channels: algorithmically generated insights, guidance from a human coach, peer interaction, educational articles, and interactive quizzes. The app has been reviewed by a range of media outlets, including newspapers such as the Chicago Tribune and USA Today; health information sources such as WebMD; and lifestyle magazines including Good Housekeeping. === Other services === In 2024, Noom launched Noom Vibe, a mobile application that encourages users to develop healthy habits by awarding "vibes"—a form of points—for activities such as walking or meeting step goals. That same year, Noom introduced a 3D body scanning feature within its app, designed to help users monitor physical changes and prevent muscle atrophy during weight loss. Also in 2024, Noom began offering a compounded GLP-1 medication as part of its weight management program. The formulation includes the same active ingredient found in the anti-obesity medications Wegovy and Ozempic. == Research == In 2016, a study published in Scientific Reports analyzed data from approximately 36,000 users of the Noom app, of whom 78% were female and 22% male. The data were collected between October 2012 and April 2014. To be included in the analysis, users had to log their weight at least twice per month over a period of six consecutive months. The study found that 78% of participants self-reported weight loss while using the app. The median duration of weight reporting was 267 days (approximately nine months). The frequency of data logging was positively correlated with weight loss. Additionally, male users had a higher average starting BMI and reported greater average weight loss compared to female users. In 2017, the Centers for Disease Control and Prevention (CDC) recognized Noom as a certified diabetes prevention program, making it the first mobile health application to receive such designation. == Criticisms == === Health programs === Noom has been criticized for promoting elements of diet culture in its advertising campaigns. The app has also faced criticism for setting calorie goals that some users and experts have deemed inappropriately low, and for employing coaches who may lack formal qualifications as registered dietitians. Coaching has been described as relying heavily on canned responses. Upon sign-up, users are prompted to complete a questionnaire consisting of over 50 questions, which is used to generate a personalized program. In 2021, the UK-based organization Privacy International alleged that Noom, along with other diet platforms, used such lengthy surveys to attract users but did not always tailor the resulting programs to the collected data. The organization claimed that many users received the same or highly similar programs regardless of their answers. It also raised concerns about the handling of potentially sensitive health data, alleging a lack of transparency regarding the sharing of such data with third parties, including Facebook, potentially in violation of the European General Data Protection Regulation (GDPR). In a follow-up investigation in 2023, Privacy International reported that Noom had made "significant positive changes" to its data handling practices. However, the organization noted that data was still being shared with Facebook and concluded that "there is still room for improvement." === Billing issues lawsuit === In August 2020, the Better Business Bureau (BBB) issued a warning to consumers regarding Noom's subscription practices. The BBB reported that numerous customers had filed complaints about difficulties canceling their subscriptions after the free trial period, as well as challenges in contacting the company to request refunds. In February 2022, Noom agreed to a $62 million settlement in a class-action lawsuit that alleged the company had used deceptive billing practices related to automatic subscription renewals. Qualifying claimants received approximately $167 each. During the case, a former senior software engineer at Noom testified that the cancellation process was intentionally designed to be difficult, with the goal of generating revenue from customers who failed to cancel in time. In response, Noom stated that it had taken steps to improve transparency around its pricing and policies, including the implementation of self-service cancellation tools.

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  MultiNet

  Multilayered extended semantic networks (MultiNets) are both a knowledge representation paradigm and a language for meaning representation of natural language expressions that has been developed by Prof. Dr. Hermann Helbig on the basis of earlier Semantic Networks. It is used in a question-answering application for German called InSicht. It is also used to create a tutoring application developed by the university of University of Hagen to teach MultiNet to knowledge engineers. MultiNet is claimed to be one of the most comprehensive and thoroughly described knowledge representation systems. It specifies conceptual structures by means of about 140 predefined relations and functions, which are systematically characterized and underpinned by a formal axiomatic apparatus. Apart from their relational connections, the concepts are embedded in a multidimensional space of layered attributes and their values. Another characteristic of MultiNet distinguishing it from simple semantic networks is the possibility to encapsulate whole partial networks and represent the resulting conceptual capsule as a node of higher order, which itself can be an argument of relations and functions. MultiNet has been used in practical NLP applications such as natural language interfaces to the Internet or question answering systems over large semantically annotated corpora with millions of sentences. MultiNet is also a cornerstone of the commercially available search engine SEMPRIA-Search, where it is used for the description of the computational lexicon and the background knowledge, for the syntactic-semantic analysis, for logical answer finding, as well as for the generation of natural language answers. MultiNet is supported by a set of software tools and has been used to build large semantically based computational lexicons. The tools include a semantic interpreter WOCADI, which translates natural language expressions (phrases, sentences, texts) into formal MultiNet expressions, a workbench MWR+ for the knowledge engineer (comprising modules for automatic knowledge acquisition and reasoning), and a workbench LIA+ for the computer lexicographer supporting the creation of large semantically based computational lexica.

