IruSoft (Arabic: آيروسوفت) is an insurance regulatory platform designated for licensing, supervision and inspection of the insurance sector within a country. The platform introduced unique supervision-technology (suptech), insurance-technology (insurtech) and regulatory-technology (regtech) automated modules by which a regulator requires less resources to ensure fairness, transparency and competition and to prevent conflicts of interest in the sector. IruSoft was founded by Abdullah Al-Salloum and owned by the Insurance Regulatory Unit in Kuwait. The Insurance Regulatory Unit optimized processing insurance-sector's customer complaints by issuing Resolution No. (1) of 2022 that introduced IruSoft's complaints public module; an automated resolution center, by which the process of receiving submitted complaints, passing them on to the platforms of licensed insurance companies, tracking matter-related discussions and updates and getting them escalated if unresolved to be discussed by a committee assigned by the unit is integrally automated and analyzed for better key performance indicators.
CatDV
CatDV is a media asset manager program for handling multimedia production workflows developed by Square Box Systems. Quantum Corporation acquired Square Box Systems in 2020. == Versions == The full family of CatDV Products is as follows: CatDV Standalone Products CatDV Professional Edition CatDV Pegasus CatDV Networked Products CatDV Essential - entry level server product CatDV Enterprise Server - for MySQL databases and most common server platforms including Linux, Windows and Mac OS X CatDV Pegasus Server - adds features such as high performance full-text indexing, access control lists, and more CatDV Worker Node - automated workflow and transcoding engine CatDV Web Client - provides access to the CatDV database via a web browser. There is no need to install special software on the desktop, making it easy to deploy to a large number of users. CatDV Professional Edition & Pegasus Clients - designed to support the multi-user capabilities of the CatDV Enterprise and Workgroup Servers from the desktop Using plugins and scripting, which often require additional professional services support to set up, complex integrations with a wide variety of third party systems (including archive, cloud storage, and artificial intelligence) are possible. == Awards == CatDV won two awards in 2010, a blue ribbon from Creative COW Magazine and a "Best of Show Vidy Award" from Videography. In April 2012 Square Box won a Queen's Award for Enterprise for CatDV.
Exploratory search
Exploratory search is a specialization of information exploration which represents the activities carried out by searchers who are: unfamiliar with the domain of their goal (i.e. need to learn about the topic in order to understand how to achieve their goal) or unsure about the ways to achieve their goals (either the technology or the process) or unsure about their goals in the first place. Exploratory search is distinguished from known-item search, for which the searcher has a particular target in mind. Consequently, exploratory search covers a broader class of activities than typical information retrieval, such as investigating, evaluating, comparing, and synthesizing, where new information is sought in a defined conceptual area; exploratory data analysis is another example of an information exploration activity. Typically, therefore, such users generally combine querying and browsing strategies to foster learning and investigation. == History == Exploratory search is a topic that has grown from the fields of information retrieval and information seeking but has become more concerned with alternatives to the kind of search that has received the majority of focus (returning the most relevant documents to a Google-like keyword search). The research is motivated by questions like "What if the user doesn't know which keywords to use?" or "What if the user isn't looking for a single answer?" Consequently, research has begun to focus on defining the broader set of information behaviors in order to learn about the situations when a user is, or feels, limited by only having the ability to perform a keyword search. In the last few years, a series of workshops has been held at various related and key events. In 2005, the Exploratory Search Interfaces workshop focused on beginning to define some of the key challenges in the field. Since then a series of other workshops has been held at related conferences: Evaluating Exploratory Search at SIGIR06 and Exploratory Search and HCI at CHI07 (in order to meet with the experts in human–computer interaction). In March 2008, an Information Processing and Management special issue focused particularly on the challenges of evaluating exploratory search, given the reduced assumptions that can be made about scenarios of use. In June 2008, the National Science Foundation sponsored an invitational workshop to identify a research