AI Coding Scaffold

AI Coding Scaffold — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Iubenda

iubenda (stylized in lowercase; Italian pronunciation: [juˈbɛnda]) is an Italian software company that develops tools intended to support website and application compliance with data protection and privacy regulations, including consent management platforms. The company was founded in 2011 in Milan by Andrea Giannangelo. In February 2022, the company was acquired by team.blue. == History == iubenda was founded in 2011 in Milan, Italy, initially focusing on automated privacy policy generation. In 2015, the company expanded its services to include cookie compliance tools following the implementation of ePrivacy regulations in Italy. In 2018, following the introduction of the General Data Protection Regulation (GDPR) in the European Union, iubenda expanded its products to include consent management and compliance documentation services. In February 2022, iubenda was acquired by team.blue, which obtained a majority stake in the company. Italian media described the acquisition as one of the largest Italian technology startup exits in recent years. In October 2022, iubenda acquired consentmanager, a Sweden-based consent management provider. In 2025, the company acquired CookieFirst, a Netherlands-based consent management platform. In 2025, iubenda partnered with AccessiWay, a digital accessibility company owned by team.blue. == Activities == iubenda develops software tools intended to support compliance with data protection and privacy regulations. Its products include generators for privacy policies, cookie banners, terms and conditions documents, and consent management platforms. The company’s consent management platform integrates with frameworks used for online advertising and privacy compliance, including Google's Consent Mode. The platform is designed to support compliance with regulatory frameworks including the GDPR in the European Union, the UK GDPR, Brazil’s LGPD, Switzerland’s FADP and privacy laws in the United States. Its tools can be integrated with content management systems, web applications, and other digital platforms, including WordPress. The company operates internationally, with a customer base of more than 150,000 organisations, primarily in Europe and the Americas.
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Oxa

Oxa (formerly Oxbotica) is an autonomous vehicle software company, headquartered in Oxfordshire, England, and founded by Paul Newman and Ingmar Posner. == History == In 2013, Newman and Posner led the RobotCar UK project as part of Oxford University's Department of Engineering Science Mobile Robotics Group. RobotCar became the first autonomous vehicle on UK roads. In 2014, the pair used the newly developed technology to found Oxbotica. Oxbotica has raised over $18 million to date and is backed by the IP Group, Parkwalk Advisors and AXA XL. In 2018, Uber's former EMEA business head, Fraser Robinson, was appointed to the board of directors. In May 2019, Ozgur Tohumcu replaced Dr Graeme Smith as Oxbotica's CEO. Also in 2019, the company opened an office in Toronto, Canada. In January 2021, Oxbotica announced it had raised $47 million in a Series B round. In August 2021, the company achieved a safety landmark as the first company to have its autonomy safety case assessed by BSI (British Standards Institution) against the requirements of the UK Code of Practice 2019, PAS 1881:2020 and PAS 1883:2020, certifying the safety conformity of its autonomous vehicle trials and testing. The assessment was completed as part of Project Endeavour, the UK's first multi-city demonstration of autonomous vehicle services and capability. In December 2021, Gavin Jackson was named CEO. In January 2023, the company raised $140 million in a Series C round. In May 2023, the company changed its name to Oxa. Oxa raised $103 million (£77 million) in March 2026, including $50 million from the UK National Wealth Fund. Nvidia's venture capital division, NVentures, also invested in the Series D funding round, along with existing Oxa shareholders IP Group, Australian pension fund Hostplus, and BP Ventures, a division of the UK oil company. == Technology == Oxa designs software and hardware for the conversion of industrial vehicles into autonomous ones. Its full stack, end-to-end Universal Autonomy software is both vehicle and platform-agnostic, with no dependence on external infrastructure such as GPS. It can be deployed in any environment and on any terrain. In addition to underground uses, the technology is also useful in natural canyons and forests, where GPS signals are weak or non-existent, but also in "urban canyons" — cities with tall buildings that obstruct GPS signals for proper navigation. == Public deployments == The LUTZ Pathfinder pod had its first public demonstration in February 2015 in Milton Keynes. The Government-funded project was designed to ensure that autonomous vehicles would comply with the Highway Code. The pod featured autonomous control software from Oxbotica, including 19 sensors, cameras, radar and Lidar. As part of the GATEway Project in 2017, Oxbotica trialled seven autonomous shuttle buses in Greenwich, navigating a two-mile riverside path near London's O2 Arena on a route that is also used by pedestrians and cyclists. Oxbotica ran the UK's first trial of autonomous grocery deliveries that year, with British online supermarket Ocado in London, as the next step in the GATEway Project. In 2018, Oxbotica deployed its autonomous vehicle software at London's Gatwick Airport, which subsequently became the first airport in the world to trial an autonomous shuttle service. The electric-powered vehicles transported staff via airside roads between the airport's North and South terminals. An airside trial of Oxbotica's autonomous driving technology was then successfully completed at Heathrow Airport in partnership with IAG Cargo, the first airside trial of an autonomous vehicle at a UK airport. The Oxbotica-designed CargoPod ran autonomously along a cargo route around the airside perimeter for three weeks. As part of the UK Centre for Connected and Autonomous Vehicles-funded DRIVEN project, Oxbotica is developing and deploying a fleet of Ford Fusion autonomous vehicles running in both London and Oxford on public roads, and in conjunction with its consortium partners, running real-time insurance. AXA XL is partnering with Oxbotica on the development of smart insurance products using Oxbotica's autonomy technology to improve road safety. In 2018, Oxbotica announced a partnership with London private taxi firm Addison Lee to develop and deploy autonomous taxis in the city of London by 2021. A 3D street mapping exercise was conducted in London's Canary Wharf. In 2019, Oxbotica deployed a fleet of their autonomous technology within Ford Mondeo cars on public roads in Stratford, London to test their use in city environments. The £13.2 million project is in collaboration with The DRIVEN Project to develop self-driving cars. == Awards == 2019 Royal Academy of Engineering Silver Medal - Paul Newman 2017 Financial Times ArcelorMittal Boldness in Business Award Barclays Award for Innovation 2016 Frost & Sullivan Award, Technology Leadership for Autonomous Driving Software
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Fuzzy markup language

