AI Assistant Gemini

AI Assistant Gemini — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Jive (software)

  Jive (formerly known as Clearspace, then Jive SBS, then Jive Engage) is a commercial Java EE-based Enterprise 2.0 collaboration and knowledge management tool produced by Jive Software. It was first released as "Clearspace" in 2006, then renamed SBS (for "Social Business Software") in March 2009, then renamed "Jive Engage" in 2011, and renamed simply to "Jive" in 2012. Jive integrates the functionality of online communities, microblogging, social networking, discussion forums, blogs, wikis, and IM under one unified user interface. Content placed into any of the systems (blog, wiki, documentation, etc.) can be found through a common search interface. Other features include RSS capability, email integration, a reputation and reward system for participation, personal user profiles, JAX-WS web service interoperability, and integration with the Spring Framework. The product is a pure-Java server-side web application and will run on any platform where Java (JDK 1.5 or higher) is installed. It does not require a dedicated server - users have reported successful deployment in both shared environments and multiple machine clusters. As of Jive 8, released March 30, 2015, there is a Jive-n version which is for internal use (hosted by the consumer or hosted by Jive as a service) and a Jive-x version which is an external version hosted as a service. Jive no longer supports wiki markup language. == Server requirements for Jive 8-n == The following are the server requirements for Jive 8-n Operating systems: RHEL version 6 or 7 for x86_64, CentOS version 6 or 7 for x86_64 or SuSE Enterprise Linux Server (SLES) 11 and 12 for x86_64 Application Servers: Jive ships with its own embedded Apache HTTPD and Tomcat servers as part of the install package. It is not possible to deploy the application onto other appservers. Databases: MySQL (5.1, 5.5, 5.6) Oracle (11gR2, 12c) Postgres (9.0, 9.1, 9.2, 9.3, 9.4 - 9.2 or higher recommended) Microsoft SQL Server (2008R2, 2012, 2014) Environment: Jive recommends a server with at least 4GB of RAM and a dual-core 2 GHz processor with x86_64 architecture The product integrates with an LDAP repository or Active Directory For optimal deployment with a large community Jive Software recommends: using dedicated cache and document-conversion servers hosting the application and database servers separately == Releases == Jive 8, released on March 30, 2015 Jive 7, released in October 2013 Jive 9.0.x, released in November 2016 Jive 9, released in November 2016, supported now

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  International Conference on Automated Planning and Scheduling

  The International Conference on Automated Planning and Scheduling (ICAPS) is a leading international academic conference in automated planning and scheduling held annually for researchers and practitioners in planning and scheduling. ICAPS is supported by the National Science Foundation, the journal Artificial Intelligence, and other supporters. == The IPC and PDDL == ICAPS conducts the International Planning Competition (IPC), a competition scheduled every few years that empirically evaluates state-of-the-art planning systems on a collection of benchmark problems. The Planning Domain Definition Language (PDDL) was developed mainly to make the 1998/2000 International Planning Competition possible, and then evolved with each competition. PDDL is an attempt to standardize Artificial Intelligence (AI) planning languages. PDDL was first developed by Drew McDermott and his colleagues in 1998, inspired by STRIPS, ADL, and other sources. == History == The ICAPS conferences began in 2003 as a merge of two bi-annual conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). == List of events ==

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  Land of Memories

  Land of Memories (Chinese: 机忆之地) is a Chinese science-fiction novel by Shen Yang (沈阳), a professor at Tsinghua University's School of Journalism and Communication. The story revolves around a former neuroscientist trying to recover her memories from the metaverse after suffering amnesia due to an accident. It contains almost 6,000 Chinese characters and was shortened from an AI-generated draft that was 43,000 characters long. The process involved 66 prompts spanning almost three hours. The novel was among 18 submissions that won the level-two prize at the Fifth Jiangsu Youth Science Education and Science Fiction Competition (第五届江苏省青年科普科幻作品大赛). The contest was restricted to participants between the age of 14 and 45 but did not forbid entries generated by AI. One of its organizers reached out to Shen after finding out that the professor had been experimenting with writing science fiction using AI. The judges were not told about the novel's origin in advance. Three of them, out of the six, approved the work. One judge, who had worked with AI models before, recognized that the novel was written by AI and criticized the work for lacking emotional appeal. The organizer who had contacted Shen said the novel's introduction was not bad but the story did not develop well. It would not meet the usual standards for publication. However, he still plans to allow AI-generated submissions in 2024. Fu Ruchu, editorial department director of the People's Literature Publishing House, said the novel was not easily identifiable as AI-generated and applauded its logical consistency. She warned that artificial intelligence could endanger the jobs of fiction writers and cause permanent damage to literary language.

