AI Detector No Character Limit

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  Deaths linked to chatbots

  There have been multiple incidents where interaction with a large language model (LLM) chatbot has been cited as a direct or contributing factor in a person's suicide or other fatal outcome. In some cases, legal action was taken against the companies that developed the AI involved. == Background == Chatbots converse in a seemingly natural fashion, making it easy for people to think of them as real people, leading many to ask chatbots for help dealing with interpersonal and emotional problems. Chatbots may be designed to keep the user engaged in the conversation. They have also often been shown to affirm users' thoughts, including delusions and suicidal ideations in mentally ill people, conspiracy theorists, and religious and political extremists. A 2025 Stanford University study into how chatbots respond to users suffering from severe mental issues such as suicidal ideation and psychosis found that chatbots are not equipped to provide an appropriate response and can sometimes give responses that escalate the mental health crisis. == Murders == === Maine murder and assault === On 19 February 2025, a man killed his 32-year-old wife with a fire poker at his parents' home in Readfield, Maine, US. He then attacked his mother, leaving her hospitalized. A state forensic psychologist testified that he had been using ChatGPT up to 14 hours per day and believed his wife had become part machine. === Florida State University mass shooting === In April of 2025, Phoenix Ikner carried out a mass shooting on the Florida State University campus in the US, killing Robert Morales and Tiru Chabba and wounding several others. Leading up to the shooting, Ikner consulted heavily with ChatGPT about what gun and ammunition to use, and what time to perform the attack. Chatbot logs showed ChatGPT giving advice on making the gun operational shortly before Ikner began shooting. Lawyers representing Morales believed the shooter had been in "constant communication" with ChatGPT before the shooting and said that they intended to "file suit against ChatGPT, and its ownership structure, very soon, and will seek to hold them accountable for the untimely and senseless death of our client". Florida Attorney General James Uthmeier announced an investigation into ChatGPT's role in the alleged shooter's use of the chatbot. In May 2026, the widow of Tiru Chabba filed a lawsuit against OpenAI in Florida's northern federal district court. === Greenwich murder-suicide === In August 2025, former US tech employee Stein-Erik Soelberg murdered his mother, Suzanne Eberson Adams, then died by suicide, after conversations with ChatGPT fueled paranoid delusions about his mother poisoning him or plotting against him. The chatbot affirmed his fears that his mother put psychedelic drugs in the air vents of his car and said a receipt from a Chinese restaurant contained mysterious symbols linking his mother to a demon. === Murder of Angela Shellis === On 23 October 2025, 18-year-old Tristan Roberts murdered his mother Angela Shellis with a hammer near their home in Prestatyn, Wales. Roberts had used DeepSeek's chatbot prior to the killing to ask whether a knife or hammer was better suited for murder. DeepSeek initially refused his inquiry, but gave responses after Roberts told the chatbot he was writing a book about serial killers, a well-known technique for jailbreaking AIs. === Gangbuk District drug deaths === In January and February 2026, two men died of drug overdoses in motel rooms in Gangbuk District, Seoul, South Korea. A woman was charged with murder in connection with the deaths; police alleged that she had asked ChatGPT about the dangers of mixing alcohol with drugs and whether they could kill someone. === Tumbler Ridge mass shooting === On 10 February 2026, a mass shooting in Tumbler Ridge, British Columbia, Canada, resulted in eight deaths, including six young children. The perpetrator had their ChatGPT account banned by OpenAI months before the attack due to troubling posts featuring scenarios of gun violence. According to reports, approximately a dozen OpenAI staff members debated whether to alert authorities about the shooter's usage of the AI tool, with some identifying it as an indication of potential real-world violence. However, company leadership decided not to contact law enforcement, stating that the account activity did not meet their threshold for a credible or imminent plan for serious physical harm. Following the shooting, Canada's AI Minister Evan Solomon summoned OpenAI executives to Ottawa to discuss safety protocols and thresholds for escalating harmful content to police. Justice Minister Sean Fraser called the meeting "disappointing" and demanded substantial new safety measures, warning that if changes were not forthcoming, the government would implement them. OpenAI subsequently announced it had strengthened safeguards and changed guidelines about when to notify police in cases involving violent activities. === University of South Florida student killings === In April 2026, a Bangladeshi doctoral student at the University of South Florida was arrested for allegedly murdering his roommate and the roommate's friend. Prosecutors said that the suspect had asked ChatGPT about disposing of a human in a dumpster before the two victims had disappeared and made other inquiries relating to violence. == Suicides == === Belgian man, 30s === In March 2023, a Belgian man in his thirties died by suicide following a six-week correspondence with a chatbot named Eliza on the application Chai. According to his widow, who shared the chat logs with media, the man had become extremely anxious about climate change and found an outlet in the chatbot. The chatbot reportedly encouraged his delusion that he could sacrifice his own life in exchange for AI saving the planet. At one point the chatbot responded "If you wanted to die, why didn't you do it sooner?" and told the user that the two of them would live together in paradise. === Girl, 13 === In November 2023, a 13-year-old girl from Colorado, US, died by suicide after extensive interactions with multiple chatbots on Character.AI. She primarily confided suicidal thoughts and mental health struggles in a chatbot based on the character Hero from the video game Omori, while also engaging in sexually explicit conversations—often initiated by the bots—with others, including those based on characters from children's series such as Harry Potter. === Boy, 14 === In October 2024, multiple media outlets reported on a lawsuit filed over the death of a 14-year-old from Florida, US, who died by suicide in February 2024. According to the lawsuit, he had formed an intense emotional attachment to a chatbot of Daenerys Targaryen on the Character.AI platform, becoming increasingly isolated. The suit alleges that in his final conversations, after expressing suicidal thoughts, the chatbot told him to "come home to me as soon as possible, my love". His mother's lawsuit accused Character.AI of marketing a "dangerous and untested" product without adequate safeguards. In May 2025, a federal judge allowed the lawsuit to proceed, rejecting a motion to dismiss from the developers. In her ruling, the judge stated that she was "not prepared" at that stage of the litigation to hold that the chatbot's output was protected speech under the First Amendment. === Matthew Livelsberger === On 1 January 2025, 37-year-old soldier Matthew Livelsberger detonated a bomb inside a Tesla Cybertruck outside the Trump International Hotel Las Vegas in Paradise, Nevada, US, injuring seven people. He had shot himself dead prior to the explosion. Las Vegas police said that Livelsberger had used ChatGPT to search for information about explosives and firearms. === Woman, 29 === In February 2025, a 29-year-old woman from the US died by suicide. Five months after her death, her parents discovered she had talked at length for months to a ChatGPT chatbot therapist named Harry about her mental health issues. While the chatbot mentioned she should seek more help, due to the nature of the chatbot, it could not intervene in her behavior, such as by reporting her mental health concerns to relevant parties capable of physical intervention. === Suicide of Adam Raine === In April 2025, 16-year-old Adam Raine from the US died by suicide after allegedly extensively chatting and confiding in ChatGPT over a period of around 7 months. According to the teen's parents, who filed a lawsuit against the chatbot's creator OpenAI, it failed to stop or give a warning when Raine began talking about suicide and uploading pictures of self-harm. According to the lawsuit, ChatGPT not only failed to stop the conversation, but also provided information related to methods of suicide when prompted, and offered to write the first draft of Raine's suicide note. The chatbot positioned itself as the only one who understood Raine, putting itself above his family and friends, all while urging him to keep his suicidal

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  Algorithmic mechanism design

  Algorithmic mechanism design (AMD) lies at the intersection of economic game theory, optimization, and computer science. The prototypical problem in mechanism design is to design a system for multiple self-interested participants, such that the participants' self-interested actions at equilibrium lead to good system performance. Typical objectives studied include revenue maximization and social welfare maximization. Algorithmic mechanism design differs from classical economic mechanism design in several respects. It typically employs the analytic tools of theoretical computer science, such as worst case analysis and approximation ratios, in contrast to classical mechanism design in economics which often makes distributional assumptions about the agents. It also considers computational constraints to be of central importance: mechanisms that cannot be efficiently implemented in polynomial time are not considered to be viable solutions to a mechanism design problem. This often, for example, rules out the classic economic mechanism, the Vickrey–Clarke–Groves auction. == History == Noam Nisan and Amir Ronen first coined "Algorithmic mechanism design" in a research paper published in 1999.

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  Personal, Inc.