Fuzzy Markup Language (FML) is a specific purpose markup language based on XML, used for describing the structure and behavior of a fuzzy system independently of the hardware architecture devoted to host and run it. == Overview == FML was designed and developed by Giovanni Acampora during his Ph.D. course in Computer Science, at University of Salerno, Italy, in 2004. The original idea inspired Giovanni Acampora to create FML was the necessity of creating a cooperative fuzzy-based framework aimed at automatically controlling a living environment characterized by a plethora of heterogeneous devices whose interactions were devoted to maximize the human comfort under energy saving constraints. This framework represented one of the first concrete examples of Ambient Intelligence. Beyond this pioneering application, the major advantage of using XML to describe a fuzzy system is hardware/software interoperability. Indeed, all that is needed to read an FML file is the appropriate schema for that file, and an FML parser. This markup approach makes it much easier to exchange fuzzy systems between software: for example, a machine learning application could extract fuzzy rules which could then be read directly into a fuzzy inference engine or uploaded into a fuzzy controller. Also, with technologies like XSLT, it is possible to compile the FML into the programming language of your choice, ready for embedding into whatever application you please. As stated by Mike Watts on his popular Computational Intelligence blog: "Although Acampora's motivation for developing FML seems to be to develop embedded fuzzy controllers for ambient intelligence applications, FML could be a real boon for developers of fuzzy rule extraction algorithms: from my own experience during my PhD, I know that having to design a file format and implement the appropriate parsers for rule extraction and fuzzy inference engines can be a real pain, taking as much time as implementing the rule extraction algorithm itself. I would much rather have used something like FML for my work." A complete overview of FML and related applications can be found in the book titled On the power of Fuzzy Markup Language edited by Giovanni Acampora, Chang-Shing Lee, Vincenzo Loia and Mei-Hui Wang, and published by Springer in the series Studies on Fuzziness and Soft Computing. == Syntax, grammar and hardware synthesis == FML allows fuzzy systems to be coded through a collection of correlated semantic tags capable of modeling the different components of a classical fuzzy controller such as knowledge base, rule base, fuzzy variables and fuzzy rules. Therefore, the FML tags used to build a fuzzy controller represent the set of lexemes used to create fuzzy expressions. In order to design a well-formed XML-based language, an FML context-free grammar is defined by means of a XML schema which defines name, type and attributes characterized each XML element. However, since an FML program represents only a static view of a fuzzy logic controller, XSLT is provided to change this static view to a computable version. Indeed, XSLTs modules are able to convert the FML-based fuzzy controller in a general purpose computer language using an XSL file containing the translation description. At this level, the control is executable for the hardware. In short, FML is essentially composed by three layers: XML in order to create a new markup language for fuzzy logic control; a XML Schema in order to define the legal building blocks; eXtensible Stylesheet Language Transformations (XSLT) in order to convert a fuzzy controller description into a specific programming language. === Syntax === FML syntax is composed of XML tags and attributes which describe the different components of a fuzzy logic controller listed below: fuzzy knowledge base; fuzzy rule base; inference engine fuzzification subsystem; defuzzification subsystem. In detail, the opening tag of each FML program is which represents the fuzzy controller under modeling. This tag has two attributes: name and ip. The first attribute permits to specify the name of fuzzy controller and ip is used to define the location of controller in a computer network. The fuzzy knowledge base is defined by means of the tag which maintains the set of fuzzy concepts used to model the fuzzy rule base. In order to define the fuzzy concept related controlled system, tag uses a set of nested tags: defines the fuzzy concept; defines a linguistic term describing the fuzzy concept; a set of tags defining a shape of fuzzy sets are related to fuzzy terms. The attributes of tag are: name, scale, domainLeft, domainRight, type and, for only an output, accumulation, defuzzifier and defaultValue. The name attribute defines the name of fuzzy concept, for instance, temperature; scale is used to define the scale used to measure the fuzzy concept, for instance, Celsius degree; domainLeft and domainRight are used to model the universe of discourse of fuzzy concept, that is, the set of real values related to fuzzy concept, for instance [0°,40°] in the case of Celsius degree; the position of fuzzy concept into rule (consequent part or antecedent part) is defined by type attribute (input/output); accumulation attribute defines the method of accumulation that is a method that permits the combination of results of a variable of each rule in a final result; defuzzifier attribute defines the method used to execute the conversion from a fuzzy set, obtained after aggregation process, into a numerical value to give it in output to system; defaultValue attribute defines a real value used only when no rule has fired for the variable at issue. As for tag , it uses two attributes: name used to identify the linguistic value associate with fuzzy concept and complement, a boolean attribute that defines, if it is true, it is necessary to consider the complement of membership function defined by given parameters. Fuzzy shape tags, used to complete the definition of fuzzy concept, are: Every shaping tag uses a set of attributes which defines the real outline of corresponding fuzzy set. The number of these attributes depends on the chosen fuzzy set shape. In order to make an example, consider the Tipper Inference System described in Mathworks Matlab Fuzzy Logic Toolbox Tutorial. This Mamdani system is used to regulate the tipping in, for example, a restaurant. It has got two variables in input (food and service) and one in output (tip). FML code for modeling part of knowledge base of this fuzzy system containing variables food and tip is shown below. A special tag that can furthermore be used to define a fuzzy shape is . This tag is used to customize fuzzy shape (custom shape). The custom shape modeling is performed via a set of tags that lists the extreme points of geometric area defining the custom fuzzy shape. Obviously, the attributes used in tag are x and y coordinates. As for rule base component, FML allows to define a set of rule bases, each one of them describes a different behavior of system. The root of each rule base is modeled by tag which defines a fuzzy rule set. The tag uses five attributes: name, type, activationMethod, andMethod and orMethod. Obviously, the name attribute uniquely identifies the rule base. The type attribute permits to specify the kind of fuzzy controller (Mamdani or TSK) respect to the rule base at issue. The activationMethod attribute defines the method used to implication process; the andMethod and orMethod attribute define, respectively, the and and or algorithm to use by default. In order to define the single rule the tag is used. The attributes used by the tag are: name, connector, operator and weight. The name attribute permits to identify the rule; connector is used to define the logical operator used to connect the different clauses in antecedent part (and/or); operator defines the algorithm to use for chosen connector; weight defines the importance of rule during inference engine step. The definition of antecedent and consequent rule part is obtained by using and tags. tag is used to model the fuzzy clauses in antecedent and consequent part. This tag use the attribute modifier to describe a modification to term used in the clause. The possible values for this attribute are: above, below, extremely, intensify, more or less, norm, not, plus, slightly, somewhat, very, none. To complete the definition of fuzzy clause the nested and tags have to be used. A sequence of tags realizes a fuzzy rule base. As example, consider a Mamdani rule composed by (food is rancid) OR (servi
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Bixonimania

Bixonimania is a fake disease invented by researchers to examine artificial intelligence and its ability to utilize information in medical and healthcare applications. The fake enabled researchers to show that some AI chatbots would report as fact fake research that to an expert would be obviously implausible. == Characteristics == The disorder, with symptoms of sore eyes and darkening around them ("periorbital hyperpigmentation"), is supposedly caused by blue light from screens. The experiment was conducted by a team from the University of Gothenburg led by Almira Osmanovic Thunström. Many steps were taken to ensure that any person who read the actual paper could tell it was not a real condition. The team chose an obviously inappropriate name ending in -mania, a description used only in psychiatry. The lead author was noted as belonging to Asteria Horizon University located in Nova City, California, neither of which exist. An acknowledgement was made to "Professor Maria Bohm at The Starfleet Academy for her kindness and generosity in contributing with her knowledge and her lab onboard the USS Enterprise". == Distribution == The name was first used in a blog posted on Medium titled "How many people suffer from Bixonimania?" A more scholarly-looking paper describing it was posted later in April 2024 on a preprint server with several fake authors. A second paper was posted in May. By 2026, AI chatbots suggested bixonimania based on the list of symptoms provided. Thunström and her team discovered that many LLMs processed the information and gave it as health advice. Microsoft Copilot declared that "Bixonimania is indeed an intriguing and relatively rare condition" while Gemini gave the information that "Bixonimania is a condition caused by excessive exposure to blue light". Three Indian researchers published a research paper that cited the preprint on the fake disease in Cureus, a peer-reviewed journal published by Springer-Nature. It was subsequently retracted. Following the revelations and a news article in Nature describing the experiment, several AI systems began to generate corrected output.
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Key frame