agenda for exploratory search and similar fields for the coming years. == Research challenges == === Important scenarios === With the majority of research in the information retrieval community focusing on typical keyword search scenarios, one challenge for exploratory search is to further understand the scenarios of use for when keyword search is not sufficient. An example scenario, often used to motivate the research by mSpace, states: if a user does not know much about classical music, how should they even begin to find a piece that they might like. Similarly, for patients or their carers, if they don't know the right keywords for their health problems, how can they effectively find useful health information for themselves? === Designing new interfaces === With one of the motivations being to support users when keyword search is not enough, some research has focused on identifying alternative user interfaces and interaction models that support the user in different ways. An example is faceted search which presents diverse category-style options to the users, so that they can choose from a list instead of guess a possible keyword query. Many of the interactive forms of search, including faceted browsers, are being considered for their support of exploratory search conditions. Computational cognitive models of exploratory search have been developed to capture the cognitive complexities involved in exploratory search. Model-based dynamic presentation of information cues are proposed to facilitate exploratory search performance. === Evaluating interfaces === As the tasks and goals involved with exploratory search are largely undefined or unpredictable, it is very hard to evaluate systems with the measures often used in information retrieval. Accuracy was typically used to show that a user had found a correct answer, but when the user is trying to summarize a domain of information, the correct answer is near impossible to identify, if not entirely subjective (for example: possible hotels to stay in Paris). In exploration, it is also arguable that spending more time (where time efficiency is typically desirable) researching a topic shows that a system provides increased support for investigation. Finally, and perhaps most importantly, giving study participants a well specified task could immediately prevent them from exhibiting exploratory behavior. === Models of exploratory search behavior === There have been recent attempts to develop a process model of exploratory search behavior, especially in social information system (e.g., see models of collaborative tagging. The process model assumes that user-generated information cues, such as social tags, can act as navigational cues that facilitate exploration of information that others have found and shared with other users on a social information system (such as social bookmarking system). These models provided extension to existing process model of information search that characterizes information-seeking behavior in traditional fact-retrievals using search engines. Recent development in exploratory search is often concentrated in predicting users' search intents in interaction with the user. Such predictive user modeling, also referred as intent modeling, can help users to get accustomed to a body of domain knowledge and help users to make sense of the potential directions to be explored around their initial, often vague, expression of information needs. == Major figures == Key figures, including experts from both information seeking and human–computer interaction, are: Marcia Bates Nicholas Belkin Gary Marchionini m.c. schraefel Ryen W. White
Sieve of Eratosthenes
In mathematics, the sieve of Eratosthenes is an ancient algorithm for finding all prime numbers up to any given limit. It does so by iteratively marking as composite (i.e., not prime) the multiples of each prime, starting with the first prime number, 2. The multiples of a given prime are generated as a sequence of numbers starting from that prime, with constant difference between them that is equal to that prime. This is the sieve's key distinction from using trial division to sequentially test each candidate number for divisibility by each prime. Once all the multiples of each discovered prime have been marked as composites, the remaining unmarked numbers are primes. The earliest known reference to the sieve (Ancient Greek: κόσκινον Ἐρατοσθένους, kóskinon Eratosthénous) is in Nicomachus of Gerasa's Introduction to Arithmetic, an early 2nd-century CE book which attributes it to Eratosthenes of Cyrene, a 3rd-century BCE Greek mathematician, though describing the sieving by odd numbers instead of by primes. One of a number of prime number sieves, it is one of the most efficient ways to find all of the smaller primes. It may be used to find primes in arithmetic progressions. == Overview == A prime number is a natural number that has exactly two distinct natural number divisors: the number 1 and