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  CatBoost

  CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. It works on Linux, Windows, macOS, and is available in Python, R, and models built using CatBoost can be used for predictions in C++, Java, C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. InfoWorld magazine awarded the library "The best machine learning tools" in 2017. along with TensorFlow, Pytorch, XGBoost and 8 other libraries. Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world. It was listed as the top-8 most frequently used ML framework in the 2020 survey and as the top-7 most frequently used ML framework in the 2021 survey. As of April 2022, CatBoost is installed about 100000 times per day from PyPI repository == Features == CatBoost has gained popularity compared to other gradient boosting algorithms primarily due to the following features Native handling for categorical features Fast GPU training Visualizations and tools for model and feature analysis Using oblivious trees or symmetric trees for faster execution Ordered boosting to overcome overfitting == History == In 2009 Andrey Gulin developed MatrixNet, a proprietary gradient boosting library that was used in Yandex to rank search results. Since 2009 MatrixNet has been used in different projects at Yandex, including recommendation systems and weather prediction. In 2014–2015 Andrey Gulin worked with a team of researchers to start a new project called Tensornet which was aimed at solving the problem of "how to work with categorical data". Their work resulted in several proprietary Gradient Boosting libraries with different approaches to handling categorical data. In 2016 the Machine Learning Infrastructure team led by Anna Dorogush started working on Gradient Boosting in Yandex, including Matrixnet and Tensornet. They implemented and open-sourced the next version of Gradient Boosting library called CatBoost, which has support for categorical and text data, GPU training, model analysis, and visualization tools. CatBoost was open-sourced in July 2017 and is under active development in Yandex and the open-source community. == Application == JetBrains uses CatBoost for code completion Cloudflare uses CatBoost for bot detection Careem uses CatBoost to predict future destinations of the rides