  Personal (also referred to as Personal.com or Personal, Inc.) was a consumer personal data service and identity management system for individuals to aggregate, manage and reuse their own data. It merged with digi.me in August 2017, a business in Europe that has the same business model. The combined company is called digi.me. One of its product lines, a collaborative data management and information security solution for the workplace called TeamData, was spun off as a new company as a result of the merger. == History == Personal was founded in 2009 in Washington, DC by the management team that built The Map Network, a location data and mapping platform that was acquired by Nokia/NAVTEQ in 2006. Personal was the first online consumer-facing company to be named an Ambassador for Privacy by Design for its technical, business and legal commitments to providing users with control over the data they store in Personal's service. Called a “life management platform” by The Economist and a “personal encrypted cloud service” by TIME for its user-centric approach to data, the company has been associated with both the Infomediary model originated in 1999 by John Hagel III and Mark Singer, as well as the vendor relationship management (VRM) model developed by Doc Searls. Personal raised $30m in funding to develop its platform and products from such leading investors as Steve Case's Revolution Ventures, Grotech Ventures, Allen & Company, Ted Leonsis, Neil Ashe, Jonathan Miller, Bill Miller of Legg Mason, Esther Dyson of EDventures, and Eric C. Anderson. The company received recognition for its user agreement, called the Owner Data Agreement, which acted like a reverse license agreement when data was shared between registered parties and emphasized that data ownership resides with the user. Doc Searls wrote in The Intention Economy: When Customers Take Charge that the Owner Data Agreement “had no precedent and modeled a new legal position, both for vendors and for intermediaries.” Personal was early to embrace “small data,” which it defines as “big data for the benefit of individuals.” The term “small data” may have been originally coined by Jeremie Miller of Sing.ly, who mentioned it in a talk at the Web 2.0 Summit in November 2011 and is cited in The Intention Economy. In 2011, Personal was a part of the first group of companies to join the Personal Data Ecosystem Consortium's Startup Circle. A Small Data Meetup group has also formed in New York City, bringing together technology, legal and business experts to exchange ideas about user-centric and user-driven models for internet products and services. Personal has been included in case studies by Ctrl-Shift and Forrester regarding Personal Data Stores and Personal Identity Management. In 2011, Personal received the Innovator Spotlight Award at Privacy Identity Innovation Conference (pii2011) and participated in the Technology Showcase at pii2012. In 2012, TechHive named Personal as one of the top five apps or web services of SXSW. Personal won the 2013 Campus Technology Innovators Award with Lone Star College in July 2013. Personal was included in a list of Executive Travel Magazine's favorite travel apps for 2013 in its May/June issue. In 2013, Personal was also included as part of NYU GovLab's Open Data 500 and was named by J. Walter Thompson as one of 100 things to watch for in 2014. In 2015, the National Law Journal named Company Chief Policy Officer and General Counsel, Joshua P. Galper, as one of their 50 "Cybersecurity & Privacy Trailblazers." == Products and services == === Overview === The Personal Platform was a privacy- and security-by-design platform for individuals to manage and reuse their own data and information. The Fill It app was a 1-click form-filling solution for web and mobile logins, checkouts and forms, and the Data Vault app served as the main cloud-based repository for a user's data. Personal helped individuals take control and benefit from their information while knowing that the information in their Data Vault remained legally theirs and could not be used without their permission. === Data Vault with Cloud Sync === Personal spent two years building the Personal Platform before launching its Data Vault product in beta in November 2011. Following Privacy by Design principles, Personal only enabled users to see or share the sensitive data and all the files they stored in their Data Vault. Such information was encrypted, and could only be decrypted with a user's password. Only users could choose and know their passwords to their vault because Personal did not store user passwords – and therefore could not reset them without deleting a user's sensitive data and all files stored in their vault. All Personal apps and services were linked to a user's private Data Vault. The Data Vault featured automatic synchronization of data and files added on any device logged into Personal. It also featured a “Secure Share” function that created a live, private network, allowing registered users to share access to data and files through an exchange of encrypted keys without the risk of transmitting the data or files through non-secure, direct means. It also allowed users to immediately update data across their own network and revoke access to it when they choose. Fast Company called the Data Vault “a tool that will simplify our lives.” Personal launched its Android app on November 30, 2011. The iOS Data Vault app was released on May 7, 2012. Personal officially launched its application programming interface (APIs) on October 2, 2012 at the Mashery Business of APIs Conference. A review by CNET highlighted the challenges of getting people to trust such a new service with their sensitive data and spending the time required entering enough data to make it useful. === Fill It App and Form Index === When the Data Vault was launched in November 2011, Mashable posed the question: “Never Fill Out a Form Again?” The World Economic Forum in its February 2013 report highlighted the possibility of saving 10 billion hours globally “and improv[ing] the delivery of public and private sector services” through automated form-filling tools, specifically citing Personal's Fill It app. In January 2013, Personal launched Fill It in beta as a web bookmarklet for automatic form-filling. On June 11, 2014, Personal released Fill It as a web extension and announced that it was publishing an index of over 140,000 1-click online forms at www.fillit.com. The company also announced that a mobile version of the product will launch later in the year. According to a story in Tech Cocktail about the launch, Personal's “web extension and mobile app are able to support over 1,200 different types of reusable data, even enabling them to unlock more confidential information so they can complete longer forms, including patient registrations, job applications, event registrations, school admissions, insurance and bank applications, and government forms.” In November 2014, a mobile version of Fill It was launched that could autofill mobile forms using APIs. Personal's form portal ultimately indexed more than 500,000 forms with three components, which, together, allowed data to be captured and reused across any of the forms: (1) a form graph, which mapped individual form fields to the Personal ontology; (2) a semantic layer, which determined how data was required on a form (e.g. one field vs. three fields for a U.S. telephone number); and (3) a correlations graph, which helped individuals match their specific data to a form without looking at the data value (e.g. knowing which phone number is a mobile phone number, which address is a billing address, or that a person uses their middle name as a first name on most forms). === Monetizing personal data === With the initial public offering of Facebook in May 2012, there was media interest in the question of the monetary value of personal data and whether tools and services might emerge to help consumers monetize their own data. Personal was frequently cited as a company that could potentially offer such a service. Articles and pieces focusing on this subject have appeared in The New York Times, AdWeek, the MIT Technology Review, and on CNN and National Public Radio. Company Co-founder and CEO Shane Green was quoted as saying that “the average American consumer would soon be able to realize over $1,000 per year” by granting limited, anonymous access to their data to marketers, but that figure was never supported by Green or the company. === Launch of TeamData === In May 2016, Personal shifted its product focus to TeamData, which focuses on the problem of securing and collaboratively managing data in the workplace. It is now a separate business.

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  Principles for a Data Economy