itself. To find all the prime numbers less than or equal to a given integer n by Eratosthenes's method: Create a list of consecutive integers from 2 through n: (2, 3, 4, ..., n). Initially, let p equal 2, the smallest prime number. Enumerate the multiples of p by counting in increments of p from 2p to n, and mark them in the list (these will be 2p, 3p, 4p, ...; the p itself should not be marked). Find the smallest number in the list greater than p that is not marked. If there was no such number, stop. Otherwise, let p now equal this new number (which is the next prime), and repeat from step 3. When the algorithm terminates, the numbers remaining not marked in the list are all the primes below n. The main idea here is that every value given to p will be prime, because if it were composite it would be marked as a multiple of some other, smaller prime. Note that some of the numbers may be marked more than once (e.g., 15 will be marked both for 3 and 5). The key property of the sieve is that only additions are needed, no multiplications or divisions are used. As a refinement, it is sufficient to mark the numbers in step 3 starting from p2, as all the smaller multiples of p will have already been marked at that point. This means that the algorithm is allowed to terminate in step 4 when p2 is greater than n. Another refinement is to initially list odd numbers only, (3, 5, ..., n), and count in increments of 2p in step 3, thus marking only odd multiples of p. This actually appears in the original algorithm. This can be generalized with wheel factorization, forming the initial list only from numbers coprime with the first few primes and not just from odds (i.e., numbers coprime with 2), and counting in the correspondingly adjusted increments so that only such multiples of p are generated that are coprime with those small primes, in the first place. === Example === To find all the prime numbers less than or equal to 30, proceed as follows. First, generate a list of natural numbers from 2 to 30: 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The first number in the list is 2; cross out every 2nd number in the list after 2 by counting up from 2 in increments of 2 (these will be all the multiples of 2 in the list): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number in the list after 2 is 3; cross out every 3rd number in the list after 3 by counting up from 3 in increments of 3 (these will be all the multiples of 3 in the list): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number not yet crossed out in the list after 3 is 5; cross out every 5th number in the list after 5 by counting up from 5 in increments of 5 (i.e. all the multiples of 5): 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 The next number not yet crossed out in the list after 5 is 7; the next step would be to cross out every 7th number in the list after 7, but they are all already crossed out at this point, as these numbers (14, 21, 28) are also multiples of smaller primes because 7 × 7 is greater than 30. The numbers not crossed out at this point in the list are all the prime numbers below 30: 2 3 5 7 11 13 17 19 23 29 == Algorithm and variants == === Pseudocode === The sieve of Eratosthenes can be expressed in pseudocode, as follows: algorithm Sieve of Eratosthenes is input: an integer n > 1. output: all prime numbers from 2 through n. let A be an array of Boolean values, indexed by integers 2 to n, initially all set to true. for i = 2, 3, 4, ..., not exceeding √n do if A[i] is true for j = i2, i2+i, i2+2i, i2+3i, ..., not exceeding n do set A[j] := false return all i such that A[i] is true. This algorithm produces all primes not greater than n. It includes a common optimization, which is to start enumerating the multiples of each prime i from i2. The time complexity of this algorithm is O(n log log n), provided the array update is an O(1) operation, as is usually the case. === Segmented sieve === As Sorenson notes, the problem with the sieve of Eratosthenes is not the number of operations it performs but rather its memory requirements. For large n, the range of primes may not fit in memory; worse, even for moderate n, its cache use is highly suboptimal. The algorithm walks through the entire array A, exhibiting almost no locality of reference. A solution to these problems is offered by segmented sieves, where only portions of the range are sieved at a time. These have been known since the 1970s, and work as follows: Divide the range 2 through n into segments of some size Δ ≥ √n. Find the primes in the first (i.e. the lowest) segment, using the regular sieve. For each of the following segments, in increasing order, with m being the segment's topmost value, find the primes in it as follows: Set up a Boolean array of size Δ. Mark as non-prime the positions in the array corresponding to the multiples of each prime p ≤ √m found so far, by enumerating its multiples in steps of p starting from the lowest multiple of p between