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  SHRDLU

  SHRDLU is an early natural-language understanding computer program that was developed by Terry Winograd at MIT in 1968–1970. In the program, the user carries on a conversation with the computer, moving objects, naming collections and querying the state of a simplified "blocks world", essentially a virtual box filled with different blocks. SHRDLU was written in the Micro Planner and Lisp programming language on the DEC PDP-6 computer and a DEC graphics terminal. Later additions were made at the computer graphics labs at the University of Utah, adding a full 3D rendering of SHRDLU's "world". The name SHRDLU was derived from ETAOIN SHRDLU, the arrangement of the letter keys on a Linotype machine, arranged in descending order of usage frequency in English. == Functionality == SHRDLU is primarily a language parser that allows user interaction using English terms. The user instructs SHRDLU to move various objects around in the "blocks world" containing various basic objects such as blocks, cones and balls. SHRDLU combined four simple ideas to make the simulation of "understanding" more convincing. One was that SHRDLU's world is so simple that the entire set of objects and locations could be described by including as few as 50 words: nouns like "block" and "cone", verbs like "place on" and "move to", and adjectives like "big" and "blue". The possible combinations of these basic language building blocks are quite simple. SHRDLU also includes a basic memory to supply context. One could ask SHRDLU to "put the green cone on the red block" and then "take the cone off"; "the cone" would be taken to mean the green cone one had just talked about. SHRDLU can search back further through the interactions to find the proper context in most cases when additional adjectives were supplied. One could also ask questions about the history; for instance, one could ask "did you pick up anything before the cone?" A side effect of this memory, and the original rules SHRDLU was supplied with, is that the program can answer questions about what was possible in the world and what was not. For instance, SHRDLU can deduce that blocks could be stacked by looking for examples, but also realize that triangles could not be stacked, after having tried it. The "world" contains basic physics to make blocks fall over, independent of the language parser. Finally, SHRDLU can also remember names given to objects, or arrangements of them. For instance one could say "a steeple is a small triangle on top of a tall rectangle"; SHRDLU can then answer questions about steeples in the blocks world, and build new ones. This is an example of a SHRDLU session: Person: Pick up a big red block. Computer: OK. Person: Grasp the pyramid. Computer: I DON'T UNDERSTAND WHICH PYRAMID YOU MEAN. Person (changing their mind): Find a block which is taller than the one you are holding and put it into the box. Computer: BY "IT", I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN THE ONE I AM HOLDING. Computer: OK. Person: What does the box contain? Computer: THE BLUE PYRAMID AND THE BLUE BLOCK. Person: What is the pyramid supported by? Computer: THE BOX. Person: How many blocks are not in the box? Computer: FOUR OF THEM. Person: Is at least one of them narrower than the one which i told you to pick up? Computer: YES, THE RED CUBE. Person: Is it supported? Computer: YES, BY THE TABLE. Person: Can the table pick up blocks? Computer: NO. Person: Can a pyramid be supported by a block? Computer: YES. Person: Can a pyramid support a pyramid? Computer: I DON'T KNOW. Person: Stack up two pyramids. Computer: I CAN'T. Person: Will you please stack up both of the red blocks and either a green cube or a pyramid? Computer: OK. Person: Which cube is sitting on the table? Computer: THE LARGE GREEN ONE WHICH SUPPORTS THE RED PYRAMID. Person: Is there a large block behind a pyramid? Computer: YES, THREE OF THEM: A LARGE RED ONE, A LARGE GREEN CUBE, AND THE BLUE ONE. Person: Put a small one onto the green cube which supports a pyramid. Computer: OK. Person: Put the littlest pyramid on top of it. Computer: OK. Person: Does the shortest thing the tallest pyramid's support supports support anything green? Computer: YES, THE GREEN PYRAMID. Person: What color is it? Computer: BY "IT", I ASSUME YOU MEAN THE SHORTEST THING THE TALLEST PYRAMID'S SUPPORT SUPPORTS. Computer: RED Person: How many things are on top of green cubes? Computer: I'M NOT SURE WHAT YOU MEAN BY "ON TOP OF" IN THE PHRASE "ON TOP OF GREEN CUBES" . DO YOU MEAN: 1 - DIRECTLY ON THE SURFACE 2 - ANWHERE ON TOP OF ? Person: 2 Computer: THREE OF THEM. Person: Had you touched any pyramid before you put the green one on the little cube? Computer: YES, THE GREEN ONE. Person: When did you pick it up? Computer: WHILE I WAS STACKING UP THE RED CUBE, A LARGE RED BLOCK AND A LARGE GREEN CUBE. Person: Why? Computer: TO GET RID OF IT. Person: Why did you do that? Computer: TO CLEAN OFF THE RED CUBE. Person: Why did you clear off that cube? Computer: TO PUT IT ON A LARGE GREEN CUBE. Person: Why did you do that? Computer: TO STACK UP THE RED CUBE , A LARGE RED BLOCK AND A LARGE GREEN CUBE. Person: Why did you do that? Computer: BECAUSE YOU ASKED ME TO. Person: How did you do it? Computer: BY PUTTING A LARGE RED BLOCK ON THE TABLE ; THEN LETTING GO OF IT ; THEN PUTTING A LARGE GREEN CUBE ON IT ; THEN LETTING GO OF THAT CUBE ; THEN PUTTING THE RED CUBE ON THAT CUBE; THEN LETTING GO OF THAT CUBE. Person: How many objects did you touch while you were doing it? Computer: FOUR OF THEM. Person: What did the red cube support before you started to clean it off? Computer: THE GREEN PYRAMID. Person: There were five blocks to the left of the box then. Computer: NO, ONLY FOUR OF THEM: THE RED CUBE, TWO LARGE GREEN CUBES AND A LARGE RED BLOCK. Person: Put the blue pyramid on the block in the box. Computer: OK. Person: Is there anything which is bigger than every pyramid but is not as wide as the thing that supports it? Computer: YES, THE BLUE BLOCK. Person: Does a steeple Computer (interrupting): SORRY, I DON'T KNOW THE WORD "STEEPLE". Person: A "steeple" is a stack which contains two green cubes and a pyramid. Computer: I UNDERSTAND. Person: Are there any steeples now? Computer: NO. Person: Build one. Computer: OK. Person: Call the biggest block "superblock". Computer: OK. Person: Have you picked superblock up since we began? Computer: YES. Person: Why did you drop it? Computer: BY "IT", I ASSUME YOU MEAN THE SUPERBLOCK. I CAN'T EXPLAIN A NON-EXISTENT EVENT. Person: Is there anything to the right of the red pyramid? Computer: YES, FOUR THINGS: THE BOX, THE RED CUBE, THE BLUE PYRAMID AND THE BLOCK NAMED SUPERBLOCK Person: Thank you. Computer: YOU'RE WELCOME! == Consequences == SHRDLU was considered a tremendously successful demonstration of artificial intelligence (AI). This led other AI researchers to excessive optimism which was soon lost when later systems attempted to deal with situations with a more realistic level of ambiguity and complexity. Subsequent efforts of the SHRDLU type, such as Cyc, have tended to focus on providing the program with considerably more information from which it can draw conclusions. In a 1991 interview, Winograd said about SHRDLU: [...] the famous dialogue with SHRDLU where you could pick up a block, and so on, I very carefully worked through, line by line. If you sat down in front of it, and asked it a question that wasn't in the dialogue, there was some probability it would answer it. I mean, if it was reasonably close to one of the questions that was there in form and in content, it would probably get it. But there was no attempt to get it to the point where you could actually hand it to somebody and they could use it to move blocks around. And there was no pressure for that whatsoever. Pressure was for something you could demo. Take a recent example, Negroponte's Media Lab, where instead of "perish or publish" it's "demo or die." I think that's a problem. I think AI suffered from that a lot, because it led to "Potemkin villages", things which - for the things they actually did in the demo looked good, but when you looked behind that there wasn't enough structure to make it really work more generally. Though not intentionally developed as such, SHRDLU is considered the first known formal example of interactive fiction, as the user interacts with simple commands to move objects around a virtual environment, though lacking the distinct story-telling normally present in the interactive fiction genre. The 1976-1977 game Colossal Cave Adventure is broadly considered to be the first true work of interactive fiction.