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  Yu-Gi-Oh! VRAINS

  Yu-Gi-Oh! VRAINS (遊☆戯☆王VRAINS, Yū Gi Ō Vureinzu) is a Japanese anime series created and animated by Nihon Ad Systems (NAS) and Gallop. It is the fifth anime spin-off in the Yu-Gi-Oh! franchise. The series aired in Japan on TV Tokyo from May 10, 2017 to September 25, 2019. It was simulcast outside of Asia by Crunchyroll courtesy of Konami Cross Media NY. It premiered in the United States on November 3, 2020 on Pluto TV. The term 'VRAINS' derives from 'Virtual Reality' (VR), 'Artificial Intelligence' (AI), 'Network System' (NS). The series revolves around the exploits of the protagonist Yusaku within the virtual world named VRAINS. In addition to featuring previous summoning mechanics, VRAINS introduces the new "Link Summon" mechanic. The series was succeeded by Yu-Gi-Oh! Sevens, which premiered in Japan on April 4, 2020. == Plot == In a place known as Den City, thousands of duelists take part in a virtual reality space known as LINK VRAINS, created by SOL Technologies, where users can create unique avatars and participate in games of Duel Monsters with each other. As a mysterious hacker organization known as the Knights of Hanoi, led by Varis, threatens this world, a high-school student and hacking genius named Yusaku Fujiki battles against them under the guise of Playmaker. Both the Knights and SOL Technologies are also after a peculiar self-aware artificial intelligence program, who holds the key to a secret area inside the network named the Cyberse World, which the Knights of Hanoi seek to destroy. As the series begins, Yusaku sees the chance to capture this AI, which he names Ai, who sets off a digital maelstrom in LINK VRAINS known as the Data Storm. As the appearance of this storm gives birth to Speed Duels, in which duellists surf the wind as they duel, Yusaku battles against Hanoi in order to uncover the truth concerning an incident that happened to him 10 years ago. With the help of two Charisma Duellists, Go Onizuka (Japanese) and Skye Zaizen, who uses the alias Blue Angel (season 1), and Blue Maiden (season 2 onwards) online, Playmaker is able to defeat Varis, saving the entire network and part ways with Ai who decides to return to his own world, the Cyberse World. Three months after Hanoi's fall, Ai discovers the Cyberse World destroyed and his friends nowhere to be found, prompting him to return to Yusaku. Meanwhile, Yusaku once again fights as Playmaker after the consciousness of the younger brother of his friend, Cal Kolter, is stolen by a mysterious enemy named Bohman. In pursuit of Bohman, Yusaku and Ai are joined by Theodore Hamilton, a victim of the Lost Incident like Yusaku who uses the alias of Soulburner online and Ai's Fire Ignis friend based on Theodore, Flame. Aqua, the Water Ignis, follows soon after by becoming Skye's partner. At the same time, Varis revives Knights of Hanoi to fight against the new enemies. It's revealed that Bohman is a sentient AI created by the Light Ignis, Lightning, who reveals that he's the one who destroyed the Cyberse World and steals Cal's brother's consciousness. Deeming Ignis superior, he decides to destroy humanity. The Wind Ignis, Windy, also assists Lightning after his program was forcefully rewritten. To defeat Lightning's team, Yusaku and his friends join forces with Knights of Hanoi and enter Lightning's stronghold. Both sides fight until only Playmaker, Ai, and Bohman are left with the latter having absorbed all other Ignis. Before perishing, both Flame and Aqua give Ai the last of their powers, allowing him and Playmaker to defeat Bohman. After the fight against Bohman, LINK VRAINS is shut down and Ai disappear together with Yusaku's robot, Roboppi. Replacing LINK VRAINS, SOL Technology develops a humanoid robot SOLtis, which Ai and Roboppi uses to infiltrate SOL Technology and attack its high executive, Queen. Knowing he'll be the next target, Skye's older brother, Akira, enlists the help of Playmaker and his friends as well as Knights of Hanoi once more to protect him. Ai and Roboppi manage to defeat everyone except Playmaker, Soulburner, and Varis, who are forced to fight decoys. After defeating Akira and taking over SOL Technology, Ai reopens LINK VRAINS and delivers a message for Playmaker that tells the whereabout of his location. Yusaku confronts Ai alone, leading the two of them to duel. Ai explains that Lightning left behind a simulation that shows the world will be destroyed if Ai is the only Ignis left. Fearing that he'll become like Lightning and Bohman, Ai decides to end his life either by Playmaker's hand if he loses or by scattering his free will into the SOLtis if he wins. Despite Playmaker's attempt to dissuade Ai, he still refuses to back down, forcing Playmaker to defeat him. In his last moment, Ai reveals that within the simulations, Yusaku always ends up dying protecting him, which is a future that he wishes to avoid. Three months after the final battle, everyone moves on with their lives and Yusaku goes on a journey. Somewhere within the network, Ai is revealed to be alive. == Production == Yu-Gi-Oh! VRAINS was first announced on December 16, 2016. It began airing on TV Tokyo in Japan on May 10, 2017. The series is being directed by Masahiro Hosoda at Studio Gallop with screenplay by Shin Yoshida and character design by Ken'ichi Hara. It would be the final anime series in the franchise to be animated by Gallop; Bridge would animate future instalments beginning with Yu-Gi-Oh! Sevens. The series ended on September 25, 2019. The series is being simulcast with English subtitles outside of Asia by Crunchyroll. This makes it the first series in the Yu-Gi-Oh! franchise to receive an official simulcast alongside its Japanese broadcast. A localized English adaptation was produced by Konami Cross Media NY. The pilot episode was previewed along with a digitally remastered screening of Yu-Gi-Oh! The Movie: Pyramid of Light on March 11, 2018 and March 12, 2018 in the US, and on June 13, 2018 in the UK. The English dub began airing on Teletoon in Canada on September 1, 2018, and on 9Go! in Australia on April 6, 2019. In November 2020, Cinedigm announced that the streaming service Pluto TV has secured exclusive rights in multiple territories, including the United States and Latin America, to VRAINS. Pluto TV would launch a channel dedicated to the Yu-Gi-Oh! franchise, featuring episodes from the entire Yu-Gi-Oh! Duel Monsters metaseries, including VRAINS, available in English and dubbed in multiple languages. == Trading Card Game == Yu-Gi-Oh! VRAINS introduces new gameplay elements to the Yu-Gi-Oh! Trading Card Game. With the release of the "Link Strike Starter Deck", it introduced the New Master Rules (also known as Master Rule 4 in some countries) to the competitive field of play. Now, only one monster can be summoned directly from each player's Extra Deck at a time, which is placed in one of the two new zones in the middle of the field called the "Extra Monster Zone". Complementing this new gameplay element are the new Link Monsters, honey-comb blue colored monsters that go into your Extra Deck. They do not have "Levels" or "Ranks", but instead have a "Link Rating", which indicates the number of arrows on the card and the required number of monsters required to summon them. A Link Monster's Link Rating can also be used as a number of materials for a Link Summon depending on their rating, subtracted from the Link Monster the player wishes to summon. Link Monsters have a number of Link Arrows equal to their Link Rating that point either vertically, horizontally, and/or diagonally. These Link Arrows that point to an empty Main Monster Zone allow the player to summon monsters from the Extra Deck, which include face-up Pendulum Monsters. The two Pendulum Zones have been moved to the far ends of the Spell & Trap Zones, though they also double as regular Spell & Trap Zones should the player wish not to use them. In 2019, a new format exclusive to the TCG was introduced separate from the main game, known as Speed Duels. The rules are similar to the main game and parallel the formatting used in the mobile game Duel Links. A format meant as a beginner's introduction to the basics, both the field and each player's decks have been drastically simplified to reflect that. Decks contain only 20-30 cards, each player gets only three Main Monster zones, and a turn will immediately end following the Battle Phase. Exclusive to Speed Duels, each player is allowed one Skill Card, which a player places face down during the beginning of a duel and can use anytime. == Reception == The series ranked 52 in Tokyo Anime Award Festival in Best 100 TV Anime 2017 category. The series' rank rose up to 8 in the same award in 2020 with 28,369 votes.