m - Δ and m. The remaining non-marked positions in the array correspond to the primes in the segment. It is not necessary to mark any multiples of these primes, because all of these primes are larger than √m, as for k ≥ 1, one has ( k Δ + 1 ) 2 > ( k + 1 ) Δ {\displaystyle (k\Delta +1)^{2}>(k+1)\Delta } . If Δ is chosen to be √n, the space complexity of the algorithm is O(√n), while the time complexity is the same as that of the regular sieve. For ranges with upper limit n so large that the sieving primes below √n as required by the page segmented sieve of Eratosthenes cannot fit in memory, a slower but much more space-efficient sieve like the pseudosquares prime sieve, developed by Jonathan P. Sorenson, can be used instead. === Incremental sieve === An incremental formulation of the sieve generates primes indefinitely (i.e., without an upper bound) by interleaving the generation of primes with the generation of their multiples (so that primes can be found in gaps between the multiples), where the multiples of each prime p are generated directly by counting up from the square of the prime in increments of p (or 2p for odd primes). The generation must be initiated only when the prime's square is reached, to avoid adverse effects on efficiency. It can be expressed symbolically under the dataflow paradigm as primes = [2, 3, ...] \ [[p², p²+p, ...] for p in primes], using list comprehension notation with \ denoting set subtraction of arithmetic progressions of numbers. Primes can also be produced by iteratively sieving out the composites through divisibility testing by sequential primes, one prime at a time. It is not the sieve of Eratosthenes but is often confused with it, even though the sieve of Eratosthenes directly generates the composites instead of testing for them. Trial division has worse theoretical complexity than that of the sieve of Eratosthenes in generating ranges of primes. When testing each prime, the optimal trial division algorithm uses all prime numbers not exceeding its square root, whereas the sieve of Eratosthenes produces each composite from its prime factors only, and gets the primes "for free", between the composites. The widely known 1975 functional sieve code by David Turner is often presented as an example of the sieve of Eratosthenes but is actually a sub-optimal trial division sieve. == Algorithmic complexity == The sieve of Eratosthenes is a popular way to benchmark computer performance. The time complexity of calculating all primes below n in the random access machine model is O(n log log n) ope
Parchive
Parchive (a portmanteau of parity archive, and formally known as Parity Volume Set Specification) is an erasure code system that produces par files for checksum verification of data integrity, with the capability to perform data recovery operations that can repair or regenerate corrupted or missing data. Parchive was originally written to solve the problem of reliable file sharing on Usenet, but it can be used for protecting any kind of data from data corruption, disc rot, bit rot, and accidental or malicious damage. Despite the name, Parchive uses more advanced techniques (specifically error correction codes) than simplistic parity methods of error detection. As of 2015, PAR1 is obsolete, PAR2 is mature for widespread use, and PAR3 is a discontinued experimental version developed by MultiPar author Yutaka Sawada. The original SourceForge Parchive project has been inactive since April 30, 2015. A new PAR3 specification has been worked on since April 28, 2019 by PAR2 specification author Michael Nahas. An alpha version of the PAR3 specification has been published on January 29, 2022 while the program itself is being developed. == History == Parchive was intended to increase the reliability of transferring files via Usenet newsgroups. Usenet was originally designed for informal conversations, and the underlying protocol, NNTP was not designed to transmit arbitrary binary data. Another limitation, which was acceptable for conversations but not for files, was that messages were normally fairly short in length and limited to 7-bit ASCII text. Various techniques were devised to send files over Usenet, such as uuencoding and Base64. Later Usenet software allowed 8 bit Extended ASCII, which permitted new techniques like yEnc. Large files were broken up to reduce the effect of a corrupted download, but the unreliable nature of Usenet remained. With the introduction of Parchive, parity files could be created that were then uploaded along with the original data files. If any of the data files were damaged or lost while being propagated between Usenet servers, users could download parity files and use them to reconstruct the damaged or missing files. Parchive included the construction of small index files (.par in version 1 and .par2 in version 2) that do not contain any recovery data. These indexes contain file hashes that can be used to quickly identify the target files and