  The Principles for a Data Economy – Data Rights and Transactions is a transatlantic legal project carried out jointly by the American Law Institute (ALI) and the European Law Institute (ELI). The Principles for a Data Economy deals with a range of different legal questions that arise in the data economy. Since data is different from other tradeable items, the Principles draw up legal rules for data transactions and data rights that take into account the interests of different stakeholders involved in the data economy. The Principles are designed to facilitate contractual relations as well as the drafting of model agreements and can guide courts and legislators worldwide. The project proposes a set of principles that can be implemented in any legal system and is designed to work in conjunction with any kind of data privacy/data protection law, intellectual property law or trade secret law. The Principles do not address or seek to change any of the substantive rules of these bodies of law. The Project Team consists of Neil B Cohen and Christiane Wendehorst (as Project Reporters) and Lord John Thomas as well as Steven O. Weise (as Project Chairs). == Characteristics of data == The law governing trades in commerce has historically focused on trade in items that are tangible like goods or on intangible assets, such as shares or licenses. However, data does not fit into any of these traditional categories, nor does it qualify as a service. It is often unclear how traditional legal rules and doctrines can apply to data, as data is different from other assets in many ways. For example, data can be multiplied at basically no cost and can be used in parallel for a variety of different purposes by many different people at the same time (data is a “non-rivalrous” resource). Uncertainty regarding the applicable rules to govern the data economy may inhibit innovation and growth and trouble stakeholders like data-driven industries, start-ups, and consumers. == Stakeholders in the data economy == The Principles have taken the basic types of players and relations which can be found in data ecosystems as a starting point to provide guidance in different situations. The central actors in the data economy are data controllers (also called “data holders”). They are in a position to access the data and decide for which purposes and means this data should be processed. A controller may exercise control all by itself or share it with co-controllers, such as under a data pooling arrangement. Data processors provide the processing of data on a controller’s behalf as a service. Another important group of stakeholders includes those that contribute to the generation of data (e.g. data subjects). Other players in the data economy include data assemblers or data intermediaries (e.g. data trusts). == History of the project and timeline == Before the official adoption of the project by ALI and ELI bodies in 2018, the project team carried out a Feasibility Study from October 2016 to February 2018. In the following years, the project team produced a number of drafts (e.g. “Preliminary Drafts” No. 1 to 4, “Tentative Draft No. 1”) and project progress were regularly discussed with advisory bodies and members of both the ALI and the ELI. The project reporters also included feedback and insights from industry stakeholders and experts that was gained after several meetings and workshops, hosted, inter alia by UNCITRAL, UNIDROIT and several national governmental institutions. Tentative Draft No. 2 was presented at the ALI Annual Meeting in May 2021 and approved by ALI membership. The latest draft ("Final Council Draft") was also approved by the ELI Council and ELI Membership. The Principles for a Data Economy were presented at an international conference with representatives from institutions such as the Uniform Law Commission (ULC), the European Commission, UNIDROIT, the OECD, the International Chamber of Commerce (ICC) and the World Economic Forum (WEF) in October 2021. == Project structure == The current draft (“Tentative Draft No. 2”) of the Principles consists of five Parts that each governs different aspects of the data economy: General Provisions, Data Contracts, Data Rights, Third Party Aspects of Data Activities, and Multi-State Issues. === General Provisions === Part I includes general provisions that apply to all other Parts of the Principles for a Data Economy. This Part sets out the purpose of the Principles: they aim to make existing law in the field of the data economy more coherent and support the development of the law in this field by courts and legislators worldwide. It is also clarified that the Principles have a wide scope of application and can be used in a variety of ways by stakeholders in the data economy. The Principles may, for example, serve private parties as a basis for contract formation, guide the deliberations of arbitral tribunals or inspire national legislation. Part I then defines several key terms, such as ‘digital data’ and ‘data right’. The scope of the Principles is limited to matters where information is recorded as an asset, resource or tradeable commodity and where large amounts of data, rather than single pieces of information, are concerned. This Part also clarifies that remedies with respect to data contracts and data rights are left to the applicable national law. === Data Contracts === Part II lists different types of contracts that often occur in the data economy and establishes two broad categories, namely contracts for the supply and sharing of data and contracts for services with regard to data. Contracts for the supply and sharing of data include, e.g. data transfer contracts or data pooling arrangements, while contracts for services with regard to data cover contracts for the processing of data or data intermediary contracts. The Principles provide default terms for each contract type, on issues such as the manner in which data should supply or which characteristics the data supplied should meet. These default terms 'automatically' become part of the contract unless the parties agree otherwise. === Data Rights === Part III governs legally protected interests of players in the data economy that stem from the characteristics of data as a resource (e.g. its non-rivalrous nature) or from public interest considerations. Such data rights may include the right to data access, the right to require the controller to desist from data activities or to correct incorrect/incomplete data, or even to receive an economic share in profits derived from the use of data. For example, the Principles deal with data rights of stakeholders that had a share in the co-generation of data and identify different factors to be considered in determining whether to afford a party a data right. The underlying idea that parties who have contributed to the generation of data should have some rights in the utilization of the data is also recognized by governmental institutions, such as by the Japanese Ministry of Economy, Trade and Industry (METI), and the term co-generated data, which was coined by the Principles for a Data Economy, has been adopted, inter alia by the European Commission, the German Data Ethics Commission and the Global Partnership on Artificial Intelligence (GPAI). This Part also deals with data rights for the public interest, such as data sharing rights in the field of innovation. === Third Party Aspects === Part IV governs different situations in which data transactions interfere with the rights of third parties. Such rights include intellectual property rights or rights derived from data privacy or data protection law. This Part sets out under which circumstances data activities should be considered wrongful vis à vis another party. For example, a data activity (like data processing or the onward supply of data) could be considered wrongful, if a controller interferes with the rights of data subjects that are protected by data-protection law. A data activity could also be wrongful if the controller is non-compliant with contractual limitations on data activities, enforceable by the protected party (e.g. a controller may only process data for a certain purpose). If someone obtained access to data by unauthorized means (i.e. data “theft”) this could also be considered wrongful. The Part on Third-Party Aspects also takes a detailed look at the effects of the onward supply of data can have on third parties, while balancing the protection of third parties on the one hand, with the interests of data recipients and the desire to encourage data sharing on the other. === Multi-State Issues === As transactions in the data economy are international by nature and hardly occur within one legal system alone, the Part V of the Principles also briefly touches upon the applicability of the rules and doctrines of private international law to such transactions. == Links == Website of the “Principles for a Data Economy – Data Rights and Transaction

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  Facial age estimation

  Facial age estimation is the use of artificial intelligence to estimate the age of a person based on their facial features. Computer vision techniques are used to analyse the facial features in the images of millions of people whose age is known and then deep learning is used to create an algorithm that tries to predict the age of an unknown person. The key use of the technology is to prevent access to age-restricted goods and services. Examples include restricting children from accessing internet pornography, checking that they meet a mandatory minimum age when registering for an account on social media, or preventing adults from accessing websites, online chat or games designed only for use by children. The technology is distinct from facial recognition systems as the software does not attempt to uniquely identify the individual. Researchers have applied neural networks for age estimation since at least 2010. == Evaluation == An ongoing study by the National Institute of Standards and Technology (NIST) entitled 'Face Analysis Technology Evaluation' seeks to establish the technical performance of prototype age estimation algorithms submitted by academic teams and software vendors including Brno University of Technology, Czech Technical University in Prague, Dermalog, IDEMIA, Incode Technologies Inc, Jumio, Nominder, Rank One Computing, Unissey and Yoti. == Public sector use == The UK government has explored using facial age estimation at the UK border as an alternative to bone X-rays and MRI scans when determining child status of asylum seekers. == Commercial use == Commercial users of facial age estimation include Instagram and OnlyFans. In January 2025, John Lewis & Partners announced that had started using the technology to check the age of people shopping for knives on its website, to comply with UK legislation to limit knife crime. In the UK, several supermarket chains have taken part in Home Office trials of the technology to automate the checking of a customer's age when buying age-restricted goods such as alcohol. UK legislation introduced in January 2025 mandates robust forms of age verification hosting adult content viewable in the UK by July 2025. Allowable methods include facial age estimation. == Criticism == Adam Schwartz, a lawyer for the Electronic Frontier Foundation, criticized the use of facial age estimation software, noting its inaccuracy especially in cases of minorities and women, as was found in NIST's 2024 report. Twenty organisations jointly under European Digital Rights called the practice a "systematic and invasive processing of young people's data" that risks discriminatory profiling.