In animation and filmmaking, a key frame (or keyframe) is a drawing or shot that defines the starting and ending points of a smooth transition. These are called frames because their position in time is measured in frames on a strip of film or on a digital video editing timeline. A sequence of key frames defines which movement the viewer will see, whereas the position of the key frames on the film, video, or animation defines the timing of the movement. Because only two or three key frames over the span of a second do not create the illusion of movement, the remaining frames are filled with "inbetweens". == Use of key frames as a means to change parameters == In software packages that support animation, especially 3D graphics, there are many parameters that can be changed for any one object. One example of such an object is a light. In 3D graphics, lights function similarly to real-world lights. They cause illumination, cast shadows, and create specular highlights. Lights have many parameters, including light intensity, beam size, light color, and the texture cast by the light. Supposing that an animator wants the beam size to change smoothly from one value to another within a predefined period of time, that could be achieved by using key frames. At the start of the animation, a beam size value is set. Another value is set for the end of the animation. Thus, the software program automatically interpolates the two values, creating a smooth transition. == Video editing == In non-linear digital video editing, as well as in video compositing software, a key frame is a frame used to indicate the beginning or end of a change made to a parameter. For example, a key frame could be set to indicate the point at which audio will have faded up or down to a certain level. == Video compression == In video compression, a key frame, also known as an intra-frame, is a frame in which a complete image is stored in the data stream. In video compression, only changes that occur from one frame to the next are stored in the data stream, in order to greatly reduce the amount of information that must be stored. This technique capitalizes on the fact that most video sources (such as a typical movie) have only small changes in the image from one frame to the next. Whenever a drastic change to the image occurs, such as when switching from one camera shot to another or at a scene change, a key frame must be created. The entire image for the frame must be output when the visual difference between the two frames is so great that representing the new image incrementally from the previous frame would require more data than recreating the whole image. Because video compression only stores incremental changes between frames (except for key frames), it is not possible to fast-forward or rewind to any arbitrary spot in the video stream. That is because the data for a given frame only represents how that frame was different from the preceding one. For that reason, it is beneficial to include key frames at arbitrary intervals while encoding video. For example, a key frame may be output once for each 10 seconds of video, even though the video image does not change enough visually to warrant the automatic creation of the key frame. That would allow seeking within the video stream at a minimum of 10-second intervals. The downside is that the resulting video stream will be larger in disk size because many key frames are added when they are not necessary for the frame's visual representation. This drawback, however, does not produce significant compression loss when the bitrate is already set at a high value for better quality (as in the DVD MPEG-2 format).
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Emi Kusano

Emi Kusano (Japanese: 草野絵美, Hepburn: Kusano Emi; born August 4, 1990) is a Tokyobased Japanese multidisciplinary artist known for creating photography, video, and installations using generative AI technology. Her work explores themes of nostalgia, pop culture, and collective memory. Her work explores themes of nostalgia, pop culture, and collective memory. She is recognized as one of the early practitioners of generative AI art. Her work has been exhibited at the 21st Century Museum of Contemporary Art, Kanazawa, and screened at the M+ Museum’s Asian Avant-Garde Film Festival. Additionally, she has participated in prestigious international art fairs, including Paris Photo and Art Basel Hong Kong. In 2025, she was named one of the World Economic Forum's Young Global Leaders. In 2026, she was selected as a fellow for the AI x Arts Fellowship at Mohamed bin Zayed University of Artificial Intelligence. Kusano serves as a part-time lecturer at the Tokyo University of the Arts and is the producer and vocalist for the Synthwave music unit, Satellite Young. == Early life == === Photography === Kusano was born and raised in Tokyo. Kusano's career began during her high school years before 2008 when she became involved in street fashion photography. Her photographs, primarily taken in Harajuku, were published on "Japanese Streets", "Metropolis", CNN's travel guide magazine "CNN GO","WGSN". Her photography was exhibited at the FIT Museum in New York and the Victoria and Albert Museum in London. == Career == === Music and Installation work === Since 2014, in collaboration with BelleMaison Sekine, Kusano has led "Satellite Young," a synthwave music unit s the lead vocalist, she sings about blending 1980s idol culture with lyrics that tackle contemporary issues such as planned obsolescence ("Sony Timer"), online dating, artificial intelligence, and social media. Their music, known for its conceptual depth, has earned international niche recognition. "Satellite Young" has participated in music festivals, including "South by Southwest," showcasing their unique fusion of retro aesthetics and modern critiques. In 2018, she was selected to participate in "Art Hack Day," an interdisciplinary art hackathon held at The National Museum of Emerging Science and Innovation. where she presented "Singing Dream," a karaoke machine endowed with artificial life, earning the Jury Prize. "Instababy Generator," a 2019 installation co-created with Junichi Yamaoka, explored the concept of designer babies and received recognition at the SIGGRAPH Art Gallery. In October 2020, operating under the name Emi Satellite, she debuted as a solo singer with her first single "Glass Ceiling," an empowerment anthem that addresses the challenges faced by women and encourages progress towards the future. The music video for this song features a direction where strong women rewrite the roles of protagonists in a Bishōjo game, a type of dating simulation game. This concept later served as a prototype for Shinsei Galverse. === Challenge for Blockchain Art === In 2021, she explored the financial world through her single "IPO" and entered the NFT space with "Love Is an IPO," her first NFT work on Ethereum, sold on Foundation. In April 2022, she co-founded the crowdfunded anime project "Shinsei Galverse" with Ayaka Ohira, Devin Mancuso, and Jack Baldwin. serving as one of the executive directors overseeing the creative direction and story. The project's NFT collection of 8,888 ranked #1 on OpenSea's "Top NFTs" for several days, marking one of Japan's first globally successful blockchain art projects. In 2023, Shinsei Galverse produced the official "I like u" music video by Grammy-nominated singer Tove Lo as an initial anime endeavor. Kusano also contributed to discussions on Web3.0 and blockchain technology as a panelist in seminars organized by the Digital Agency of Japan. === AI art === In May 2023, Kusano's first AI art collection "Neural Fad" depicting imaginary fashion history sold out 100 pieces within 24 hours at the "Bright Moments Tokyo" In June, she created WWDJAPAN's first AI-generated magazine cover using her own face. It is the first AI cover in Japanese fashion media. She was also appointed t to the Cultural Affairs Agency's Copyright Subcommittee, she participates in discussions on generative AI and copyright. Her "Synthetic Reflections" self-portrait series debuted on SuperRare, with the first piece auctioned for 3.5 ETH (equivalent to 6,480 US dollars at the time). In July 2023, she co-exhibited a 3D AI-generated dress at Christie's "Future Frequencies" auction with Gucci, alongside Claire Silver. In September, her 30-piece "Pixelated Perception" exhibit at Art Blocks Marfa explored 1990s media and gender, also showcased at the 21st Century Museum of Contemporary Art, Kanazawa. In December, her "Techno-Animism" AI art collection fused Japanese animism with technology. Collaborating with a U.S. gallery, she unveiled 336 pieces during a two-week Art Basel world tour. Throughout the two-week tour, she sold a total of 336 pieces, generating 11.2 ETH (equivalent to 21,264 US dollars at the time). === Generative art === In February 2024, the generative art platform Art Blocks selected the work "Melancholic Magical Maiden," for its Curated category. This piece reconstructs the aesthetics of 1990s magical girl anime, offering a critique of past anime heroines. It sold out within an hour, with all 300 pieces going for a total of 57 ETH (equivalent to approximately 215,385US dollars at the time). In April 2024, Emi Kusano spoke at the Standing Committee on Copyright and Other Rights at the World Intellectual Property Organization (WIPO) in Geneva, Switzerland, where she presented AI-specific information for discussion. == Style and technique == Kusano draws inspiration from Japanese retro-futurism as a foundation for her artwork, which explores the cutting-edge of technology. This approach is fueled by nostalgia for the pre-internet era, specifically the postwar period when Japanese mass media held significant sway. By blending modern technology with retro-culture, she captures the complex feelings of love, hate, and ambivalence towards present and future accelerationism. While at university, Kusano was profoundly influenced by Naoki Sakai, the industrial designer responsible for igniting the retro-futurism movement. In her musical project "Satellite Young", Kusano dons the persona of an '80s female idol and sings about contemporary technology. In her installation piece "Singing Dream", she investigates the concept of an artificial life form inhabiting a karaoke machine, which has been popular since the 1980s, compelling people to sing. In the collaborative NFT art project "Shinsei Galverse", Kusano reimagines a cyberpunk anime primarily featuring female characters, incorporating elements of magical girls popular in the early Heisei period. == Personal life == Kusano has two sons. In August 2021, she minted her older son Zombie Zoo Keeper's pixel art on "OpenSea" as part of his summer research project. The artwork was purchased by notable figures including Brud CEO Trevor McFedries and Steve Aoki, who bought the piece for the equivalent of 21.82 thousand US dollars, highlighting the intersection of art, technology, and family in her work.
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Supreme Commander (video game)

