Transaction data or transaction information is a category of data describing transactions. Transaction data/information gather variables generally referring to reference data or master data – e.g. dates, times, time zones, currencies. Typical transactions are: Financial transactions about orders, invoices, payments; Work transactions about plans, activity records; Logistic transactions about deliveries, storage records, travel records, etc.. == Management == Recording and storing transactions is called records management. The record of the transaction is stored in a place where the retention can be guaranteed and where data is archived or removed following a retention period. Formats of recorded transactions can be digital data in databases and spreadsheets, or handwritten texts in physical documents like former bankbooks. Transaction processing systems are application software that generate transactions and manage transaction data/information, e.g. SAP and Oracle Financials. == Data warehousing == Transaction data can be summarised in a data warehouse, which helps accessibility and analysis of the data.
17LIVE
17LIVE is an international entertainment platform. As of 2024, 17LIVE is the #3 live broadcasting platform globally, formed by its flagship live stream app 17LIVE (LIVIT in English markets), MEME Live and live stream e-commerce platforms HandsUP and OrderPally. == History == 17LIVE was first founded in Taiwan in 2015 by Jeffery Huang. The company has maintained its leading position since its entry into the Japan market in 2017, becoming the biggest platform for live entertainment in Japan, Taiwan, Hong Kong, and other countries. In 2017, 17 closed out US$33M in series B round to merge with dating software Paktor, with Joseph Phua (Co-founder of Paktor) taking over the leadership of 17LIVE as CEO and Co-founder, as well as to enter the Japan and Hong Kong market. Within one year, 17 Media became the #1 market leader in Japan. In 2018, the company raised $25M in series C round as it got ready for US IPO, which failed to materialize. 17LIVE had an unsuccessful US IPO attempt in 2018. Since then, the company reformed and transformed the business. Some key initiatives include the hiring of current CEO Hirofumi Ono, spin-off of Paktor (dating software business unit), full buy-out of founder Jeffery Huang, acquisition of MEME and HandsUp, and more. Despite the failed IPO attempt, the company continued to push for international expansion, including creating ‘LIVIT’ for the English-speaking markets to enter US, India, and North Africa. In 2019, 17's flagship live streaming app reached 10M downloads in Japan, and the business continues to push for both organic and inorganic expansion. Some key M&A highlights in the year include the acquisition of MEME Live in Southeast Asia, as well as HandsUp, a live e-commerce platform. In 2020, M17 closed out $26.5M in Series D round to continue organic growth in Japan, US and Middle East. In the same year, the company also sold its dating app business, Parktor, to rationalise M17 into a live-stream pure play business, followed by the appointment of its current Chairman, Joseph Phua, and previous Global CEO, Hirofumi Ono. With the buy-out and departure of founder Jeff Huang, the parent holding company M17 Entertainment Limited was officially renamed as 17 LIVE Group. An estimated 60 million users registered in 154 countries and territories in April 2022. In 2022, September, 17LIVE announced Group CEO Hirofumi Ono steps down. Alex Lien takes over the leadership as new Group COO; Jing Shen Ng appointed Group CTO. In 2023, March, 17LIVE announced Alex Lien promoted to Global CEO. Kenta Masuda appointed as Global CFO. === Collaboration with Ayumi Hamasaki === To celebrate its 4th anniversary, 17LIVE collaborated with Japanese singer-songwriter Ayumi Hamasaki, who led the 17LIVE 4th Anniversary meets Ayumi Hamasaki series starting October 18, 2021. Along with composer and arranger Yuta Nakano, Hamasaki judged auditioning artists competing for the chance to work with her and her production team for a debut single. The series was streamed live on the 17LIVE website, the final airing on November 11. The eventual winner was named as Yoshitaka_song. When asked why she collaborated with 17LIVE as a producer, Hamasaki commented: "Although the world has become like this (during COVID-19), I believe that the art of entertainment can give people dreams, hope, courage, and strength. I hope that kind of light will continue to shine through the entertainment industry." == Features == On 17LIVE, artists (LIVERs) are able to broadcast live, and post photos and videos from their album. The app has been designed for LIVERs to simply open the App, and start sharing contents without the need to edit or professionally curate their videos. The platform cultivates LIVERs, supports them with a local content management team, and provides artists with various functions, such as real time chatting, gifting, fan clubs, interactive competition and events. Today, 17LIVE has 46 thousands contracted artists and more than 2.3 million MAU, who spend 44 minutes on the platform every day. 17LIVE continues to advocate content-driven philosophy and delivers diverse topics, from politics and music to entertainment, to broaden its audience groups. 