The core idea of artificial intelligence systems integration is making individual software components, such as speech synthesizers, interoperable with other components, such as common sense knowledgebases, in order to create larger, broader and more capable A.I. systems. The main methods that have been proposed for integration are message routing, or communication protocols that the software components use to communicate with each other, often through a middleware blackboard system. Most artificial intelligence systems involve some sort of integrated technologies, for example, the integration of speech synthesis technologies with that of speech recognition. However, in recent years, there has been an increasing discussion on the importance of systems integration as a field in its own right. Proponents of this approach are researchers such as Marvin Minsky, Aaron Sloman, Deb Roy, Kristinn R. Thórisson and Michael A. Arbib. A reason for the recent attention A.I. integration is attracting is that there have already been created a number of (relatively) simple A.I. systems for specific problem domains (such as computer vision, speech synthesis, etc.), and that integrating what's already available is a more logical approach to broader A.I. than building monolithic systems from scratch. == Integration focus == The focus on systems' integration, especially with regard to modular approaches, derive from the fact that most intelligences of significant scales are composed of a multitude of processes and/or utilize multi-modal input and output. For example, a humanoid-type of intelligence would preferably have to be able to talk using speech synthesis, hear using speech recognition, understand using a logical (or some other undefined) mechanism, and so forth. In order to produce artificially intelligent software of broader intelligence, integration of these modalities is necessary. == Challenges and solutions == Collaboration is an integral part of software development as evidenced by the size of software companies and the size of their software departments. Among the tools to ease software collaboration are various procedures and standards that developers can follow to ensure quality, reliability and that their software is compatible with software created by others (such as W3C standards for webpage development). However, collaboration in fields of A.I. has been lacking, for the most part not seen outside the respected schools, departments or research institutes (and sometimes not within them either). This presents practitioners of A.I. systems integration with a substantial problem and often causes A.I. researchers to have to 're-invent the wheel' each time they want a specific functionality to work with their software. Even more damaging is the "not invented here" syndrome, which manifests itself in a strong reluctance of A.I. researchers to build on the work of others. The outcome of this in A.I. is a large set of "solution islands": A.I. research has produced numerous isolated software components and mechanisms that deal with various parts of intelligence separately. To take some examples: Speech synthesis FreeTTS from CMU Speech recognition Sphinx from CMU Logical reasoning OpenCyc from Cycorp Open Mind Common Sense Net from MIT With the increased popularity of the free software movement, a lot of the software being created, including A.I. systems, is available for public exploit. The next natural step is to merge these individual software components into coherent, intelligent systems of a broader nature. As a multitude of components (that often serve the same purpose) have already been created by the community, the most accessible way of integration is giving each of these components an easy way to communicate with each other. By doing so, each component by itself becomes a module, which can then be tried in various settings and configurations of larger architectures. Some challenging and limitations of using A.I. software is the uncontrolled fatal errors. For example, serious and fatal errors have been discovered in very precise fields such as human oncology, as in an article published in the journal Oral Oncology Reports entitled "When AI goes wrong: Fatal errors in oncological research reviewing assistance". The article pointed out a grave error in artificial intelligence based on GBT in the field of biophysics. Many online communities for A.I. developers exist where tutorials, examples, and forums aim at helping both beginners and experts build intelligent systems. However, few communities have succeeded in making a certain standard, or a code of conduct popular to allow the large collection of miscellaneous systems to be integrated with ease. == Methodologies == === Constructionist design methodology === The constructionist design methodology (CDM, or 'Constructionist A.I.') is a formal methodology proposed in 2004, for use in the development of cognitive robotics, communicative humanoids and broad AI systems. The creation of such systems requires the integration of a large number of functionalities that must be carefully coordinated to achieve coherent system behavior. CDM is based on iterative design steps that lead to the creation of a network of named interacting modules, communicating via explicitly typed streams and discrete messages. The OpenAIR message protocol (see below) was inspired by the CDM and has frequently been used to aid in the development of intelligent systems using CDM. == Examples == ASIMO, Honda's humanoid robot, and QRIO, Sony's version of a humanoid robot. Cog, M.I.T. humanoid robot project under the direction of Rodney Brooks. AIBO, Sony's robot dog, integrates vision, hearing and motorskills. TOPIO, TOSY's humanoid robot can play ping-pong with human
Global serializability