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  Multicloud

  Multicloud (also written as multi-cloud or multi cloud) is a term with varying interpretations, generally referring to a system using multiple cloud computing providers. According to ISO/IEC 22123-1: "multi-cloud is a cloud deployment model in which a customer uses public cloud services provided by two or more cloud service providers". Multi-cloud can involve various deployment models, including public, private, and hybrid clouds, and multiple service models, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Multicloud incorporates workload, data, traffic, and workflow portability options, which can result in varying implementation complexity. When effectively implemented, multicloud solutions can enhance architectural resilience, reduce dependence on a single vendor, and improve flexibility by leveraging services from different providers. However, multicloud strategies also present challenges, including increased operational complexity, security risks, higher costs, and integration difficulties. According to the 2024 State of the Cloud Report by Flexera, multi-cloud adoption has continued to rise in 2024. Enterprises increasingly silo applications into specific clouds and select best-fit services. Key use cases include data analysis in separate clouds and cross-cloud disaster recovery. == Advantages and challenges == There are several advantages to using a multicloud approach, including the ability to negotiate better pricing with cloud providers, the ability to quickly switch to another provider if needed, and the ability to avoid vendor lock-in. Multicloud can also be a good way to hedge against the risks of obsolescence, as it allows you to rely on multiple vendors and open standards, which can prolong the life of your systems. Additional benefits of the multicloud architecture include adherence to local policies that require certain data to be physically present within the area/country, geographical distribution of processing requests from physically closer cloud unit which in turn reduces latency and protect against disasters. Various issues and challenges also present themselves in a multicloud environment. Security and governance is more complicated, and more "moving parts" may create resiliency issues. == Difference between multicloud and hybrid cloud == Multicloud differs from hybrid cloud in that it refers to multiple cloud services from different vendors rather than multiple deployment modes (on-premises hardware, and public and private, cloud hosting). However, when considering a broad definition of multi-cloud, hybrid cloud can still be regarded as a special form of multi-cloud.

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  Spatial–temporal reasoning