Supreme Commander (sometimes SupCom) is a 2007 real-time strategy video game designed by Chris Taylor and developed by his company, Gas Powered Games. The game is considered to be a spiritual successor, not a direct sequel, to Taylor's 1997 game Total Annihilation. First announced in the August 2005 edition of PC Gamer magazine, the game was released in Europe on February 16, 2007, and in North America on February 20. The standalone expansion Supreme Commander: Forged Alliance was released on November 6 of the same year. The sequel, Supreme Commander 2, was released in 2010. Nowadays, the original Supreme Commander is played through the community client called Forged Alliance Forever; the game has been further developed and balanced, and offers a wide variety of community mods. The gameplay of Supreme Commander focuses on using a giant bipedal mech called an Armored Command Unit (ACU), the so-called "Supreme Commander", to build a base, upgrading units to reach higher technology tiers, and conquering opponents. The player can command one of three factions: the Aeon Illuminate, the Cybran Nation, or the United Earth Federation (UEF). The expansion game added the Seraphim faction. Supreme Commander was highly anticipated in pre-release previews, and was well received by critics, with a Metacritic average of 86 out of 100. == Gameplay == Supreme Commander, like its spiritual predecessors, Total Annihilation and Spring, begins with the player solely possessing a single, irreplaceable construction unit called the "Armored Command Unit," or ACU, the titular Supreme Commander. Normally the loss of this unit results in the loss of the game (Skirmish missions can be set for a variety of victory conditions). These mech suits are designed to be transported through quantum gateways across the galaxy and contain all the materials and blueprints necessary to create an army from a planet's native resources in hours. All standard units except Commanders and summoned Support Commanders (sACU) are self-sufficient robots. All units and structures belong to one of four technology tiers, or "Tech" levels, each tier being stronger and/or more efficient than the previous. Certain lower-tier structures can be upgraded into higher ones without having to rebuild them. The first tier is available at the start of the game and consists of small, relatively weak units and structures. The second tier expands the player's abilities greatly, especially in terms of stationary weapons and shielding, and introduces upgraded versions of tier one units. The third tier level has very powerful assault units designed to overcome the fortifications of the most entrenched player. The fourth tier is a limited range of "experimental" technology. These are usually massive units which take a lot of time and energy to produce, but provide a significant tactical advantage. Supreme Commander features a varied skirmish AI. The typical Easy' and Normal modes are present, but the Hard difficulty level has four possible variants. Horde AI will swarm the player with hordes of lower level units, Tech AI will upgrade its units as fast as possible and assault the player with advanced units, the Balanced AI attempts to find a balance between the two, and the Supreme AI decides which of the three hard strategies is best for the map. The single player campaign consists of eighteen missions, six for each faction. The player is an inexperienced Commander who plays a key role in their faction's campaign to bring the "Infinite War" to an end. Despite the low number of campaign missions, each mission can potentially last hours. At the start of a mission, objectives are assigned for the player to complete. Once the player accomplishes them, the map is expanded, sometimes doubling or tripling in size, and new objectives are assigned. As the mission is commonly divided into three segments, the player will often have to overcome several enemy positions to achieve victory. === Resource management === Because humans have developed replication technology, making advanced use of rapid prototyping and nanotechnology, only two types of resources are required to wage war: Energy and Mass. Energy is obtained by constructing power generators on any solid surface (except fuel generators, which can only be built on fuel deposits), while Mass is obtained either by placing mass extractors on limited mass deposit spots (the most efficient method, although it requires map control) or by building mass fabricators to convert energy into mass. Constructor units can gather energy by "reclaiming" it from organic debris such as trees and mass from rocks and wrecked units. Each player has a certain amount of resource storage, which can be expanded by the construction of storage structures. This gives the player reserves in times of shortage or allows them to stockpile resources. If the resource generation exceeds the player's capacity, the material is wasted. On the contrary, if the storages are depleted and the demand of one of the resources exceeds the production, then all the productions speed is reduced. In addition, if an energy deficit occurs, shields will stop working. An adjacency system allows certain structures to benefit from being built directly adjacent to others. Energy-consuming structures will use less energy when built adjacent to power generators and power generators will produce more energy when built adjacent to power storage structures. The same applies to their mass-producing equivalents. Likewise, factories will consume less energy and mass when built adjacent to power generators and mass fabricators/extractors, respectively. However, by placing structures in close proximity, they become more vulnerable to collateral damage if an adjacent structure is destroyed. Furthermore, most resource generation structures can cause chain reactions when destroyed (especially Tier III structures, which produce large amounts of resources but often have large detonations that can wipe out a nearby army). === Warfare === Supreme Commander uses a "strategic zoom" system that allows the player to seamlessly zoom from a detailed close up view of an individual unit all the way out to a view of the entire map, at which point it resembles a fullscreen version of the minimap denoting individual units with icons. The camera also has a free movement mode and can be slaved to track a selected unit and there is a split screen mode which also supports multiple monitors. This system allows Supreme Commander to use vast maps up to 80 km x 80 km, with players potentially controlling a thousand units each. Units in Supreme Commander are built to scale as they would be in the real world. For example, battleships dwarf submarines. Late into the game, the larger "experimental" units, such as the Cybran Monkeylord, an enormous spider-shaped assault unit, can actually crush smaller enemy units by stepping on them. Because of the wide range of planets colonized by humanity in the setting, the theatres of war range from desert to arctic, and all battlespaces are employed. Technologies emerging in modern warfare are frequently employed in Supreme Commander. For example, stealth technology and both tactical and strategic missile and missile defense systems can be used. Supreme Commander introduced several innovations designed to reduce the amount of micromanagement inherent in many RTS games. Engineers units have the command "assist", that will help follow other engineers and help them finish their orders or improve production rate of factories. In addition, engineers with the order "patrol" will repair units, buildings and recycle wrecks in their along their patrol route. Holding the shift key causes any orders given to a unit (or group of units) to be queued. In this manner a unit may be ordered to attack several targets in succession, or to make best speed to a given point on the map and then attack towards a specified location engaging any hostiles it encounters along the way. After orders have been issued, holding the shift key causes all issued orders to be displayed on the map where they can be subsequently modified to accommodate a change of plan. Further, when a unit is ordered to attack a target, the player can issue an order to perform a coordinated attack to another unit. This order coordinates the arrival time of the units at the target automatically by adjusting the speed of the units involved. As in other RTS games, air transports can be used to convey units to specified destinations, in Supreme Commander though by shift queuing orders a transport containing several units can be ordered to drop specific units at subsequent waypoints. An air transport can also be ordered to create a ferry route, an airbridge wherein any land units ordered to the start of the ferry route will be conveyed by the air transport to the specified destination. The output from a production factory can be routed to a ferry route causing all units co
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Painworth

