AI Grammar Solver

AI Grammar Solver — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Chatbot

    Chatbot

    A chatbot (originally chatterbot) is a software application or web interface designed to converse through text or speech. Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner. Such chatbots often use deep learning and natural language processing. Simpler chatbots have existed for decades. Chatbots have gained popularity during the AI boom of the 2020s, with the releases of generative AI chatbots such as ChatGPT, Gemini, Claude, and Grok. These chatbots typically use fine-tuned large language models to generate text. A major area where chatbots have long been used is customer service and support, with various sorts of virtual assistants. == History == === Turing test === In 1950, Alan Turing published an article entitled "Computing Machinery and Intelligence" in which he proposed what is now called the Turing test as a criterion of intelligence. This criterion depends on the ability of a computer program to impersonate a human in a real-time written conversation with a human judge, to the extent that the judge is incapable of reliably distinguishing, on the basis of the conversational content alone, between the program and a real human. === Early chatbots === Joseph Weizenbaum's program ELIZA was first published in 1966. Weizenbaum did not claim that ELIZA was genuinely intelligent, and the introduction to his paper presented it more as a debunking exercise:In artificial intelligence, machines are made to behave in wondrous ways, often sufficient to dazzle even the most experienced observer. But once a particular program is unmasked, once its inner workings are explained, its magic crumbles away; it stands revealed as a mere collection of procedures. The observer says to himself "I could have written that". With that thought, he moves the program in question from the shelf marked "intelligent", to that reserved for curios. The object of this paper is to cause just such a re-evaluation of the program about to be "explained". Few programs ever needed it more. ELIZA's key method of operation involves the recognition of clue words or phrases in the input, and the output of the corresponding pre-prepared or pre-programmed responses that can move the conversation forward in an apparently meaningful way (e.g. by responding to any input that contains the word 'MOTHER' with 'TELL ME MORE ABOUT YOUR FAMILY'). Thus an illusion of understanding is generated, even though the processing involved has been merely superficial. ELIZA showed that such an illusion is surprisingly easy to generate because human judges are ready to give the benefit of the doubt when conversational responses are capable of being interpreted as "intelligent". Following ELIZA, psychiatrist Kenneth Colby developed PARRY in 1972. From 1978 to some time after 1983, the CYRUS project led by Janet Kolodner constructed a chatbot simulating Cyrus Vance (57th United States Secretary of State). It used case-based reasoning, and updated its database daily by parsing wire news from United Press International. The program was unable to process the news items subsequent to the surprise resignation of Cyrus Vance in April 1980, and the team constructed another chatbot simulating his successor, Edmund Muskie. In 1984, an interactive version of the program Racter was released which acted as a chatbot. A.L.I.C.E. was released in 1995. This uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so-called, Alicebots. A.L.I.C.E. is a weak AI without any reasoning capabilities. It is based on a similar pattern matching technique as ELIZA in 1966. This is not strong AI, which would require sapience and logical reasoning abilities. Jabberwacky, released in 1997, learns new responses and context based on real-time user interactions, rather than being driven from a static database. Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are the Loebner Prize and The Chatterbox Challenge (the latter has been offline since 2015, however, materials can still be found from web archives). Pre-dating the current generation of large language models, Gavagai, a Swedish language technology startup, created a Twitter-based bot in 2015 and DBpedia created a chatbot during the 2017 Google Summer of Code that communicated through Facebook Messenger. === Modern chatbots based on large language models === Modern chatbots like ChatGPT are often based on foundational large language models called generative pre-trained transformers (GPT). They are based on a deep learning architecture called the transformer, which contains artificial neural networks. They generate text after being trained on a large text corpus, and have emergent abilities that they are not specifically trained for. Chatbots integrated into apps and websites can call image-generation models or search the web. Some platforms also enable users to interact with conversational interfaces directly through web-based chat environments, allowing real-time assistance, content generation, and task automation without requiring software installation. == Application == === Messaging apps === Many companies' chatbots run on messaging apps or simply via SMS. They are used for B2C customer service, sales and marketing. In 2016, Facebook Messenger allowed developers to place chatbots on their platform. There were 30,000 bots created for Messenger in the first six months, rising to 100,000 by September 2017. Since September 2017, this has also been as part of a pilot program on WhatsApp. Airlines KLM and Aeroméxico both announced their participation in the testing; both airlines had previously launched customer services on the Facebook Messenger platform. The bots usually appear as one of the user's contacts, but can sometimes act as participants in a group chat. Many banks, insurers, media companies, e-commerce companies, airlines, hotel chains, retailers, health care providers, government entities, and restaurant chains have used chatbots to answer simple questions, increase customer engagement, for promotion, and to offer additional ways to order from them. Chatbots are also used in market research to collect short survey responses. A 2017 study showed 4% of companies used chatbots. In a 2016 study, 80% of businesses said they intended to have one by 2020. ==== As part of company apps and websites ==== Previous generations of chatbots were present on company websites, e.g. Ask Jenn from Alaska Airlines which debuted in 2008 or Expedia's virtual customer service agent which launched in 2011. The newer generation of chatbots includes IBM Watson-powered "Rocky", introduced in February 2017 by the New York City-based e-commerce company Rare Carat to provide information to prospective diamond buyers. ==== Chatbot sequences ==== Used by marketers to script sequences of messages, very similar to an autoresponder sequence. Such sequences can be triggered by user opt-in or the use of keywords within user interactions. After a trigger occurs a sequence of messages is delivered until the next anticipated user response. Each user response is used in the decision tree to help the chatbot navigate the response sequences to deliver the correct response message. === Company internal platforms === Companies have used chatbots for customer support, human resources, or in Internet-of-Things (IoT) projects. Overstock.com, for one, has reportedly launched a chatbot named Mila to attempt to automate certain processes when customer service employees request sick leave. Other large companies such as Lloyds Banking Group, Royal Bank of Scotland, Renault and Citroën are now using chatbots instead of call centres with humans to provide a first point of contact. In large companies, like in hospitals and aviation organizations, chatbots are also used to share information within organizations, and to assist and replace service desks. === Customer service === Chatbots have been proposed as a replacement for customer service departments. In 2026, The Financial Times reported on agentic chatbots that could do shopping for customers once given instructions. In 2016, Russia-based Tochka Bank launched a chatbot on Facebook for a range of financial services, including a possibility of making payments. In July 2016, Barclays Africa also launched a Facebook chatbot. === Healthcare === Chatbots are also appearing in the healthcare industry. A study suggested that physicians in the United States believed that chatbots would be most beneficial for scheduling doctor appointments, locating health clinics, or providing medication information. A 2025 review found that participants often rated chatbot responses as more empathic than those from clinicians. In 2020, WhatsApp worked with th