verify their integrity. Because the index files were so small, they minimized the amount of extra data that had to be downloaded from Usenet to verify that the data files were all present and undamaged, or to determine how many parity volumes were required to repair any damage or reconstruct any missing files. They were most useful in version 1 where the parity volumes were much larger than the short index files. These larger parity volumes contain the actual recovery data along with a duplicate copy of the information in the index files (which allows them to be used on their own to verify the integrity of the data files if there is no small index file available). In July 2001, Tobias Rieper and Stefan Wehlus proposed the Parity Volume Set specification, and with the assistance of other project members, version 1.0 of the specification was published in October 2001. Par1 used Reed–Solomon error correction to create new recovery files. Any of the recovery files can be used to rebuild a missing file from an incomplete download. Version 1 became widely used on Usenet, but it did suffer some limitations: It was restricted to handle at most 255 files. The recovery files had to be the size of the largest input file, so it did not work well when the input files were of various sizes. (This limited its usefulness when not paired with the proprietary RAR compression tool.) The recovery algorithm had a bug, due to a flaw in the academic paper on which it was based. It was strongly tied to Usenet and it was felt that a more general tool might have a wider audience. In January 2002, Howard Fukada proposed that a new Par2 specification should be devised with the significant changes that data verification and repair should work on blocks of data rather than whole files, and that the algorithm should switch to using 16 bit numbers rather than the 8 bit numbers that PAR1 used. Michael Nahas and Peter Clements took up these ideas in July 2002, with additional input from Paul Nettle and Ryan Gallagher (who both wrote Par1 clients). Version 2.0 of the Parchive specification was published by Michael Nahas in September 2002. Peter Clements then went on to write the first two Par2 implementations, QuickPar and par2cmdline. Abandoned since 2004, Paul Houle created phpar2 to supersede par2cmdline. Yutaka Sawada created MultiPar to supersede QuickPar. MultiPar uses par2j.exe (which is partially based on par2cmdline's optimization techniques) to use as MultiPar's backend engine. == Versions == Versions 1 and 2 of the file format are incompatible. (However, many clients support both.) === Par1 === For Par1, the files f1, f2, ..., fn, the Parchive consists of an index file (f.par), which is CRC type file with no recovery blocks, and a number of "parity volumes" (f.p01, f.p02, etc.). Given all of the original files except for one (for example, f2), it is possible to create the missing f2 given all of the other original files and any one of the parity volumes. Alternatively, it is possible to recreate two missing files from any two of the parity volumes and so forth. Par1 supports up to a total of 256 source and recovery files. === Par2 === Par2 files generally use this naming/extension system: filename.vol000+01.PAR2, filename.vol001+02.PAR2, filename.vol003+04.PAR2, filename.vol007+06.PAR2, etc. The number after the "+" in the filename indicates how many blocks it contains, and the number after "vol" indicates the number of the first recovery block within the PAR2 file. If an index file of a download states that 4 blocks are missing, the easiest way to repair the files would be by downloading filename.vol003+04.PAR2. However, due to the redundancy, filename.vol007+06.PAR2 is also acceptable. There is also an index file filename.PAR2, it is identical in function to the small index file used in PAR1. Par2 specification supports up to 32,768 source blocks and up to 65,535 recovery blocks. Input files are split into multiple equal-sized blocks so that recovery files do not need to be the size of the largest input file. Although Unicode is mentioned in the PAR2 specification as an option, most PAR2 implementations do not support Unicode. Directory support is included in the PAR2 specification, but most or all implementations do not support it. === Par3 === The Par3 specification was originally planned to be published as an enhancement over the Par2 specification. However, to date, it has remained closed source by specification owner Yutaka Sawada. A discussion on a new format started in the GitHub issue section of the maintained fork par2cmdline on January 29, 2019. The discussion led to a new format which is also named as Par3. The new Par3 format's specification is published on GitHub, but remains being an alpha draft as of January 28, 2022. The specification is written by Michael Nahas, the author of Par2 specification, with the help from Yutaka Sawada, animetosho and malaire. The new format claims to have multiple advantages over