17LIVE also hosts offline flash events and concerts to attract new users and support LIVERs better connect with their fans. == Operation == 17LIVE has over 700 employees globally. The app provides few monetization models for LIVERs on the platform, including: Gifting: user / fans buy virtual gifts on the app to send to their favored LIVERs. Subscription: monthly subscription fan club service for access to exclusive content Pay-per-view: ticket service for online streaming concerts E-commerce: live e-commerce platform In the past, 17LIVE has encountered some regulatory headwinds with reported incidents of inappropriate livestream content on the platform. The incidents were direct results of the lack of oversight and supervision capability in place in the business at the time. Over the years, 17LIVE claims to have put in tremendous manpower and effort into improving, monitoring and maintaining control over both the live stream content and the KYC procedures and systems.
Computer game bot Turing test
The computer game bot Turing test is a variant of the Turing test, where a human judge viewing and interacting with a virtual world must distinguish between other humans and video game bots, both interacting with the same virtual world. This variant was first proposed in 2008 by Associate Professor Philip Hingston of Edith Cowan University, and implemented through a tournament called the 2K BotPrize. == History == The computer game bot Turing test was proposed to advance the fields of artificial intelligence (AI) and computational intelligence with respect to video games. It was considered that a poorly implemented bot implied a subpar game, so a bot that would be capable of passing this test, and therefore might be indistinguishable from a human player, would directly improve the quality of a game. It also served to debunk a flawed notion that "game AI is a solved problem." Emphasis is placed on a game bot that interacts with other players in a multiplayer environment. Unlike a bot that simply needs to make optimal human-like decisions to play or beat a game, this bot must make the same decisions while also convincing another in-game player of its human-likeness. == Implementation == The computer game bot Turing test was designed to test a bot's ability to interact with a game environment in comparison with a human player; simply 'winning' was insufficient. This evolved into a contest with a few important goals in mind: There are three participants: a human player, a computer-game bot, and a judge. The bot needs to appear more human-like than the human player. Judge scores are not bipolar — both human and bot can be scored anywhere on a scale from 1 to 5 (1=not humanlike, 5=human). All three participants are to be indistinguishable in the arena, with the exception of a randomly generated name tag, so as to reduce the chance of random elements such as name or appearance influencing the judges. Chat is disabled throughout the match. Bots were not given omniscient powers as they may be in other games. Bots must react only to the data that might be reasonably available to a human player. Human participants were of a moderate skill range, with no participant either ignorant to the game or capable of playing at a professional level. In 2008, the first 2K BotPrize tournament took place. The contest was held with the game Unreal Tournament 2004 as the platform. Contestants created their bots in advance using the GameBots interface. GameBots had some modifications made so as to adhere to the above conditions, such as removing data about vantage points or weapon damage that unfairly informed the bots of relevant strengths/weaknesses that a human would otherwise need to learn. == Tournament == The first BotPrize Tournament was held on 17 December 2008, as part of the 2008 IEEE Symposium on Computational Intelligence and Games in Australia. Each competing team was given time to set up and adjust their bots to the modified game client, although no coding changes were allowed at that point. The tournament was run in rounds, each a 10-minute death match. Judges were the last to join the server and every judge observed every player and every bot exactly once, although the pairing of players and bots did change. When the tournament ended, no bot was rated as more human than any player. In