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  • Amaq News Agency

    Amaq News Agency

    Amaq News Agency (Arabic: وكالة أعماق الإخبارية, romanized: Wakālat Aʻmāq al-Ikhbārīyah) is a news outlet linked to the Islamic State (IS). Amaq is often the "first point of publication for claims of responsibility" for terrorist attacks in Western countries by the Islamic State. In March 2019, Amaq News Agency was designated as a foreign terrorist organization by the United States Department of State. == History == Among the founders of Amaq was Syrian journalist Baraa Kadek, who joined IS in late 2013, Abu Muhammad al-Furqan, and seven others who originally worked for Halab News Network. According to The New York Times, it has a direct connection with IS, from which it "gets tips". Its name was taken from Amik Valley in Hatay Province, which is mentioned in a hadith as the site of an "apocalyptic victory over non-believers". Amaq News Agency was first noticed by SITE during the Siege of Kobanî (Syria) in 2014, when its updates were shared among IS fighters. It became more widely known after it began reporting claims of responsibility for terrorist attacks in Western countries, such as the 2015 San Bernardino attack, for which IS officially claimed responsibility the next day. An Amaq cameraman shot the first footage of the capture of Palmyra in 2015. Amaq launched an official mobile app in 2015 and has warned against unofficial versions that reportedly have been used to spy on its users. It also uses a Telegram account. It had a WordPress-based blog, but it was removed without explanation in April 2016. On 12 June 2016, IS claimed responsibility for the Pulse nightclub shooting through Amaq, without prior knowledge of the attack. The shooter, Omar Mateen had later pledged allegiance to IS via a phone call with emergency services. On 31 May 2017, a Facebook post announced Amaq's founder, Baraa Kadek AKA Rayan Meshaal, had been killed with his daughter by an American airstrike on Mayadin. The post was reportedly made by his younger brother. Reuters could not immediately verify this account. On 27 July 2017, the US confirmed that Kadek had been killed by a coalition airstrike near Mayadin between 25 and 27 May 2017. In June 2017, German police arrested a 23-year-old Syrian man identified only as Mohammed G., accusing him of communicating with the alleged perpetrator of the 2016 Malmö Muslim community centre arson in order to report to Amaq. On 21 March 2019, the U.S. Department of State officially deemed Amaq an alias of IS, and thus a Foreign Terrorist Organization. On 22 March 2024, the Islamic State claimed responsibility for the Crocus City Hall attack through Amaq, U.S. officials confirmed the claim shortly after. A day after the attack, Amaq published a video of the attack, filmed by one of the attackers. It showed the attackers shooting victims and slitting the throat of another, while the filming attacker praises Allah and speaks against infidels. == Character == Amaq publishes a stream of short news reports, both text and video, on the mobile app Telegram. The reports take on the trappings of mainstream journalism, with "Breaking News" headings, and embedded reporters at the scenes of IS battles. The reports try to appear neutral, toning down the jihadist language and sectarian slurs IS uses in its official releases. Charlie Winter of the Transcultural Conflict and Violence Initiative at Georgia State University, and Rita Katz of SITE Intelligence Group in Washington say Amaq functions much like the state-owned news agency of IS, though the group does not acknowledge it as such. Katz said it behaves "like a state media". Amaq appears to have been allowed to develop by IS as a way to have a news outlet that is controlled by the group but is somewhat removed from it, giving IS more of the appearance of legitimacy. == Reliability == According to Rukmini Callimachi in The New York Times: "Despite a widespread view that the Islamic State opportunistically claims attacks with which it has little genuine connection, its track record—minus a handful of exceptions—suggests a more rigorous protocol. At times, the Islamic State has got details wrong, or inflated casualty figures, but the gist of its claims is typically correct." According to Callimachi, the group considers itself responsible for acts carried out by people who were inspired by its propaganda, as well as acts carried out by its own personnel and in some instances, had claimed attacks before the identities of the killers were known. Graeme Wood writing in The Atlantic in October 2017, wrote "The idea that the Islamic State simply scans the news in search of mass killings, then sends out press releases in hope of stealing glory, is false. Amaq may learn details of the attacks from mainstream media ... but its claim of credit typically flows from an Amaq-specific source." An October 2017 article in The Hill, points to two false claims made in the summer of 2017, the Resorts World Manila attack and a false claim that bombs had been planted at Charles de Gaulle Airport in Paris. Also, a claimed IS connection to the 2017 Las Vegas shooting proved to be false. According to Rita Katz on the SITE Intelligence Group website, calling a terrorist a "soldier of the caliphate (warrior from the caliphate)" in a statement issued by Amaq, was the usual way in which IS indicated that it inspired an attack. Centrally coordinated attacks were usually described as "executed by a detachment belonging to the Islamic State", and were often announced by both Amaq and by IS' central media command. == Online presence == In November 2019, Belgian police said they had carried out a successful cyberattack on Amaq, thus leaving IS without an operational communication channel. However, Amaq has since regained online presence, primarily on dark web platforms to make it harder for law enforcement to take them down without physical access to the server hosting the specific platform.

