AI Art Detector

AI Art Detector — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Electronic business

  Electronic business (also known as online business or e-business) is any kind of business or commercial activity that includes sharing information across the internet. Commerce constitutes the exchange of products and services between businesses, groups, and individuals; and can be seen as one of the essential activities of any business. E-commerce focuses on the use of ICT to enable the external activities and relationships of the business with individuals, groups, and other organizations, while e-business does not only deal with online commercial operations of enterprises, but also deals with their other organizational matters such as human resource management and production. The term "e-business" was coined by IBM's marketing and Internet team in 1996. == Market participants == Electronic business can take place between a very large number of market participants; it can be between business and consumer, private individuals, public administrations, or any other organizations such as non-governmental organizations (NGOs). These various market participants can be divided into three main groups: Business (B) Consumer (C) Administration (A) All of them can be either buyers or service providers within the market. There are nine possible combinations for electronic business relationships. B2C and B2B belong to E-commerce, while A2B and A2A belong to the E-government sector which is also a part of the electronic business. == History == One of the founding pillars of electronic business was the development of the Electronic Data Interchange (EDI) electronic data interchange. This system replaced traditional mailing and faxing of documents with a digital transfer of data from one computer to another, without any human intervention. Michael Aldrich is considered the developer of the predecessor to online shopping. In 1979, the entrepreneur connected a television set to a transaction processing computer with a telephone line and called it "teleshopping", meaning shopping at distance. From the mid-nineties, major advancements were made in the commercial use of the Internet. Amazon, which launched in 1995, started as an online bookstore and grew to become nowadays the largest online retailer worldwide, selling food, toys, electronics, apparel and more. Other successful stories of online marketplaces include eBay or Etsy. In 1994, IBM, with its agency Ogilvy & Mather, began to use its foundation in IT solutions and expertise to market itself as a leader of conducting business on the Internet through the term "e-business." Then CEO Louis V. Gerstner, Jr. was prepared to invest $1 billion to market this new brand. After conducting worldwide market research in October 1997, IBM began with an eight-page piece in The Wall Street Journal that would introduce the concept of "e-business" and advertise IBM's expertise in the new field. IBM decided not to trademark the term "e-business" in the hopes that other companies would use the term and create an entirely new industry. However, this proved to be too successful and by 2000, to differentiate itself, IBM launched a $300 million campaign about its "e-business infrastructure" capabilities. Since that time, the terms, "e-business" and "e-commerce" have been loosely interchangeable and have become a part of the common vernacular. According to the U.S. Department Of Commerce, the estimated retail e-commerce sales in Q1 2020 were representing almost 12% of total U.S. retail sales, against 4% for Q1 2010. == Business model == The transformation toward e-business is complex and in order for it to succeed, there is a need to balance between strategy, an adapted business model (e-intermediary, marketplaces), right processes (sales, marketing) and technology (Supply Chain Management, Customer Relationship Management). When organizations go online, they have to decide which e-business models best suit their goals. A business model is defined as the organization of product, service and information flows, and the source of revenues and benefits for suppliers and customers. The concept of the e-business model is the same but used in online presence. === Revenue model === A key component of the business model is the revenue model or profit model, which is a framework for generating revenues. It identifies which revenue source to pursue, what value to offer, how to price the value, and who pays for the value. It is a key component of a company's business model. It primarily identifies what product or service will be created in order to generate revenues and the ways in which the product or service will be sold. Without a well-defined revenue model, that is, a clear plan of how to generate revenues, new businesses will more likely struggle due to costs that they will not be able to sustain. By having a revenue model, a business can focus on a target audience, fund development plans for a product or service, establish marketing plans, begin a line of credit and raise capital. ==== E-commerce ==== E-commerce (short for "electronic commerce") is trading in products or services using computer networks, such as the Internet. Electronic commerce draws on technologies such as mobile commerce, electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection. Modern electronic commerce typically uses the World Wide Web for at least one part of the transaction's life cycle, although it may also use other technologies such as e-mail. == Concerns == While much has been written of the economic advantages of Internet-enabled commerce, there is also evidence that some aspects of the internet such as maps and location-aware services may serve to reinforce economic inequality and the digital divide. Electronic commerce may be responsible for consolidation and the decline of mom-and-pop, brick and mortar businesses resulting in increases in income inequality. === Security === E-business systems naturally have greater security risks than traditional business systems, therefore it is important for e-business systems to be fully protected against these risks. A far greater number of people have access to e-businesses through the internet than would have access to a traditional business. Customers, suppliers, employees, and numerous other people use any particular e-business system daily and expect their confidential information to stay secure. Hackers are one of the great threats to the security of e-businesses. Some common security concerns for e-Businesses include keeping business and customer information private and confidential, the authenticity of data, and data integrity. Some of the methods of protecting e-business security and keeping information secure include physical security measures as well as data storage, data transmission, anti-virus software, firewalls, and encryption to list a few. ==== Privacy and confidentiality ==== Confidentiality is the extent to which businesses makes personal information available to other businesses and individuals. With any business, confidential information must remain secure and only be accessible to the intended recipient. However, this becomes even more difficult when dealing with e-businesses specifically. To keep such information secure means protecting any electronic records and files from unauthorized access, as well as ensuring safe transmission and data storage of such information. Tools such as encryption and firewalls manage this specific concern within e-business. ==== Authenticity ==== E-business transactions pose greater challenges for establishing authenticity due to the ease with which electronic information may be altered and copied. Both parties in an e-business transaction want to have the assurance that the other party is who they claim to be, especially when a customer places an order and then submits a payment electronically. One common way to ensure this is to limit access to a network or trusted parties by using a virtual private network (VPN) technology. The establishment of authenticity is even greater when a combination of techniques are used, and such techniques involve checking "something you know" (i.e. password or PIN), "something you need" (i.e. credit card), or "something you are" (i.e. digital signatures or voice recognition methods). Many times in e-business, however, "something you are" is pretty strongly verified by checking the purchaser's "something you have" (i.e. credit card) and "something you know" (i.e. card number). ==== Data integrity ==== Data integrity answers the question "Can the information be changed or corrupted in any way?" This leads to the assurance that the message received is identical to the message sent. A business needs to be confident that data is not changed in transit, whether deliberately or by accident. To help with data integrity, firewalls protect stored data against unauthorized access, while