In concurrency control of databases, transaction processing (transaction management), and other transactional distributed applications, global serializability (or modular serializability) is a property of a global schedule of transactions. A global schedule is the unified schedule of all the individual database (and other transactional object) schedules in a multidatabase environment (e.g., federated database). Complying with global serializability means that the global schedule is serializable, has the serializability property, while each component database (module) has a serializable schedule as well. In other words, a collection of serializable components provides overall system serializability, which is usually incorrect. A need in correctness across databases in multidatabase systems makes global serializability a major goal for global concurrency control (or modular concurrency control). With the proliferation of the Internet, Cloud computing, Grid computing, and small, portable, powerful computing devices (e.g., smartphones), as well as increase in systems management sophistication, the need for atomic distributed transactions and thus effective global serializability techniques, to ensure correctness in and among distributed transactional applications, seems to increase. In a federated database system or any other more loosely defined multidatabase system, which are typically distributed in a communication network, transactions span multiple (and possibly distributed) databases. Enforcing global serializability in such system, where different databases may use different types of concurrency control, is problematic. Even if every local schedule of a single database is serializable, the global schedule of a whole system is not necessarily serializable. The massive communication exchanges of conflict information needed between databases to reach conflict serializability globally would lead to unacceptable performance, primarily due to computer and communication latency. Achieving global serializability effectively over different types of concurrency control has been open for several years. == The global serializability problem == === Problem statement === The difficulties described above translate into the following problem: Find an efficient (high-performance and fault tolerant) method to enforce Global serializability (global conflict serializability) in a heterogeneous distributed environment of multiple autonomous database systems. The database systems may employ different concurrency control methods. No limitation should be imposed on the operations of either local transactions (confined to a single database system) or global transactions (span two or more database systems). === Quotations === Lack of an appropriate solution for the global serializability problem has driven researchers to look for alternatives to serializability as a correctness criterion in a multidatabase environment (e.g., see Relaxing global serializability below), and the problem has been characterized as difficult and open. The following two quotations demonstrate the mindset about it by the end of the year 1991, with similar quotations in numerous other articles: "Without knowledge about local as well as global transactions, it is highly unlikely that efficient global concurrency control can be provided... Additional complications occur when different component DBMSs [Database Management Systems] and the FDBMSs [Federated Database Management Systems] support different concurrency mechanisms... It is unlikely that a theoretically elegant solution that provides conflict serializability without sacrificing performance (i.e., concurrency and/or response time) and availability exists." === Proposed solutions === Several solutions, some partial, have been proposed for the global serializability problem. Among them: Global conflict graph (serializability graph, precedence graph) checking Distributed Two-phase locking (Distributed 2PL) Distributed Timestamp ordering Tickets (local logical timestamps which define local total orders, and are propagated to determine global partial order of transactions) == Relaxing global serializability == Some techniques have been developed for relaxed global serializability (i.e., they do not guarantee global serializability; see also Relaxing serializability). Among them (with several publications each): Quasi serializability Two-level serializability Another common reason nowadays for Global serializability relaxation is the requirement of availability of internet products and services. This requirement is typically answered by large scale data replication. The straightforward solution for synchronizing replicas' updates of a same database object is including all these updates in a single atomic distributed transaction. However, with many replicas such a transaction is very large, and may span several computers and networks that some of them are likely to be unavailable. Thus such a transaction is likely to end with abort and miss its purpose. Consequently, Optimistic replication (Lazy replication) is often utilized (e.g., in many products and services by Google, Amazon, Yahoo, and alike), while global serializability is relaxed and compromised for eventual consistency. In this case relaxation is done only for applications that are not expected to be harmed by it. Classes of schedules defined by relaxed global serializability properties either contain the global serializability class, or are incomparable with it. What differentiates techniques for relaxed global conflict serializability (RGCSR) properties from those of relaxed conflict serializability (RCSR) properties that are not RGCSR is typically the different way global cycles (span two or more databases) in the global conflict graph are handled. No distinction between global and local cycles exists for RCSR properties that are not RGCSR. RCSR contains RGCSR. Typically RGCSR techniques eliminate local cycles, i.e., provide local serializability (which can be achieved effectively by regular, known concurrency control methods); however, obviously they do not eliminate all global cycles (which would achieve global serializability).