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  • Pixlr

    Pixlr

    Pixlr is a group of SaaS creative tools including Pixlr.com, Designs.ai and Vectr.com. Pixlr.com is a cloud-based set of image editing tools and utilities, including AI image generation and enhancements. The Pixlr suite targets users who require subjectively simple, or more advanced, photo editing as well as graphic design. It features a freemium business model with subscription plans—Plus, Premium and Teams. The platform can be used on desktop and also smartphones and tablets. Pixlr is compatible with various image formats such as JPEG, PNG, WEBP, GIF, PSD (Photoshop Document) and PXZ (native Pixlr document format). Designs.ai lets users create content using AI, with a goal of being within two minutes, across different media types including videos, text, banners and audio. Vectr.com was acquired in 2017 before being spun out into Pixlr Group in 2023. == History == Pixlr was founded in 2008 and built on Macromedia Flash. On 19 July 2011, Autodesk announced that they had acquired the Pixlr suite. In 2013, Time listed Pixlr as one of the top 50 websites of the year. In 2017, Pixlr was acquired from Autodesk. It was subsequently rebuilt and relaunched in HTML5 in 2019. In September 2023, Pixlr was awarded as the Top 13 GenAi Web Product by the world's top venture firm Andreessen Horowitz. In November 2023, Pixlr, Designs.ai and Vectr were combined as a new business group named Pixlr Group focusing on generative AI and creative software solutions. In May 2024, Pixlr was featured as one of the top 18 progressive web applications highlighted on Google I/O. == Versions == Pixlr.com rebranded itself as a full creative suite in 2019 by introducing Pixlr X, Pixlr E and Pixlr M. The platform introduced more features in December 2021 with a new logo and added tools which included: Brushes, the 'Heal tool', Animation, and Batch upload. The brush feature enables the creation of hand-drawn effects. The Heal tool allows users to remove unwanted objects from their images whereas the Animation feature can be used to include movements into their edits. Users can also utilize Batch upload to edit up to 50 images simultaneously. In November 2022, Pixlr 2023 was launched, adding more tools such as "AI smart resize", colorization, text wrapping and other additional effects. In November 2023, Pixlr 2024 was launched with Pixlr Designer and new AI-powered updates which includes AI image generation, AI infill, AI inpainting and more.

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  • Scientific Working Group – Imaging Technology

    Scientific Working Group – Imaging Technology

    The Scientific Working Group on Imaging Technology was convened by the Federal Bureau of Investigation in 1997 to provide guidance to law enforcement agencies and others in the criminal justice system regarding the best practices for photography, videography, and video and image analysis. This group was terminated in 2015. == History == As technology has advanced through the years, law enforcement has needed to stay abreast of emerging technological advances and use these in the investigation of crime. A factor that is considered when new technology is used in these investigations is the determination of whether the use of that new technology will be admissible in court. The judicial system in the United States currently has two standards used in the determination of admissibility of testimony regarding scientific evidence; the Daubert Standard and the Frye Standard. These standards guide the courts in the admissibility of testimony derived from the use of new technologies and scientific techniques. The Federal Bureau of Investigation (FBI), seeking to address possible admissibility issues with such testimony, established Scientific Working Groups starting with the Scientific Working Group on DNA Analysis and Methods (SWGDAM) in 1988. The goal of these groups is to open lines of communication between law enforcement agencies and forensic laboratories around the world while providing guidance on the use of new and innovative technologies and techniques. This guidance can lead to admissibility of evidence and/or testimony, provided proper methods in the collection of evidence and its analysis are employed. In 2009, the National Academy of Sciences released a report entitled, "Strengthening Forensic Science in the United States: A Path Forward." This report addresses many topics including challenges and disparities facing the forensic science community, standardization, certification of practitioners and accreditation of their respective entities, problems related to the interpretation of forensic evidence, the need for research, and the admission of forensic science evidence in litigation. This report mentions the Scientific Working Groups and their role in forensic science. The history of imaging technology (photography) can be said to extend back to the times of Chinese philosopher Mo-Ti (470-390 B.C.) who described the principles behind the precursor to the camera obscura. Since that time, advances in imaging technology include the discovery of chemical photographic processes in the 19th century and the use of electronic imaging technology that includes analog video cameras and digital video and still cameras. By the mid 1990s, it was apparent that technologically advanced camera systems such as these were being adopted for use in the criminal justice system. This led the FBI to convene a meeting of individuals working in the field of forensic imaging from federal, state, local, and foreign law enforcement, and the U.S. military, during the summer of 1997. As a result of this meeting, the Technical Working Group on Imaging Technology was formed from a core group of the meeting’s participants. This group later became the Scientific Working Group on Imaging Technology (SWGIT). Prior to the inception of SWGIT, some law enforcement agencies began adopting digital imaging technology. Due to the lack of guidelines or standards, some of these agencies attempted to replace all their film cameras with substandard digital cameras, only to find that the equipment they had purchased was not capable of accomplishing the mission for which they were intended. At that time only low resolution digital cameras were deemed affordable by some law enforcement agencies. Some of these agencies were forced to rethink their photography procedures and reverted to the use of film cameras or replaced their low-resolution digital cameras with higher quality, more expensive equipment. Also lacking at this early stage was guidance on how to store and archive digital image files. When SWGIT was formed, it was tasked with providing guidance to law enforcement and others in the criminal justice system by releasing documents that describe the best practices and guidelines for the use of imaging technology, to include these concerns and many others. This group was terminated in 2015. == SWGIT Function == During its existence, SWGIT provided information on the appropriate use of various imaging technologies including both established and new. This was accomplished through the release of documents such as the SWGIT Best Practices documents. As changes in technology occurred, these documents were updated. Over the course of its existence, SWGIT collaborated with other Scientific Working Groups to address imaging concerns within their respective disciplines. SWGIT published over 20 documents that dealt specifically with imaging technology. SWGIT also co-published documents with the Scientific Working Group on Digital Evidence (SWGDE) that had a component or components dealing with imaging technology. SWGIT also provided imaging technology guidance and input for documents from the Scientific Working Group on Friction Ridge Analysis, Study and Technology (SWGFAST), the Scientific Working Group for Forensic Document Examination (SWGDOC), and the Scientific Working Group on Shoeprint and Tire Tread Evidence (SWGTREAD). SWGIT assisted the American Society of Crime Lab Directors/Laboratory Accreditation Board (ASCLD/LAB) in the writing of definitions and standards for the accreditation of Digital and Multimedia Evidence sections of crime laboratories. In addition to releasing documents, SWGIT members disseminated best practices for law enforcement professionals where imaging technology was concerned. This was carried out by attending and lecturing at meetings and conferences of various forensic organizations that included: The American Academy of Forensic Sciences (AAFS) The International Association for Identification (IAI) The Law Enforcement and Emergency Services Video Association (LEVA) The American Society of Crime Lab Directors (ASCLD) The SWGIT membership consisted of approximately fifty scientists, photographers, instructors, and managers from more than two dozen federal, state, and local law enforcement agencies, as well as from the academic and research communities. The membership elected its officers from within. SWGIT was composed of the Executive Committee, four standing subcommittees, and ad hoc subcommittees appointed on an as-needed basis. The standing subcommittees were: Image Analysis, Forensic Photography, Video, and Outreach. This group was terminated in 2015. == Legal Proceedings == The following court cases have conducted Daubert v. Merrell Dow Pharm., Inc., 509 U.S. 579 (1993) hearings in which SWGIT best practice documents have been cited as accepted protocol, methodology, and as generally accepted techniques in the forensic community: U. S. v. Rudy Frabizio, U.S. District Court, Boston, MA, 2008 (Image Authentication) U.S. v. Nobumochi Furukawa, U.S. District Court, Minnesota, 2007 (Video Authentication) U.S. v. John Stroman, U.S. District Court, South Carolina, 2007 (Facial Comparison Analysis) State of Texas v. Daniel Day, Tarrant County Texas, 2005 (Camera Identification to Images) U.S. v. Marc Watzman, U.S. District Court, Northern Illinois, 2004 (Video Authentication) U.S. v. McKreith, U.S. District Court, Fort Lauderdale, FL, 2002 (Photo comparison of shirt) == Termination == This group was unfunded by the FBI in 2015.