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  ChatScript

  ChatScript is a combination Natural Language engine and dialog management system designed initially for creating chatbots, but is currently also used for various forms of NL processing. It is written in C++. The engine is an open source project at SourceForge. and GitHub. ChatScript was written by Bruce Wilcox and originally released in 2011, after Suzette (written in ChatScript) won the 2010 Loebner Prize, fooling one of four human judges. == Features == In general ChatScript aims to author extremely concisely, since the limiting scalability of hand-authored chatbots is how much/fast one can write the script. Because ChatScript is designed for interactive conversation, it automatically maintains user state across volleys. A volley is any number of sentences the user inputs at once and the chatbots response. The basic element of scripting is the rule. A rule consists of a type, a label (optional), a pattern, and an output. There are three types of rules. Gambits are something a chatbot might say when it has control of the conversation. Rejoinders are rules that respond to a user remark tied to what the chatbot just said. Responders are rules that respond to arbitrary user input which is not necessarily tied to what the chatbot just said. Patterns describe conditions under which a rule may fire. Patterns range from extremely simplistic to deeply complex (analogous to Regex but aimed for NL). Heavy use is typically made of concept sets, which are lists of words sharing a meaning. ChatScript contains some 2000 predefined concepts and scripters can easily write their own. Output of a rule intermixes literal words to be sent to the user along with common C-style programming code. Rules are bundled into collections called topics. Topics can have keywords, which allows the engine to automatically search the topic for relevant rules based on user input. == Example code == Words starting with ~ are concept sets. For example, ~fruit is the list of all known fruits. The simple pattern (~fruit) reacts if any fruit is mentioned immediately after the chatbot asks for favorite food. The slightly more complex pattern for the rule labelled WHATMUSIC requires all the words what, music, you and any word or phrase meaning to like, but they may occur in any order. Responders come in three types. ?: rules react to user questions. s: rules react to user statements. u: rules react to either. ChatScript code supports standard if-else, loops, user-defined functions and calls, and variable assignment and access. == Data == Some data in ChatScript is transient, meaning it will disappear at the end of the current volley. Other data is permanent, lasting forever until explicitly killed off. Data can be local to a single user or shared across all users at the bot level. Internally all data is represented as text and is automatically converted to a numeric form as needed. === Variables === User variables come in several kinds. Variables purely local to a topic or function are transient. Global variables can be declared as transient or permanent. A variable is generally declared merely by using it, and its type depends on its prefix ($, $$, $_). === Facts === In addition to variables, ChatScript supports facts – triples of data, which can also be transient or permanent. Functions can query for facts having particular values of some of the fields, making them act like an in-memory database. Fact retrieval is very quick and efficient the number of available in-memory facts is largely constrained to the available memory of the machine running the ChatScript engine. Facts can represent record structures and are how ChatScript represents JSON internally. Tables of information can be defined to generate appropriate facts. The above table links people to what they invented (1 per line) with Einstein getting a list of things he did. == External communication == ChatScript embeds the Curl library and can directly read and write facts in JSON to a website. == Server == A ChatScript engine can run in local or server mode. == Pos-tagging, parsing, and ontology == ChatScript comes with a copy of English WordNet embedded within, including its ontology, and creates and extends its own ontology via concept declarations. It has an English language pos-tagger and parser and supports integration with TreeTagger for pos-tagging a number of other languages (TreeTagger commercial license required). == Databases == In addition to an internal fact database, ChatScript supports PostgreSQL, MySQL, MSSQL and MongoDB both for access by scripts, but also as a central filesystem if desired so ChatScript can be scaled horizontally. A common use case is to use a centralized database to host the user files and multiple servers to scale the ChatScript engine. == JavaScript == ChatScript also embeds DukTape, ECMAScript E5/E5.1 compatibility, with some semantics updated from ES2015+. == Spelling Correction == ChatScript has built-in automatic spell checking, which can be augmented in script as both simple word replacements or context sensitive changes. With appropriate simple rules you can change perfect legal words into other words or delete them. E.g., if you have a concept of ~electronic_goods and don't want an input of Radio Shack (a store name) to be detected as an electronic good, you can get the input to change to Radio_Shack (a single word), or allow the words to remain but block the detection of the concept. This is particularly useful when combined with speech-to-text code that is imperfect, but you are familiar with common failings of it and can compensate for them in script. == Control flow == A chatbot's control flow is managed by the control script. This is merely another ordinary topic of rules, that invokes API functions of the engine. Thus control is fully configurable by the scripter (and functions exist to allow introspection into the engine). There are pre-processing control flow and post-processing control flow options available, for special processing.

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  European Conference on Artificial Intelligence

  The European Conference on Artificial Intelligence (ECAI) is the leading conference in the field of Artificial Intelligence in Europe, and is commonly listed together with IJCAI and AAAI as one of the three major general AI conferences worldwide. The conference series has been held without interruption since 1974, originally under the name AISB. The conference was originally held biennially, but has been organized annually since ECAI 2022. The conferences are held under the auspices of the European Coordinating Committee for Artificial Intelligence (ECCAI) and organized by one of the member societies. The journal AI Communications, sponsored by the same society, regularly publishes special issues in which conference attendees report on the conference. Publication of a paper in ECAI is considered by some journals to be archival: the paper should be considered equivalent to a journal publication and that the contents of ECAI papers cannot be reformulated as separate journal submissions unless a significant amount of new material is added. == List of ECAI conferences == ECAI-1992 took place in Vienna, Austria. ECAI-1996 took place in Budapest, Hungary. ECAI-1998 tool place in Brighton, United Kingdom. ECAI-2000 took place in Berlin, Germany. ECAI-2004 took place in Valencia, Spain. ECAI-2006 took place in Riva del Garda, Italy. ECAI-2008 took place in Patras, Greece. ECAI-2010 took place in Lisbon, Portugal. ECAI-2012 took place in Montpellier, France. ECAI-2014 took place in Prague, Czech Republic. ECAI-2016 took place in The Hague, Netherlands. ECAI-2018 took place in Stockholm, Sweden. ECAI-2020 took place in Santiago de Compostela, Spain. ECAI-2022 took place in Vienna, Austria. ECAI-2023 took place in Kraków, Poland. ECAI-2024 took place in Santiago de Compostela, Spain. ECAI-2025 took place in Bologna, Italy.

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  Taylor Swift deepfake pornography controversy