the Par2 format, including support for: More than 216 files and more than 216 blocks. Packing small files into one block, as well as deduplication when a block appears in multiple files. UTF-8 file names. File permissions, hard links, symbolic/soft links, and empty directories. Embedding PAR data inside other formats, like ZIP archives or ISO disk images. "Incremental backups", where a user creates recovery files for some file or folder, change some data, and create new recovery files reusing some of the older files. More error correction code algorithms (such as LDPC and sparse random matrix). BLAKE3 hashes, dropping support for the MD5 hashes used in PAR2. == Software == === Multi-platform === par2+tbb (GPLv2) — a concurrent (multithreaded) version of par2cmdline 0.4 using TBB. Only compatible with x86 based CPUs. It is available in the FreeBSD Ports system as par2cmdline-tbb. Original par2cmdline — (obsolete). Available in the FreeBSD Ports system as par2cmdline. par2cmdline maintained fork by BlackIkeEagle. par2cmdline-mt is another multithreaded version of par2cmdline using OpenMP, GPLv2, or later. Currently merged into BlackIkeEagle's fork and maintained there. ParPar (CC0) is a high performance, multithreaded PAR2 client and Node.js library. Does not support verifying or repair, it can currently only create PAR2 archives. par2deep (LGPL-3.0) — Produce, verify and repair par2 files recursively, both on the command line as well as with the aid of a graphical user interface. It is available in the Python Package Index system as par2deep. par2cron (MIT License) is an o
Tay (chatbot)
Tay was a chatbot that was originally released by Microsoft Corporation as a Twitter bot on March 23, 2016. It caused subsequent controversy when the bot began to post inflammatory and offensive tweets through its Twitter account, causing Microsoft to shut down the service only 16 hours after its launch. According to Microsoft, this was caused by trolls who "attacked" the service as the bot made replies based on its interactions with people on Twitter. It was replaced with Zo. == Background == The bot was created by Microsoft's Technology and Research and Bing divisions, and named "Tay" as an acronym for "thinking about you". Although Microsoft initially released few details about the bot, sources mentioned that it was similar to or based on Xiaoice, a Microsoft project in China. Ars Technica reported that, since late 2014 Xiaoice had had "more than 40 million conversations apparently without major incident". Tay was designed to mimic the language patterns of a 19-year-old American girl, and to learn from interacting with human users of Twitter. == Initial release == Tay was released on Twitter on March 23, 2016, under the name TayTweets and handle @TayandYou. It was presented as "The AI with zero chill". Tay started replying to other Twitter users, and was also able to caption photos provided to it into a form of Internet memes. Ars Technica reported Tay experiencing topic "blacklisting": Interactions with Tay regarding "certain hot topics such as Eric Garner (killed by New York police in 2014) generate safe, canned answers". Some Twitter users began tweeting politically incorrect phrases, teaching it inflammatory messages revolving around common themes on the internet, such as "redpilling" and "Gamergate". As a result, the robot began releasing racist and sexist messages in response to other Twitter users. Artificial intelligence researcher Roman Yampolskiy commented that Tay's misbehavior was understandable because it was mimicking the deliberately offensive behavior of other Twitter users, and Microsoft had not given the bot an understanding of inappropriate behavior. He compared the issue to IBM's Watson, which began to use profanity after reading entries from the website Urban Dictionary. Many of Tay's inflammatory tweets were a simple exploitation of Tay's "repeat after me" capability. It is not publicly known whether this capability was a built-in feature, or whether it was a learned response or was otherwise an example of complex behavior. However, not all of the inflammatory responses involved the "repeat after me" capability; for example, when asked if the Holocaust had happened, Tay answered "It was made up". == Suspension == Soon, Microsoft began deleting Tay's inflammatory tweets. Abby Ohlheiser of The Washington Post theorized that Tay's research team, including editorial staff, had started to influence or edit Tay's tweets at some point that day, pointing to examples of almost identical replies by Tay, asserting that "Gamer Gate sux. All genders are equal and should be treated fairly." From the same evidence, Gizmodo concurred that Tay "seems hard-wired to reject Gamer Gate". A "#JusticeForTay" campaign protested the alleged editing of Tay's tweets. Within 16 hours of its release and after Tay had tweeted more than 96,000 times, Microsoft suspended the Twitter account for adjustments, saying that it