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  • Jeremy Renner Official

    Jeremy Renner Official

    Jeremy Renner Official (or Jeremy Renner on the Google Play Store) was a mobile app created by American actor Jeremy Renner. He created the app in March 2017 to hear the input and comments of his fans. The app was shut down in September 2019 in part due to the frequent bullying and trolling that the platform had experienced. The app featured optional microtransactions, with some ranging up to roughly US$400 despite the app itself being free. Upon shutting down the app, Renner issued a mass-refund for the collectible "stars" in the app for purchases made within the last ninety days, from the day the announcement was posted. He then posted an apology to the app itself, and the app was deleted from both the Google Play Store and the App Store shortly after. == Usage == Upon downloading the app, the user was faced with a video of Renner speaking about his fans and superfans, regular giveaways, and real-life updates. While the app was active, Renner posted regular questions and comments for fans. Renner occasionally livestreamed about his work and day-to-day life. The community developed to include memes, selfies, and a "Happy Rennsday" event on Wednesdays. == History == === 2017–2019 === The app launched in March 2017 with a promotional contest. Renner's fans were encouraged to download the app and create comments about being Renner's biggest fan; Renner would then choose a winner and transport the winner and a guest to have lunch with him at the Calgary Expo. In the first few months Renner teased behind-the-scenes of projects he was working on, which he now sporadically does on Instagram. The app was similarly designed to Instagram as well, with a near identically styled layout. Around midway through 2019, a hoax account of Renner was made to mock the celebrity, joking about masturbating to porn and defending another hoax account of Casey Anthony. FastCompany wrote extensively about Renner's app in April 2019, calling it "a surprising new kind of social media". The Ringer stated "Jeremy Renner's Jeremy Renner app is the Jeremy Renner of apps." === After deletion (2019–2020) === After the shutdown of the app, a comedy-based pseudo-app with modular endings was released, called "The Jeremy Renner App Experience", in which the player plays as Jeremy Renner on the day of the Jeremy Renner Official app's shutdown. The app details several different choices on how Renner handles the situation. A six-part podcast was also created to mock the app's deletion, called The Renner Files, featuring Carolyn Goldfarb and Sarah Ramos. == Controversies == === Marketing === One of the main controversies of Renner's app was its marketing. The app's developers, Escapex, specialized in and grew famous for making similar monetized apps for celebrities. The marketing campaign was based on direct contact with Renner, whose chances were increased with regular payments for "stars", although very few encounters seemed to happen with Renner himself. The multiple problems with the app led the CEO of Escapex, Sephi Shapira, to call the app a "freak situation", and added "Am I concerned about this? Not more than I'm concerned about 50 other things I'm dealing with as a startup company." Along with the marketing failures, the app was seen as misrepresenting itself as seemingly erotic with some advertisements featuring Renner suggestively staring at the camera, despite the actual app being initially considered safe for children. === Harassment === After its release in 2017, the app was met with waves of harassment and bullying by many users on the app, most frequently by using impersonation — referenced in Renner's apology/deletion notice. Some death threats were made across the app by fraud accounts pretending to be several controversial celebrities, including O. J. Simpson and Casey Anthony. As early as October 2017, there were claims of censorship, bullying, and "contest-rigging". In September 2019, comedian Stefan Heck publicized his discovery of the fact that replies through the app appeared as if they were sent by Renner himself in push notifications. After several users abused this feature, Renner asked Escapex to shut down the app.