Trebel (music app)
Trebel is an on-demand music download and discovery platform developed by M&M Media Inc. The company's business model aims to combat digital music piracy by giving users access to on-demand music at no cost while delivering fair compensation to artists and music rights holders. Trebel has a patent that allows it to market itself as the only international music service in which users can legally download music and listen to it offline for free. As of March 2023, Trebel has a catalog of 75 million songs from record labels such as Universal Music Group, Sony Music Entertainment, Warner Music Group and hundreds of independent labels. Trebel is based in Stamford, Connecticut. with additional locations in Mexico City, Jakarta, Bogota, Los Angeles and Miami. The app is available in the Apple App Store, Google Play Store, and Huawei AppGallery. == History == Trebel was founded in 2014 by Gary Mekikian, who was previously the co-founder of answerFriend, Inc., which commercialized web based question-answering technologies and merged with Electric Knowledge, forming InQuira. This company was eventually acquired by Oracle Corporation in 2011. His co-founders at Trebel include Stanford classmates Corey Jones and Luis Soto Durazo, as well as his daughters Grace and Juliette. Mekikian envisioned Trebel as an alternative to music piracy after a high school classmate of his daughters was targeted by cyberattackers while illegally downloading music online. Trebel was initially released in 2015 under the name Project Carmen to students at Ohio State, Santa Monica College, Cal State Fullerton, UCLA and Long Beach State. In its original incarnation, the service planned to target students at 3,000 universities and 30,000 high schools in the United States. A beta version of the app was introduced in 2016 with content from Universal Music Group and Warner Music Group. Trebel launched commercially in the United States and Mexico in 2018. In 2018, Mexican mass-media corporation Televisa also became a minority investor in Trebel. In May 2020, during the early months of the COVID-19 pandemic, Trebel was a digital broadcast partner for Se Agradece, a concert produced in Mexico by Televisa to honor frontline COVID workers that featured artists such as Rosalia, J Balvin, Maluma and Ricky Martin. In June 2021, Trebel reached 3 million monthly active users. In October 2021, Trebel signed a music licensing agreement with Merlin Network, the licensing agency for the independent music sector that controls an estimated 12% of the global digital recorded music market. In January 2022, Trebel announced a strategic alliance with MNC Corporation, an Indonesian media conglomerate, which also became a minority backer of the company. In March 2022, Trebel reported 5.2 million monthly active users as a result of growth in Latin America. In the same month,, Latin music star Maluma became a backer of Trebel and an advisor to Gary Mekikian, helping expand the service throughout Latin America. On April 18, 2022, Trebel launched in Indonesia during the finale of the music competition show X Factor Indonesia. Trebel also signed a deal that month with Soccer Media Solutions, a sports and entertainment marketing agency in Mexico, to sell Trebel’s premium advertising inventory through Soccer Media. In May 2022, Guillermo Ochoa, goalkeeper for the Mexican national soccer team, invested in Trebel and became an ambassador for the company. On October 2, 2022, Trebel collaborated with Musica Studios, one of the largest music companies in Indonesia, on the production of a music festival in Jakarta titled Trebel Music Fest. The event featured performances by top Indonesian music artists such as Noah, Nidji, and d'Masiv. In October 2022, Trebel launched in Colombia. The service reached 1.2 million monthly active users in Colombia six months after launching. In December 2022, Trebel collaborated with KFC in Indonesia on the release of a KFC digital music program using a product called Trebel Max. As part of the program, KFC customers who bought the Crazy Superstar Combo package at KFC received a subscription to Trebel Max for 30 days. Trebel announced the launch of Trebel AI in May 2023. Trebel AI uses ChatGPT-powered technology to generate playlists based on natural language queries from users. In Indonesia, the Trebel AI feature was announced during a broadcast of the show Indonesian Idol XII that took place on May 8, 2023. In July 2023, Trebel reached more than 13 million monthly active users. In November 2023, Trebel became a featured app on the Discord app directory. Discord users that add the Trebel bot to their servers have access to Trebel's on-demand music library and have the exclusive privilege of being DJ's during server sessions with up to 150 concurrent listeners. == Platform == === Features === Trebel has a patent that allows it to market itself as the only international music service in which users can legally download music and listen to it offline for free. As of March 2023, Trebel has a catalog of 75 million songs from record labels such as Universal Music Group, Sony Music Entertainment, Warner Music Group and hundreds of independent labels. Trebel offers unlimited music downloads that are playable in the app by registered users only. Offline listening is free to all users and not blocked by a paywall. Users can search for music based on song, artist, album, browsing