  In late January 2024, sexually explicit AI-generated deepfake images of American musician Taylor Swift were proliferated on social media platforms 4chan and X (formerly Twitter). Several artificial images of Swift of a sexual or violent nature were quickly spread, with one post reported to have been seen over 47 million times before its eventual removal. The images led Microsoft to enhance Microsoft Designer's text-to-image model to prevent future abuse. Moreover, these images prompted responses from anti-sexual assault advocacy groups, US politicians, Swifties, and Microsoft CEO Satya Nadella, among others, and it has been suggested that Swift's influence could result in new legislation regarding the creation of deepfake pornography. A similar controversy emerged in August 2025, when The Verge reported AI image and video tool Grok Imagine generated sexually explicit images and videos of Swift from an otherwise innocuous text prompt. == Background == American musician Taylor Swift has been the target of misogyny and slut-shaming throughout her career. American technology corporation Microsoft offers AI image creators called Microsoft Designer and Bing Image Creator, which employ censorship safeguards to prevent users from generating unsafe or objectionable content. Members of a Telegram group discussed ways to circumvent these censors to create pornographic images of celebrities. Graphika, a disinformation research firm, traced the creation of the images back to a 4chan community. == Reactions == For some, the deepfake images of Swift immediately became a source of controversy and outrage. Other internet users found them humorous and absurd, such as the image making it appear as though Swift was to engage in sexual intercourse with Oscar the Grouch. The images drew condemnations from Rape, Abuse & Incest National Network and SAG-AFTRA. The latter group, who had been following issues regarding AI-generated media prior to Swift's involvement, considered the images "upsetting, harmful and deeply concerning." Microsoft CEO Satya Nadella, whose company's products were believed to be used to make these images, responded to the controversy as "alarming and terrible", further stating his belief that "we all benefit when the online world is a safe world." === Taylor Swift === A source close to Swift told the Daily Mail that she would be considering legal action, saying, "Whether or not legal action will be taken is being decided, but there is one thing that is clear: These fake AI-generated images are abusive, offensive, exploitative, and done without Taylor's consent and/or knowledge." === Politicians === White House press secretary Karine Jean-Pierre expressed concern over the counterfeit images, deeming them "alarming", and emphasized the obligation of social media platforms to curb the dissemination of misinformation. Several members of American politics called for legislation against AI-generated pornography. Later in the month, a bipartisan bill was introduced by US senators Dick Durbin, Lindsey Graham, Amy Klobuchar and Josh Hawley. The bill would allow victims to sue individuals who produced or possessed "digital forgeries" with intent to distribute, or those who received the material knowing it was made without consent. The European Union struck a deal in February 2024 on a similar bill that would criminalize deepfake pornography, as well as online harassment and revenge porn, by mid-2027. === Social media platforms === X responded to the sharing of these images on their own website with claims they would suspend accounts that participated in their spread. Despite this, the photos continued to be reshared among accounts of X, and spread to other platforms including Instagram and Reddit. X enforces a "synthetic and manipulated media policy", which has been criticized for its efficacy. They briefly blocked searches of Swift's name on January 27, 2024, reinstating them two days later. === Swifties === Fans of Taylor Swift, known as Swifties, responded to the circulation of these images by pushing the hashtag #ProtectTaylorSwift to trend on X. They also flooded other hashtags related to the images with more positive images and videos of her live performances. == Cultural significance == Deepfake pornography has remained highly controversial and has affected figures from other celebrities to ordinary people, most of whom are women. Journalists have opined that the involvement of a prominent public figure such as Swift in the dissemination of AI-generated pornography could bring public awareness and political reform to the issue.

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  Conference on Artificial General Intelligence

  The Conference on Artificial General Intelligence (AGI) is a meeting of researchers in the field of artificial general intelligence (AGI) organized by the AGI Society steered by Marcus Hutter and Ben Goertzel. It has been held annually since 2008. The conference was initiated by the 2006 Bethesda Artificial General Intelligence Workshop and has since been hosted at various international venues. == Locations and history == AGI-2026 San Francisco State University, California, USA AGI-2025 Reykjavík University, Reykjavík, Iceland AGI-2024 University of Washington, Seattle, Washington, USA AGI-2023 KTH Royal Institute of Technology, Stockholm, Sweden AGI-2022 The Crocodile, Seattle, Washington, USA AGI-2021 Computer History Museum, Mountain View, California, USA AGI-2020 Virtual Conference AGI-2019 Sheraton Shenzhen Futian, Shenzhen, China AGI-2018 Czech Technical University, Prague, Czech Republic AGI-2017 ibis Melbourne, Melbourne, Australia AGI-2016 The New School, New York, New York, USA AGI-2015 Berlin-Brandenburg Academy of Sciences and Humanities, Berlin, Germany AGI-2014 Université Laval, Quebec City, Canada (sponsored by the Cognitive Science Society and the AAAI) AGI-2013 Peking University, Beijing, China (sponsored by the Cognitive Science Society and the AAAI) AGI-2012 University of Oxford, Oxford, United Kingdom (sponsored by the Future of Humanity Institute and Ray Kurzweil) AGI-2011 Google Headquarters, Mountain View, California, USA (sponsored by Google, AAAI, and Ray Kurzweil) AGI-2010 University of Lugano, Lugano, Switzerland (In Memoriam Ray Solomonoff and sponsored by AAAI and Ray Kurzweil) AGI-2009 Crowne Plaza Crystal City, Arlington, Virginia, USA (sponsored by AAAI and Ray Kurzweil) AGI-2008 University of Memphis, Tennessee, USA (sponsored by AAAI) == Notable speakers == The conference has attracted many speakers over the years including Turing Award winners Yoshua Bengio and Richard S. Sutton as well as Ben Goertzel, Marcus Hutter, Jürgen Schmidhuber, Gary Marcus, John E. Laird, Peter Norvig, Joscha Bach, François Chollet, John L. Pollock, Bill Hibbard, Hugo de Garis, Stan Franklin, Steve Omohundro, Randal A. Koene, Ernst Dickmanns, Margaret Boden, David Hanson, Roman Yampolskly, Selmer Bringsjord, Kristinn R. Thórisson and Nick Bostrom.

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  System requirements specification

  A System Requirements Specification (SysRS) (abbreviated SysRS to be distinct from a software requirements specification (SRS)) is a structured collection of information that embodies the requirements of a system. A business analyst (BA), sometimes titled system analyst, is responsible for analyzing the business needs of their clients and stakeholders to help identify business problems and propose solutions. Within the systems development life cycle domain, the BA typically performs a liaison function between the business side of an enterprise and the information technology department or external service providers.