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  • DoorDash

    DoorDash

    DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States. It also has a 60% market share in the convenience delivery category. As of December 31, 2020, the platform was used by 450,000 merchants, 20 million consumers, and had over one million delivery couriers. Founded by Tony Xu, Andy Fang, Stanley Tang and Evan Moore, DoorDash made its debut on the Fortune 500 list in 2024, ranking No. 443. DoorDash has been sued for or held legally liable for withholding tips, reducing tip transparency, antitrust price manipulation, listing restaurants without permission, misclassifying workers, withholding sick time, and illegally selling personal data. As of April 2026, DoorDash operates in the United States (including Puerto Rico), Canada, Australia, and New Zealand. Through its subsidiaries Deliveroo and Wolt, the company also operates across Europe, as well as in Azerbaijan, Georgia, Israel, Kazakhstan, Kuwait, and the United Arab Emirates. == History == In January 2013, Stanford University students Tony Xu, Stanley Tang, Andy Fang and Evan Moore launched PaloAltoDelivery.com in Palo Alto, California. In the summer of 2013, it received US$120,000 in seed money from Y Combinator in exchange for a 7% stake. It incorporated as DoorDash in June 2013. DoorDash's first partnership with a fast food burger restaurant chain was in April 2016, when it partnered with CKE Restaurants, parent company of Carl's Jr. and Hardee's, for food delivery. In December 2017, DoorDash announced its partnership with Wendy's for delivery from its restaurants. In December 2018, DoorDash overtook Uber Eats to hold the second position in total US food delivery sales, behind GrubHub. By March 2019, it had exceeded GrubHub in total sales, at 27.6% of the on-demand delivery market. By early 2019, DoorDash was the largest food delivery provider in the U.S., as measured by consumer spending. In October 2019, DoorDash opened its first ghost kitchen, DoorDash Kitchen, in Redwood City, California, with four restaurants operating at the location. By June 2020, DoorDash had raised more than $2.5 billion over several financing rounds from investors including Y Combinator, Charles River Ventures, SV Angel, Khosla Ventures, Sequoia Capital, SoftBank Group, GIC, and Kleiner Perkins. DoorDash announced a partnership with KFC in September 2020, followed by Taco Bell in October 2020. In November 2020, DoorDash announced the opening of its first physical restaurant location, partnering up with Bay Area restaurant Burma Bites to offer delivery and pick-up orders. In December 2020, it became a public company via an initial public offering, raising $3.37 billion. In November 2021, DoorDash acquired Finland's Wolt for €7bn. In August 2022, DoorDash announced it would end its partnership with Walmart in September, ending the companies' cooperation agreement from 2018. In November 2022, DoorDash announced plans to lay off 1,250 corporate employees, or about six percent of its workforce, to rein in expenses. In June 2023, DoorDash announced it would give its drivers the option of earning an hourly minimum wage instead of being paid per delivery. However, drivers are only paid hourly when on an active delivery. In September 2023, the company transferred its stock listing from the New York Stock Exchange to the Nasdaq. On December 18, 2023, DoorDash was added to the Nasdaq-100 index. In March 2025, DoorDash announced a partnership with Klarna, a Buy Now, Pay Later (BNPL) service, letting customers schedule small payments over a set period of time. DoorDash received widespread criticism from this decision, including internet mockery, given concerns about the increase of household debt in America. In 2025, DoorDash acquired the UK-based delivery service Deliveroo for $3.88 billion. The combined company operates in 40 countries and serves 50 million users monthly. In September 2025, DoorDash and Ace Hardware (the largest hardware cooperative) announced their partnership to offer delivery for home use products from over 4,000 Ace locations. == Lawsuits against DoorDash == === 2017 class-action lawsuit for misclassifying workers === In 2017, a class-action lawsuit was filed against DoorDash for allegedly misclassifying delivery drivers in California and Massachusetts as independent contractors. In 2022, a tentative settlement was reached in which DoorDash would pay $100 million total, with $61 million going to over 900,000 drivers, paying out just over $130 per driver, and $28 million for the lawyers. Gizmodo criticized the settlement, noting that the $413 million that DoorDash CEO Tony Xu received the previous year was one of the largest CEO compensation packages of all time. === 2019 data breach lawsuit === On May 4, 2019, DoorDash confirmed 4.9 million customers, delivery workers and merchants had sensitive information stolen via a data breach. Those who joined the platform after April 5, 2018, were unaffected by the breach. A class-action lawsuit for the breach was filed against DoorDash in October 2019. === Withholding of tips and subsequent class-action lawsuits === In July 2019, the company's tipping policy was criticized by The New York Times, and later The Verge and Vox and Gothamist. Drivers receive a guaranteed minimum per order that is paid by DoorDash by default. When a customer added a tip, instead of going directly to the driver, it first went to the company to cover the guaranteed minimum. Drivers then only directly received the part of the tip that exceeded the guaranteed minimum per order. In January 2020, it was reported that DoorDash had lied about skimming tips from its drivers, causing them to earn an average of $1.45 an hour after expenses, and that after the company had allegedly overhauled its tipping system, DoorDash was still manipulating per-delivery payouts at the expense of drivers. A DoorDash customer filed a class action lawsuit against the company for its "materially false and misleading" tipping policy. The case was referred to arbitration in August 2020. Under pressure, the company revised its policy. The company settled a lawsuit with District of Columbia Attorney General Karl Racine for $2.5 million, with funds going to deliverers, the government, and to charity. ==== 2021 driver strike for tip transparency ==== In July 2021, DoorDash drivers went on strike to protest lack of tip transparency and to ask for higher pay. At the time of the strike, and, as of June 2022, DoorDash did not allow drivers to see the full tip amounts prior to accepting a delivery in the app. If customers tip over a set amount for the order total, Doordash hides a portion of the tip until the delivery is complete. The strike occurred after DoorDash rewrote its code to cut off access to Para, a third-party app that drivers had been using to see the full tip amounts. ==== 2025 class-action lawsuit settlement ==== In 2025, DoorDash agreed to pay around $17 million for "misleading both consumers and delivery workers" with tips being docked from drivers' pay instead of directly going to drivers. === 2020 antitrust litigation === In April 2020, in the case of Davitashvili v. GrubHub Inc. DoorDash, Grubhub, Postmates, and Uber Eats were accused of monopolistic power by only listing restaurants on its apps if the restaurant owners signed contracts which include clauses that require prices be the same for dine-in customers as for customers receiving delivery. The plaintiffs stated that this arrangement increases the cost for dine-in customers, as they are required to subsidize the cost of delivery; and that the apps charge "exorbitant" fees, which range from 13% to 40% of revenue, while the average restaurant's profit ranges from 3% to 9% of revenue. The lawsuit seeks treble damages, including for overcharges, since April 14, 2016, for dine-in and delivery customers in the United States at restaurants using the defendants’ delivery apps. Although several preliminary documents in the case have now been filed, a trial date has not yet been set. === Litigation for illegal unauthorized restaurant listing === In May 2021, DoorDash was criticized for unauthorized listings of restaurants who had not given permission to appear on the app. The company was sued by Lona's Lil Eats in St. Louis, with the lawsuit claiming that DoorDash had listed them without permission, then prevented any orders to the restaurant from going through and redirecting customers to other restaurants instead, because Lona's was "too far away," when in reality it had not paid DoorDash a fee for listing. This aspect of DoorDash's business practice is illegal in California. === 2021 lawsuit by the city of Chicago === In August 2021, the city of Chicago sued DoorDash and GrubHub. According to Chicago mayor Lori Lightfoot, the companies broke the law by using "unfair and deceptive t

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  • Graphics suite

    Graphics suite

    A graphics suite is a software suite for graphics work that are distributed together. The programs are usually able to interact with each other on a higher level than the operating system would normally allow. There is no hard, fast rule regarding the programs to be included in a graphics application suite, but most will include at least a bitmap graphics editor and a vector graphics editor. In addition to these, the suite may contain VRML editors, animation editors, and morphing tools.