subsequent tournaments, run during 2009–2011, bots achieved scores that were increasingly human-like, but no contestant had won the BotPrize in any of these contests. In 2012, the 2K BotPrize was held once again, and two teams programmed bots that achieved scores greater than those of human players. == Successful bots == To date, there have been two successfully programmed bots that passed the computer game bot Turing test: UT^2, a team from the University of Texas at Austin, emphasized a bot that adjusted its behaviour based on previously observed human behaviour and neuroevolution. The team has made their bot available, although a copy of Unreal Tournament 2004 is required. Mihai Polceanu, a doctoral student from Romania, focused on creating a bot that would mimic opponent reactions, in a sense 'borrowing' the human-like nature of the opponent. These victors succeeded in the year 2012, Alan Turing's centenary year. == Aftermath == The outcome of a bot that appears more human-like than a human player is possibly overstated, since in the tournament in which the bots succeeded, the average 'humanness' rating of the human players was only 41.4%. This showcases some limits of this Turing test, since the results demonstrate that human behaviour is more complicated and quantitative than was accounted for. In light of this, the BotPrize competition organizers will increase the difficulty in upcoming years with new challenges, forcing competitors to improve their bots. It is also believed that methods and techniques developed for the computer game bot Turing test will be useful in fields other than video games, such as virtual training environments and in improving Human–robot interaction. == Contrasts to the Turing test == The computer game bot Turing test differs from the traditional or generic Turing test in a number of ways: Unlike the traditional Turing test, for example the Chatterbot-style contest held annually by the Loebner Prize competition, the humans who played against the Computer Game Bots are not trying to convince judges they are the human; rather, they want to win the game (i.e., by achieving the highest kill score). Judges are not restricted to awarding only one participant in a match as the 'human' and the other as the 'non-human.' This emphasizes more qualitative rather than polarized findings. With regards to a successful video game bot, this is not to be confused with a claim that the bot is 'intelligent,' whereas a machine that 'passed' the Turing test would arguably have some evidence for its Chatterbot's 'intelligence.' The game Unreal Tournament 2004 was chosen for its commercial availability and its interface for creating bots, GameBots. This limitation on medium is a sharp contrast to the Turing test, which emphasizes a conversation, where possible questions are vastly more numerous than the set of possible actions available in any specific video game. The available information to the participants, humans and bots, is not equal. Humans interact through vision and sound, whereas bots interact with data and events. The judges cannot introduce new events (e.g., a lava pit) to aid in differentiating between human and bot, whereas in a Chatterbot designed system, judges may theoretically ask any question in any manner. The two participants and the judge take part in a three-way interaction, unlike, for example, the paired two-way interaction of the Loebner Prize Contest.
Computer Power and Human Reason
Computer Power and Human Reason: From Judgment to Calculation is a 1976 nonfiction book by German-American computer scientist Joseph Weizenbaum in which he contends that while artificial intelligence may be possible, we should never allow computers to make important decisions, as they will always lack human qualities such as compassion and wisdom. == Background == Before writing Computer Power and Human Reason, Weizenbaum had garnered significant attention for creating the ELIZA program, an early milestone in conversational computing. His firsthand observation of people attributing human-like qualities to a simple program prompted him to reflect more deeply on society's readiness to entrust moral and ethical considerations to machines. == Reception and legacy == Computer Power and Human Reason sparked scholarly debate on the acceptable scope of AI applications, particularly in fields where human welfare and ethical considerations are paramount. Early academic reviews highlighted that Weizenbaum's stance pushed readers to recognize that even as computers grow more capable, they lack the intrinsic moral compass and empathy required for certain kinds of judgment. The book caused disagreement with, and separation from, other members of the artificial intelligence research community, a status the author later said he'd come to take pride in.