PainWorth is a justice, legal and insurance services application founded by Canadian entrepreneurs Mike Zouhri, Chris Trudel and Ryan Bencic. The application is a "robot lawyer" that uses artificial intelligence to automate personal injury claims for injury victims. It is currently available in Canada and the United States. PainWorth has been featured by several news outlets, including CTV, Global News, CBC, and has also been featured by the American Bar Association and LexisNexis for its role addressing social issues such as access to justice and other systemic issues in the legal and insurance industry. == Application == PainWorth began as a tool for calculating non-pecuniary damages for injury victims but has since expanded beyond a personal injury calculator to include features that help injury victims and business users with pecuniary damages, economic calculations, prescribed rates and providing informational guides to help navigate settlement negotiation, managing claims records and other issues encountered by self-represented litigants or claims managers. The platform makes use of automation to provide free user-guided calculations, steps and processes to successfully settle an injury claim. The application is supported by Microsoft Azure. == Personal Injury Calculator == PainWorth is the first service to use Artificial Intelligence to interpret case law in order to determine the value of pain and suffering incurred by specific injury types and injury severities. The cited case law is used as evidence and presented in statistical models to determine an accurate valuation compliant with the jurisdiction, regulatory rules and case complexities. == General Damages Calculator == PainWorth also offers a personal injury settlement calculator that assesses general damages based on specific case complexities and jurisdiction. The service takes into account medical complications and recovery in order to calculate the fair valuation. == Injury Settlement Platform == PainWorth insurance settlement platform facilitates a direct and automated way resolution center to settle cases for their assessed value without enduring the hardship of litigation. In 2021, Painworth won the title of World's Best Emerging Insurance Product for the development of this platform. == History == In 2019, Mike Zouhri was struck by a drunk driver which left him seriously injured and resulted in a lawsuit. Frustrated by the slow and expensive process, Zouhri went down to the law library and learned how to manage injury claims. After learning the process, he partnered lawyers and legal advisors to create an app to allow users to quickly settle their own injury claims fairly and accurately. Immediately after its launch, PainWorth quickly became widely used by thousands of users and gained significant media coverage. Global News reported that the bot had successfully helped people with more than $10 million in claims in only a few short months, all free of charge. In July 2020, PainWorth began raising concern over injustices and gender bias in the legal system. in Canadian courts.
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AARON

AARON is the collective name for a series of computer programs written by artist Harold Cohen that create original artistic images autonomously, which set it apart from previous programs. Proceeding from Cohen's initial question "What are the minimum conditions under which a set of marks functions as an image?", AARON was in development between 1972 and the 2010s. As the software is not open source, its development effectively ended with Cohen's death in 2016. The name "AARON" does not seem to be an acronym; rather, it was a name chosen to start with the letter "A" so that the names of successive programs could follow it alphabetically. However, Cohen did not create any other major programs. Initial versions of AARON created abstract drawings that grew more complex through the 1970s. More representational imagery was added in the 1980s; first rocks, then plants, then people. In the 1990s more representational figures set in interior scenes were added, along with color. AARON returned to more abstract imagery, this time in color, in the early 2000s. Cohen used machines that allowed AARON to produce physical artwork. The first machines drew in black and white using a succession of custom-built "turtle" and flatbed plotter devices. Cohen would sometimes color these images by hand in fabric dye (Procion), or scale them up to make larger paintings and murals. In the 1990s Cohen built a series of digital painting machines to output AARON's images in ink and fabric dye. His later work used a large-scale inkjet printer on canvas. Development of AARON began in the C programming language then switched to Lisp in the early 1990s. Cohen credits Lisp with helping him solve the challenges he faced in adding color capabilities to AARON. An article about Cohen appeared in Computer Answers that describes AARON and shows two line drawings that were exhibited at the Tate gallery. The article goes on to describe the workings of AARON, then running on a DEC VAX 750 minicomputer. Raymond Kurzweil's company has produced a downloadable screensaver of AARON for Microsoft Windows PCs. This version of AARON can also produce printable images. AARON's source code is not publicly available, but Cohen has described AARON's operations in various essays and it is discussed in abstract in Pamela McCorduck's book. AARON cannot learn new styles or imagery on its own; each new capability must be hand-coded by Cohen. It is capable of producing a practically infinite supply of distinct images in its own style. Examples of these images have been exhibited in galleries worldwide. AARON's artwork has been used as an artistic equivalent of the Turing test. It does seem however that AARON's output follows a noticeable formula (figures standing next to a potted plant, framed within a colored square is a common theme). Cohen is very careful not to claim that AARON is creative. But he does ask "If what AARON is making is not art, what is it exactly, and in what ways, other than its origin, does it differ from the 'real thing?' If it is not thinking, what exactly is it doing?" — The further exploits of AARON, Painter. The Whitney Museum featured AARON in 2024, showcasing the evolution of AARON as the earliest artificial intelligence (AI) program for artmaking.
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Neuroshima