friends' recent activity, and through other users' playlists. The app also offers free cloud storage for downloaded songs. Trebel also contains a feature called SongID, which identifies music being played nearby using a short sample, then offers it for download on the service. Podcasts are available for free listening on the service as well. === Business model === Trebel uses a business model that generates revenue from the sale of digital advertising as well as user interactions with branded experiences, and consumption of virtual goods within the app (akin to mobile games). The app also features a brand takeover feature called Trebel Max, which offers unlimited access in exchange for users engaging with experiences offered by specific brands. Trebel’s brand partners include Uber, KFC, Walmart, Coca-Cola, Amazon and P&G. === Content === In September 2022, Trebel secured an exclusive release of the song “Suara Hatiku” by Indonesian actress Amanda Monopo. As of March 2023, Trebel offers 75 million songs through licensing agreements with Universal Music Group, Sony Music Entertainment, Warner Music Group and global indie rights agency Merlin. == Awards == In 2023, Trebel won three Google Play awards including "Best App of 2023", "Best Everyday Essentials" and "Users' Choice".
Synonym (database)
In databases, a synonym is an alias or alternate name for a table, view, sequence, or other schema object. They are used mainly to make it intuitive for users to access database objects owned by other users. They also hide the underlying object's identity and make it harder for a malicious program or user to target the underlying object (security through obscurity). Because a synonym is just an alternate name for an object, it requires no storage other than its definition. When an application uses a synonym, the DBMS forwards the request to the synonym's underlying base object. By coding your programs to use synonyms instead of database object names, you insulate yourself from any changes in the name, ownership, or object locations, at the cost of adding another layer that also needs to be maintained. Users can also have different needs, for example some may wish to use a shorter name to refer to database objects they often query, which can be done with aliases without having to rename the underlying object and alter the code referring to it. Synonyms are very powerful from the point of view of allowing users access to objects that do not lie within their schema. All synonyms have to be created explicitly with the CREATE SYNONYM command and the underlying objects can be located in the same database or in other databases that are connected by database links There are two major uses of synonyms: Object invisibility: Synonyms can be created to keep the original object hidden from the user. Location invisibility: Synonyms can be created as aliases for tables and other objects that are not part of the local database. When a table or a procedure is created, it is created in a particular schema, and other users can access it only by using that schema's name as a prefix to the object's name. The way around for this is for the schema owner creates a synonym with the same name as the table name. == Public synonyms == Public synonyms are owned by special schema in the Oracle Database called PUBLIC. As mentioned earlier, public synonyms can be referenced by all users in the database. Public synonyms are usually created by the application owner for the tables and other objects such as procedures and packages so the users of the application can see the objects The following code shows how to create a public synonym for the employee table: Now any user can see the table by just typing the original table name. If you wish, you could provide a different table name for that table in the CREATE SYNONYM statement. Remember that the DBA must create public synonyms. Just because you can see a table through public (or private) synonym doesn’t mean that you can also perform SELECT, INSERT, UPDATE or DELETE operations on the table. To be able to perform those operations, a user needs specific privileges for the underlying object, either directly or through roles from the application owner. == Private synonyms == A private synonym is a synonym within a database schema that a developer typically uses to mask the true name of a table, view stored procedure, or other database object in an application schema. Private synonyms, unlike public synonyms, can be referenced only by the schema that owns the table or object. You may want to create private synonyms when you want to refer to the same table by different contexts. Private synonym overrides public synonym definitions. You create private synonyms the same way you create public synonyms, but you omit the PUBLIC keyword in the CREATE statement. The following example shows how to create a private synonym called addresses for the locations table. Note that once you create the private synonym, you can refer to the synonym exactly as you would the original table name. == Drop a synonym == Synonyms, both private and public, are dropped in the same manner by using the DROP SYNONYM command, but there is one important difference. If you are dropping a public synonym; you need to add the keyword PUBLIC after the keyword DROP. The ALL_SYNONYMS (or DBA_SYNONYMS) view provides information on all synonyms in your database.