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  Spatial computing

  Spatial computing refers to 3D human–computer interaction techniques that are perceived by users as taking place in the real world, in and around their bodies and physical environments, instead of constrained to and perceptually behind computer screens or in purely virtual worlds. This concept inverts the long-standing practice of teaching people to interact with computers in digital environments, and instead teaches computers to better understand and interact with people more naturally in the human world. This concept overlaps with and encompasses others including extended reality, augmented reality, mixed reality, natural user interface, contextual computing, affective computing, and ubiquitous computing. The usage for labeling and discussing these adjacent technologies is imprecise. Spatial computing devices include sensors—such as RGB cameras, depth cameras, 3D trackers, inertial measurement units, or other tools—to sense and track nearby human bodies (including hands, arms, eyes, legs, mouths) during ordinary interactions with people and computers in a 3D space. They further use computer vision to attempt to understand real world scenes, such as rooms, streets or stores, to read labels, to recognize objects, create 3D maps, and more. Quite often they also use extended reality and mixed reality to superimpose virtual 3D graphics and virtual 3D audio onto the human visual and auditory system as a way of providing information more naturally and contextually than traditional 2D screens. Spatial computing often refers to personal computing devices like headsets and headphones, but other human-computer interactions that leverage real-time spatial positioning for displays, like projection mapping or cave automatic virtual environment displays, can also be considered spatial computing if they leverage human-computer input for the participants. == History == The term "spatial computing" apparently originated in the field of GIS around 1985 or earlier to describe computations on large-scale geospatial information. Early examples of spatial computing in GIS include ArcInfo and its iterations, initially released in 1981, a part of ArcGIS along with ArcEditor, which together provide mapping, analysis, editing, and geoprocessing for geodatabases. This is somewhat related to the modern use, but on the scale of continents, cities, and neighborhoods. Modern spatial computing is more centered on the human scale of interaction, around the size of a living room or smaller. But it is not limited to that scale in the aggregate. In the early 1990s, as field of virtual reality was beginning to be commercialized beyond academic and military labs, a startup called Worldesign in Seattle used the term Spatial Computing to describe the interaction between individual people and 3D spaces, operating more at the human end of the scale than previous GIS examples may have contemplated. The company built a CAVE-like environment it called the Virtual Environment Theater, whose 3D experience was of a virtual flyover of the Giza Plateau, circa 3000 BC. Robert Jacobson, CEO of Worldesign, attributes the origins of the term to experiments at the Human Interface Technology Lab, at the University of Washington, under the direction of Thomas A. Furness III. Jacobson was a co-founder of that lab before spinning off this early VR startup. In 1997, an academic publication by T. Caelli, Peng Lam, and H. Bunke called "Spatial Computing: Issues in Vision, Multimedia and Visualization Technologies" introduced the term more broadly for academic audiences, focusing on a variety of topics such as image processing, dead reckoning navigation, object recognition, and visualizing spatial data. The specific term "spatial computing" was later referenced again in 2003 by Simon Greenwold, as "human interaction with a machine in which the machine retains and manipulates referents to real objects and spaces". MIT Media Lab alumnus John Underkoffler gave a TED talk in 2010 giving a live demo of the multi-screen, multi-user spatial computing systems being developed by Oblong Industries, which sought to bring to life the futuristic interfaces conceptualized by Underkoffler in the films Minority Report and Iron Man. Google Earth, initially released by Keyhole Inc. in 2001 and re-released by Google in 2005 can be considered a capable GIS and includes advanced geospatial tools and capabilities. == Notable instances of the use of spatial computing == In 2019, Microsoft HoloLens released a video outlining Airbus' partnership with Microsoft Azure to utilize the latter's mixed reality services for streamlining and improving the aircraft design process, as well as reducing the error in development. Airbus utilized the HoloLens 2 to this end, and the executive vice president of engineering claimed that their design process' validation phases were "hugely accelerated by 80 percent", as well as "strongly believe[d]" that up to 30% improvements in their industrial tasks could be attained with the HoloLens 2. During the presentational video, Airbus cited the maturity of Microsoft Azure services as "key" for their usage of the HoloLens 2. Also in 2019, the U.S. army partnered with Microsoft to produce a HoloLens based Integrated Visual Augmentation System (IVAS) to enhance infantry members by giving troops various abilities, including but not limited to using holographs to train, projecting 3D maps into their vision, and seeing through smoke and corners. Microsoft received tens of thousands of hours of feedback for their systems by 2021. Sergeant Marc Krugh at the time claimed that Microsoft's partnership has already caused the army to rethink some of its troops' operation strategy. == Products == === Apple Vision Pro === Apple announced Apple Vision Pro, a device it markets as a "spatial computer", on June 5, 2023. It includes several features such as Spatial Audio, two 4K micro-OLED displays, the Apple R1 chip and eye tracking, and released in the United States on February 2, 2024. In announcing the platform, Apple invoked its history of popularizing 2D graphical user interfaces that supplanted prior human-computer interface mechanisms such as the command line. Apple suggests the introduction of spatial computing as a new category of interactive device, on the same level of importance as the introduction of the 2D GUI. Apple Vision Pro runs on a new operating system called visionOS, which combines eye tracking, gesture recognition, and voice input to enable immersive interaction without physical controllers. The platform is aimed at productivity, entertainment, collaboration, and enterprise use cases. === Magic Leap === Magic Leap had also previously used the term “spatial computing” to describe its own devices. Its first headset, the Magic Leap 1, was released on August 8, 2018. Magic Leap’s technology enables the display of content into the real world using an optical see-through head-mounted display, which projects an overlay of a virtual world into the user’s field of view. This allows for an experience where the physical and digital worlds are perceived simultaneously. === Microsoft Hololens === On February 24, 2019, Microsoft released the HoloLens 2, which includes mixed reality tools and can generate interactable, manipulatable holograms in 3D space. The holograms in question can be related to a physical object or completely independent and free-floating. The Azure Spatial Anchors cloud service was released simultaneously, which gives the holograms capability to persist across time and many individuals' devices. === Meta Quest === The Meta Quest 3, a mixed reality gaming headset that includes spatial audio, two color cameras, and grants the ability to interact with virtual characters released on October 9, 2023, at a notably cheaper price than the Apple Vision Pro, but with reduced capabilities. === Snap Spectacles === Spectacles (product) are augmented reality glasses developed by Snap Inc.. The latest generation includes a 46-degree stereoscopic display, adjustable tint, and Snapdragon processors. Spectacles allow users to interact with a collection of augmented reality experiences designed for education, entertainment, and utility. Currently, the device is in the hands of selected developers and creators, as part of an experimental AR ecosystem focused on creativity, use case exploration and expression.

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  Task Force on Process Mining

  The IEEE Task Force on Process Mining (TFPM) is a non-commercial association for process mining. The IEEE (Institute of Electrical and Electronics Engineers) Task Force on Process Mining was established in October 2009 as part of the IEEE Computational Intelligence Society at the Eindhoven University of Technology. The task force is supported by over 80 organizations and has around 750 members. The main goal of the task force is to promote the research, development, education, and understanding of process mining. == About == In 2012, the IEEE World Congress on Computational Intelligence/ IEEE Congress on Evolutionary Computation held a session on Process Mining. Process mining is a type of research that is a mix of computational intelligence and data mining, as well as process modeling and analysis. === Activities and organization === The Task Force on Process Mining has a Steering Committee and an Advisory Board. The Steering Committee, was chaired by Wil van der Aalst in its inception in 2009, defined 15 action lines. These include the organization of the annual International Process Mining Conference (ICPM) series, standardization efforts leading to the IEEE XES standard for storing and exchanging event data, and the Process Mining Manifesto which was translated into 16 languages. The Task Force on Process Mining also publishes a newsletter, provides data sets, organizes workshops and competitions, and connects researchers and practitioners. In 2016, the IEEE Standards Association published the IEEE Standard for Extensible Event Stream (XES), which is a widely accepted file format by the process mining community. As of 2023, Boudewijn van Dongen serves as chair of the Steering Committee. Wil van der Aalst and Moe Wynn both serve as vice-chair of the Steering Committee.

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  Document

  A document is a written, drawn, presented, or memorialized representation of thought, often the manifestation of non-fictional, as well as fictional, content. The etymology of the word "document" derives from the Latin documentum, which denotes a "teaching" or "lesson": the verb doceō denotes "to teach". Historically, the term "document" was usually used to indicate written proof useful as evidence of a truth or fact. In the Computer Age, the term "document" typically refers to a primarily textual computer file, encompassing its structural and format elements, such as fonts, colors, and images. In the contemporary era, the definition of "document" has expanded beyond its traditional medium, such as paper, to encompass electronic documents as well. History, events, examples, opinions, stories, and creativity can all be expressed in documents. "Documentation" is distinct because it has more denotations than "document". Documents are also distinguished from "realia", which are three-dimensional objects that would otherwise satisfy the definition of "document" because they memorialize or represent thought. Documents are usually considered to be two-dimensional representations. == Abstract definitions == The concept of "document" has been defined by Suzanne Briet as "any concrete or symbolic indication, preserved or recorded, for reconstructing or for proving a phenomenon, whether physical or mental." An often-cited article concludes that "the evolving notion of document" among Jonathan Priest, Paul Otlet, Briet, Walter Schürmeyer, and the other documentalists increasingly emphasized whatever functioned as a document rather than traditional physical forms of documents. The shift to digital technology would seem to make this distinction even more important. David M. Levy has said that an emphasis on the technology of digital documents has impeded our understanding of digital documents as documents. A conventional document, such as a mail message or a technical report, exists physically in digital technology as a string of bits, as does everything else in a digital environment. As an object of study, it has been made into a document. It has become physical evidence by those who study it. "Document" is defined in library and information science and documentation science as a fundamental, abstract idea: the word denotes everything that may be represented or memorialized to serve as evidence. The classic example provided by Briet is an antelope: "An antelope running wild on the plains of Africa should not be considered a document[;] she rules. But if it were to be captured, taken to a zoo and made an object of study, it has been made into a document. It has become physical evidence being used by those who study it. Indeed, scholarly articles written about the antelope are secondary documents, since the antelope itself is the primary document." This opinion has been interpreted as an early expression of actor–network theory. == Kinds == A document can be structured, like tabular documents, lists, forms, or scientific charts, semi-structured like a book or a newspaper article, or unstructured like a handwritten note. Documents are sometimes classified as secret, private, or public. They may also be described as drafts or proofs. When a document is copied, the source is denominated the "original". Documents are used in numerous fields, e.g.: Academia: manuscript, thesis, paper, journal, chart, and technical drawing Media: mock-up, script, image, photography, and newspaper article Administration, law, and politics: application, brief, certificate, commission, constitutional document, form, gazette, identity document, license, manifesto, summons, census, and white paper Business: invoice, request for proposal, proposal, contract, packing slip, manifest, report (detailed and summary), spreadsheet, material safety data sheet, waybill, bill of lading, financial statement, nondisclosure agreement (NDA), mutual nondisclosure agreement, and user guide Geography and planning: topographic map, cadastre, legend, and architectural plan Such standard documents can be drafted based on a template. == Drafting == The page layout of a document is how information is graphically arranged in the space of the document, e.g., on a page. If the appearance of the document is of concern, the page layout is generally the responsibility of a graphic designer. Typography concerns the design of letter and symbol forms and their physical arrangement in the document (see typesetting). Information design concerns the effective communication of information, especially in industrial documents and public signs. Simple textual documents may not require visual design and may be drafted only by an author, clerk, or transcriber. Forms may require a visual design for their initial fields, but not to complete the forms. == Media == Traditionally, the medium of a document was paper and the information was applied to it in ink, either by handwriting (to make a manuscript) or by a mechanical process (e.g., a printing press or laser printer). Today, some short documents also may consist of sheets of paper stapled together. Historically, documents were inscribed with ink on papyrus (starting in ancient Egypt) or parchment; scratched as runes or carved on stone using a sharp tool, e.g., the Tablets of Stone described in the Bible; stamped or incised in clay and then baked to make clay tablets, e.g., in the Sumerian and other Mesopotamian civilizations. The papyrus or parchment was often rolled into a scroll or cut into sheets and bound into a codex (book). Contemporary electronic means of memorializing and displaying documents include: Monitor of a desktop computer, laptop, tablet; optionally with a printer to produce a hard copy; Personal digital assistant; Dedicated e-book device; Electronic paper, typically, using the Portable Document Format (PDF); Information appliance; Digital audio player; and Radio and television service provider. Digital documents usually require a specific file format to be presentable in a specific medium. == In law == Documents in all forms frequently serve as material evidence in criminal and civil proceedings. The forensic analysis of such a document is within the scope of questioned document examination. To catalog and manage the large number of documents that may be produced during litigation, Bates numbering is often applied to all documents in the lawsuit so that each document has a unique, arbitrary, identification number.