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  Clanker

  Clanker is a derogatory term for robots and artificial intelligence (AI) software. The term has been used in Star Wars media, first appearing in the franchise's 2005 video game Star Wars: Republic Commando. In 2025, the term became widely used to express hatred or distaste for machines ranging from delivery robots to large language models. This trend has been attributed to anxiety around the negative societal effects of AI. == In science fiction == The term has been previously used in science fiction literature, first appearing in a 1958 article by William Tenn in which he uses it to describe robots from science fiction films like Metropolis. The Star Wars franchise began using the term as a slur against droids in the 2005 video game Star Wars: Republic Commando before being prominently used in the animated series Star Wars: The Clone Wars, which follows a galaxy-wide war between the Galactic Republic's clone troopers and the Confederacy of Independent Systems' battle droids. In Star Wars media, robots—more commonly known as droids—are routinely depicted as the subjects of discrimination. For example, in the original Star Wars film, C-3PO and R2-D2 are abducted by Jawas and sold to the family of Luke Skywalker. When visiting a cantina in Mos Eisley, both droids are refused service by the bartender, who remarks that "We don't serve their kind." In Star Wars lore, the term clanker had entered use by the time of the franchise's High Republic Era and became prominent during the Clone Wars, in which clone troopers regularly use the phrase against battle droids. == AI backlash == The growing popularity of the term clanker reflects an increase in direct contact between people and AI systems. On sidewalks, delivery robots impede mobility and cause safety issues. In digital spaces, cybersecurity experts have raised concerns about the rising number of bots online, which now make up a large portion of internet traffic. A 2025 report estimated that about one in five social media accounts are automated. The term is also a reaction to AI advocacy from industrialists like Elon Musk and Sam Altman, who have championed the integration of AI into nearly every aspect of modern life. This includes efforts by major companies and startups alike, such as Amazon's development of humanoid robots to replace human workers in service industries. Such initiatives have further fueled public skepticism, reinforcing the association of clanker with unease over automation and the displacement of human roles. A global survey conducted by the research firm Gartner in December 2023 found that 64% of customers would prefer companies to avoid using AI in customer service, with another 53% stating they would consider switching to a different company if they discovered AI was handling their service interactions. Another report by Ernst & Young, published in July 2025, found that 42% of employees across Europe are worried that the use of AI in the workplace may threaten their employment. Criticism has also been directed at the technology itself. Some of the backlash stems from concerns about the resource consumption of AI systems, their frequent reliance on copyrighted material without consent, and questions about the intentions of the corporations behind them. There are also concerns about the potential cognitive effects of relying heavily on AI. A study, authored by researchers at Microsoft and Carnegie Mellon University, warns that regular dependence on AI may leave users mentally unprepared for real-world problem solving, likening the effect to cognitive atrophy. In June 2025, United States Senator Ruben Gallego tweeted that his "new bill makes sure you don't have to talk to a clanker if you don't want to", referring to proposed legislation that would require call centers to disclose their use of automated customer service agents to callers in the United States and offer the option to switch to a human representative. == Analysis == Linguist Adam Aleksic has described clanker as an evolution of racial slurs that anthropomorphize robotic systems. Internet memes incorporating the term often reference historical discrimination against marginalized groups such as African Americans. Based on the work of linguist Geoffrey Nunberg, American news website Axios has argued that clanker is merely a derogatory word, rather than a slur, because it does not perpetuate social inequities. NPR has noted the irony that the word robot was coined by Karel Čapek for his 1920 science-fiction play R.U.R. as a similar criticism of industrialization forcing workers to become devoid of their humanity. Aleksic has observed that robot can be further traced to the Proto-Slavic noun orbъ, which means 'slave'. While other science fiction media include pejoratives for androids and robots, such as skinjob and toaster from the Blade Runner and Battlestar Galactica franchises, respectively, clanker is believed to have gained popularity because its usage is intuitive and flexible. Whereas AI slop describes low-quality output from artificial intelligence, clanker belittles the underlying computer systems.

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  Estimation of distribution algorithm

  Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), are stochastic optimization methods that guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Optimization is viewed as a series of incremental updates of a probabilistic model, starting with the model encoding an uninformative prior over admissible solutions and ending with the model that generates only the global optima. EDAs belong to the class of evolutionary algorithms. The main difference between EDAs and most conventional evolutionary algorithms is that evolutionary algorithms generate new candidate solutions using an implicit distribution defined by one or more variation operators, whereas EDAs use an explicit probability distribution encoded by a Bayesian network, a multivariate normal distribution, or another model class. Similarly as other evolutionary algorithms, EDAs can be used to solve optimization problems defined over a number of representations from vectors to LISP style S expressions, and the quality of candidate solutions is often evaluated using one or more objective functions. The general procedure of an EDA is outlined in the following: t := 0 initialize model M(0) to represent uniform distribution over admissible solutions while (termination criteria not met) do P := generate N>0 candidate solutions by sampling M(t) F := evaluate all candidate solutions in P M(t + 1) := adjust_model(P, F, M(t)) t := t + 1 Using explicit probabilistic models in optimization allowed EDAs to feasibly solve optimization problems that were notoriously difficult for most conventional evolutionary algorithms and traditional optimization techniques, such as problems with high levels of epistasis. Nonetheless, the advantage of EDAs is also that these algorithms provide an optimization practitioner with a series of probabilistic models that reveal a lot of information about the problem being solved. This information can in turn be used to design problem-specific neighborhood operators for local search, to bias future runs of EDAs on a similar problem, or to create an efficient computational model of the problem. For example, if the population is represented by bit strings of length 4, the EDA can represent the population of promising solution using a single vector of four probabilities (p1, p2, p3, p4) where each component of p defines the probability of that position being a 1. Using this probability vector it is possible to create an arbitrary number of candidate solutions. == Estimation of distribution algorithms (EDAs) == This section describes the models built by some well known EDAs of different levels of complexity. It is always assumed a population P ( t ) {\displaystyle P(t)} at the generation t {\displaystyle t} , a selection operator S {\displaystyle S} , a model-building operator α {\displaystyle \alpha } and a sampling operator β {\displaystyle \beta } . == Univariate factorizations == The most simple EDAs assume that decision variables are independent, i.e. p ( X 1 , X 2 ) = p ( X 1 ) ⋅ p ( X 2 ) {\displaystyle p(X_{1},X_{2})=p(X_{1})\cdot p(X_{2})} . Therefore, univariate EDAs rely only on univariate statistics and multivariate distributions must be factorized as the product of N {\displaystyle N} univariate probability distributions, D Univariate := p ( X 1 , … , X N ) = ∏ i = 1 N p ( X i ) . {\displaystyle D_{\text{Univariate}}:=p(X_{1},\dots ,X_{N})=\prod _{i=1}^{N}p(X_{i}).} Such factorizations are used in many different EDAs, next we describe some of them. === Univariate marginal distribution algorithm (UMDA) === The UMDA is a simple EDA that uses an operator α U M D A {\displaystyle \alpha _{UMDA}} to estimate marginal probabilities from a selected population S ( P ( t ) ) {\displaystyle S(P(t))} . By assuming S ( P ( t ) ) {\displaystyle S(P(t))} contain λ {\displaystyle \lambda } elements, α U M D A {\displaystyle \alpha _{UMDA}} produces probabilities: p t + 1 ( X i ) = 1 λ ∑ x ∈ S ( P ( t ) ) x i , ∀ i ∈ 1 , 2 , … , N . {\displaystyle p_{t+1}(X_{i})={\dfrac {1}{\lambda }}\sum _{x\in S(P(t))}x_{i},~\forall i\in 1,2,\dots ,N.} Every UMDA step can be described as follows D ( t + 1 ) = α UMDA ∘ S ∘ β λ ( D ( t ) ) . {\displaystyle D(t+1)=\alpha _{\text{UMDA}}\circ S\circ \beta _{\lambda }(D(t)).} === Population-based incremental learning (PBIL) === The PBIL, represents the population implicitly by its model, from which it samples new solutions and updates the model. At each generation, μ {\displaystyle \mu } individuals are sampled and λ ≤ μ {\displaystyle \lambda \leq \mu } are selected. Such individuals are then used to update the model as follows p t + 1 ( X i ) = ( 1 − γ ) p t ( X i ) + ( γ / λ ) ∑ x ∈ S ( P ( t ) ) x i , ∀ i ∈ 1 , 2 , … , N , {\displaystyle p_{t+1}(X_{i})=(1-\gamma )p_{t}(X_{i})+(\gamma /\lambda )\sum _{x\in S(P(t))}x_{i},~\forall i\in 1,2,\dots ,N,} where γ ∈ ( 0 , 1 ] {\displaystyle \gamma \in (0,1]} is a parameter defining the learning rate, a small value determines that the previous model p t ( X i ) {\displaystyle p_{t}(X_{i})} should be only slightly modified by the new solutions sampled. PBIL can be described as D ( t + 1 ) = α PIBIL ∘ S ∘ β μ ( D ( t ) ) {\displaystyle D(t+1)=\alpha _{\text{PIBIL}}\circ S\circ \beta _{\mu }(D(t))} === Compact genetic algorithm (cGA) === The CGA, also relies on the implicit populations defined by univariate distributions. At each generation t {\displaystyle t} , two individuals x , y {\displaystyle x,y} are sampled, P ( t ) = β 2 ( D ( t ) ) {\displaystyle P(t)=\beta _{2}(D(t))} . The population P ( t ) {\displaystyle P(t)} is then sorted in decreasing order of fitness, S Sort ( f ) ( P ( t ) ) {\displaystyle S_{{\text{Sort}}(f)}(P(t))} , with u {\displaystyle u} being the best and v {\displaystyle v} being the worst solution. The CGA estimates univariate probabilities as follows p t + 1 ( X i ) = p t ( X i ) + γ ( u i − v i ) , ∀ i ∈ 1 , 2 , … , N , {\displaystyle p_{t+1}(X_{i})=p_{t}(X_{i})+\gamma (u_{i}-v_{i}),\quad \forall i\in 1,2,\dots ,N,} where, γ ∈ ( 0 , 1 ] {\displaystyle \gamma \in (0,1]} is a constant defining the learning rate, usually set to γ = 1 / N {\displaystyle \gamma =1/N} . The CGA can be defined as D ( t + 1 ) = α CGA ∘ S Sort ( f ) ∘ β 2 ( D ( t ) ) {\displaystyle D(t+1)=\alpha _{\text{CGA}}\circ S_{{\text{Sort}}(f)}\circ \beta _{2}(D(t))} == Bivariate factorizations == Although univariate models can be computed efficiently, in many cases they are not representative enough to provide better performance than GAs. In order to overcome such a drawback, the use of bivariate factorizations was proposed in the EDA community, in which dependencies between pairs of variables could be modeled. A bivariate factorization can be defined as follows, where π i {\displaystyle \pi _{i}} contains a possible variable dependent to X i {\displaystyle X_{i}} , i.e. | π i | = 1 {\displaystyle |\pi _{i}|=1} . D Bivariate := p ( X 1 , … , X N ) = ∏ i = 1 N p ( X i | π i ) . {\displaystyle D_{\text{Bivariate}}:=p(X_{1},\dots ,X_{N})=\prod _{i=1}^{N}p(X_{i}|\pi _{i}).} Bivariate and multivariate distributions are usually represented as probabilistic graphical models (graphs), in which edges denote statistical dependencies (or conditional probabilities) and vertices denote variables. To learn the structure of a PGM from data linkage-learning is employed. === Mutual information maximizing input clustering (MIMIC) === The MIMIC factorizes the joint probability distribution in a chain-like model representing successive dependencies between variables. It finds a permutation of the decision variables, r : i ↦ j {\displaystyle r:i\mapsto j} , such that x r ( 1 ) x r ( 2 ) , … , x r ( N ) {\displaystyle x_{r(1)}x_{r(2)},\dots ,x_{r(N)}} minimizes the Kullback–Leibler divergence in relation to the true probability distribution, i.e. π r ( i + 1 ) = { X r ( i ) } {\displaystyle \pi _{r(i+1)}=\{X_{r(i)}\}} . MIMIC models a distribution p t + 1 ( X 1 , … , X N ) = p t ( X r ( N ) ) ∏ i = 1 N − 1 p t ( X r ( i ) | X r ( i + 1 ) ) . {\displaystyle p_{t+1}(X_{1},\dots ,X_{N})=p_{t}(X_{r(N)})\prod _{i=1}^{N-1}p_{t}(X_{r(i)}|X_{r(i+1)}).} New solutions are sampled from the leftmost to the rightmost variable, the first is generated independently and the others according to conditional probabilities. Since the estimated distribution must be recomputed each generation, MIMIC uses concrete populations in the following way P ( t + 1 ) = β μ ∘ α MIMIC ∘ S ( P ( t ) ) . {\displaystyle P(t+1)=\beta _{\mu }\circ \alpha _{\text{MIMIC}}\circ S(P(t)).} === Bivariate marginal distribution algorithm (BMDA) === The BMDA factorizes the joint probability distribution in bivariate distributions. First, a randomly chosen variable is added as a node in a graph, the most dependent variable to one of those in the graph is chosen among those not yet in the graph, this procedure is repeated until no remain

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  Xaitment

  xaitment is a German-based company that develops and sells artificial intelligence (AI) software to video game developers and simulation developers. The company was founded in 2004 by Dr. Andreas Gerber, and is a spin-off of the German Research Centre for Artificial Intelligence, or DFKI. xaitment has its main office in Quierschied, Germany, and field offices in San Francisco and China. == Products == xaitment currently sells two AI software modules: xaitMap and xaitControl. xaitMap provides runtime libraries and graphical tools for navigation mesh generation (also called NavMesh generation), pathfinding, dynamic collision avoidance, and individual and crowd movement. xaitControl is a finite-state machine for game logic and character behavior modeling that also includes a real-time debugger. On January 11, 2012, xaitment announced that it making its source code for these modules available to "all current and future US and European licensees". On February 22, 2012 xaitment released two new plug-ins, xaitMap and xaitControl for the Unity Game Engine. The full versions are available for PC (Windows and Linux), PlayStation 3, Xbox 360 and Wii. The pathfinding plug-in is available with a Windows dev environment, but can deployed on iOS, Mac, Android and the Unity Web Player. == Partners == xaitment's AI software is currently integrated into the Unity game engine, Havok's Vision Engine, Bohemia Interactive's VBS2 Simulation Engine, GameBase's Gamebryo game engine. == Customers == xaitment sells its AI software products to video game developers and military and civil simulation developers. Current customers include Tencent, gamania, TML Studios, Emobi Games, IP Keys and others. A full list of customers can be found on xaitment's website.