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  • Ulead MediaStudio Pro

    Ulead MediaStudio Pro

    Ulead MediaStudio Pro (MSP) is real-time, timeline based prosumer level video editing software by Ulead Systems. It is a suite of 5 digital video and audio applications, including: Video Capture, Video Paint, CG Infinity, Audio Editor and Video Editor. MSP is only available on the Windows platform. Since version 8.0, CG Infinity and Video Paint are separate from the MSP suite, and are being sold as a combination product called VideoGraphics Lab (VGL). On June 18, 2008, Corel formally announced that MediaStudio Pro would be discontinued. The final MediaStudio Pro version was 8.10.0039 (Pro 8 Service Pack 1) released June 2, 2006. Corel discontinued support for MediaStudio Pro in June 2009. Version 6.0 is last version to support Windows 95, although recent versions are not compatible with Windows Vista or Windows 7. == Modules == There are 5 stand-alone modules in MSP before version 8.0, they are: Video Capture – The video capturing module in MSP. Video Paint – A frame-by-frame editor which can let user to make some image or hand-drawing effects on video frames. CG Infinity – A vector-based video editing tool which allows user to create logo animation or vector graphics on video frames. Audio Editor – The audio editing tool in MSP. It can utilize DirectX audio filters and Ulead audio filters to do audio effect processing. Video Editor – The module that users do video editing with audio/video effects. It can also utilize DirectX audio filters and 3rd party video filters to do the video editing. Since version 8.0, CG Infinity and Video Paint have been separated from the MSP suite and are being sold as a combination product called VideoGraphics Lab (VGL). == Editions == Ulead MediaStudio Pro had several editions before version 7.0. They are: Full edition: this edition includes all 5 modules. Director's Cut edition: this edition has 3 modules including Video Capture, Video Editor and Audio Editor. SE edition: SE means Simple Edition or Special Edition and is an OEM bundle version. It also includes the 3 modules as Director's Cut, however, is feature limited. Sometimes it will be given freely in video magazines. After version 7.0 only Full edition is available in the MSP suite. On June 18, 2008, Corel formally announced that MediaStudio Pro would be discontinued. == Release history ==

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  • Open Cloud Computing Interface

    Open Cloud Computing Interface

    The Open Cloud Computing Interface (OCCI) is a set of specifications delivered through the Open Grid Forum, for cloud computing service providers. OCCI has a set of implementations that act as proofs of concept. It builds upon World Wide Web fundamentals by using the Representational State Transfer (REST) approach for interacting with services. == Scope == The aim of the Open Cloud Computing Interface is the development of an open specification and API for cloud offerings. The focus was on Infrastructure-as-a-Service (IaaS) based offerings but the interface can be extended to support Platform and Software as a Service offerings as well. IaaS is one of three primary segments of the cloud computing industry in which compute, storage and network resources are provided as services. The API is based on a review of existing service-provider functionality and a set of use cases contributed by the working group. OCCI is a boundary API that acts as a service front-end to an IaaS provider’s internal infrastructure management framework. OCCI provides commonly understood semantics, syntax and a means of management in the domain of consumer-to-provider IaaS. It covers management of the entire life-cycle of OCCI-defined model entities and is compatible with existing standards such as the Open Virtualization Format (OVF) and the Cloud Data Management Interface (CDMI). Notably, it serves as an integration point for standardization efforts including Distributed Management Task Force, Internet Engineering Task Force and the Storage Networking Industry Association. == Context == OCCI began in March 2009 and was initially led by RabbitMQ and the Complutense University of Madrid. Today, the working group has over 250 members and includes numerous individuals, industry and academic parties. The OCCI operates under the umbrella of the Open Grid Forum (OGF), using a wiki and a mailing list for collaboration. == Goals == Interoperability: allow different Cloud providers to work together without data schema/format translation, facade/proxying between APIs and understanding and/or dependency on multiple APIs Portability: no technical/vendor lock-in and enable services to move between providers allows clients to easily switch between providers based on business objectives (e.g., cost) with minimal technical costs, thus enabling and fostering competition. Integration: the specification can be implemented with both the latest infrastructures or legacy ones. Extensibility: thanks to the use of a meta-model and capabilities discovery features, an OCCI client is able to interact with any OCCI server using provider-specific OCCI extensions. == Specific Implementations == They implement specific extensions of OCCI for a particular service: IaaS, PaaS, brokering, etc. Several implementations have been announced or released. == Generic Implementations (frameworks) == Here are frameworks to build OCCI APIs. Complementing these are a variety of developer tools. == Alternatives == Alternative approaches include the use of the Cloud Infrastructure Management Interface (CIMI) and related standards set from DMTF and the Amazon Web Services interfaces from Amazon. (The latter have not been endorsed by any known Standards organization). OpenNebula conducted a survey of their users in which the results showed, 38% do not expose cloud APIs, their users only interface through the Sunstone GUI, 36% mostly use the Amazon Web Services API, and 26% mostly use the OpenNebula’s OCCI API or the OCCI API offered by rOCCI.