Big data
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offers greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sources. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data that have only volume, velocity, and variety can pose challenges in sampling. A fourth concept, veracity, which refers to the level of reliability of data, was thus added. Without sufficient investment in expertise to ensure big data veracity, the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture value from big data. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on". Scientists, business executives, medical practitioners, advertising and governments alike regularly meet difficulties with large datasets in areas including Internet searches, fintech, healthcare analytics, geographic information systems, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology, and environmental research. The size and number of available data sets have grown rapidly as data is collected by devices such as mobile devices, cheap and numerous information-sensing Internet of things devices, aerial (remote sensing) equipment, software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.17×260 bytes) of data are generated. Based on an IDC report prediction, the global data volume was predicted to grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data. According to IDC, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. Statista reported that the global big data market is forecasted to grow to $103 billion by 2027. In 2011 McKinsey & Company reported, if US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data. And users of services enabled by personal-location data could capture $600 billion in consumer surplus. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization. Relational database management systems and desktop statistical software packages used to visualize data often have difficulty processing and analyzing big data. The processing and analysis of big data may require "massively parallel software running on tens, hundreds, or even thousands of servers". What qualifies as "big data" varies depending on the capabilities of those analyzing it and their tools. Furthermore, expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration." == Definition == The term big data has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data philosophy encompasses unstructured, semi-structured and structured data; however, the main focus is on unstructured data. Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. Big data requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex, and of a massive scale. Variability is often included as an additional quality of big data. A 2018 definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd's relational model." In a comparative study of big datasets, Kitchin and McArdle found that none of the commonly considered characteristics of big data appear consistently across all of the analyzed cases. For this reason, other studies identified the redefinition of power dynamics in knowledge discovery as the defining trait. Instead of focusing on the intrinsic characteristics of big data, this alternative perspective pushes forward a relational understanding of the object claiming that what matters is the way in which data is collected, stored, made available and analyzed. === Big data vs. business intelligence === The growing maturity of the concept more starkly delineates the difference between "big data" and "business intelligence": Business intelligence uses applied mathematics tools and descriptive statistics with data with high information density to measure things, detect trends, etc. Big data uses mathematical analysis, optimization, inductive statistics, and concepts from nonlinear system identification to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density to reveal relationships and dependencies, or to perform predictions of outcomes and behaviors. == Characteristics == Big data can be described by the following characteristics: Volume The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not. The size of big data is usually larger than terabytes and petabytes. Variety The type and nature of the data. Earlier technologies like RDBMSs were capable to handle structured data efficiently and effectively. However, the change in type and nature from structured to semi-structured or unstructured challenged the existing tools and technologies. Big data technologies evolved with the prime intention to capture, store, and process the semi-structured and unstructured (variety) data generated with high speed (velocity), and huge in size (volume). Later, these tools and technologies were explored and used for handling structured data also but preferable for storage. Eventually, the processing of structured data was still kept as optional, either using big data or traditional RDBMSs. This helps in analyzing data towards effective usage of the hidden insights exposed from the data collected via social media, log files, sensors, etc. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion. Velocity The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. Compared to small data, big data is produced more continually. Two kinds of velocity related to big data are the frequency of generation and the frequency of handling, recording, and publishing. Veracity The truthfulness or reliability of the data, which refers to the data quality and the data value. Big data must not only be large in size, but also must be reliable in order to achieve value in the analysis of it. The data quality of captured data can vary greatly, affecting an accurate analysis. Value The worth in information that can be achieved by the processing and analysis of large datasets. Value also can be measured by an assessment of the other qualities of big data. Value may also represent the profitability of information that is retrieved from the analysis of big data. Variability The characteristic of the changing formats, structure, or sources of big data. Big data can include structured, unstructured,