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  IRCF360

  Infrared Control Freak 360 (IRCF360) is a 360-degree proximity sensor and a motion sensing devices, developed by ROBOTmaker. The sensor is in BETA developers release as a low cost (software configurable) sensor for use within research, technical and hobby projects. == Overview == The 360-degree sensor was originally designed as a short range micro robot proximity sensor and mainly intended for Swarm robotics, Ant robotics, Swarm intelligence, autonomous Qaudcopter, Drone, UAV, multi-robot simulations e.g. Jasmine Project where 360 proximity sensing is required to avoid collision with other robots and for simple IR inter-robot communications. To overcome certain limitation with Infra-red (IR) proximity sensing (e.g. detection of dark surfaces) the sensing module includes ambient light sensing and basic tactile sensing functionality during forward movement sensing/probing providing photovore and photophobe robot swarm behaviours and characteristics. A project named Sensorium Project was started aimed at broadening the Sensors audience beyond its typical robot sensor usage. To demonstrate the sensor's functionality, opensource Java based Integrated Development Environments (IDE) are used, such as Arduino and Processing (programming language).

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  Lighthill report

  Artificial Intelligence: A General Survey, commonly known as the Lighthill report, is a scholarly article by James Lighthill, published in Artificial Intelligence: a paper symposium in 1973. It was compiled by Lighthill for the British Science Research Council as an evaluation of academic research in the field of artificial intelligence (AI). The report gave a very pessimistic prognosis for many core aspects of research in this field, stating that "In no part of the field have the discoveries made so far produced the major impact that was then promised". It "formed the basis for the decision by the British government to end support for AI research in most British universities", contributing to an AI winter in the United Kingdom. == Publication history == It was commissioned by the SRC in 1972 for Lighthill to "make a personal review of the subject [of AI]". Lighthill completed the report in July. The SRC discussed the report in September, and decided to publish it, together with some alternative points of view by Stuart Sutherland, Roger Needham, Christopher Longuet-Higgins, and Donald Michie. The SRC's decision to invite the report was partly a reaction to high levels of discord within the University of Edinburgh's Department of Artificial Intelligence, one of the earliest and biggest centres for AI research in the UK. On May 9, 1973, Lighthill debated several leading AI researchers (Donald Michie, John McCarthy, Richard Gregory) at the Royal Institution in London concerning the report. == Content == While the report was supportive of research into the simulation of neurophysiological and psychological processes, it was "highly critical of basic research in foundational areas such as robotics and language processing". The report stated that AI researchers had failed to address the issue of combinatorial explosion when solving problems within real-world domains. That is, the report states that whilst AI techniques may have worked within the scope of small problem domains, the techniques would not scale up well to solve more realistic problems. The report represents a pessimistic view of AI that began after early excitement in the field. The report divides AI research into three categories: Advanced Automation ("A"): applications of AI, such as optical character recognition, mechanical component design and manufacture, missile perception and guidance, etc. Computer-based Central Nervous System research ("C"): building computational models of human brains (neurobiology) and behavior (psychology). Bridge, or Building Robots ("B"): research that combines categories A and C. This category is intentionally vague. Projects in category A had had some success, but only in restricted domains where a large quantity of detailed knowledge was used in designing the program. This was disappointing to researchers who hoped for generic methods. Due to the issue of the combinatorial explosion, the amount of detailed knowledge required by the program quickly grew too large to be entered by hand, thus restricting projects to restricted domains. Projects in category C had had some measure of success. Artificial neural networks were successfully used to model neurobiological data. SHRDLU demonstrated that human use of language, even in fine details, depends on the semantics or knowledge, and is not purely syntactical. This was influential in psycholinguistics. Attempts to extend SHRDLU to larger domains of discourse was considered impractical, again due to the issue of the combinatorial explosion. Projects in category B were held to be failures. One important project, that of "programming and building a robot that would mimic human ability in a combination of eye-hand co-ordination and common-sense problem solving", was considered entirely disappointing. Similarly, chess playing programs were no better than human amateurs. Due to the combinatorial explosion, the run-time of general algorithms quickly grew impractical, requiring detailed problem-specific heuristics. The report stated that it was expected that within the next 25 years, category A would simply become applied technologies engineering, C would integrate with psychology and neurobiology, while category B would be abandoned.