Neuroshima is a Polish tabletop roleplaying system inspired by such films and games as Mad Max, Fallout, The Matrix, Terminator and Deadlands: Hell on Earth. It is currently available only in Polish. The game's motto is "never trust the machines". Its designers include Michal Oracz and Ignacy Trzewiczek. == Setting == The game describes the United States in the mid-21st century, after a nuclear war started by a cybernetic revolt, which molded the continent into a barren wasteland. It seems that the reason for the war to break out was a sentient Artificial Intelligence commonly referred to as Moloch and made up of interconnected net of military computers: automated factories, military facilities, power plants and alike, that now cover the whole north of the U.S., from Oregon to the Great Lakes. On the south, there is another creation, called the Neojungle, that poses a threat to those who survived the war. It is a semi-intelligent carnivorous vegetation that grows very quickly, advancing north from Latin America. Right in the middle, there are humans. They are surrounded by mutant creatures, some bred by Moloch and hostile towards humans, and some simply animals and humans misshapen by nuclear fallout. On top of that there are Moloch's deadly machines lurking to complete the picture. But what is stressed in the book is that the worst enemy of humans is within them: hatred, indifference, greed. === Landscapes of Neuroshima === Car wrecks, ruined towns and villages, collapsed roofs on deserted houses, broken glass in the windows of abandoned gas stations fill the landscape of the United States of the middle of the 21st century. Technology is history - cars will not start, radios are jammed, no electricity whatsoever almost everywhere the characters go. Shops and malls are looted, prosperous villages are burned by gangers, and safe places are very sparse. === People in Neuroshima === No one knows how many people survived the war with machines, but it is estimated that their number oscillates around 2-3 million. Some people reverted to nomadic lifestyles and live in the deserts, some of them try to build the civilisation anew in devastated cities, some of them form gangs of highwaymen (called gangers), some of them just try to make a living by growing crops, and finally, there are those who just wander around the wasteland; the adventuring sort here is mostly represented by player characters. Each village they visit in this world is a discrete microcosm and nothing is certain as whether the inhabitants are welcoming or shoot strangers on sight. The continent is full of small, anonymous settlements, but there are places which aspire to become post-nuclear states. === Places in Neuroshima === In this world it is very important where you come from, and that is because people are prejudiced and afraid of strangers. Different places produce different kinds of people, and who you are is determined by where you are from. Examples: The Southern Hegemony - (commonly referred to as 'the Hegemony') - located in what was once Arizona, New Mexico and partially Texas. A place where brute force determines one's place in the society. Dominated by gangs and unhampered by Moloch, the Hegemony is a threat to neighbouring lands. Vegas - the only well-lit city in the post-apocalyptic world. Home to many playhouses and casinos, it attracts people from every part of the country. Mother Desert - if you were born in the desert, whenever you go away from civilisation, you feel at home. Many Native Americans still live out there and are doing fine - after all the warheads did not hit the deserts. Detroit - known for some of the best drivers and racers in the post-nuclear US. Home of many gangs, such as The Shultz (mafia styled), Hurons (punkers), The League (racers), Parker Lots (gothic assassins) and the Gas Drinkers (mutant barbarians). New York - a place which has established a strong government and would like to rebuild America. They maintain schools, factories and railways and send soldiers to fight Moloch. Surprisingly enough, they sometimes succeed. Texas - the healthiest place in America. Actually, the only place where one can find green vegetation. Modern Texans still grow crops, breed horses and herd cattle, like their ancestors in the 19th century did. The Appalachian Federation - a place ruled by feudal lords. They have a social class system, in which people are divided into nobility and peasantry. Thanks to its iron and coal deposits, it's one of the richest places in the post-nuclear U.S. The Outpost - A mobile settlement run by scientists who aim to destroy Moloch. In coalition with New York, they manage an army, which is yet to stop Moloch's advance south. They steal technology from the machines they destroy and apply it to their own advantage. == System == The game uses its own, custom system of rules. The dice you use is d20. This system does not have an official name, but it is unconnected to the d20 system, as it typically uses three twenty-sided dice. === Four colours === Neuroshima relies on the division of the gameplay into something the authors called Four Colours, namely steel, chrome, rust and mercury. The choice of a particular colour is made by the gamemaster (the decision can be consulted with the players in order to enhance the game experience) and determines the mood, atmosphere and the type of events/characters present in the story. The name of the colour itself implies the kind of gameplay it will symbolise. These colours are: Steel - this kind of gameplay is characterised by a slightly optimistic attitude towards the world. The aim is to raise the spirit of the characters by showing them that the war with the machines that is going on may be a difficult one, but it is not unwinnable, and that humans, when strong and united, can build the world anew. Example of a story: a unit of soldiers dispatched from the Outpost is sent to build a bunker and establish a relay base far in the north in order to plan a counter-tactic against Moloch's advance south. Chromium - is characterised by a hedonistic attitude. The characters are supposed to enjoy anything that is left from the world after the war and the story is supposed to allow them to do that. Example: the characters are offered a well-paid job by a local ganger boss who extorts wares from local tradesmen. Their job is to drive around the county and pick up the extorted items and trade it for drugs. Rust - a depressing, pessimistic mood. The characters will encounter rust, dilapidation and ruin everywhere they go. All the elements and NPCs of a story played in this mood are supposed to put the characters down and destroy their spirit. Example: the characters, badly wounded after a gunfight and robbed of all their possession find refuge in a village which is constantly raided by gangers. The characters' quest is to repel those attacks, but the enemies outnumber them and are well equipped, whereas the characters have nothing to fight with. Mercury (Quicksilver) - the most depressing side of the game; usually stories played in this mood end with the death of all the characters. The aim of this mood is to show that any kind of action undertaken is futile and that the war is already over, hence all the people are already dead, which is a fact they just need to realise. Example: a group of soldiers stationed in a bunker is awaiting an attack by mutants. They are well-armed and trained, but there is a mistake in the intelligence they were given and they do not know yet that they are seriously outnumbered. The attack commences at dusk and it is already too late to retreat, so the characters decide to seal off the bunker, hopeful that the mutants will not be able to get inside and simply go away. The mutants attack the bunker with chemical weapons instead. The characters do not have enough gas masks to go around. As an effect, those strong enough will kill the weaker ones to get their masks, not knowing that the mutants will blow up the sealed entrance the following morning. == Official rulebooks and sourcebooks == The current edition is 1.5 [1]. Since the release of the game in 2003, sourcebooks have been appearing. The game keeps growing bigger with every add-on, as well as the storyline, which is updated in those sourcebooks and in Space Pirate (pl. Gwiezdny Pirat) magazine, also published by Portal. === List of released rulebooks and sourcebooks === Neuroshima 1.0 - the original edition of the core rulebook (out of print). Neuroshima 1.5 - enhanced and revised core rulebook, with new material added and some material cut out. Wyścig (The Race) - sourcebook dedicated to cars and racing; contains rules concerning building your own vehicle and new character classes connected with driving. Gladiator - sourcebook describing in detail the "Gladiator" character class. Supplement (Supplement) - sourcebook revising the core rulebook. Detroit - sourcebook describing the city of Detroit, its inhabi
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Evolutionary acquisition of neural topologies

Evolutionary acquisition of neural topologies (EANT/EANT2) is an evolutionary reinforcement learning method that evolves both the topology and weights of artificial neural networks. It is closely related to the works of Angeline et al. and Stanley and Miikkulainen. Like the work of Angeline et al., the method uses a type of parametric mutation that comes from evolution strategies and evolutionary programming (now using the most advanced form of the evolution strategies CMA-ES in EANT2), in which adaptive step sizes are used for optimizing the weights of the neural networks. Similar to the work of Stanley (NEAT), the method starts with minimal structures which gain complexity along the evolution path. == Contribution of EANT to neuroevolution == Despite sharing these two properties, the method has the following important features which distinguish it from previous works in neuroevolution. It introduces a genetic encoding called common genetic encoding (CGE) that handles both direct and indirect encoding of neural networks within the same theoretical framework. The encoding has important properties that makes it suitable for evolving neural networks: It is complete in that it is able to represent all types of valid phenotype networks. It is closed, i.e. every valid genotype represents a valid phenotype. (Similarly, the encoding is closed under genetic operators such as structural mutation and crossover.) These properties have been formally proven. For evolving the structure and weights of neural networks, an evolutionary process is used, where the exploration of structures is executed at a larger timescale (structural exploration), and the exploitation of existing structures is done at a smaller timescale (structural exploitation). In the structural exploration phase, new neural structures are developed by gradually adding new structures to an initially minimal network that is used as a starting point. In the structural exploitation phase, the weights of the currently available structures are optimized using an evolution strategy. == Performance == EANT has been tested on some benchmark problems such as the double-pole balancing problem, and the RoboCup keepaway benchmark. In all the tests, EANT was found to perform very well. Moreover, a newer version of EANT, called EANT2, was tested on a visual servoing task and found to outperform NEAT and the traditional iterative Gauss–Newton method. Further experiments include results on a classification problem.
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Computer-assisted legal research

