AI Chat Exporter Extension

AI Chat Exporter Extension — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Universal psychometrics

  Universal psychometrics encompasses psychometrics instruments that could measure the psychological properties of any intelligent agent. Up until the early 21st century, psychometrics relied heavily on psychological tests that require the subject to cooperate and answer questions, the most famous example being an intelligence test. Such methods are only applicable to the measurement of human psychological properties. As a result, some researchers have proposed the idea of universal psychometrics - they suggest developing testing methods that allow for the measurement of non-human entities' psychological properties. For example, it has been suggested that the Turing test is a form of universal psychometrics. This test involves having testers (without any foreknowledge) attempt to distinguish a human from a machine by interacting with both (while not being to see either individuals). It is supposed that if the machine is equally intelligent to a human, the testers will not be able to distinguish between the two, i.e., their guesses will not be better than chance. Thus, Turing test could measure the intelligence (a psychological variable) of an AI. Other instruments proposed for universal psychometrics include reinforcement learning and measuring the ability to predict complexity.

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  March algorithm

  The March algorithm is a widely used algorithm that tests SRAM memory by filling all its entries test patterns. It carries out several passes through an SRAM checking the patterns and writing new patterns. The SRAM read and write operations performed on each pass are called a March element and each element is repeated for each entry. The March algorithm is often used to find functional faults in SRAM during testing such as: Stuck-at Faults (SAFs) Transition Faults (TFs) Address Decoder Faults (AFs) Coupling Faults (CFs), such as Inversion (CFin), Idempotent (CFid), and State (CFst) coupling faults It has been suggested to test SRAM modules using the algorithm before sale using a built-in self-test mechanism. == Notation == Each pass in a test sequence is represented by an "element". An element consists of a vertical arrow to indicate the direction in which the memory is scanned followed by a list of read/write operations to be applied to each memory cell. Multiple elements can be listed, separated by semicolons, to form a "test". For example, { ⇕ ( w 0 ) ; ⇑ ( r 0 , w 1 ) ; ⇓ ( r 1 , w 0 , r 0 ) } {\displaystyle \{\Updownarrow (w0);\Uparrow (r0,w1);\Downarrow (r1,w0,r0)\}} specifies to: Scan in both directions, writing 0. Scan from lowest to highest address, reading 0 and writing 1. Scan from highest to lowest address, reading 1, writing 0 and reading 0. == Variants == Many variants of the March algorithm exist with different sequences of tests. Each variant makes a different tradeoff between what faults it can detect and the complexity of the algorithm. Several variants have been given names:

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  Upper ontology

  In information science, an upper ontology (also known as a top-level ontology, upper model, or foundation ontology) is an ontology (in the sense used in information science) that consists of very general terms (such as "object", "property", "relation") that are common across all domains. An important function of an upper ontology is to support broad semantic interoperability among a large number of domain-specific ontologies by providing a common starting point for the formulation of definitions. Terms in the domain ontology are ranked under the terms in the upper ontology, e.g., the upper ontology classes are superclasses or supersets of all the classes in the domain ontologies. A number of upper ontologies have been proposed, each with its own proponents. Library classification systems predate upper ontology systems. Though library classifications organize and categorize knowledge using general concepts that are the same across all knowledge domains, neither system is a replacement for the other. == Development == Any standard foundational ontology is likely to be contested among different groups, each with its own idea of "what exists". One factor exacerbating the failure to arrive at a common approach has been the lack of open-source applications that would permit the testing of different ontologies in the same computational environment. The differences have thus been debated largely on theoretical grounds, or are merely the result of personal preferences. Foundational ontologies can however be compared on the basis of adoption for the purposes of supporting interoperability across domain ontologies. No particular upper ontology has yet gained widespread acceptance as a de facto standard. Different organizations have attempted to define standards for specific domains. The 'Process Specification Language' (PSL) created by the National Institute of Standards and Technology (NIST) is one example. Another important factor leading to the absence of wide adoption of any existing upper ontology is the complexity. Some upper ontologies—Cyc is often cited as an example in this regard—are very large, ranging up to thousands of elements (classes, relations), with complex interactions among them and with a complexity similar to that of a human natural language, and the learning process can be even longer than for a natural language because of the unfamiliar format and logical rules. The motivation to overcome this learning barrier is largely absent because of the paucity of publicly accessible examples of use. As a result, those building domain ontologies for local applications tend to create the simplest possible domain-specific ontology, not related to any upper ontology. Such domain ontologies may function adequately for the local purpose, but they are very time-consuming to relate accurately to other domain ontologies. To solve this problem, some genuinely top level ontologies have been developed, which are deliberately designed to have minimal overlap with any domain ontologies. Examples are Basic Formal Ontology and the DOLCE (see below). === Arguments for the infeasibility of an upper ontology === Historically, many attempts in many societies have been made to impose or define a single set of concepts as more primal, basic, foundational, authoritative, true or rational than all others. A common objection to such attempts points out that humans lack the sort of transcendent perspective — or God's eye view — that would be required to achieve this goal. Humans are bound by language or culture, and so lack the sort of objective perspective from which to observe the whole terrain of concepts and derive any one standard. Thomasson, under the headline "1.5 Skepticism about Category Systems", wrote: "category systems, at least as traditionally presented, seem to presuppose that there is a unique true answer to the question of what categories of entity there are – indeed the discovery of this answer is the goal of most such inquiries into ontological categories. [...] But actual category systems offered vary so much that even a short survey of past category systems like that above can undermine the belief that such a unique, true and complete system of categories may be found. Given such a diversity of answers to the question of what the ontological categories are, by what criteria could we possibly choose among them to determine which is uniquely correct?" Another objection is the problem of formulating definitions. Top level ontologies are designed to maximize support for interoperability across a large number of terms. Such ontologies must therefore consist of terms expressing very general concepts, but such concepts are so basic to our understanding that there is no way in which they can be defined, since the very process of definition implies that a less basic (and less well understood) concept is defined in terms of concepts that are more basic and so (ideally) more well understood. Very general concepts can often only be elucidated, for example by means of examples, or paraphrase. There is no self-evident way of dividing the world up into concepts, and certainly no non-controversial one There is no neutral ground that can serve as a means of translating between specialized (or "lower" or "application-specific") ontologies Human language itself is already an arbitrary approximation of just one among many possible conceptual maps. To draw any necessary correlation between English words and any number of intellectual concepts, that we might like to represent in our ontologies, is just asking for trouble. (WordNet, for instance, is successful and useful, precisely because it does not pretend to be a general-purpose upper ontology; rather, it is a tool for semantic / syntactic / linguistic disambiguation, which is richly embedded in the particulars and peculiarities of the English language.) Any hierarchical or topological representation of concepts must begin from some ontological, epistemological, linguistic, cultural, and ultimately pragmatic perspective. Such pragmatism does not allow for the exclusion of politics between persons or groups, indeed it requires they be considered as perhaps more basic primitives than any that are represented. Those who doubt the feasibility of general purpose ontologies are more inclined to ask "what specific purpose do we have in mind for this conceptual map of entities and what practical difference will this ontology make?" This pragmatic philosophical position surrenders all hope of devising the encoded ontology version of "The world is everything that is the case." (Wittgenstein, Tractatus Logico-Philosophicus). Finally, there are objections similar to those against artificial intelligence. Technically, the complex concept acquisition and the social / linguistic interactions of human beings suggest any axiomatic foundation of "most basic" concepts must be cognitive biological or otherwise difficult to characterize since we don't have axioms for such systems. Ethically, any general-purpose ontology could quickly become an actual tyranny by recruiting adherents into a political program designed to propagate it and its funding means, and possibly defend it by violence. Historically, inconsistent and irrational belief systems have proven capable of commanding obedience to the detriment or harm of persons both inside and outside a society that accepts them. How much more harmful would a consistent rational one be, were it to contain even one or two basic assumptions incompatible with human life? === Arguments for the feasibility of an upper ontology === Many of those who doubt the possibility of developing wide agreement on a common upper ontology fall into one of two traps: they assert that there is no possibility of universal agreement on any conceptual scheme; but they argue that a practical common ontology does not need to have universal agreement, it only needs a large enough user community (as is the case for human languages) to make it profitable for developers to use it as a means to general interoperability, and for third-party developer to develop utilities to make it easier to use; and they point out that developers of data schemes find different representations congenial for their local purposes; but they do not demonstrate that these different representations are in fact logically inconsistent. In fact, different representations of assertions about the real world (though not philosophical models), if they accurately reflect the world, must be logically consistent, even if they focus on different aspects of the same physical object or phenomenon. If any two assertions about the real world are logically inconsistent, one or both must be wrong, and that is a topic for experimental investigation, not for ontological representation. In practice, representations of the real world are created as and known to be approximations to the basic reality, and their use is circumscribed by the limits of e