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  Geometric primitive

  In vector computer graphics, CAD systems, and geographic information systems, a geometric primitive (or prim) is the simplest (i.e. 'atomic' or irreducible) geometric shape that the system can handle (draw, store). Sometimes the subroutines that draw the corresponding objects are called "geometric primitives" as well. The most "primitive" primitives are point and straight line segments, which were all that early vector graphics systems had. In constructive solid geometry, primitives are simple geometric shapes such as a cube, cylinder, sphere, cone, pyramid, torus. Modern 2D computer graphics systems may operate with primitives which are curves (segments of straight lines, circles and more complicated curves), as well as shapes (boxes, arbitrary polygons, circles). A common set of two-dimensional primitives includes lines, points, and polygons, although some people prefer to consider triangles primitives, because every polygon can be constructed from triangles (polygon triangulation). All other graphic elements are built up from these primitives. In three dimensions, triangles or polygons positioned in three-dimensional space can be used as primitives to model more complex 3D forms. In some cases, curves (such as Bézier curves, circles, etc.) may be considered primitives; in other cases, curves are complex forms created from many straight, primitive shapes. == Common primitives == The set of geometric primitives is based on the dimension of the region being represented: Point (0-dimensional), a single location with no height, width, or depth. Line or curve (1-dimensional), having length but no width, although a linear feature may curve through a higher-dimensional space. Planar surface or curved surface (2-dimensional), having length and width. Volumetric region or solid (3-dimensional), having length, width, and depth. In GIS, the terrain surface is often spoken of colloquially as "2 1/2 dimensional," because only the upper surface needs to be represented. Thus, elevation can be conceptualized as a scalar field property or function of two-dimensional space, affording it a number of data modeling efficiencies over true 3-dimensional objects. A shape of any of these dimensions greater than zero consists of an infinite number of distinct points. Because digital systems are finite, only a sample set of the points in a shape can be stored. Thus, vector data structures typically represent geometric primitives using a strategic sample, organized in structures that facilitate the software interpolating the remainder of the shape at the time of analysis or display, using the algorithms of Computational geometry. A Point is a single coordinate in a Cartesian coordinate system. Some data models allow for Multipoint features consisting of several disconnected points. A Polygonal chain or Polyline is an ordered list of points (termed vertices in this context). The software is expected to interpolate the intervening shape of the line between adjacent points in the list as a parametric curve, most commonly a straight line, but other types of curves are frequently available, including circular arcs, cubic splines, and Bézier curves. Some of these curves require additional points to be defined that are not on the line itself, but are used for parametric control. A Polygon is a polyline that closes at its endpoints, representing the boundary of a two-dimensional region. The software is expected to use this boundary to partition 2-dimensional space into an interior and exterior. Some data models allow for a single feature to consist of multiple polylines, which could collectively connect to form a single closed boundary, could represent a set of disjoint regions (e.g., the state of Hawaii), or could represent a region with holes (e.g., a lake with an island). A Parametric shape is a standardized two-dimensional or three-dimensional shape defined by a minimal set of parameters, such as an ellipse defined by two points at its foci, or three points at its center, vertex, and co-vertex. A Polyhedron or Polygon mesh is a set of polygon faces in three-dimensional space that are connected at their edges to completely enclose a volumetric region. In some applications, closure may not be required or may be implied, such as modeling terrain. The software is expected to use this surface to partition 3-dimensional space into an interior and exterior. A triangle mesh is a subtype of polyhedron in which all faces must be triangles, the only polygon that will always be planar, including the Triangulated irregular network (TIN) commonly used in GIS. A parametric mesh represents a three-dimensional surface by a connected set of parametric functions, similar to a spline or Bézier curve in two dimensions. The most common structure is the Non-uniform rational B-spline (NURBS), supported by most CAD and animation software. == Application in GIS == A wide variety of vector data structures and formats have been developed during the history of Geographic information systems, but they share a fundamental basis of storing a core set of geometric primitives to represent the location and extent of geographic phenomena. Locations of points are almost always measured within a standard Earth-based coordinate system, whether the spherical Geographic coordinate system (latitude/longitude), or a planar coordinate system, such as the Universal Transverse Mercator. They also share the need to store a set of attributes of each geographic feature alongside its shape; traditionally, this has been accomplished using the data models, data formats, and even software of relational databases. Early vector formats, such as POLYVRT, the ARC/INFO Coverage, and the Esri shapefile support a basic set of geometric primitives: points, polylines, and polygons, only in two dimensional space and the latter two with only straight line interpolation. TIN data structures for representing terrain surfaces as triangle meshes were also added. Since the mid 1990s, new formats have been developed that extend the range of available primitives, generally standardized by the Open Geospatial Consortium's Simple Features specification. Common geometric primitive extensions include: three-dimensional coordinates for points, lines, and polygons; a fourth "dimension" to represent a measured attribute or time; curved segments in lines and polygons; text annotation as a form of geometry; and polygon meshes for three-dimensional objects. Frequently, a representation of the shape of a real-world phenomenon may have a different (usually lower) dimension than the phenomenon being represented. For example, a city (a two-dimensional region) may be represented as a point, or a road (a three-dimensional volume of material) may be represented as a line. This dimensional generalization correlates with tendencies in spatial cognition. For example, asking the distance between two cities presumes a conceptual model of the cities as points, while giving directions involving travel "up," "down," or "along" a road imply a one-dimensional conceptual model. This is frequently done for purposes of data efficiency, visual simplicity, or cognitive efficiency, and is acceptable if the distinction between the representation and the represented is understood, but can cause confusion if information users assume that the digital shape is a perfect representation of reality (i.e., believing that roads really are lines). == In 3D modelling == In CAD software or 3D modelling, the interface may present the user with the ability to create primitives which may be further modified by edits. For example, in the practice of box modelling the user will start with a cuboid, then use extrusion and other operations to create the model. In this use the primitive is just a convenient starting point, rather than the fundamental unit of modelling. A 3D package may also include a list of extended primitives which are more complex shapes that come with the package. For example, a teapot is listed as a primitive in 3D Studio Max. == In graphics hardware == Various graphics accelerators exist with hardware acceleration for rendering specific primitives such as lines or triangles, frequently with texture mapping and shaders. Modern 3D accelerators typically accept sequences of triangles as triangle strips.