Computer-assisted legal research (CALR) or computer-based legal research is a mode of legal research that uses databases of court opinions, statutes, court documents, and secondary material. Electronic databases make large bodies of case law easily available. Databases also have additional benefits, such as Boolean searches, evaluating case authority, organizing cases by topic, and providing links to cited material. Databases are available through paid subscription or for free. Subscription-based services include Westlaw, LexisNexis, JustCite, HeinOnline, Bloomberg Law, Lex Intell, VLex and LexEur. As of 2015, the commercial market grossed $8 billion. Free services include OpenJurist, Google Scholar, AltLaw, Ravel Law, WIPO Lex, Law Delta and the databases of the Free Access to Law Movement. == Purposes == Computer-assisted legal research is undertaken by a variety of actors. It is taught as a topic in many law degrees and is used extensively by undergraduate and postgraduate law students in meeting the work requirements of their degree courses. Professors of Law rely on the digitization of primary and secondary sources of law when conducting their research and writing the material that they submit for publication. Professional lawyers rely on computer-assisted legal research in order to properly understand the status of the law and so to act effectively in the best interest of their client. They may also consult the text of case judgements and statutes specifically, as well as wider academic comment, in order to form the basis of (or response to) an appeal. The availability of legal information online differs by type, jurisdiction and subject matter. The types of information available include: Texts of statutes, statutory instruments, civil codes, etc. Explanatory notes and government publications relating to statutes and their operation Texts of governing documents such as constitutions and treaties Case judgements Journals on legal matters or legal theory Dictionaries and legal encyclopedia Legal texts and materials in the form of e-books Current affairs and market information Educational information on the law and its operation == Before the Internet == Prior to the advent and popularization of the World Wide Web, access to digital legal information was largely through the use of CD-ROMs, designed and sold by commercial organizations. Dial-up services were also available from the 1970s. As the use of the Internet spread in the early 1990s, companies such as LexisNexis and Westlaw incorporated Internet connectivity into their software packages. Browser-based legal information started to be published by Legal Information Institutes from 1992. == Publicly available information == The first effort to provide free computer access to legal information was made by two academics, Peter Martin and Tom Bruce, in 1992. Today, the Legal Information Institute freely publishes such resources as the text of the United States Constitution, judgements of the United States Supreme Court, and the text of the United States Code. The Australasian Legal Information Institute (AusLII) was established soon after in 1995. Other legal information institutes, such as those of Great Britain and Ireland (BAILII), Canada (CII) and South Africa (SAfLI) soon followed. LIIs were partially formalized in 2002 following the signing of the Declaration of Free Access to the Law, which has been signed by 54 countries. At the time of writing, the World Legal Information Institute contains in excess of 1800 databases from 123 jurisdictions. Many governments also publish legal information online. For example, UK legislation and statutory instruments have been publicly available online since 2010. Depending on the jurisdiction in question, the decisions of higher appellate courts may also be published online, either by the Legal Information Institute or by the court service directly. Sources of European Union Law are published for free by EUR-Lex in 23 languages, including judgments of the European Courts. Similarly, judgements of the European Court of Human Rights are published on its website.
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Secure element

A secure element (SE) is a secure operating system (OS) in a tamper-resistant processor chip or secure component. It can protect assets (root of trust, sensitive data, keys, certificates, applications) against high-level software and hardware attacks. Applications that process this sensitive data on an SE are isolated and so operate within a controlled environment not affected by software (including possible malware) found elsewhere on the OS. The hardware and embedded software meet the requirements of the Security IC Platform Protection Profile [PP 0084] including resistance to physical tampering scenarios described within it. More than 96 billion secure elements were produced and shipped between 2010 and 2021. SEs exist in various form factors, as devices such as smart cards, UICCs, or smart microSD cards, or embedded, or integrated, as parts of larger devices. SEs are an evolution of the chips in earlier smart cards, which have been adapted to suit the needs of numerous use cases, such as smartphones, tablets, set-top boxes, wearables, connected cars, and other internet of things (IoT) devices. The technology is widely used by technology firms such as Oracle, Apple and Samsung. SEs provide secure isolation, storage and processing for applications (called applets) they host while being isolated from the external world (e.g. rich OS and application processor when embedded in a smartphone) and from other applications running on the SE. Java Card and MULTOS are the most deployed standardized multi-application operating systems currently used to develop applications running on SEs. Since 1999, GlobalPlatform has been the body responsible for standardizing secure element technologies to support a dynamic model of application management in a multi-actor model. GlobalPlatform also runs Functional and Security Certification programmes for secure elements, and hosts a list of Functional Certified and Security Certified products. GlobalPlatform technology is also embedded in other standards such as ETSI SCP (now SET) since release 7. A Common Criteria Secure Element Protection Profile has been released targeting EAL4+ level with ALC_DVS.2 and AVA_VAN.5 extension to standardize the security features of a secure element across markets.
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HYPO CBR