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  • Cepstral mean and variance normalization

    Cepstral mean and variance normalization

    Cepstral mean and variance normalization (CMVN) is a computationally efficient normalization technique for robust speech recognition. The performance of CMVN is known to degrade for short utterances. This is due to insufficient data for parameter estimation and loss of discriminable information as all utterances are forced to have zero mean and unit variance. CMVN minimizes distortion by noise contamination for robust feature extraction by linearly transforming the cepstral coefficients to have the same segmental statistics. Cepstral Normalization has been effective in the CMU Sphinx for maintaining a high level of recognition accuracy over a wide variety of acoustical environments. == Cepstral Normalization Techniques == There are multiple algorithms that achieve Cepstral Normalization in different ways. === Fixed codeword-dependent cepstral normalization (FCDCN) === FCDCN was developed to provide a form of compensation that provides greater recognition accuracy than SDCN but in a more computationally-efficient manner than the CDCN algorithm. The FCDCN algorithm applies an additive correction that depends on the instantaneous SNR of the input (like SDCN), but that can also vary from codeword to codeword (like CDCN). === Multiple Fixed Codeword-dependent Cepstral Normalization (MFCDCN) === MFCDCN is a simple extension of FCDCN algorithm that does not need environment specific training. In MFCDCN, compensation vectors are pre-computed in parallel for a set of target environments, using the FCDCN algorithm. === Incremental Multiple Fixed Codeword-dependent Cepstral Normalization (IMFCDCN) === While environment selection for the compensation vectors of MFCDCN is generally performed on an utterance-by-utterance basis, IMFCFCN improves on it by allowing the classification process to make use of cepstral vectors from previous utterances in a given session. == Cepstral Noise Subtraction == Automatic speech recognition (ASR) describes the steps of transcribing speech utterances represented as acoustic wave forms to written words. As is, CMVN has been used in different applications as this technique has proven to provide better speech recognitions results in different environments. CMVN has the capabilities to reduce differences between test and training data produced by channel distortions and colorizations . CMVN has also been found to be able to reduce differences in feature representation between speakers can also partly reduce the influence of background noise.

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  • EasyA

    EasyA

    EasyA is a web3 technology company and education platform based in London (United Kingdom), founded in 2022 by Phil Kwok and Dom Kwok. EasyA was officially launched in 2022, focusing on web3 technologies. This community was influenced by the founders' experiences during the COVID-19 pandemic and early collaborations with universities and other educational institutions. Subsequently, the community was used as a foundation for developing Web3-related initiatives, including the organisation of EasyA's first Web3 hackathon in 2022. The EasyA app has over one million users and provides educational content on various blockchain technologies. EasyA Labs is a separate initiative focused on developing products intended to improve accessibility to cryptocurrency for a broader audience.

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  • Multiple satellite imaging

    Multiple satellite imaging

    Multiple satellite imaging is the process of using multiple satellites to gather more information than a single satellite so that a better estimate of the desired source is possible. Something that cannot be resolved with one telescope might be visible with two or more telescopes. == Background == Interferometry is the process of combining waves in such a way that they constructively interfere. When two or more independent sources detect a signal at the same given frequency those signals can be combined and the result is better than each one individually. An overview of Astronomical interferometers and a History of astronomical interferometry can be referenced from their respective pages. The NASA Origins Program was created in the 1990s to ultimately search for the origin of the universe. The theory that the Origins Program is based on is: since light travels at a constant speed until it is absorbed by something; there is still light that was part of the first light ever created traveling about the universe and ultimately some of that light is coming in the general direction of Earth. So a satellite system capable of collecting light from the beginning of the universe would be able to tell us more about where we came from. There is also the constant search for life in other worlds. A satellite system using the interferometric technologies mentioned above would be able to have a much higher resolution than any of the current deep space imaging systems. == Future == NASA is currently focused on the Vision for Space Exploration and has reduced current funding for scientific unmanned space exploration in favor of human exploration. These budget cuts have slowed the multiple satellite imaging development and relevant scientific missions as Project Prometheus and Terrestrial Planet Finder have ended as well but research continues.