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  Mathematical model

  A mathematical model is an abstract description of a concrete system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in many fields, including applied mathematics, natural sciences, social sciences and engineering. In particular, the field of operations research studies the use of mathematical modelling and related tools to solve problems in business or military operations. A model may help to characterize a system by studying the effects of different components, which may be used to make predictions about behavior or solve specific problems. == Elements of a mathematical model == Mathematical models can take many forms, including dynamical systems, statistical models, differential equations, or game theoretic models. These and other types of models can overlap, with a given model involving a variety of abstract structures. In many cases, the quality of a scientific field depends on how well the mathematical models developed on the theoretical side agree with results of repeatable experiments. Lack of agreement between theoretical mathematical models and experimental measurements often leads to important advances as better theories are developed. In the physical sciences, a traditional mathematical model contains most of the following elements: Governing equations Supplementary sub-models Defining equations Constitutive equations Assumptions and constraints Initial and boundary conditions Classical constraints and kinematic equations == Classifications == Mathematical models are of different types: === Linear vs. nonlinear === If all the operators in a mathematical model exhibit linearity, the resulting mathematical model is defined as linear. All other models are considered nonlinear. The definition of linearity and nonlinearity is dependent on context, and linear models may have nonlinear expressions in them. For example, in a statistical linear model, it is assumed that a relationship is linear in the parameters, but it may be nonlinear in the predictor variables. Similarly, a differential equation is said to be linear if it can be written with linear differential operators, but it can still have nonlinear expressions in it. In a mathematical programming model, if the objective functions and constraints are represented entirely by linear equations, then the model is regarded as a linear model. If one or more of the objective functions or constraints are represented with a nonlinear equation, then the model is known as a nonlinear model. Linear structure implies that a problem can be decomposed into simpler parts that can be treated independently or analyzed at a different scale, and therefore that the results will remain valid if the initial is recomposed or rescaled. Nonlinearity, even in fairly simple systems, is often associated with phenomena such as chaos and irreversibility. Although there are exceptions, nonlinear systems and models tend to be more difficult to study than linear ones. A common approach to nonlinear problems is linearization, but this can be problematic if one is trying to study aspects such as irreversibility, which are strongly tied to nonlinearity. === Static vs. dynamic === A dynamic model accounts for time-dependent changes in the state of the system, while a static (or steady-state) model calculates the system in equilibrium, and thus is time-invariant. Dynamic models are typically represented by differential equations or difference equations. === Explicit vs. implicit === If all of the input parameters of the overall model are known, and the output parameters can be calculated by a finite series of computations, the model is said to be explicit. But sometimes it is the output parameters which are known, and the corresponding inputs must be solved for by an iterative procedure, such as Newton's method or Broyden's method. In such a case the model is said to be implicit. For example, a jet engine's physical properties such as turbine and nozzle throat areas can be explicitly calculated given a design thermodynamic cycle (air and fuel flow rates, pressures, and temperatures) at a specific flight condition and power setting, but the engine's operating cycles at other flight conditions and power settings cannot be explicitly calculated from the constant physical properties. === Discrete vs. continuous === A discrete model treats objects as discrete, such as the particles in a molecular model or the states in a statistical model; while a continuous model represents the objects in a continuous manner, such as the velocity field of fluid in pipe flows, temperatures and stresses in a solid, and electric field that applies continuously over the entire model due to a point charge. === Deterministic vs. probabilistic (stochastic) === A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables; therefore, a deterministic model always performs the same way for a given set of initial conditions. Conversely, in a stochastic model—usually called a "statistical model"—randomness is present, and variable states are not described by unique values, but rather by probability distributions. === Deductive, inductive, or floating === A deductive model is a logical structure based on a theory. An inductive model arises from empirical findings and generalization from them. If a model rests on neither theory nor observation, it may be described as a 'floating' model. Application of mathematics in social sciences outside of economics has been criticized for unfounded models. Application of catastrophe theory in science has been characterized as a floating model. === Strategic vs. non-strategic === Models used in game theory are distinct in the sense that they model agents with incompatible incentives, such as competing species or bidders in an auction. Strategic models assume that players are autonomous decision makers who rationally choose actions that maximize their objective function. A key challenge of using strategic models is defining and computing solution concepts such as the Nash equilibrium. An interesting property of strategic models is that they separate reasoning about rules of the game from reasoning about behavior of the players. == Construction == In business and engineering, mathematical models may be used to maximize a certain output. The system under consideration will require certain inputs. The system relating inputs to outputs depends on other variables too: decision variables, state variables, exogenous variables, and random variables. Decision variables are sometimes known as independent variables. Exogenous variables are sometimes known as parameters or constants. The variables are not independent of each other as the state variables are dependent on the decision, input, random, and exogenous variables. Furthermore, the output variables are dependent on the state of the system (represented by the state variables). Objectives and constraints of the system and its users can be represented as functions of the output variables or state variables. The objective functions will depend on the perspective of the model's user. Depending on the context, an objective function is also known as an index of performance, as it is some measure of interest to the user. Although there is no limit to the number of objective functions and constraints a model can have, using or optimizing the model becomes more involved (computationally) as the number increases. For example, economists often apply linear algebra when using input–output models. Complicated mathematical models that have many variables may be consolidated by use of vectors where one symbol represents several variables. === A priori information === Mathematical modeling problems are often classified into black box or white box models, according to how much a priori information on the system is available. A black-box model is a system of which there is no a priori information available. A white-box model (also called glass box or clear box) is a system where all necessary information is available. Practically all systems are somewhere between the black-box and white-box models, so this concept is useful only as an intuitive guide for deciding which approach to take. Usually, it is preferable to use as much a priori information as possible to make the model more accurate. Therefore, the white-box models are usually considered easier, because if you have used the information correctly, then the model will behave correctly. Often the a priori information comes in forms of knowing the type of functions relating different variables. For example, if we make a model of how a medicine works in a human system, we know that usually the amount of medicine in the blood is an exponentially decaying function, but we are still left with several unknown parameters; how