Cortica
Headquartered in Tel Aviv Cortica utilizes unsupervised learning methods to recognize and analyze digital images and video. The technology developed by the Cortica team is based on research of the function of the human brain. == Company Founding == Cortica was founded in 2007 by Igal Raichelgauz, Karina Odinaev and Yehoshua Zeevi. Together, the founders developed the company’s core technology while at Technion – Israel Institute of Technology. By combining discoveries in neuroscience with developments in computer programming, the team created technology that possesses the ability to interpret large amounts of visual data with increased accuracy. This technology, called Image2Text, is based on the founders’ work in digitally replicating cortical neural networks’ ability to identify complex patterns within massive quantities of ambiguous and noisy data. Cortica’s offerings have application in the automotive industry, media industries, as well as the smart city and medical industries. Industry experts suggest that the self-driving automotive industry alone will be worth upwards of $7 trillion while each connected car is expected to generate 4,000 GB of data per day. Beyond that, industry analysts expect the proliferation of surveillance cameras to continue leading to an expected 2,500 Petabytes of data being generated daily by new surveillance cameras. Cortica operates in these high scale industries. The company currently employs professionals from many domains including AI researchers as well as veterans of intelligence units within the Israeli Defense Forces. == Research and Technology == In 2006, Founders Raichelgauz, Odinaev, and Zeevi shared their findings with the 28th IEEE EMBS Annual International Conference in New York in a paper titled, “Natural Signal Classification by Neural Cliques and Phase-Locked Attractors”. That same year, the team also published “Cliques in Neural Ensembles as Perception Carriers" CB Insights recently identified Cortica as the number one patent holder among AI companies. Cortica is researching to develop a machine-learning driving system which can identify objects and pedestrians. Connecting to it, Elon Musk has been rumored to partner with Cortica for his electric car company, Tesla. However, Tesla denies it stating that Musk did not discuss a collaboration with artificial intelligence firm Cortica. == Funding == Cortica raised $7 million in its Series A funding round, announced in August 2012. Investors included Horizons Ventures (the investment firm of Hong Kong billionaire Li Ka-Shing), and Ynon Kreiz, the former chairman and CEO of the Endemol Group. In May 2013, it was announced that Cortica had raised $1.5 million from Russian firm Mail.ru Group. It later transpired that this was a part of Cortica's Series B funding round for $6.4 million, announced in June 2013. The round was led by Horizons Ventures, with participation from the Russian firm Mail.ru Group and other angel investors. In its fourth funding round, Cortica has raised $20 million, bringing the total investments to $38 million. According to a report from The Israeli lead Daily economic newspaper, TheMarker, the fourth round was led by a strategic Chinese investor who will probably help the company expand into the Asian market. == Media coverage == GigaOm listed Cortica as one of the top deep learning startups in a November 2013 article surveying the field, along with AlchemyAPI, Ersatz, and Semantria. Business Insider ranked Cortica as one of the coolest tech companies in Israel. CB Insights has identified Cortica as the top patent holding AI company. In 2017 several leading automotive media outlets covered the launch of Cortica's automotive business unit
Couch to 5K
Couch to 5K, abbreviated C25K, is an exercise plan that gradually progresses from beginner running toward a 5 kilometre (3.1 mile) run over nine weeks. == Operations == The Couch to 5K running plan, also known as C25K, created by Josh Clark in 1996, was developed with the expectation of creating a plan for new runners to start running. The plan is aimed to have users work out for 20 to 30 minutes, three days a week. Within the program, users can be expected to perform different tasks such as intervals of running with period of short walks in between to help build endurance in the weeks up to the final goal of a 5K run. During the nine weeks leading up to the race, the runner will learn to set their own pace and where their strengths and weaknesses are within running. Often, the daily workouts start with a five-minute warm-up walk and works up to running five kilometres without a walking break within nine weeks. Users are not expected to have any experience in running and can be some of the first running that they ever do. The main goal is to turn that unexperienced runner into someone who can run a 5K. Clark started the website Kick and featured C25K on the site. In 2001, Kick merged with Cool Running, a New England–based running site. Clark later sold his stake in Cool Running and the Couch to 5K program. Cool Running was absorbed into Active.com, operated by Active Network, LLC. Active Network provides mobile apps for Couch to 5K, as well as 5K to 10K, a follow-up program. The NHS in the UK provides downloadable podcasts and a smartphone app (Android and iOS) for the plan. A mobile app, created by Zen Labs, has training plans that are based on the Couch to 5K running plan from CoolRunning.com. It is one of the highest-rated health and fitness apps available on Android and iOS. As of 2016, the C25K app has been used by over 5 million people.