ISLRN
The ISLRN or International Standard Language Resource Number is Persistent Unique Identifier for Language Resources. == Context == On November 18, 2013, 12 major organisations (see list below) from the fields Language Resources and Technologies, Computational Linguistics, and Digital Humanities held a cooperation meeting in Paris (France) and agreed to announce the establishment of the International Standard Language Resource Number (ISLRN), to be assigned to each Language Resource. Among the 12 organisations, 4 institutions constitute the ISLRN Steering Committee (ST) ADHO ACL Asian Federation of Natural Language Processing ST COCOSDA, International Committee for the Coordination & Standardisation of Speech Databases and Assessment Techniques ICCL (COLING) European Data Forum ELRA ST IAMT, International Association for Machine Translation Archived 2010-06-24 at the Wayback Machine ISCA LDC ST Oriental COCOSDA ST RMA, Language Resource Management Agency == Size and Content == The Joint Research Centre(JRC), the [European Commission]'s in-house science service, was the first organisation to adopt the ISLRN initiative and requested. 2500 resources and tools have already been allocated an ISLRN. These resources include written data (Annotated corpus, Annotated text, List of misspelled word, Terminological database, Treebank, Wordnet, etc.) and speech corpora (Synthesised Speech, Transcripts and Audiovisual Recordings, Conversational Speech, Folk Sayings, etc.) == Objectives == Providing Language Resources with unique names and identifiers using a standardized nomenclature ensures the identification of each Language Resources and streamlines the citation with proper references in activities within Human Language Technology as well as in documents and scientific publications. Such unique identifier also enhances the reproducibility, an essential feature of scientific work.
Zynn
Zynn was a Chinese video-sharing social networking service owned by Kuaishou, a Beijing-based internet technology company established in 2011 by Su Hua and Cheng Yixiao. It was used to create and share short videos, and it pays its users for using the app and referring others. Zynn was launched on May 7, 2020. It became the most-downloaded app in the App Store in the same month. It has also been criticized for being a "pyramid scheme", and it has faced accusations of plagiarism and stealing content. Aside from Zynn in North America, Kuaishou is available under the name Kwai in Russia, South Korea, Japan, Thailand, Vietnam, Philippines, Malaysia, Indonesia, Brazil, America, India, and the Middle East. Kwai used to be available in Australia and the United States on the App Store, but was removed at an unknown date. Zynn was permanently shut down on the 20th of August, 2021. == History == In 2011, entrepreneur Su Hua co-founded Kuaishou with business partner Cheng Yixiao. Originally a GIF-making app, Kuaishou soon moved to short video content. Su Hua also serves as the current Kuaishou CEO. In December 2019, Chinese internet conglomerate Tencent invested $2 billion in Kuaishou, reportedly to compete with rival ByteDance. In December 2019, Kuaishou acquired an app developer called Owlii, which is the developer of Zynn. Zynn was developed to be a North American Market edition of Kuaishou. On May 7, 2020, the app was launched and it was downloaded over 2 million times in that month. On May 12, 2020, Kuaishou filed a lawsuit seeking compensation for "unfair competition", and accused Douyin, the sister app of TikTok, of "interfering" with search results on app stores. Zynn shut down on the 20th of August, 2021. == Features == Zynn allows its users to create, edit and share short videos of themselves. Its interface has been described as a "complete clone" of TikTok, its main competitor. The Zynn app was unique in the way that they paid users for using the platform. Each user earned $1 for signing up, and they could earn money for referring users to the platform. Watching videos resulted in earning "points", which could be redeemed for gift cards or be cashed out via PayPal.[1] == Criticisms and controversies == Multiple TikTok users had reported seeing their entire accounts plagiarized, with one account pretending to be Addison Rae. Despite being launched in May, many videos were posted in February. Zynn has employed "intermittent variable rewards" in its point system, which has been criticized as being the "same reinforcement strategy used to addict people to slot machines". Cash payouts for using the app have resulted in criticism and accusations of anti-competitive behavior. The app was taken down from the Google Play store on June 10. Zynn blamed it on an "isolated incident". Six days later, it was taken down from the App Store as well. US Senator Josh Hawley has criticized the platform, calling it "predatory" and "anti-competitive" in a letter to the Federal Trade Commission asking for an investigation into Zynn. He said "[Zynn] smacks of a textbook predatory-pricing scheme, one calculated to attain immediate market dominance for Zynn by driving competitors out of the market."