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  T Layout

  The T-Layout is an architectural and design concept for web applications, specifically tailored to improve the user experience on mobile devices. It features a horizontally scrollable container divided into three distinct sections, each spanning the full width of the screen, and was developed to optimise space usage and streamline navigation. == Background == The T-Layout introduces horizontal scrolling as a complementary method to the conventional pop-up-based navigation system in mobile web applications. In this layout, the central section which is visible by default upon accessing the application, facilitates the main content of a URL address and is flanked by two "helper" sections. This approach minimises the need for extensive user movements, in order to reach navigation controls typically located at the top of the screen. It is aimed at enhancing the user experience on mobile devices by providing an easier way to access essential content such as the main navigation, e-commerce related screens, or user account related information, ensuring that those elements are readily accessible while requiring minimal user effort. The T-Layout was first implemented by E (e-streetwear.com) in their mobile web app layout, and it was inspired by the interfaces of well-tested native mobile apps like Instagram and Revolut. A study titled "Mobile Navigation and User Preferences Survey" indicated a preference among mobile app users for one-handed usage, primarily navigating with their thumb. These insights led to the T-Layout Experiment, which compared the efficiency of using swipe gestures to access navigational elements against reaching traditional navigation controls. == Development history == It was first released as the mobile layout of E in early 2023. It was originally developed based on six principles: user-centric functionality, lightweight filesize, HTML and CSS implementation with minimal or no use of JavaScript required, suitable both for browser and server-rendering architectures, intuitive design, and improved SEO. The development of the T-Layout was driven by the necessity for more ergonomic and user-friendly interfaces in mobile web applications. Its design, reminiscent of the letter 'T', emerged as a solution to several usability challenges mobile device users face, emphasising ease of access and efficient screen space utilisation. In July 2023, E formalised the concept and its technical specifications, introducing it to the web design and development community. In October 2023 the "Mobile Navigation and User Preferences Survey" was conducted, establishing that the vast majority of individuals prefer to use mobile applications by holding the phone in a one-handed grip, utilising only the thumb for gestures when possible. The subsequent "T-Layout Experiment", designed to measure the time in seconds and the distance (user effort) in pixels, required to access navigational elements by traditionally tapping on fixed-positioned controls compared to swiping anywhere on the screen. The results proved that swipe gestures require less time and much less effort. == Styling and features == The main characteristic of the T-Layout is its horizontal scrolling feature, which can improve navigation efficiency while preserving the functionality of traditionally structured user interfaces. Its Implementation can be achieved with a combination of HTML and styling with CSS as well as precompiled Scss and Sass, CSS-in-JS, and styled JSX. It can be either a purely HTML/CSS solution but JavaScript can be utilised as well to add more specific functionalities, while It can be implemented to both existing and new applications. Its application in server-side rendering architectures will ensure that all its underlying principles apply. Although principally each section in the layout has a distinct role and facilitates specific types of content, the T-Layout as a concept is versatile, and it is adaptable allowing modifications in the layout or how it's implemented to cater to the specific needs of different applications.

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  Alec Radford

  Alec Radford is an American artificial intelligence researcher. == Biography == Radford grew up in Texas. He graduated from Cistercian Preparatory School in 2011, where he became an Eagle Scout, and dropped out of Olin College in August 2014, where he and fellow students Slater Victoroff, Diana Yuan, and Madison May had formed the startup Indico in their dorm room. In 2015, the quartet were joined by Luke Metz and the firm and the Facebook AI research lab in New York used generative adversarial networks to create realistic low pixel images. A demonstration of Indico's technology was used without proper attribution in an April 2016 demonstration by Nvidia chief executive Jensen Huang. Radford joined OpenAI around 2016, where he worked on natural-language processing. The following year, Radford trained a neural network on Amazon reviews. The model was fairly basic, with layers which allowed for human understanding. Upon exploring it, he saw that it had a special neuron linked to the sentiment of the reviews, which it had created on its own. This was a drastic improvement from previous neural networks that had analysed sentiment, because they had to be told to do so and specially trained on data that was explicitly labeled according to sentiment. This development made OpenAI chief scientist Ilya Sutskever consider that a future model, using more diverse language data, could map far more structures of meaning, eventually becoming a "learned core module" for superintelligence. In 2018, Radford was the lead author on OpenAI's seminal research paper on generative pre-trained transformers, which form the foundation of ChatGPT. At OpenAI, he worked on early GPT models, Whisper, a speech recognition model, and the image generator DALL-E. He left OpenAI in December 2024 to pursue independent research. Around March 2025, Radford joined Thinking Machines Lab as an advisor. He joined along with Bob McGrew who was previously the chief research officer of OpenAI. In April 2026, Radford, Nick Levine, and David Duvenaud released Talkie, an AI model trained on books, newspapers, scientific journals, patents, and case law published before December 31, 1930. When asked about the state of the world in 2026, it stated that one billion people would live in Europe, that London and New York would be connected by steamships that transit between the two in ten days, and "winter will be passed in Paris, and the summer in London."

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  Parents & Kids Safe AI Coalition

  The Parents & Kids Safe AI Coalition is a political action committee that advocates for regulation of artificial intelligence on child safety. As of April 2026, the group is funded solely by the artificial intelligence company OpenAI, which pledged $10 million to the effort. == History == In October 2025, California Gov. Gavin Newsom vetoed Assembly Bill 1064. Sponsored by Common Sense Media, the bill would have introduced stronger child safety protections for AI chatbots. The following month, Common Sense Media founder Jim Steyer filed a ballot initiative intended to restore the "guardrails" lost in the veto. In response, OpenAI introduced a competing initiative. In January 2026, Common Sense Media and OpenAI announced that they would be working together on a compromise ballot initiative, the Parents & Kids Safe AI Act. Reporting indicated that initial outreach emails to child safety organizations failed to disclose OpenAI's involvement. Several advocacy groups signed an open letter claiming the initiative would shield AI companies from liability and undermine age verification, among other concerns. After Common Sense Media met with opposing groups in February, the ballot initiative was put on hold and the organizations involved sought to negotiate with the Legislature instead. The Parents & Kids Safe AI Coalition was founded to support this effort. In March 2026, the group reached out to some of the same groups contacted earlier, asking them to endorse its list of policy priorities. Again, some organizations reported being unaware of OpenAI's level of involvement. At least two groups withdrew from the coalition after learning about the financial ties. The priorities themselves were described as "vague but fairly uncontroversial" by The San Francisco Standard.