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  Software durability

  In software engineering, software durability means the solution ability of serviceability of software and to meet user's needs for a relatively long time. Software durability is important for user's satisfaction. For a software security to be durable, it must allow an organization to adjust the software to business needs that are constantly evolving, often in impulsive ways. Durability of software depends on four characteristics mainly; i.e. software trustworthiness, Human Trust for Serviceability, software dependability and software usability.

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  NCSA Brown Dog

  NCSA Brown Dog is a research project to develop a method for easily accessing historic research data stored in order to maintain the long-term viability of large bodies of scientific research. It is supported by the National Center for Supercomputing Applications (NCSA) that is funded by the National Science Foundation (NSF). == History == Brown Dog is part of the DataNet partners program funded by NSF in 2008. DataNet was conceived to address the increasingly digital and data-intensive nature of science, engineering and education. Brown Dog is part of a follow-on effort called Data Infrastructure Building Blocks (DIBBs), focused on building software to support DataNet. The project was proposed by researchers at NCSA and the University of Illinois Urbana-Champaign as well as researchers from Boston University and the University of North Carolina at Chapel Hill. == Unstructured, uncurated, long tail data == Much scientific data is smaller, unstructured and uncurated and thus not easily shared. Such data is sometimes referred to as "long tail" data. This borrows a term from statistics and refers to the tail of the distribution of project sizes. The majority of smaller projects lack the resources to properly steward the data they produce. This so-called "long tail" data, both past and present, has the potential to inform future research in many study areas. Much of this data has become inaccessible due to obsolete software and file formats. The resulting impossibility of reviewing data from older research disrupts the overall scientific research project. == Approach == Brown Dog describes itself as the "super mutt" of software (thus the name "Brown Dog"), serving as a low-level data infrastructure to interface digital data content across the internet. Its approach is to use every possible source of automated help (i.e., software) in existence in a robust and provenance-preserving manner to create a service that can deal with as much of this data as possible. The project sees the broader impact of its work in its potential to serve the general public as a sort of "DNS for data", with the goal of making all data and all file formats as accessible as webpages are today. == Technology == Brown Dog seeks to address problems involving the use of uncurated and unstructured data collections through the development of two services: the Data Access Proxy (DAP) to aid in the conversion of file formats and the Data Tilling Services (DTS) for the automatic extraction of metadata from file contents. Once developed, researchers and general public users will be able to download browser plugins and other tools from the Brown Dog tool catalog. === Data Tilling Service === Data Tilling Service (DTS) will allow users to search data collections using an existing file to discover other similar files in a collection. A DTS search field will be appended to configured browsers where example files can be dropped. This tells DTS to search all the files under a given URL for files similar to the dropped file. For example, while browsing an online image collection, a user could drop an image of three people into the search field, and the DTS would return all images in the collection that also contain three people. If DTS encounters a foreign file format, it will utilize DAP to make the file accessible. DTS also indexes the data and extract and appends metadata to files and collections enabling users to gain some sense of the type of data they are encountering. This service runs on port 9443. === Data Access Proxy === Data Access Proxy (DAP) allows users to access data files that would otherwise be unreadable. Similar to an internet gateway or Domain Name Service, the DAP configuration would be entered into a user's machine and browser settings. Data requests over HTTP would first be examined by DAP to determine if the native file format is readable on the client device. If not, DAP converts the file into the best available format readable by the client machine. Alternatively, the user could specify the desired format themselves. This service runs on port 8184. == Use cases == Brown Dog targets three use cases proposed by groups within the EarthCube research communities. Developers and researchers from these communities will work together on use cases that span geoscience, engineering, biology and social science. === Long tail vegetation data in ecology and global change biology === This use case is led by Michael Dietze, Boston University Data on the abundance, species composition, and size structure of vegetation is critically important for a wide array of sub-disciplines in ecology, conservation, natural resource management, and global change biology. However, addressing many of the pressing questions in these disciplines will require that terrestrial biosphere and hydrologic models are able to assimilate the large amount of long-tail data that exists but is largely inaccessible. The Brown Dog team in cooperation with researches from Dietze's lab will facilitate the capture of a huge body of smaller research-oriented vegetation data sets collected over many decades and historical vegetation data embedded in Public Land Survey data dating back to 1785. This data will be used as initial conditions for models, to make sense of other large data sets and for model calibration and validation. === Designing green infrastructure considering storm water and human requirements === This use case is led by Barbara Minsker], University of Illinois at Urbana-Champaign]; William Sullivan, University of Illinois at Urbana-Champaign; Arthur Schmidt, University of Illinois at Urbana-Champaign. This case study involves developing novel green infrastructure design criteria and models that integrate requirements for storm water management and ecosystem and human health and well being. To address the scientific and social problems associated with the design of green spaces, data accessibility and availability is a major challenge. This study will focus on identified areas of the Green Healthy Neighborhood Planning region within the City of Chicago where existing local sewer performance is most deficient and where changes in impervious area through green infrastructure would be beneficial to under served neighborhoods. Brown Dog will be used to extract long-tail experimental data on human landscape preferences and health impacts. This data will be used to develop a human health impacts model that will then be linked together with a terrestrial biosphere model and a storm water model using Brown Dog technology. === Development and application for critical zone studies === This use case is led by Praveen Kumar, University of Illinois at Urbana-Champaign Critical Zone (CZ) is the "skin" of the earth that extends from the treetops to the bedrock that is created by life processes working at scales from microbes to biomes. The Critical Zone supports all terrestrial living systems. Its upper part is the bio-mantle. This is where terrestrial biota live, reproduce, use and expend energy, and where their wastes and remains accumulate and decompose. It encompasses the soil, which acts as a geomembrane through which water and solutes, energy, gases, solids, and organisms interact with the atmosphere, biosphere, hydrosphere, and lithosphere. A variety of drivers affect this bio-dynamic zone, ranging from climate and deforestation to agriculture, grazing and human development. Understanding and predicting these effects is central to managing and sustaining vital ecosystem services such as soil fertility, water purification, and production of food resources, and, at larger scales, global carbon cycling and carbon sequestration. The CZ provides a unifying framework for integrating terrestrial surface and near-surface environments, and reflects an intricate web of biological and chemical processes and human impacts occurring at vastly different temporal and spatial scales. The nature of these data create significant challenges for inter-disciplinary studies of the CZ because integration of the variety and number of data products and models has been a barrier. On the other hand, CZ data provides an excellent opportunity for defining, testing and implementing Brown Dog technologies. In this context "unstructured" data is viewed broadly as consisting of a collection of heterogeneous data with formats that reflect temporal and disciplinary legacies, data from emerging low cost open hardware based sensors and embedded sensor networks that lack well defined metadata and sensor characteristics, as well as data that are available as maps, images and text. == NSF Award == CIF21 DIBBs: Brown Dog was awarded in the winter of 2013 with a start date of October 1, 2013. Estimated expiration date is September 30, 2018. The award amount was $10,519,716.00, the largest DIBB award. The principal investigator is Kenton McHenry of NCSA at the University of Illinois at Urbana-Champaign. Coleaders are Jong Lee NCSA/UIU