Artificial intelligence in Wikimedia projects
Some editors of Wikimedia projects use artificial intelligence (AI) and machine learning programs to edit existing articles or create new ones. Some applications of artificial intelligence, like using large language models (LLMs) to create new articles from scratch, have been more controversial than others for the Wikipedia community. In August 2025, English Wikipedia adopted a policy that allowed editors to nominate suspected LLM-generated articles for speedy deletion. This was followed by a March 2026 decision to prohibit the use of LLMs to generate or rewrite article content, with exceptions for copyediting one's own writing and machine translation from another language's Wikipedia. Wikipedia has also been a significant source of training data for some of the earliest artificial intelligence projects. This has received mixed reactions including concern about companies not citing Wikipedia when relying on it to answer a question as well as Wikipedia's increased costs from data scraping. == AI usage == === Earliest use of automated tools, machine learning and AI === Since 2002, bots have been allowed to run on Wikipedia but must be approved and supervised by a human. A bot created in 2002, rambot, transformed census data into short new articles about towns in the United States; the vast majority of town, city, and county articles were started by it. Fighting vandalism has been a major focus of machine learning and AI bots and tools. The 2007 ClueBot relied on simple heuristics to identify likely vandalism, while its 2010 successor, ClueBot NG, uses machine learning through an artificial neural network. Machine translation software has also been used by Wikimedia contributors for a number of years. Aaron Halfaker's Objective Revision Evaluation Service (ORES) project was launched in late 2015 as an artificial intelligence service for grading the quality of Wikipedia edits. === Generative AI and LLMs === In 2022, the public release of ChatGPT inspired more experimentation with AI and writing Wikipedia articles. A debate was sparked about whether and to what extent such large language models are suitable for such purposes in light of their tendency to generate plausible-sounding misinformation, including fake references; to generate prose that is not encyclopedic in tone; and to reproduce biases. An early experiment on December 6, 2022 by a Wikipedia contributor named Pharos occurred when he created the article "Artwork title" using ChatGPT for the initial draft. Another editor who experimented with this early version of ChatGPT said that ChatGPT's overview of "Weaponized incompetence" was decent, but that the citations were fabricated. Since 2023, work has been done to draft an English Wikipedia policy regarding ChatGPT and similar LLMs, at times recommending that users who are unfamiliar with LLMs should avoid using them due to the aforementioned risks, as well as noting the potential for libel or copyright infringement. In early 2023, the Wiki Education Foundation reported that some experienced editors found AI to be useful in starting drafts or creating new articles. It said that ChatGPT "knows" what Wikipedia articles look like and can easily generate one that is written in the style of Wikipedia, but warned that ChatGPT had a tendency to use promotional language, among other issues. In 2023, a ban on AI was deemed "too harsh" by the community given the productivity benefits it offered editors. In 2023, members of the English Wikipedia community created a WikiProject named AI Cleanup to assist in the removal of poor quality AI content from Wikipedia. Miguel García, a former Wikimedia member from Spain, said in 2024 that when ChatGPT was originally launched, the number of AI-generated articles on the site peaked. He added that the rate of AI articles has now stabilized due to the community's efforts to combat it. He said that majority of the articles that have no sources are deleted instantly or are nominated for deletion. In October 2024, a study by Princeton University found that about 5% of 3,000 newly created articles (created in August 2024) on English Wikipedia were created using AI. The study said that some of the AI articles were on innocuous topics and that AI had likely only been used to assist in writing. For some other articles, AI had been used to promote businesses or political interests. In October 2024, Ilyas Lebleu, founder of WikiProject AI Cleanup, said that they and their