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  Graphics Turing test

  In computer graphics the graphics Turing test is a variant of the Turing test, the twist being that a human judge viewing and interacting with an artificially generated world should be unable to reliably distinguish it from reality. The original formulation of the test is: "The subject views and interacts with a real or computer generated scene. The test is passed if the subject can not determine reality from simulated reality better than a random guess. (a) The subject operates a remotely controlled (or simulated) robotic arm and views a computer screen. (b) The subject enters a door to a controlled vehicle or motion simulator with computer screens for windows. An eye patch can be worn on one eye, as stereo vision is difficult to simulate." The "graphics Turing scale" of computer power is then defined as the computing power necessary to achieve success in the test. It was estimated in, as 1036.8 TFlops peak and 518.4 TFlops sustained. Actual rendering tests with a Blue Gene supercomputer showed that current supercomputers are not up to the task scale yet. A restricted form of the graphic Turing test has been investigated, where test subjects look into a box, and try to tell whether the contents are real or virtual objects. For the very simple case of scenes with a cardboard pyramid or a styrofoam sphere, subjects were not able to reliably tell reality and graphics apart.

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  Smartphone kill switch

  A smartphone kill switch is a software-based security feature that allows a smartphone's owner to remotely render it inoperable if it is lost or stolen, thereby deterring theft. There have been a number of initiatives to legally require kill switches on smartphones. Smartphones have high resale value, and are therefore often the target of theft, with thieves selling them to cartels for resale. A kill switch can deter theft by making devices worthless. == Legal requirements == In the United States, Minnesota was the first state to pass a bill requiring smartphones to have such a feature, and California was the first to require that the feature be turned on by default. The California law requires the kill switch to be resistant to reinstallation of the phone's operating system. The CTIA initially resisted the legislation, fearing that it would make phones easier to hack, but later supported kill switches. There is evidence that this legislation has been effective, with smartphone theft declining by 50% between 2013 and 2017 in San Francisco. Secure Our Smartphones (S.O.S.), a New York State and San Francisco initiative started by New York State Attorney General Eric Schneiderman and San Francisco District Attorney George Gascón. The initiative is co-chaired by Schneiderman, Gascón and Boris Johnson, and has 105 members. == Examples == An Android phone signed into a Google account can be remotely locked and erased via Google's Find My Device service, as long as it is connected to the Internet. To prevent this, a thief must sign the device out of Google before the owner locks or erases it. iPhones have a similar service.

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  Daisy Intelligence

  Daisy Intelligence is a Canadian artificial intelligence (AI) company that provides data analysis services to help retailers, mainly grocers and supermarkets, to determine optimal pricing and promotional mix. The company also helps insurance companies detect fraudulent claims. The company uses a subset of AI known as reinforcement learning. In October 2019, the company moved from the suburban Vaughan, Ontario, to downtown Toronto, joining other AI and technology startups concentrated in the King Street East area. In 2019, the company was ranked No. 39 on The Globe and Mail's annual list of Canada's "top growing companies by three-year revenue growth."

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  ELVIS Act

  The ELVIS Act or Ensuring Likeness Voice and Image Security Act, signed into law by Tennessee Governor Bill Lee on March 21, 2024, marked a significant milestone in the area of regulation of artificial intelligence and public sector policies for artists in the era of artificial intelligence (AI) and AI alignment. It was noted as the first enacted legislation in the United States specifically designed to protect musicians from the unauthorized use of their voices through artificial intelligence technologies and against audio deepfakes and voice cloning. This legislation distinguishes itself by adding penalties for copying a performer's voice. == Origin and advocacy == The inception of the ELVIS Act has been attributed to Gebre Waddell, founder of Sound Credit, who initially conceptualized a framework in 2023 that later evolved into the legislation. Representative Justin J. Pearson acknowledged Waddell's pivotal role during the March 4 House Floor Session on the bill. Leading Tennessee musicians supported the ELVIS Act. Tennessee Governor Bill Lee endorsed it as a Governor's Bill, and it was introduced in the Tennessee Legislature as House Bill 2091 by William Lamberth (R-44) and Senate Bill 2096 by Jack Johnson (R-27). The ELVIS Act is an amendment to a 1984 law that was the result of the Elvis Presley estate litigation for controlling how his likeness could be used after death. == Lobbying from the recording industry == The legislative journey of the ELVIS Act included a broad coalition of music industry stakeholders, including: These organizations, led by the Recording Academy and the RIAA, played roles in drafting the legislation, advocating for passage, and rallying support among the industry and legislators. The act gained momentum through discussions that bridged industry concerns with legislative action. This collaborative process led to a proposal that specifically targets the use of AI to create unauthorized reproductions of artists' voices and images. == Opposition == The ELVIS Act saw industry opposition from the Motion Picture Association, including testimony in the House Banking & Consumer Affairs Subcommittee, including remarks that the law risks "interference with our members’ ability to portray real people and events." TechNet, representing companies such as OpenAI, Google and Amazon, expressed their opposition in the hearing to the bill as drafted, asserting that the language was too broadly written and could have unintended consequences. Other concerns included its potential application to cover bands, but lawmakers assured people that this was not the intention. The bill passed the Tennessee House and Senate with a unanimous, bi-partisan vote including 93 ayes and 0 Noes in the House, and 30 ayes and 0 noes in the Senate. == Passage == By explicitly addressing AI impersonation, the ELVIS Act originated a legal approach to safeguarding personal rights, in the context of digital and technological advancements. It extends protections to an artist's voice and likeness, areas vulnerable to exploitation with the proliferation of AI technologies that occurred in 2023. The legislation received widespread support from the music industry, signaling a significant step forward in the ongoing effort to balance innovation with the protection of individual rights and creative integrity. It was reported as underscoring Tennessee's commitment to its musical heritage and showed the state as a leader in adapting copyright and privacy protections to the modern technological landscape. Artists including Chris Janson and Luke Bryan appeared at the signing ceremony hosted at Robert's Western World to support the new law and commemorate its passing. == Legal precedent == The ELVIS Act was reported as representing a development in the discourse surrounding AI, intellectual property, and personal rights. It was hoped by proponents to set a precedent for future legislative efforts both within and beyond Tennessee, offering a model for how states and potentially the federal government could address similar challenges. As AI technology continues to evolve, the act represents a foundational framework for protecting the authenticity and rights of artists, ensuring contributions remain protected. The act prohibits usage of AI to clone the voice of an artist without consent and can be criminally enforced as a Class A misdemeanor. This legislation's success was hoped by its supporters to inspire similar actions in other states, contributing to a unified approach to copyright and privacy in the digital age. Such a national response would reinforce the importance of safeguarding artists' rights against unauthorized use of their voices and likenesses.