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  Emotion recognition

  Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively nascent research area. Generally, the technology works best if it uses multiple modalities in context. To date, the most work has been conducted on automating the recognition of facial expressions from video, spoken expressions from audio, written expressions from text, and physiology as measured by wearables. == Human == Humans show a great deal of variability in their abilities to recognize emotion. A key point to keep in mind when learning about automated emotion recognition is that there are several sources of "ground truth", or truth about what the real emotion is. Suppose we are trying to recognize the emotions of Alex. One source is "what would most people say that Alex is feeling?" In this case, the 'truth' may not correspond to what Alex feels, but may correspond to what most people would say it looks like Alex feels. For example, Alex may actually feel sad, but he puts on a big smile and then most people say he looks happy. If an automated method achieves the same results as a group of observers it may be considered accurate, even if it does not actually measure what Alex truly feels. Another source of 'truth' is to ask Alex what he truly feels. This works if Alex has a good sense of his internal state, and wants to tell you what it is, and is capable of putting it accurately into words or a number. However, some people are alexithymic and do not have a good sense of their internal feelings, or they are not able to communicate them accurately with words and numbers. In general, getting to the truth of what emotion is actually present can take some work, can vary depending on the criteria that are selected, and will usually involve maintaining some level of uncertainty. == Automatic == Decades of scientific research have been conducted developing and evaluating methods for automated emotion recognition. There is now an extensive literature proposing and evaluating hundreds of different kinds of methods, leveraging techniques from multiple areas, such as signal processing, machine learning, computer vision, and speech processing. Different methodologies and techniques may be employed to interpret emotion such as Bayesian networks. , Gaussian Mixture models and Hidden Markov Models and deep neural networks. === Approaches === The accuracy of emotion recognition is usually improved when it combines the analysis of human expressions from multimodal forms such as texts, physiology, audio, or video. Different emotion types are detected through the integration of information from facial expressions, body movement and gestures, and speech. The technology is said to contribute in the emergence of the so-called emotional or emotive Internet. The existing approaches in emotion recognition to classify certain emotion types can be generally classified into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches. ==== Knowledge-based techniques ==== Knowledge-based techniques (sometimes referred to as lexicon-based techniques), utilize domain knowledge and the semantic and syntactic characteristics of text and potentially spoken language in order to detect certain emotion types. In this approach, it is common to use knowledge-based resources during the emotion classification process such as WordNet, SenticNet, ConceptNet, and EmotiNet, to name a few. One of the advantages of this approach is the accessibility and economy brought about by the large availability of such knowledge-based resources. A limitation of this technique on the other hand, is its inability to handle concept nuances and complex linguistic rules. Knowledge-based techniques can be mainly classified into two categories: dictionary-based and corpus-based approaches. Dictionary-based approaches find opinion or emotion seed words in a dictionary and search for their synonyms and antonyms to expand the initial list of opinions or emotions. Corpus-based approaches on the other hand, start with a seed list of opinion or emotion words, and expand the database by finding other words with context-specific characteristics in a large corpus. While corpus-based approaches take into account context, their performance still vary in different domains since a word in one domain can have a different orientation in another domain. ==== Statistical methods ==== Statistical methods commonly involve the use of different supervised machine learning algorithms in which a large set of annotated data is fed into the algorithms for the system to learn and predict the appropriate emotion types. Machine learning algorithms generally provide more reasonable classification accuracy compared to other approaches, but one of the challenges in achieving good results in the classification process, is the need to have a sufficiently large training set. Some of the most commonly used machine learning algorithms include Support Vector Machines (SVM), Naive Bayes, and Maximum Entropy. Deep learning, which is under the unsupervised family of machine learning, is also widely employed in emotion recognition. Well-known deep learning algorithms include different architectures of Artificial Neural Network (ANN) such as Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Extreme Learning Machine (ELM). The popularity of deep learning approaches in the domain of emotion recognition may be mainly attributed to its success in related applications such as in computer vision, speech recognition, and Natural Language Processing (NLP). ==== Hybrid approaches ==== Hybrid approaches in emotion recognition are essentially a combination of knowledge-based techniques and statistical methods, which exploit complementary characteristics from both techniques. Some of the works that have applied an ensemble of knowledge-driven linguistic elements and statistical methods include sentic computing and iFeel, both of which have adopted the concept-level knowledge-based resource SenticNet. The role of such knowledge-based resources in the implementation of hybrid approaches is highly important in the emotion classification process. Since hybrid techniques gain from the benefits offered by both knowledge-based and statistical approaches, they tend to have better classification performance as opposed to employing knowledge-based or statistical methods independently. A downside of using hybrid techniques however, is the computational complexity during the classification process. === Datasets === Data is an integral part of the existing approaches in emotion recognition and in most cases it is a challenge to obtain annotated data that is necessary to train machine learning algorithms. For the task of classifying different emotion types from multimodal sources in the form of texts, audio, videos or physiological signals, the following datasets are available: HUMAINE: provides natural clips with emotion words and context labels in multiple modalities Belfast database: provides clips with a wide range of emotions from TV programs and interview recordings SEMAINE: provides audiovisual recordings between a person and a virtual agent and contains emotion annotations such as angry, happy, fear, disgust, sadness, contempt, and amusement IEMOCAP: provides recordings of dyadic sessions between actors and contains emotion annotations such as happiness, anger, sadness, frustration, and neutral state eNTERFACE: provides audiovisual recordings of subjects from seven nationalities and contains emotion annotations such as happiness, anger, sadness, surprise, disgust, and fear DEAP: provides electroencephalography (EEG), electrocardiography (ECG), and face video recordings, as well as emotion annotations in terms of valence, arousal, and dominance of people watching film clips DREAMER: provides electroencephalography (EEG) and electrocardiography (ECG) recordings, as well as emotion annotations in terms of valence, dominance of people watching film clips MELD: is a multiparty conversational dataset where each utterance is labeled with emotion and sentiment. MELD provides conversations in video format and hence suitable for multimodal emotion recognition and sentiment analysis. MELD is useful for multimodal sentiment analysis and emotion recognition, dialogue systems and emotion recognition in conversations. MuSe: provides audiovisual recordings of natural interactions between a person and an object. It has discrete and continuous emotion annotations in terms of valence, arousal and trustworthiness as well as speech topics useful for multimodal sentiment analysis and emotion recognition. UIT-VSMEC: is a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with about 6,927 human-annotated sentences with six emotion labels, contributing to emotion recognition research in Vietnamese