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  • Computer appliance

    Computer appliance

    A computer appliance is a computer system with a combination of hardware, software, or firmware that is specifically designed to provide a particular computing resource. Such devices became known as appliances because of the similarity in role or management to a home appliance, which are generally closed and sealed, and are not serviceable by the user or owner. The hardware and software are delivered as an integrated product and may even be pre-configured before delivery to a customer, to provide a turn-key solution for a particular application. Unlike general purpose computers, appliances are generally not designed to allow the customers to change the software and the underlying operating system, or to flexibly reconfigure the hardware. Another form of appliance is the virtual appliance, which has similar functionality to a dedicated hardware appliance, but is distributed as a software virtual machine image for a hypervisor-equipped device. == Overview == Traditionally, software applications run on top of a general-purpose operating system, which uses the hardware resources of the computer (primarily memory, disk storage, processing power, and networking bandwidth) to meet the computing needs of the user. The main issue with the traditional model is related to complexity. It is complex to integrate the operating system and applications with a hardware platform, and complex to support it afterwards. By tightly constraining the variations of the hardware and software, the appliance becomes easily deployable, and can be used without nearly as wide (or deep) IT knowledge. Additionally, when problems and errors appear, the supporting staff very rarely needs to explore them deeply to understand the matter thoroughly. The staff needs merely training on the appliance management software to be able to resolve most of problems. In all forms of the computer appliance model, customers benefit from easy operations. The appliance has exactly one combination of hardware and operating system and application software, which has been pre-installed at the factory. This prevents customers from needing to perform complex integration work, and dramatically simplifies troubleshooting. In fact, this "turnkey operation" characteristic is the driving benefit that customers seek when purchasing appliances. To be considered an appliance, the (hardware) device needs to be integrated with software, and both are supplied as a package. This distinguishes appliances from "home grown" solutions, or solutions requiring complex implementations by integrators or value-added resellers (VARs). The appliance approach helps to decouple the various systems and applications, for example in the data center. Once a resource is decoupled, in theory it can be also centralized to become shared among many systems, centrally managed and optimized, all without requiring changes to any other system. == Tradeoffs of the computer appliance approach == The major disadvantage of deploying a computer appliance is that since they are designed to supply a specific resource, they most often include a customized operating system running over specialized hardware, neither of which are likely to be compatible with the other systems previously deployed. Customers lose flexibility. One may believe that a proprietary embedded operating system, or operating system within an application, can make the appliance much more secure from common cyber attacks. However, the opposite is true. Security by obscurity is a poor security decision, and appliances are often plagued by security issues as evidenced by the proliferation of IoT devices. == Types of appliances == The variety of computer appliances reflects the wide range of computing resources they provide to applications. Some examples: Storage appliances provide large amounts of storage, often available to many machines on the network. See Network-attached storage and Storage area network. Network appliances are general purpose routers which may also provide firewall protection, Transport Layer Security (TLS), messaging, access to specialized networking protocols (like the ebXML Message Service) and bandwidth multiplexing for the multiple systems they front-end. Backup and disaster recovery appliances computer appliances that are integrated backup software and backup targets, sometimes with hypervisors to support local DR of protected servers. They are often a gateway to a full DRaaS solution. Firewall and Security appliances Dedicated network appliances that are designed to protect computer networks from unwanted traffic. IIoT and MES Gateway appliances Computer appliances that are designed to translate data bidirectionally between control systems and enterprise systems. Proprietary, embedded, firmware applications running on the appliance use point-to-point connections to translate data between field devices in their native automation protocols and MES systems through their APIs, ODBC, or RESTful interfaces. Anti-spam appliances for e-mail spam Software appliances A single application server appliance, with just enough operating system (JeOS) for it to run. Virtual machine appliances consist of a "hypervisor style" embedded operating system running on appliance hardware. The hypervisor layer is matched to the hardware of the appliance, and cannot be varied by the customer, but the customer may load other operating systems and applications onto the appliance in the form of virtual machines. == Consumer appliances == Aside from its deployment within data centers, many computer appliances are directly used by the general public. These include: Digital video recorder Residential gateway Network-attached storage (NAS) Video game console Consumer uses stress the need for an appliance to have easy installation, configuration, and operation, with little or no technical knowledge being necessary. == Appliances in industrial automation == The world of industrial automation has been rich in appliances. These appliances have been hardened to withstand temperature and vibration extremes. These appliances are also highly configurable, enabling customization to meet a wide variety of applications. The key benefits of an appliance in automation are: Reduced downtime - a failed appliance is typically replaced with a COTS replacement and its task is quickly and easily reloaded from a backup. Highly scalable - appliances are typically targeted solutions for an area of a plant or process. As the requirements change, scalability is achieved through the installation of another appliance. Automation concepts are easily replicated throughout the enterprise by standardizing on appliances to perform the needed tasks, as opposed to the development of custom automation schemes for each task. Low TCO (total cost of ownership) - appliances are developed, tested and supported by automation product vendors and undergo a much broader level of quality testing than custom designed automation solutions. The use of appliances in automation reduce the level of testing needed in each individual application. Reduced design time - appliances perform specific functions and although they are highly configurable, they are typically self documenting. This enables appliance based solutions to be transferred from engineer to engineer with minimal need for training and documentation. Types of automation appliances: PLC (programmable logic controller) - Programmable logic controllers are appliances that are typically used for discrete control and offer a wide range of Input and Output options. They are configured through standardized programming languages such as IEC-1131. PID (proportional–integral–derivative controller) - PID controllers are appliances that monitor a process variable and, based on an error term, effect change on a control output (manipulated variable) to drive the process variable to a setpoint. PAC (programmable automation controller) - Programmable automation controllers are appliances that embody properties of both PLCs and PID controllers enabling the integration of both analog and discrete control. Universal gateway - A universal gateway appliance has the ability to communicate with a variety of devices through their respective communication protocols, and will affect data transactions between them. This in increasingly important as manufacturing strives to improve agility, quality, production rates, production costs and reduce downtime through enhanced M2M (machine to machine) communications. EATMs (Enterprise Appliance Transaction Modules) - Enterprise appliance transaction modules are appliances that affect data transactions from plant floor automation systems to enterprise business systems. They communicate to plant floor equipment through various vendor automation protocols, and communicate to business systems through database communication protocols such as JMS (Java Message Service) and SQL (Structured Query Language). == Internal structure == There are several

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  • Eat App

    Eat App

    Eat App is a global restaurant technology company that provides a cloud-based management platform for restaurants, hotels, and other venues. The platform enables venues to accept online reservations seamlessly, manage tables, and enhance customer relationship management (CRM). It utilizes AI to improve operational efficiency, provides marketing automation, and helps build a comprehensive guestbook. The company also offers a consumer app and website for discovering and booking restaurant tables online. According to the company, the system has seated over 100 million guests, and the number continues to grow. Eat was founded by Nezar Kadhem and David Feuillard in 2015 and has raised $13M to date from Silicon Valley's 500 startups, Middle East Venture Partners (MEVP), Derayah VC, amongst other business angels. The company is currently operational across the world, with offices in Dubai and the United States. == Product overview == === For restaurants === Eat App’s reservation system allows for a digital record of all reservations, all guests that have previously visited the restaurant, as well as analytics on the performance of the restaurant. The table management feature simplifies traditional restaurant operations by providing a live snapshot of current status, seating optimization, and shift management. The CRM and analytics suite gathers and monitors data to build a segmented guestbook for personalized marketing and provides dashboards for data-driven decision-making. Additionally, the review feature makes it easy for restaurants to automatically collect reviews from their guests. Additionally, Eat App includes a chit printer function that seamlessly prints reservation details at host stands and a review management feature that allows restaurants to manage online reviews directly within the platform. == History == In February 2015, Eat App raised $300k from Bahrain-based business angel group TENMOU. In June 2018, Eat raised $1.2 million from Dubai-based Middle East Venture Partners (MEVP). In February 2020, Eat App raised $5 million in a Series B funding round led by 500 Startups, Derayah Venture Fund, and MEVP, with participation from a few angel investors and family members. In February 2021, Eat App launched its technology with The Emaar Hospitality Group, implementing it across over 50 restaurants in Emaar properties and hotels. The cloud-based system runs natively on iPads in each restaurant, providing Emaar staff access to reservations and guest information, and integrates with the U by Emaar loyalty app to personalize service. On September 28, 2022, Eat App announced the closing of an $11 million Series B funding round. The investment was led by Middle East Venture Partners (MEVP), 500 Startups, Derayah Venture Capital, Dallah Albaraka, Ali Zaid Al Quraishi & Brothers Company, and Rasameel Investment Company, with participation from existing investors.

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  • Resel

    Resel

    In image analysis, a resel (from resolution element) represents the actual spatial resolution in an image or a volumetric dataset. The number of resels in the image may be lower or equal to the number of pixel/voxels in the image. In an actual image the resels can vary across the image and indeed the local resolution can be expressed as "resels per pixel" (or "resels per voxel"). In functional neuroimaging analysis, an estimate of the number of resels together with random field theory is used in statistical inference. Keith Worsley has proposed an estimate for the number of resels/roughness. The word "resel" is related to the words "pixel", "texel", and "voxel". Waldo R. Tobler is probably among the first to use the word.

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