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  Oracle Database

  Oracle AI Database (commonly referred to as Oracle Database, Oracle DBMS, Oracle Autonomous Database, or simply as Oracle) is a proprietary multi-model database management system produced and marketed by Oracle Corporation. It is a database commonly used for running online transaction processing (OLTP), data warehousing (DW) and mixed (OLTP & DW) database workloads. Oracle AI Database uses SQL for database updating and retrieval. Oracle Database runs on-premises, on Oracle engineered systems such as Oracle Exadata, on Oracle Cloud Infrastructure, and as a managed Autonomous Database service. It is also offered inside Microsoft Azure, Google Cloud, and Amazon Web Services data centers through Oracle's multicloud offerings. The current long-term support release, Oracle AI Database 26ai, became available in the cloud and on Oracle engineered systems in October 2025 and on-premises for Linux x86-64 in January 2026. == History == Larry Ellison and his two friends and former co-workers, Bob Miner and Ed Oates, started a consultancy called Software Development Laboratories (SDL) in 1977, later Oracle Corporation. SDL developed the original version of the Oracle software. The name Oracle comes from the code-name of a Central Intelligence Agency-funded project Ellison had worked on while formerly employed by Ampex; the CIA was Oracle's first customer, and allowed the company to use the code name for the new product. Ellison wanted his database to be compatible with IBM System R, but that company's Don Chamberlin declined to release its error codes. By 1985 Oracle advertised, however, that "Programs written for SQL/DS or DB2 will run unmodified" on the many non-IBM mainframes, minicomputers, and microcomputers its database supported "Because all versions of ORACLE are identical". Later releases introduced capabilities associated with successive eras of the product, including PL/SQL stored procedures and triggers in Oracle7 (1992), Real Application Clusters in Oracle9i (2001), grid infrastructure and automatic management in Oracle 10g (2003), the multitenant architecture and In-Memory Column Store in Oracle Database 12c (2013), and AI Vector Search and JSON Relational Duality in Oracle Database 23ai (2024). In October 2025 Oracle rebranded the 23ai line as Oracle AI Database 26ai. (see Release History) == Architecture == An Oracle Database system consists of an instance and a database. The instance is a set of memory structures and background processes; the database is the set of files that store data. The instance exists only in memory, and a single instance is associated with one multitenant container database. The principal memory structures are the System Global Area, which is shared, and the Program Global Areas, which are private to individual processes. The shared pool, database buffer cache, and redo log buffer are components of the System Global Area, and the optional In-Memory Column Store also resides there. Background processes operate on the database files and use these memory structures; they include the database writer, the log writer, the checkpoint process, and the system and process monitor processes. Server processes handle connections from client programs and run their SQL statements. Storage is organized logically and physically. Logically, data is held in tablespaces composed of segments, extents, and data blocks. Physically, the database comprises datafiles, control files, and online redo log files, with archived redo logs supporting media recovery. == High Availability and Scalability == Oracle Database includes several technologies for high availability, disaster recovery, and scale. Oracle Real Application Clusters allows multiple instances on separate servers to access one shared database concurrently; it was introduced with Oracle9i in 2001. Oracle Data Guard maintains standby databases synchronized with a primary database, and Active Data Guard additionally allows read-only workloads on a standby while it applies changes. Oracle GoldenGate performs logical replication and data integration across heterogeneous systems. Native sharding, introduced in Oracle Database 12c Release 2, distributes one logical database across independent shards. Oracle Exadata is an engineered system that pairs database servers with storage servers and offloads operations such as filtering to the storage tier; it is available on-premises, in Oracle Cloud Infrastructure, and through Cloud@Customer. == Notable Features == AI Vector Search adds a vector data type, vector indexes, and vector distance operators to the database. These allow similarity search over machine-learning embeddings to be expressed in SQL and combined with queries over relational, JSON, spatial, and graph data. It became generally available in Oracle Database 23ai. JSON Relational Duality exposes the same data both as relational tables and as JSON documents through duality views, so that an application can read and write either representation of the data. It became generally available in Oracle Database 23ai. In-Memory Column Store maintains a column-oriented copy of selected tables in memory in addition to the row-oriented format, and the optimizer can use the columnar copy for analytic queries. It was introduced in Oracle Database 12c Release 1.Partitioning divides large tables and indexes into independently managed pieces. Advanced Compression and Hybrid Columnar Compression are compression features for transactional and warehouse data respectively. == Data Types == Oracle AI Database supports a variety of data types and data models within a single system. These include traditional relational data types as well as semi-structured, unstructured, and specialized data formats, enabling different types of data to be stored and queried together. == Releases and versions == Oracle products follow a custom release-numbering and -naming convention. The "ai" in the current release, Oracle AI Database 26ai, stands for "Artificial Intelligence". Previous releases (e.g. Oracle Database 19c, 10g, and Oracle9i Database) have used suffixes of "c", "g", and "i" which stand for "Cloud", "Grid", and "Internet" respectively. Prior to the release of Oracle8i Database, no suffixes featured in Oracle AI Database naming conventions. There was no v1 of Oracle AI Database, as Ellison "knew no one would want to buy version 1". For some database releases, Oracle also provides an Express Edition (XE) that is free to use. Oracle AI Database release numbering has used the following codes: The Introduction to Oracle AI Database includes a brief history on some of the key innovations introduced with each major release of Oracle AI Database. See My Oracle Support (MOS) note Release Schedule of Current Database Releases (Doc ID 742060.1) for the current Oracle AI Database releases and their patching end dates. == Patch updates and security alerts == Prior to Oracle Database 18c, Oracle Corporation released Critical Patch Updates (CPUs) and Security Patch Updates (SPUs) and Security Alerts to close security vulnerabilities. These releases are issued quarterly; some of these releases have updates issued prior to the next quarterly release. Starting with Oracle Database 18c, Oracle Corporation releases Release Updates (RUs) and Release Update Revisions (RURs). RUs usually contain security, regression (bug), optimizer, and functional fixes which may include feature extensions as well. RURs include all fixes from their corresponding RU but only add new security and regression fixes. However, no new optimizer or functional fixes are included. == Competition == In the market for relational databases, Oracle AI Database competes against commercial products such as IBM Db2 and Microsoft SQL Server. Oracle and IBM tend to battle for the mid-range database market on Unix and Linux platforms, while Microsoft dominates the mid-range database market on Microsoft Windows platforms. However, since they share many of the same customers, Oracle and IBM tend to support each other's products in many middleware and application categories (for example: WebSphere, PeopleSoft, and Siebel Systems CRM), and IBM's hardware divisions work closely with Oracle on performance-optimizing server-technologies (for example, Linux on IBM Z). Niche commercial competitors include Teradata (in data warehousing and business intelligence), Software AG's ADABAS, Sybase, and IBM's Informix, among many others. In the cloud, Oracle AI Database competes against the database services of AWS, Microsoft Azure, and Google Cloud Platform. Increasingly, the Oracle AI Database products compete against open-source software relational and non-relational database systems such as PostgreSQL, MongoDB, Couchbase, Neo4j, ArangoDB and others. Oracle acquired Innobase, supplier of the InnoDB codebase to MySQL, in part to compete better against open source alternatives, and acquired Sun Microsystems, owner of MySQL, in 2010. Database products licensed as open