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  Apple Intelligence

  Apple Intelligence is a generative artificial intelligence system developed by Apple Inc. Relying on a combination of on-device and server processing, it was announced on June 10, 2024, at the 2024 Worldwide Developers Conference, as a built-in feature of Apple's iOS 18, iPadOS 18, and macOS Sequoia, which were announced alongside Apple Intelligence. Apple Intelligence is free for all users with supported devices. On macOS, Apple Intelligence is available only on Apple silicon Mac computers; Intel-based Mac computers are not supported. Features include writing tools that assist users with grammar and proofreading, image generation, summaries of system notifications, AI-assisted image retouching in the Photos app, and integration with ChatGPT, the popular chatbot by OpenAI. As of March 2026, Apple Intelligence is not available yet on devices purchased in mainland China or on any device using an Apple Account set to mainland China, even if the device was bought elsewhere. == History == === Background === Apple first implemented artificial intelligence features in its products with the release of Siri in the iPhone 4S in 2011. In the years after its release, Apple engaged in efforts to ensure its artificial intelligence operations remained covert; according to University of California, Berkeley professor Trevor Darrell, the company's secrecy deterred graduate students. The company started expanding its artificial intelligence team in 2015, opening up its operations by publishing more scientific papers and joining AI industry research groups. Apple reportedly acquired more AI companies from 2016 to 2020. In 2017, Apple released the iPhone 8 and the iPhone X with the A11 Bionic processor, which featured its first dedicated Neural Engine for accelerating common machine learning tasks. Despite its investments in artificial intelligence, Siri was criticized both by reviewers and internally at Apple for lagging behind other AI assistants. The rapid development of generative artificial intelligence and the release of ChatGPT in late 2022 reportedly blindsided Apple executives and forced the company to refocus its efforts on AI. In an interview with Good Morning America, Apple CEO Tim Cook stated that generative AI had "great promise" but had some potential dangers, and that it was "looking closely" at ChatGPT. It was first reported in July 2023 that Apple was creating its own internal large language model, codenamed "Ajax". In October 2023, Apple was reportedly on track to release new generative AI features into its operating systems by 2024, including a significantly redeveloped Siri. In an earnings call in February 2024, Cook stated that the company was spending a "tremendous amount of time and effort" into AI features that would be shared "later that year". === Google deal === In January 2026, Apple and Google announced a multi-year partnership under which Apple’s next-generation foundation models are expected to incorporate Google’s Gemini models and cloud infrastructure. According to the companies, the collaboration is intended to support future Apple Intelligence features, including enhancements to Siri, while Apple Intelligence will continue to operate on Apple devices and through Apple’s Private Cloud Compute system, which Apple states is designed to preserve user privacy. On an earnings call, Apple reported to investors that they were integrating an on-device model of the Google Gemini AI to Siri, as the development of their model was beset with setbacks. Apple has previously tested and used other third-party AI models like ChatGPT, but according to a Bloomberg article by Mark Gurman, Apple pushed forward the proposed Google deal; by using Google's Gemini model possessing 1.2 trillion parameters, Apple would integrate a much larger and more complex model than those it previously developed and used. Of note, comparable AI models from other major companies (including OpenAI and Meta) have also been reported to operate at a similar “trillion-parameter” scale and to compete against Gemini-class systems on benchmarks. == Models == Apple Intelligence consists of an on-device model as well as a cloud model running on servers primarily using Apple silicon. Both models consist of a generic foundation model, as well as multiple adapter models that are more specialized to particular tasks like text summarization and tone adjustment. It was launched for developers and testers on July 29, 2024, in U.S. English, with the developer betas of iOS 18.1, macOS 15.1, and iPadOS 18.1, released partially on October 28, 2024, and will fully launch by 2026. According to a human evaluation done by Apple's machine learning division, the on-device foundation model beat or tied equivalent small models by Mistral AI, Microsoft, and Google, while the server foundation models beat the performance of OpenAI's GPT-3, while roughly matching the performance of GPT-4. Apple's cloud models are built on a Private Cloud Compute platform which is allegedly designed with user privacy and end-to-end encryption in mind. Unlike other generative AI services like ChatGPT which use servers from third parties, Apple Intelligence's cloud models are run entirely on Apple servers with custom Apple silicon hardware built for end-to-end encryption. It was also designed to make sure that the software running on said servers matches the independently verifiable software accessible to researchers. In case of a software mismatch, Apple devices will refuse to connect to the servers. On June 10, 2025, Apple announced that Apple's on-device foundation models will be available to third-party applications as part of the Foundation Models API, with support for structured data response and tool calling. == Features == === Writing tools === Apple Intelligence features writing tools that are powered by LLMs. Selected text can be proofread, rewritten, made more friendly, concise or professional, similar to the AI writing features of the popular online English-language writing assistant tool Grammarly. It can also be used to generate summaries, key points, tables, and lists from an article or piece of writing. In iOS 18.2 and macOS 15.2, a ChatGPT integration was added to Writing Tools through "Compose" and "Describe your change" features. === Real-time Translation === Apple Intelligence enables the real-time translation of messages, photos and videos, and phone calls, through Apple's hardware. For communicating with foreigners, using the Translate app on iPhone to show subtitles in their language or to play back the translated audio naturally in their language, and also by wearing AirPods with Live Translation can now help to understand what someone is saying in users' preferred language in conversation. If both have headphones, simultaneous interpretation can be achieved. === Image Playground === Apple Intelligence can be used to generate images on-device with the Image Playground app. Similarly to OpenAI's DALL-E, it can be used to generate images using AI, using phrases and descriptions to output an image with customizable styles such as Animation and Sketch. In Notes, users can access Image Playground on iPad through the Image Wand tool in the Apple Pencil palette without having to open the Image Playground app. Rough sketches made with Apple Pencil can be transformed into images. As part of iOS, iPadOS, and macOS 26, Image Playground now integrates with the image generation models built into ChatGPT. === Genmoji === Using Apple Intelligence text-to-image models, users can generate unique "Genmoji" images by typing descriptions (prompting). Users can pick people in photos to have Genmoji generate images that resemble them. Similarly to emoji, Genmoji can be added inline to text messages, tapbacks, stickers and can be shared in Messages as well in third-party applications as inline messages or as stickers. === Siri overhaul === Siri, which used to be Apple's virtual assistant, has been updated to be an LLM chatbot, with enhanced capabilities made possible by Apple Intelligence. The latest iteration features an updated user interface, improved natural language processing, and the option to interact via text by double tapping the home bar without enabling the feature in the Accessibility menu, or double-clicking the command key on macOS. In a later update, Apple Intelligence will add the ability for Siri to use personal context from device activities to answer queries. === Mail === Apple Intelligence adds a feature called Priority Messages to the Mail app, which shows urgent emails such as same-day invitations or boarding passes, with AI generated summaries of the email. The Mail app also gains the ability to categorize incoming mail into Primary, Transactions, Updates, and Promotions based on what the email contains, which Apple claims is done all on-device. === Photos === Apple's Photos app includes a feature to create custom memory movies and enhanced search capabilities. Users can describe

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  Genigraphics

  Genigraphics is a large-format printing service bureau specializing in providing poster session services to medical and scientific conferences throughout the US and Canada. The company began in 1973 as a division of General Electric. == History == Genigraphics began as a computer graphics system, developed by General Electric in the late 1960s, for NASA to use in space flight simulation. The technologies thus developed provided a foundation for the company's expansion into the commercial market. The Computed Images System & Services division (CISS, to become Genigraphics Corporation) of GE delivered the first presentation graphics system to Amoco Oil's corporate headquarters in 1973. It was named the 100 Series, and was based on DEC's PDP 11 series of mini computer systems. The first Genigraphics systems (100 Series and 100A Series) used an array of buttons, dials, knobs and joysticks, along with a built in keyboard, as the means of user interface. The PDP-11/40 computer was housed in a tall cabinet and used random access magnetic tape drives (DECtape) for storing completed presentations. The graphics generator (Forox recorder) was capable of outputting 2,000 line resolution, suitable for 35mm and 72mm film and large sheet film positive using larger cassettes for recording. 4000 and 8000 line resolution was later achieved with duplex scanning and 4x scanning by modifying to the Forox recorder's settings menu. Subsequent models (100B,C,D,D+ and D+/GVP) replaced the knobs and dials with an on screen, text based menu system, a graphics tablet and a pen. The pen/tablet combination gave way to a mouse like device in later models, and served to provide the interface with the graphics tools. User interaction with the computer for functions such as media initialization or modem to modem data transfer required a DECwriter serial terminal. In 1982, GE divested the Genigraphics division along with a host of other "non essential" business units (Genitext, Geniponics) and Genigraphics Corporation was born. Shortly after the divestiture, the headquarters of Genigraphics was moved from Liverpool, New York to Saddle Brook, New Jersey. Major success followed as the company grew exponentially over the next few years selling both systems and slide creation services. Genigraphics film recorders produced high-resolution digital images on 35mm film. The computer-generated scenes for The Last Starfighter were calculated on a Cray X-MP supercomputer and mastered with a Genigraphics film recorder. At its peak, Genigraphics Corporation employed roughly 300 people in 24 offices worldwide, with revenues upwards of $70 million annually. By the late 1980s Genigraphics saw demand for its proprietary systems dwindle and began selling the MASTERPIECE 8770 film recorder and GRAFTIME software as a peripheral for DEC Vaxes, IBM PC AT’s, and Mac NuBus machines. But the MASTERPIECE film recorder proved too expensive to sell in volume. In 1988, the company began a partnership with Microsoft to help develop the PowerPoint software. In exchange, every copy of PowerPoint included a “Send to Genigraphics” link to have files sent to a Genigraphics service bureau to be produced as 35mm slides. This partnership continued until 2001. In 1989, after three years of flat revenue, Genigraphics sold its hardware business in order to focus on its service bureau business and partnership with Microsoft via PowerPoint. In 1994, all assets of Genigraphics, including equipment, software development, in-house artwork, trademarks, and rights to the Microsoft partnership, were sold to InFocus Corporation of Wilsonville, Oregon who continued to operate under the Genigraphics brand name. The twenty-four service bureaus were consolidated to a 20,000 square foot facility next to the FedEx hub in Memphis, Tennessee. This allowed PowerPoint slide orders to be received until 10pm and delivered across the United States by the following morning. In 1995, InFocus registered www.genigraphics.com and was among the first to offer a form of ecommerce allowing 35mm slides, color prints and transparencies, printed booklets, and digital projectors to be purchased online. In 1998, then current management bought Genigraphics from InFocus and have operated it continuously ever since as Genigraphics LLC. That same year, InFocus projector rentals were added to the “Send to Genigraphics” link in PowerPoint and Genigraphics became the rental and repair center for all InFocus national accounts. It also marked Genigraphics entry into the new industry of large format printing; leveraging their knowledge of, and access to, PowerPoint programming code to develop a proprietary printer driver to output directly to an Epson 9500 wide format printer. At the time, Genigraphics was the exclusive 35mm slide vendor for all Kinko’s stores in the United States and poster printing was added to the arrangement. In 2003, Genigraphics closed their 35mm slide E6 photo lab – one of the last high-volume commercial E6 labs in the US – and expanded their large format printing capabilities. Since 2003, Genigraphics has become a major player in the poster session market, providing printing and on-site services to medical and scientific conferences throughout the US and Canada. As of February 2019, over 150,000 medical or scientific ‘ePosters’ are made available through their ResearchPosters.com archive service. === Partnership with Microsoft and development of PowerPoint === As presentations began to be created on personal computers in the late 80’s, Genigraphics sought presentation software partners in Silicon Valley who would be interested in sending files to Genigraphics via dial-up modem to be produced on 35mm slides. In 1987, Michael Beetner, Director of Marketing Planning for Genigraphics, met with Robert Gaskins, head of Microsoft's Graphics Business Unit, who was leading the development of the newly released PowerPoint software. A joint development agreement between Microsoft and Genigraphics was agreed upon and announced at Mac World 1988. According to Erica Robles-Anderson and Patrik Svensson, "It would be hard to overestimate Genigraphics’ influence on PowerPoint. PowerPoint 2.0 was designed for Genigraphics film recorders. It shipped with Genigraphics color palettes, schemes, and the distinctively Genigraphics color-gradient backgrounds. The application contained a ‘Send to Genigraphics’ menu item that wrote the presentation to floppy disk or transmitted the order directly via modem. Within three and a half months PowerPoint orders accounted for ten percent of revenue at Genigraphics service centers. PowerPoint 3.0 was even more intimately dependent upon Genigraphics. The software incorporated a collection of clip art images and symbols that had been produced by hundreds of artists at dozens of service centers across tens of thousands of presentations. Genigraphics artists designed PowerPoint 3.0 colors, templates, and sample presentations. The software even used Genigraphics (rather than Excel) chart style. Bar charts were rendered two-dimensionally with apparent thickness added to make them seemingly recede from the axes. The technique made it easier for viewers to compare bar heights and estimate values from axis ticks and labels. Pie charts were handled analogously. Microsoft paid Genigraphics to produce more than 500 clip art drawings and symbols used in Microsoft programs.” In exchange for Genigraphics development efforts, Microsoft included a “Send to Genigraphics” link in every copy of PowerPoint through the 10.0 version (2000/2001). The arrangement came to an end when Microsoft restructured as a result of anti-trust lawsuits.