fellow editors noticed a pattern of unnatural writing that could be connected to ChatGPT. They added that AI is able to mass-produce content that sounds real while being completely fake, leading to the creation of hoax articles on Wikipedia that they were tasked to delete. In June 2025, the Wikimedia Foundation started testing a "Simple Article Summaries" feature which would provide AI-generated summaries of Wikipedia articles, similar to Google Search's AI Overviews. The decision was met with immediate and harsh criticism from some Wikipedia editors, who called the feature a "ghastly idea" and a "PR hype stunt." They criticized a perceived loss of trust in the site due to AI's tendency to hallucinate and questioned the necessity of the feature. The criticism led the Wikimedia Foundation to halt the rollout of Simple Article Summaries that same month while still expressing interest in integrating generative AI more into Wikipedia. The project hints at tensions within the community and with the Foundation over when to use AI.In August 2025, the English Wikipedia community created a policy that allowed users to nominate suspected AI-generated articles for speedy deletion. Editors might recognize AI-generated articles because they use citations that are not related to the subject of the article or fabricated citations or the wording has particular quirks. If an article uses language that reads like an LLM response to a user, such as "Here is your Wikipedia article on" or "Up to my last training update", the article is typically tagged for speedy deletion. Other signs of AI use include excessive use of em dashes, overuse of the word "moreover", promotional material in articles that describes something as "breathtaking" and formatting issues like using curly quotation marks instead of straight versions. During the discussion on implementing the speedy deletion policy, one user, who is an article reviewer, said that he is "flooded non-stop with horrendous drafts" created using AI. Other users said that AI articles have a large amount of "lies and fake references" and that it takes a significant amount of time to fix the issues. English Wikipedia created a guide on how to spot signs of AI-generated writing in August 2025, titled "Signs of AI writing". In January 2026, the Wiki Education Foundation continued to caution against copying and pasting outputs from generative AI into Wikipedia and to avoid it for creating new articles explaining that the text often failed verification with the sources provided. The foundation created a training module that encourages editors to use AI for identifying gaps in articles, finding access to sources and finding relevant sources. In March 2026, the English Wikipedia community prohibited the use of AI to add content to articles, with exceptions for copy editing and machine translation from another language's Wikipedia. The English Wikipedia community holds the position that LLMs often violate core content policies. == Using Wikipedia for artificial intelligence == A 2017 paper described Wikipedia as the mother lode for human-generated text available for machine learning. In the development of the Google's Perspective API that identifies toxic comments in online forums, a dataset containing hundreds of thousands of Wikipedia talk page comments with human-labelled toxicity levels was used. As of 2023, subsets of the Wikipedia corpus were considered one of the largest well-curated data sets available for AI training, used to train every LLM to-date according to Stephen Harrison. This use of Wikipedia was divisive as of 2023. The Wikimedia Foundation and many of its projects supporters worry that attribution to Wikipedia articles is missing in many large-language models like ChatGPT (as well as AI like Siri and Alexa). While Wikipedia's licensing policy lets anyone use its texts, including in modified forms, it does have the condition that credit is given, implying that using its contents in answers by AI models without clarifying the sourcing may violate its terms of use. The Foundation expressed concern that without attribution, people will not visit the site as much or be as motivated to donate to support the project if they do not know when they are benefiting from it. They also noticed an 8% decrease in visitors to Wikipedia in 2025 which they attributed both to the increased popularity of generative AI and social media. In 2025, the Wikimedia Foundation has cited absorbing increased costs associated with scra