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  Knowledge as a service

  Knowledge as a service (KaaS) is a computing service that delivers information to users, backed by a knowledge model, which might be drawn from a number of possible models based on decision trees, association rules, or neural networks. A knowledge as a service provider responds to knowledge requests from users through a centralised knowledge server, and provides an interface between users and data owners. KaaS is one of several cloud computing-dependent business models in which computer resources are sold on an on-demand and pay-as-you-use basis. == Overview == At the International Semantic Web Conference 2019, it was described how knowledge can be made live and evolve on the web allowing users to learn directly from elaborated knowledge, now appearing in the form of knowledge graphs. KaaS appear when knowledge graphs are accessed via services This is opposed to DaaS which might "compute large volumes of data; integrate and analyzes that data; and publish it in real-time, using Web service APIs" (from Data as a Service) where the KaaS is able to exploit context - both the context of the user in relation to their information requests of the KaaS (where and when they make the request) and also the context of the information in relation to some objective or purpose of the users either understood by the KaaS automatically or indicated to it by the user. == Differentiating knowledge from data == Conceptual models that make such a differentiation such as the so-called DIKW pyramid have existed for perhaps more than 40 years (see a 1974 journal article about this) however definitions are not stable and universally accepted (see the discussion about the conceptualizations of DIKW within the DIKW Wikipedia article that question value of wisdom). The knowledge component of DIKW is generally agreed to be an elusive concept which is difficult to define, however Rowley 2007, in a well known student textbook differentiated knowledge from data by stating that knowledge is "defined with reference to information" and that it contains more than just facts but also "beliefs and expectations". In relation to knowledge graphs, knowledge may be additional content they provide over and above pure data which is the definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse (see the definition of Ontology). The ability to represent "beliefs and expectations", or other forms of not so straightforwardly explicit knowledge is an on-going area of improvement in information sciences (see Tacit knowledge) and, with relation to KaaS, the establishment of recent informatics mechanics to do so it critical to the legitimacy of KaaS as it is differentiated from just value-added DaaS. Knowledge graphs' ability to represent context via the definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse that they provide (see the definition of Ontology) has led to the idea that supplying access to KNs might be a required competency of a KaaS. == Delivery of knowledge == Much service-delivered content is dependent on a session to provide much of the context that the user (client) needs to understand answers to questions. For example, using current HTTP internet protocols, a GET request to retrieve information identified by a URI, such as a web page, a client (a human or a machine) may have access information supplied automatically to enable that client to bypass paywalls or other content access controls. Such context, in this case about the client's information access allowances, can alter the information provided. In a logical extension to this internet protocols example, a server would receive from the client, either manually or automatically, a full context which would be information about the situation the client is in and this would allow the server to best interpret the client's request. Current internet protocols allow for formats, languages and related preferences to be expressed by clients but make no mention of what a client already knows and what they may understand. The recent Content Negotiation by Profile proposes additions to both the HTTP internet protocols and related services that allow clients to also request information - a response from the server - that accords with an identified information model. This then allows clients to indicate not just formats and languages that they understand (technically that they prefer) but also domains of discourse that that do, which is a step towards comprehensive client context provision.