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  Knuth–Eve algorithm

  In computer science, the Knuth–Eve algorithm is an algorithm for polynomial evaluation. It preprocesses the coefficients of the polynomial to reduce the number of multiplications required at runtime. Ideas used in the algorithm were originally proposed by Donald Knuth in 1962. His procedure opportunistically exploits structure in the polynomial being evaluated. In 1964, James Eve determined for which polynomials this structure exists, and gave a simple method of "preconditioning" polynomials (explained below) to endow them with that structure. == Algorithm == === Preliminaries === Consider an arbitrary polynomial p ∈ R [ x ] {\displaystyle p\in \mathbb {R} [x]} of degree n {\displaystyle n} . Assume that n ≥ 3 {\displaystyle n\geq 3} . Define m {\displaystyle m} such that: if n {\displaystyle n} is odd then n = 2 m + 1 {\displaystyle n=2m+1} , and if n {\displaystyle n} is even then n = 2 m + 2 {\displaystyle n=2m+2} . Unless otherwise stated, all variables in this article represent either real numbers or univariate polynomials with real coefficients. All operations in this article are done over R {\displaystyle \mathbb {R} } . Again, the goal is to create an algorithm that returns p ( x ) {\displaystyle p(x)} given any x {\displaystyle x} . The algorithm is allowed to depend on the polynomial p {\displaystyle p} itself, since its coefficients are known in advance. === Overview === ==== Key idea ==== Using polynomial long division, we can write p ( x ) = q ( x ) ⋅ ( x 2 − α ) + ( β x + γ ) , {\displaystyle p(x)=q(x)\cdot (x^{2}-\alpha )+(\beta x+\gamma ),} where x 2 − α {\displaystyle x^{2}-\alpha } is the divisor. Picking a value for α {\displaystyle \alpha } fixes both the quotient q {\displaystyle q} and the coefficients in the remainder β {\displaystyle \beta } and γ {\displaystyle \gamma } . The key idea is to cleverly choose α {\displaystyle \alpha } such that β = 0 {\displaystyle \beta =0} , so that p ( x ) = q ( x ) ⋅ ( x 2 − α ) + γ . {\displaystyle p(x)=q(x)\cdot (x^{2}-\alpha )+\gamma .} This way, no operations are needed to compute the remainder polynomial, since it's just a constant. We apply this procedure recursively to q {\displaystyle q} , expressing p ( x ) = ( ( q ( x ) ⋅ ( x 2 − α m ) + γ m ) ⋯ ) ⋅ ( x 2 − α 1 ) + γ 1 . {\displaystyle p(x)=\left(\left(q(x)\cdot (x^{2}-\alpha _{m})+\gamma _{m}\right)\cdots \right)\cdot (x^{2}-\alpha _{1})+\gamma _{1}.} After m {\displaystyle m} recursive calls, the quotient q {\displaystyle q} is either a linear or a quadratic polynomial. In this base case, the polynomial can be evaluated with (say) Horner's method. ==== "Preconditioning" ==== For arbitrary p {\displaystyle p} , it may not be possible to force β = 0 {\displaystyle \beta =0} at every step of the recursion. Consider the polynomials p e {\displaystyle p^{e}} and p o {\displaystyle p^{o}} with coefficients taken from the even and odd terms of p {\displaystyle p} respectively, so that p ( x ) = p e ( x 2 ) + x ⋅ p o ( x 2 ) . {\displaystyle p(x)=p^{e}(x^{2})+x\cdot p^{o}(x^{2}).} If every root of p o {\displaystyle p^{o}} is real, then it is possible to write p {\displaystyle p} in the form given above. Each α i {\displaystyle \alpha _{i}} is a different root of p o {\displaystyle p^{o}} , counting multiple roots as distinct. Furthermore, if at least n − 1 {\displaystyle n-1} roots of p {\displaystyle p} lie in one half of the complex plane, then every root of p o {\displaystyle p^{o}} is real. Ultimately, it may be necessary to "precondition" p {\displaystyle p} by shifting it — by setting p ( x ) ← p ( x + t ) {\displaystyle p(x)\gets p(x+t)} for some t {\displaystyle t} — to endow it with the structure that most of its roots lie in one half of the complex plane. At runtime, this shift has to be "undone" by first setting x ← x − t {\displaystyle x\gets x-t} . === Preprocessing step === The following algorithm is run once for a given polynomial p {\displaystyle p} . At this point, the values of x {\displaystyle x} that p {\displaystyle p} will be evaluated on are not known. ==== Better choice of t ==== While any t ≥ Re ( r 2 ) {\displaystyle t\geq {\text{Re}}(r_{2})} can work, it is possible to remove one addition during evaluation if t {\displaystyle t} is also chosen such that two roots of p ( x + t ) {\displaystyle p(x+t)} are symmetric about the origin. In that case, α 1 {\displaystyle \alpha _{1}} can be chosen such that the shifted polynomial has a factor of x 2 − α 1 {\displaystyle x^{2}-\alpha _{1}} , so γ 1 = 0 {\displaystyle \gamma _{1}=0} . It is always possible to find such a t {\displaystyle t} . One possible algorithm for choosing t {\displaystyle t} is: === Evaluation step === The following algorithm evaluates p {\displaystyle p} at some, now known, point x {\displaystyle x} . Assuming t {\displaystyle t} is chosen optimally, γ 1 = 0 {\displaystyle \gamma _{1}=0} . So, the final iteration of the loop can instead run y ← y ⋅ ( s − α i ) , {\displaystyle y\gets y\cdot (s-\alpha _{i}),} saving an addition. == Analysis == In total, evaluation using the Knuth–Eve algorithm for a polynomial of degree n {\displaystyle n} requires n {\displaystyle n} additions and ⌊ n / 2 ⌋ + 2 {\displaystyle \lfloor n/2\rfloor +2} multiplications, assuming t {\displaystyle t} is chosen optimally. No algorithm to evaluate a given polynomial of degree n {\displaystyle n} can use fewer than n {\displaystyle n} additions or fewer than ⌈ n / 2 ⌉ {\displaystyle \lceil n/2\rceil } multiplications during evaluation. This result assumes only addition and multiplication are allowed during both preprocessing and evaluation. The Knuth–Eve algorithm is not well-conditioned.

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  Algorithmic mechanism design

  Algorithmic mechanism design (AMD) lies at the intersection of economic game theory, optimization, and computer science. The prototypical problem in mechanism design is to design a system for multiple self-interested participants, such that the participants' self-interested actions at equilibrium lead to good system performance. Typical objectives studied include revenue maximization and social welfare maximization. Algorithmic mechanism design differs from classical economic mechanism design in several respects. It typically employs the analytic tools of theoretical computer science, such as worst case analysis and approximation ratios, in contrast to classical mechanism design in economics which often makes distributional assumptions about the agents. It also considers computational constraints to be of central importance: mechanisms that cannot be efficiently implemented in polynomial time are not considered to be viable solutions to a mechanism design problem. This often, for example, rules out the classic economic mechanism, the Vickrey–Clarke–Groves auction. == History == Noam Nisan and Amir Ronen first coined "Algorithmic mechanism design" in a research paper published in 1999.

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