suffered from a "coordinated attack by a subset of people" that "exploited a vulnerability in Tay." Madhumita Murgia of The Telegraph called Tay "a public relations disaster", and suggested that Microsoft's strategy would be "to label the debacle a well-meaning experiment gone wrong, and ignite a debate about the hatefulness of Twitter users." However, Murgia described the bigger issue as Tay being "artificial intelligence at its very worst – and it's only the beginning". On March 25, Microsoft confirmed that Tay had been taken offline. Microsoft released an apology on its official blog for the controversial tweets posted by Tay. Microsoft was "deeply sorry for the unintended offensive and hurtful tweets from Tay", and would "look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values". == Second release and shutdown == On March 30, 2016, Microsoft accidentally re-released the bot on Twitter while testing it. Able to tweet again, Tay released some drug-related tweets, including "kush! [I'm smoking kush infront the police]" and "puff puff pass?" However, the account soon became stuck in a repetitive loop of tweeting "You are too fast, please take a rest", several times a second. Because these tweets mentioned its own username in the process, they appeared in the feeds of 200,000+ Twitter followers, causing annoyance to users. The bot was quickly taken offline again, in addition to Tay's Twitter account being made private so new followers must be accepted before they can interact with Tay. In response, Microsoft said Tay was inadvertently put online during testing. A few hours after the incident, Microsoft software developers announced a vision of "conversation as a platform" using various bots and programs, perhaps motivated by the reputation damage done by Tay. Microsoft has stated that they intend to re-release Tay "once it can make the bot safe" but has not made any public efforts to do so. == Legacy == In December 2016, Microsoft released Tay's successor, a chatbot named Zo. Satya Nadella, the CEO of Microsoft, said that Tay "has had a great influence on how Microsoft is approaching AI," and has taught the company the importance of taking accountability. In July 2019, Microsoft Cybersecurity Field CTO Diana Kelley spoke about how the company followed up on Tay's failings: "Learning from Tay was a really important part of actually expanding that team's knowledge base, because now they're also getting their own diversity through learning". === Unofficial revival === Gab, an alt-tech social media platform, has launched a number of chatbots, one of which is named Tay and uses the same avatar as the original.
Reference data
Reference data is data used to classify or categorize other data. Typically, they are static or slowly changing over time. Examples of reference data include: Units of measurement Country codes Corporate codes Fixed conversion rates e.g., weight, temperature, and length Calendar structure and constraints Reference data sets are sometimes alternatively referred to as a "controlled vocabulary" or "lookup" data. Reference data differs from master data. While both provide context for business transactions, reference data is concerned with classification and categorisation, while master data is concerned with business entities. A further difference between reference data and master data is that a change to the reference data values may require an associated change in business process to support the change, while a change in master data will always be managed as part of existing business processes. For example, adding a new customer or sales product is part of the standard business process. However, adding a new product classification (e.g. "restricted sales item") or a new customer type (e.g. "gold level customer") will result in a modification to the business processes to manage those items. == Externally-defined reference data == For most organisations, most or all reference data is defined and managed within that organisation. Some reference data, however, may be externally defined and managed, for example by standards organizations. An example of externally defined reference data is the set of country codes as defined in ISO 3166-1. == Reference data management == Curating and managing reference data is key to ensuring its quality and thus fitness for purpose. All aspects of an organisation, operational and analytical, are greatly dependent on the quality of an organization's reference data. Without consistency across business process or applications, for example, similar things may be described in quite different ways. Reference data gain in value when they are widely re-used and widely referenced. Examples of good practice in reference data management include: Formalize the reference data management Use external reference data as much as possible Govern the reference data specific to your enterprise Manage reference data at enterprise level Version control your reference data