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  Darkforest

  Darkforest is a computer go program developed by Meta Platforms, based on deep learning techniques using a convolutional neural network. Its updated version Darkfores2 combines the techniques of its predecessor with Monte Carlo tree search. The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them. With the update, the system is known as Darkfmcts3. Darkforest is of similar strength to programs like CrazyStone and Zen. It has been tested against a professional human player at the 2016 UEC cup. Google's AlphaGo program won against a professional player in October 2015 using a similar combination of techniques. Darkforest is named after Liu Cixin's science fiction novel The Dark Forest. == Background == Competing with top human players in the ancient game of Go has been a long-term goal of artificial intelligence. Go's high branching factor makes traditional search techniques ineffective, even on cutting-edge hardware, and Go's evaluation function could change drastically with one stone change. However, by using a Deep Convolutional Neural Network designed for long-term predictions, Darkforest has been able to substantially improve the win rate for bots over more traditional Monte Carlo Tree Search based approaches. === Matches === Against human players, Darkfores2 achieves a stable 3d ranking on KGS Go Server, which roughly corresponds to an advanced amateur human player. However, after adding Monte Carlo Tree Search to Darkfores2 to create a much stronger player named darkfmcts3, it can achieve a 5d ranking on the KGS Go Server. ==== Against other AI ==== darkfmcts3 is on par with state-of-the-art Go AIs such as Zen, DolBaram and Crazy Stone, but lags behind AlphaGo. It won 3rd place in January 2016 KGS Bot Tournament against other Go AIs. === News coverage === After Google's AlphaGo won against Fan Hui in 2015, Facebook made its AI's hardware designs public, alongside releasing the code behind DarkForest as open-source, in addition to heavy recruiting to strengthen its team of AI engineers. == Style of play == Darkforest uses a neural network to sort through the 10100 board positions, and find the most powerful next move. However, neural networks alone cannot match the level of good amateur players or the best search-based Go engines, and so Darkfores2 combines the neural network approach with a search-based machine. A database of 250,000 real Go games were used in the development of Darkforest, with 220,000 used as a training set and the rest used to test the neural network's ability to predict the next moves played in the real games. This allows Darkforest to accurately evaluate the global state of the board, but local tactics were still poor. Search-based engines have poor global evaluation, but are good at local tactics. Combining these two approaches is difficult because search-based engines work much faster than neural networks, a problem which was solved in Darkfores2 by running the processes in parallel with frequent communication between the two. === Conventional strategies === Go is generally played by analyzing the position of the stones on the board. Various advanced players have described it as playing in some part subconsciously. Unlike chess and checkers, where AI players can simply look further forward at moves than human players, but with each round of Go having on average 250 possible moves, that approach is ineffective. Instead, neural networks copy human play by training the AI systems on images of successful moves, the AI can effectively learn how to interpret how the board looks, as many grandmasters do. In November 2015, Facebook demonstrated the combination of MCTS with neural networks, which played with a style that "felt human". === Flaws === It has been noted that Darkforest still has flaws in its playstyle. The bot sometimes plays tenuki ("move elsewhere") pointlessly when local powerful moves are required. When the bot is losing, it shows the typical behavior of MCTS, it plays bad moves and loses more. The Facebook AI team has acknowledged these as areas of future improvement. == Program architecture == The family of Darkforest computer go programs is based on convolution neural networks. The most recent advances in Darkfmcts3 combined convolutional neural networks with more traditional Monte Carlo tree search. Darkfmcts3 is the most advanced version of Darkforest, which combines Facebook's most advanced convolutional neural network architecture from Darkfores2 with a Monte Carlo tree search. Darkfmcts3 relies on a convolution neural networks that predicts the next k moves based on the current state of play. It treats the board as a 19x19 image with multiple channels. Each channel represents a different aspect of board information based upon the specific style of play. For standard and extended play, there are 21 and 25 different channels, respectively. In standard play, each players liberties are represented as six binary channels or planes. The respective plane is true if the player one, two, or three or more liberties available. Ko (i.e. illegal moves) is represented as one binary plane. Stone placement for each opponent and empty board positions are represented as three binary planes, and the duration since a stone has been placed is represented as real numbers on two planes, one for each player. Lastly, the opponents rank is represented by nine binary planes, where if all are true, the player is a 9d level, if 8 are true, an 8d level, and so forth. Extended play additionally considers the border (binary plane that is true at the border), position mask (represented as distance from the board center, i.e. x ( − 0.5 ∗ d i s t a n c e 2 ) {\displaystyle x^{(-0.5distance^{2})}} , where x {\displaystyle x} is a real number at a position), and each player's territory (binary, based on which player a location is closer to). Darkfmct3 uses a 12-layer full convolutional network with a width of 384 nodes without weight sharing or pooling. Each convolutional layer is followed by a rectified linear unit, a popular activation function for deep neural networks. A key innovation of Darkfmct3 compared to previous approaches is that it uses only one softmax function to predict the next move, which enables the approach to reduce the overall number of parameters. Darkfmct3 was trained against 300 random selected games from an empirical dataset representing different game stages. The learning rate was determined by vanilla stochastic gradient descent. Darkfmct3 synchronously couples a convolutional neural network with a Monte Carlo tree search. Since the convolutional neural network is computationally taxing, the Monte Carlo tree search focuses computation on the more likely game play trajectories. By running the neural network synchronously with the Monte Carlo tree search, it is possible to guarantee that each node is expanded by the moves predicted by the neural network. == Comparison with other systems == Darkfores2 beats Darkforest, its neural network-only predecessor, around 90% of the time, and Pachi, one of the best search-based engines, around 95% of the time. On the Kyu rating system, Darkforest holds a 1-2d level. Darkfores2 achieves a stable 3d level on KGS Go Server as a ranked bot. With the added Monte Carlo tree search, Darkfmcts3 with 5,000 rollouts beats Pachi with 10k rollouts in all 250 games; with 75k rollouts it achieves a stable 5d level in KGS server, on par with state-of-the-art Go AIs (e.g., Zen, DolBaram, CrazyStone); with 110k rollouts, it won the 3rd place in January KGS Go Tournament.

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  Juergen Pirner

  Juergen Pirner (born 1956) is the German creator of Jabberwock, a chatterbot that won the 2003 Loebner prize. Pirner created Jabberwock modelling the Jabberwocky from Lewis Carroll's poem of the same name. Initially, Jabberwock would just give rude or fantasy-related answers; but over the years, Pirner has programmed better responses into it. As of 2007 he has taught it 2.7 million responses. Pirner lives in Hamburg, Germany.

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