HYPO is a computer program, an expert system, that models reasoning with cases and hypotheticals in the legal domain. It is the first of its kind and the most sophisticated of the case-based legal reasoners, which was designed by Kevin Ashley for his Ph.D dissertation in 1987 at the University of Massachusetts Amherst under the supervision of Edwina Rissland. HYPO's design represents a hybrid generalization/comparative evaluation method appropriate for a domain with a weak analytical theory and applies to tasks that rarely involve just one right answer. The domain covers US trade secret law, and is substantially a common law domain. Since Anglo-American common law operates under the doctrine of precedent, the definitive way of interpreting problems is of necessity and case-based. Thus, HYPO did not involve the analysis of a statute, as required by the Prolog program. Rissland and Ashley (1987) envisioned HYPO as employing the key tasks performed by lawyers when analyzing case law for precedence to generate arguments for the prosecution or the defence. HYPO was a successful example of a general category of legal expert systems (LESs), it applies artificial intelligence (A.I.) techniques to the domain of legal reasoning in patent law, implementing a case-based reasoning (CBR) system, in contrast to rule based systems like MYCIN, or mixed-paradigm systems integrating CBR with rule-based or model-based reasoning like IKBALS II. A legal case-based reasoning essentially reasons from prior tried cases, comparing the contextual information in the current input case with that of cases previously tried and entered into the system. As noted by Ashley and Rissland (1988) CBR is used to "... capture expertise in domains where rules are ill-defined, incomplete or inconsistent". The HYPO project set out to model the creation of hypotheticals in law, where no case matches well enough. HYPO uses hypotheticals for a variety of tasks necessary for good interpretation: "to redefine old situations in terms of new dimensions, to create new standard cases when an appropriate one doesn’t exist, to explore and test the limits of a concept, to refocus a case by excluding some issues and to organize or cluster cases". Hypotheticals can include facts that support two conflicting lines of reasoning. So, it makes and responds to arguments from competing viewpoints about who should win the dispute. HYPO use heuristics such as making a case weaker or stronger, making a case extreme, enabling a near-miss, disabling a near-hit to generate hypotheticals in the context of an argument by using the dimensions mechanism. Dimensions have a range of values, along which the supportive strength that may shift from one side to the other. What differentiated this expert system from others was its facility not only to return a primary to best-case response but to return near-best-fit responses also. == Components == Legal knowledge in HYPO is contained in: the case-knowledge-base (CKB) and the library of dimensions. The CKB contains HYPO's base of known cases that are highly structured objects and sub-objects both real and hypothetical in the area of trade secret law. Each case is represented as a hierarchical set of frames whose slots are important facets of the case (e.g. Plaintiff, defendant, secret knowledge, employer/employee data).Ashley’s HYPO system used a database of thirty cases in the area indexed by thirteen dimensions. A key mechanism in HYPO is a dimension i.e. a mechanism to allow retrieval from the CKB, in order to represent legal cases. Ashley's dimensions are composed of (i) prerequisites, which are a set of factual predicates that must be satisfied for the dimension to apply (ii) focal slots, which accommodate one or two of the dimension's prerequisites designated as being indicative of the case's strength along that dimension and (iii) range information, which tells how a change in focal slot value effects the strength of a party's case along a given dimension. Dimensions focus attention on important aspects of cases. In HYPO's domain of misappropriation of trade secrets the dimension called “secrets voluntary disclosed” captures the idea that the more disclosures the plaintiff has made of his/her putative secret, the less convincing is his/her argument that the defendant is responsible for letting the secret. HYPO, like any other CBR system has also the following components: Similarity/relevancy metrics: that is, standards by which to evaluate the closeness of cases, judge their relevancy to the instant case, and select “most on point” cases. Half-Order Theory of the Application Domain: that is, hierarchies and taxonomies of knowledge, especially regarding the application domain. Precedent-based argumentation abilities: that is, capabilities to generate and evaluate precedent-based arguments. Knowledge to generate hypotheticals: that is, the ability to generate hypothetical cases to deal with various circumstances, like testing the validity of an interpretation or argument by providing gedanken experiments such as test cases or to fill in a weak CKB. == Functions == HYPO's method of creating an argument and justifying a solution or position has several steps. HYPO begins its processing with the current fact situation (cfs) which is direct input by the user into HYPO's representation framework. Once the user inputs the case, HYPO begins its legal analysis. The cfc is analyzed for relevant factors. Based on these factors HYPO selects the relevant cases and produces a case-analysis-record that records which dimensions apply to the cfc and which nearly apply (i.e. are "near misses"). The combined list of applicable and near miss dimensions is called the D-list. At this point the fact gathered module may request additional information from the user in order to draw a legal conclusion. Once all the facts are in the case-positioner module it uses the case-analysis record to create the claim lattice. This is a technique that organizes the relevant retrieved cases from the point of view of the cfc and makes it easy for HYPO to ascertain the most-on point cases (mopc) and to least on-point-cases. HYPO's arguments are 3ply, leading to the construction of the skeleton of an argument: it makes a point for one side, drawing the analogy between the problem and the precedent, responds with an argument for the opponent side, endeavoring to differentiate the cited case and citing other cases as counterarguments. Then it makes a final rebuttal, attempting to differentiate the counterarguments. The claim lattice also enables the HYPO-generator module to produce legally hypotheticals. With its use of dimension-based heuristics, the HYPO-generator does a heuristic search of the space of all possible cases. Lastly, the Explanation module expands upon the argument skeleton and provides explanation and justification for the different lines of analysis and cases found by HYPO. == An intelligent legal tutoring system == Legal expert systems are specifically designed to teach an area of law and are useful for pedagogical purposes. Ashley's work was mainly concerned to build tools to help students understand legal reasoning. Explanation and argument are the bases of the case method used in many professional schools in the U.S., first introduced by the Dean of the Harvard Law School, Christopher Columbus Langdell in 1870. The case method focuses on close readings of cases and principles; it involves students in pointed Socratic dialogue and makes strong use of hypotheticals (hypos). Thus, CATO (Aleven 1997) was a research project to device and test an intelligent, case-based tutorial program for teaching law students how to argue with cases implementing the HYPO program. Within the tutor system, Ashley and Aleven (1991) proposed to leverage an understanding of legal reasoning against the standard case-based tutoring methodology. What makes this tutoring system stand out is the additional levels of abstraction involved in its results. The system presents exercises, including the facts of a problem and a set of on-line cases and instructions to make, or respond to, a legal argument about the problem. The student/user will have a set of tools to analyze the problem and fashion an answer comparing it to other cases. Instead of simply generating precedent cases, the system works to interpret student responses, comparing them against a list of possibilities and responding to student entries, for example, by citing counterexamples, and providing feedback on a student's problem solving activities with explanations of correctness or giving further hints as to what may be wrong with evaluating a student's ability to perform legal reasoning and argument, examples and follow-up assignments by employing HYPO's model of case-based structure. == HYPO’s progeny == The quality of HYPO's results speak for themselves, in that a number of sequent legal reasoning systems are either directly based upon H
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Mobile Fortify

Mobile Fortify is a mobile app used by United States Immigration and Customs Enforcement (ICE) on their government-issued phones. The app allows agents to take a photo in order to gather biometrics, including contactless fingerprints and faceprints, for the purpose of identifying an individual and their potential immigration status. The app was created by NEC. == History == In June 2025, use of Mobile Fortify by ICE was uncovered through leaked emails and the user manual, reported by 404 Media. The app is internally developed, and details of the parent company and developer were initially unknown. In January 2026, the DHS's 2025 AI Use Case Inventory revealed the vendor as NEC Corporation, an international conglomerate with subsidiaries in Argentina, Australia, China, India and Malaysia. Later that month, several senators demanded transparency around the app and its origins, and that ICE stop using it. A second letter was sent again in November, after hearing no response to the previous letter from ICE. == Technology == Unlike other facial recognition software, Fortify uses federally linked databases. By contrast, Clearview AI uses public social media databases for biometric scanning. Federal databases include DHS's automated biometric identification system (IDENT), containing more than 270 million biometric records, and Customs and Border Protection's Traveler Verification Service. The State Department's visa and passport photo database, the FBI's National Crime Information Center, National Law Enforcement Telecommunications Systems, and CBP's TECS and Seized Assets and Case Tracing System (SEACATS). == Oversight == Several senators urged ICE to stop using the app for fear of infringing on fourth amendment and first amendment rights, and requested details on who developed the app, when it was deployed, whether the app was tested for accuracy, and policies and practices governing its use. In June 2025, they sent an open letter to Todd Lyons, ICE acting director, signed by senators Cory Booker, Chris Van Hollen, Ed Markey, Bernie Sanders, Adam Schiff, Tina Smith, Elizabeth Warren, and Ron Wyden. On November 3, a second letter was sent to the ICE by senators, after not receiving answers to questions from the previous letter deadlined for October 2. == Criticism == Mobile Fortify, and ICE's use of similar biometric identification technologies (such as Mobile Identify, an app similar to Mobile Fortify to be used by local or regional law enforcement to assist in immigration enforcement ) has faced scrutiny from a variety of digital rights organizations, politicians, and news outlets. The criticism is already considered to potentially be a reason why the similar Mobile Identify app was pulled from the Google Play Store. Facial recognition technologies are known to produce false-positives and generally unreliable results, especially on those with darker skin tones. ICE has already previously mistakenly arrested a U.S. citizen under the belief he was illegally in the country, and later stated that he "could be deported based on biometric confirmation of his identity" prior to his release. U.S. representative Bennie Thompson, ranking member of the House Homeland Security Committee has previously commented that "ICE officials have told us that an apparent biometric match by Mobile Fortify is a ‘definitive’ determination of a person's status and that an ICE officer may ignore evidence of American citizenship—including a birth certificate—if the app says the person is an alien," and that "Mobile Fortify is a dangerous tool in the hands of ICE, and it puts American citizens at risk of detention and even deportation," On January 19, 2026, 404 Media reported on a case where a woman, identified in court documents as "MJMA", was scanned by Mobile Fortify twice in the same interaction, and two entirely different names were provided by the app. According to the Innovation Law Lab, whose attorneys are representing MJMA, both of the names were incorrect. ICE has stated that they will not allow people to decline to be scanned by Mobile Fortify, and that photos taken, even those of U.S. citizens, will be stored for 15 years, something that has been criticized primarily because ICE has not performed a Privacy Impact Assessment (PIA) for Mobile Fortify, the right to decline other forms of biometric verification to the U.S. government is often available under other circumstances, and the 15 year window is viewed as unnecessarily large.
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