  Spatial–temporal reasoning is an area of artificial intelligence that draws from the fields of computer science, cognitive science, and cognitive psychology. The theoretic goal—on the cognitive side—involves representing and reasoning spatial-temporal knowledge in mind. The applied goal—on the computing side—involves developing high-level control systems of automata for navigating and understanding time and space. == Influence from cognitive psychology == A convergent result in cognitive psychology is that the connection relation is the first spatial relation that human babies acquire, followed by understanding orientation relations and distance relations. Internal relations among the three kinds of spatial relations can be computationally and systematically explained within the theory of cognitive prism as follows: the connection relation is primitive; an orientation relation is a distance comparison relation: you being in front of me can be interpreted as you are nearer to my front side than my other sides; a distance relation is a connection relation using a third object: you being one meter away from me can be interpreted as a one-meter-long object connected with you and me simultaneously. == Fragmentary representations of temporal calculi == Without addressing internal relations among spatial relations, AI researchers contributed many fragmentary representations. Examples of temporal calculi include Allen's interval algebra, and Vilain's & Kautz's point algebra. The most prominent spatial calculi are mereotopological calculi, Frank's cardinal direction calculus, Freksa's double cross calculus, Egenhofer and Franzosa's 4- and 9-intersection calculi, Ligozat's flip-flop calculus, various region connection calculi (RCC), and the Oriented Point Relation Algebra. Recently, spatio-temporal calculi have been designed that combine spatial and temporal information. For example, the spatiotemporal constraint calculus (STCC) by Gerevini and Nebel combines Allen's interval algebra with RCC-8. Moreover, the qualitative trajectory calculus (QTC) allows for reasoning about moving objects. == Quantitative abstraction == An emphasis in the literature has been on qualitative spatial-temporal reasoning which is based on qualitative abstractions of temporal and spatial aspects of the common-sense background knowledge on which our human perspective of physical reality is based. Methodologically, qualitative constraint calculi restrict the vocabulary of rich mathematical theories dealing with temporal or spatial entities such that specific aspects of these theories can be treated within decidable fragments with simple qualitative (non-metric) languages. Contrary to mathematical or physical theories about space and time, qualitative constraint calculi allow for rather inexpensive reasoning about entities located in space and time. For this reason, the limited expressiveness of qualitative representation formalism calculi is a benefit if such reasoning tasks need to be integrated in applications. For example, some of these calculi may be implemented for handling spatial GIS queries efficiently and some may be used for navigating, and communicating with, a mobile robot. == Relation algebra == Most of these calculi can be formalized as abstract relation algebras, such that reasoning can be carried out at a symbolic level. For computing solutions of a constraint network, the path-consistency algorithm is an important tool. == Software == GQR, constraint network solver for calculi like RCC-5, RCC-8, Allen's interval algebra, point algebra, cardinal direction calculus, etc. qualreas is a Python framework for qualitative reasoning over networks of relation algebras, such as RCC-8, Allen's interval algebra, and Allen's algebra integrated with Time Points and situated in either Left- or Right-Branching Time.

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  Tabnine

  Tabnine is a code completion tool which uses generative artificial intelligence to assist users by autocompleting code. It was created in 2018 by Jacob Jackson, a student at the University of Waterloo. It is now developed by Tabnine, a software company founded under the name Codota by Dror Weiss and Eran Yahav in Tel Aviv, Israel, in 2013, and renamed to Tabnine in 2021. Initially established under the name Codota, the company underwent a rebranding in May 2021 following the release of the company’s first large language model based AI coding assistant, adopting the name Tabnine. == History == Tabnine was established as Codota in 2013 by Dror Weiss and Eran Yahav in Tel Aviv, Israel. Tabnine, initially founded under the name Codota, was created to develop tools based on over a decade of academic research at the Technion. Codota, the predecessor of Tabnine, secured $2 million in seed investment in June 2017. Following this, in June 2018, the company introduced the first AI-based code completion for Java IDE. In 2019, Codota acquired a product called Tabnine, which used the newly available large-language model technology to provide generative AI for software code across a broader range of programming languages across five IDEs. Codota replaced its earlier approach to code generation with this new approach to generative AI. The company secured a Series A round of funding in April 2020, raising $12 million. On May 26, 2021, Codota changed its name to Tabnine and underwent a corresponding rebranding. By April 2022, Tabnine reached over one million users. In June of the same year, Tabnine launched models that could predict full lines and snippets of code. The same year it raised $15.5 mln in a funding round led co-led by Qualcomm Ventures. In June 2023, Tabnine introduced an AI-powered chat agent, enabling developers to use natural language to generate code, to explain code, to generate tests and documentation, and to propose fixes to code. In November 2023, Tabnine closed a Series B round of funding, raising $25 million to scale the company’s operations. == Operations == Tabnine's headquarters is located in Tel Aviv, Israel, with an additional corporate entity in the United States. As of November 2023, Tabnine generative AI for software development is used by a million developers. It has 10 million installations across VS Code and JetBrains. Since its founding, Dror Weiss has served as CEO, with Eran Yahav as CTO.

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