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  A Very Fatal Murder

  A Very Fatal Murder is a podcast produced by the satirical publication The Onion. A parody of true crime podcasts, A Very Fatal Murder is hosted by fictional New York City reporter David Pascall, who travels to the small town Bluff Springs, Nebraska to investigate the murder of prom queen Hayley Price. Pascall is voiced by David Sidorov, who also wrote for the podcast. The podcast premiered on January 23, 2018, and consists of 7 episodes. Season 2 was released in its entirety on May 11, 2019. == Production == A Very Fatal Murder satirizes popular true crime podcasts such as Serial, S-Town, and My Favorite Murder. According to head writer Katy Yeiser, the podcast is not meant as a take down of any particular podcast, but rather an ode to the genre. == Synopsis == The podcast follows fictional investigative reporter David Pascall (voiced by David Sidorov) who is searching for the perfect murder to create an award-winning podcast about. He is assisted by ETHL (the Extremely Timely Homicide Locator), an MIT-created computer programmed to find "the most interesting, violent, culturally relevant murder cases in America". == Episodes == === Season 1 === === Season 2 === == Reception == The podcast received mostly positive reviews, and was largely praised for attacking true-crime tropes such as the "hot dead girl" and the romanticization of small-town America. === Awards ===

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  Argument technology

  Argument technology is a sub-field of collective intelligence and artificial intelligence that focuses on applying computational techniques to the creation, identification, analysis, navigation, evaluation and visualisation of arguments and debates. In the 1980s and 1990s, philosophical theories of arguments in general, and argumentation theory in particular, were leveraged to handle key computational challenges, such as modeling non-monotonic and defeasible reasoning and designing robust coordination protocols for multi-agent systems. At the same time, mechanisms for computing semantics of Argumentation frameworks were introduced as a way of providing a calculus of opposition for computing what it is reasonable to believe in the context of conflicting arguments. With these foundations in place, the area was kick-started by a workshop held in the Scottish Highlands in 2000, the result of which was a book coauthored by philosophers of argument, rhetoricians, legal scholars and AI researchers. Since then, the area has been supported by various dedicated events such as the International Workshop on Computational Models of Natural Argument (CMNA) which has run annually since 2001; the International Workshop on Argument in Multi Agent Systems (ArgMAS) annually since 2004; the Workshop on Argument Mining, annually since 2014, and the Conference on Computational Models of Argument (COMMA), biennially since 2006. Since 2010, the field has also had its own journal, Argument & Computation, which was published by Taylor & Francis until 2016 and since then by IOS Press. One of the challenges that argument technology faced was a lack of standardisation in the representation and underlying conception of argument in machine readable terms. Many different software tools for manual argument analysis, in particular, developed idiosyncratic and ad hoc ways of representing arguments which reflected differing underlying ways of conceiving of argumentative structure. This lack of standardisation also meant that there was no interchange between tools or between research projects, and little re-use of data resources that were often expensive to create. To tackle this problem, the Argument Interchange Format set out to establish a common standard that captured the minimal common features of argumentation which could then be extended in different settings. Since about 2018, argument technology has been growing rapidly, with, for example, IBM's Grand Challenge, Project Debater, results for which were published in Nature in March 2021; German research funder, DFG's nationwide research programme on Robust Argumentation Machines, RATIO, begun in 2019; and UK nationwide deployment of The Evidence Toolkit by the BBC in 2019. A 2021 video narrated by Stephen Fry provides a summary of the societal motivations for work in argument technology. Argument technology has applications in a variety of domains, including education, healthcare, policy making, political science, intelligence analysis and risk management and has a variety of sub-fields, methodologies and technologies. == Technologies == === Argument assistant === An argument assistant is a software tool which support users when writing arguments. Argument assistants can help users compose content, review content from one other, including in dialogical contexts. In addition to Web services, such functionalities can be provided through the plugin architectures of word processor software or those of Web browsers. Internet forums, for instance, can be greatly enhanced by such software tools and services. === Argument blogging === ArguBlogging is software which allows its users to select portions of hypertext on webpages in their Web browsers and to agree or disagree with the selected content, posting their arguments to their blogs with linked argument data. It is implemented as a bookmarklet, adding functionality to Web browsers and interoperating with blogging platforms such as Blogger and Tumblr. === Argument mapping === Argument maps are visual, diagrammatic representations of arguments. Such visual diagrams facilitate diagrammatic reasoning and promote one's ability to grasp and to make sense of information rapidly and readily. Argument maps can provide structured, semi-formal frameworks for representing arguments using interactive visual language. One avenue of research and development is the design of online platforms to leverage collective intelligence to populate such maps and to integrate data, optimize and assess arguments. === Argument mining === Argument mining, or argumentation mining, is a research area within the natural language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs. === Argument search === An argument search engine is a search engine that is given a topic as a user query and returns a list of arguments for and against the topic or about that topic. Such engines could be used to support informed decision-making or to help debaters prepare for debates. === Automated argumentative essay scoring === The goal of automated argumentative essay scoring systems is to assist students in improving their writing skills by measuring the quality of their argumentative content. === Debate technology === Debate technology focuses on human-machine interaction and in particular providing systems that support, monitor and engage in debate. One of the most high-profile examples of debating technology is IBM's Project Debater which combines scripted communication with very large-scale processing of news articles to identify and construct arguments on the fly in a competitive debating setting. Debating technology also encompasses tools aimed at providing insight into debates, typically using techniques from data science. These analytics have been developed in both academic and commercial settings. === Decision support system === Argument technology can reduce both individual and group biases and facilitate more accurate decisions. Argument-based decision support systems do so by helping users to distinguish between claims and the evidence supporting them, and express their confidence in and evaluate the strength of evidence of competing claims. They have been used to improve predictions of housing market trends, risk analysis, ethical and legal decision making. ==== Ethical decision support system ==== An ethical decision support system is a decision support system which supports users in moral reasoning and decision-making. ==== Legal decision support system ==== A legal decision support system is a decision support system which supports users in legal reasoning and decision-making. === Explainable artificial intelligence === An explainable or transparent artificial intelligence system is an artificial intelligence system whose actions can be easily understood by humans. === Intelligent tutoring system === An intelligent tutoring system is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. The intersection of argument technology and intelligent tutoring systems includes computer systems which aim to provide instruction in: critical thinking, argumentation, ethics, law, mathematics, and philosophy. === Legal expert system === A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. === Machine ethics === Machine ethics is a part of the ethics of artificial intelligence concerned with the moral behavior of artificially intelligent beings. As humans argue with respect to morality and moral behavior, argument can be envisioned as a component of machine ethics systems and moral reasoning components. === Proof assistant === In computer science and mathematical logic, a proof assistant or interactive theorem prover is a software tool to assist with the development of formal proofs by human-machine collaboration. This involves some sort of interactive proof editor, or other interface, with which a human can guide the search for proofs, the details of which are stored in, and some steps provided by, a computer. === Ethical considerations === Ethical considerations of argument technology include privacy, transparency, societal concerns, and diversity in representation. These factors cut across different levels such as technology, user interface design, user, service context, and society. There is concern about unethical misuse for "generating arguments on controversial topics with specific stances and deploying them on social platforms". Another issue may concern the design of conclusion-making algorithms, such as e.g. enabling such to conclude that certain key data is needed instead of only making lists of best-fit conclusions or enabling the generation of multi

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