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  Cancer Likelihood in Plasma

  Cancer Likelihood in Plasma (CLiP) refers to a set of ensemble learning methods for integrating various genomic features useful for the noninvasive detection of early cancers from blood plasma. An application of this technique for early detection of lung cancer (Lung-CLiP) was originally described by Chabon et al. (2020) from the labs of Ash Alizadeh and Max Diehn at Stanford. This method relies on several improvements to cancer personalized profiling by deep sequencing (CAPP-Seq) for analysis of circulating tumor DNA (ctDNA). The CLiP technique integrates multiple distinctive genomic features of a cancer of interest findings within a machine-learning framework for cancer detection. For example, studies have shown that the majority of somatic mutations found in cell-free DNA (cfDNA) are not tumor derived, but instead reflect clonal hematopoeisis (also known as CHIP). Even though CHIP tends to target specific genes, it also involves many generally non-recurrent mutations that can be shed from leukocytes and detected in cfDNA, regardless of whether profiling patients with cancer and healthy adults. However, genuine tumor derived ctDNA mutations can be distinguished from CHIP-derived mutations. This is because unlike tumor-derived mutations, CHIP-derived mutations that are shed from leukocytes into plasma tend to occur on longer cfDNA fragments, and to lack specific mutational signatures such as those associated with tobacco smoking in lung cancer that are also found in tumor derived ctDNA molecules. CLiP integrates these features within hierarchical ensemble machine learning models that consider somatic mutations and copy number alternations, among other features. While the CLiP method is unique in relying exclusively on mutations and copy number alterations, it is related to a variety of other liquid biopsy methods being commercially developed for early cancer detection using ctDNA and proteins (e.g., CancerSEEK / DETECT-A ), cfDNA fragmentation patterns (e.g., DELFI), and DNA methylation (e.g., cfMeDIP-Seq, Grail). While the CLiP method has not yet been broadly applied for population-based cancer screening, it has been shown to distinguish discriminate early-stage lung cancers from risk-matched controls across multiple cohorts of patients enrolled across the US.

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  ShareMethods

  ShareMethods is a Web 2.0 document management and collaboration service with a focus on sales, marketing, and the extended selling network. It offers a software as a service (SaaS) subscription to companies and is available as a stand-alone application or as an integrated program with CRM tools such as Oracle CRM On Demand or salesforce.com. == History == ShareMethods was launched in 2004 to provide collaboration and communication services for sales and marketing teams, business partners, and customers. The founders have a background of building software-as-a-service applications and creating digital media applications. In September 2005, ShareMethods launched "ShareNow" as one of the first applications on the salesforce.com AppExchange. In September 2006, ShareMethods moved its operations into a SAS 70 Type II data center owned by SunGard. In March 2009, ShareMethods launched "ShareSpaces" to provide on-demand portals or workspaces. In 2013, ShareMethods announced that its platform is available in a private cloud (on-premises) version. == Products == ShareMethods: Combines document management, collaboration, analytics, and CRM integration into a single solution. Key content can be centrally managed and delivered to sales channels, while providing feedback to marketing. ShareMethods is often used as a sales portal for internal sales and a partner portal for external partners. ShareNow: Integrates ShareMethods with salesforce.com providing Single Sign On for salesforce.com users and access to files related to accounts opportunities, etc. including custom objects. Also facilitates collaboration between salesforce.com users and non-users. ShareMethods for Oracle CRM On Demand: Integrates ShareMethods with Oracle CRM On Demand providing Single Sign On for Oracle users and easy access to files related to accounts opportunities, etc. ShareOffice: An on-demand intranet/extranet solution. Features include full-text search, version history, server sync-up, email updates, audit trail/analytics, check-in/check-out, multilingual user interface. ShareSpaces: Independent workspaces or portals where users can collaborate with business partners, teammates, or individuals to work together on content and documents. == Integration and interoperability == ShareMethods is available on Salesforce.com's AppExchange platform. ShareMethods also integrates with Oracle CRM On Demand to provide document management within the CRM application. Customers also can integrate proprietary systems via single-sign-on and self-registration. In addition, developers can make use of the ShareMethods API based on WebDAV to integrate document management functionality.

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  FAIR data

  FAIR data is data which meets the 2016 FAIR principles of findability, accessibility, interoperability, and reusability (FAIR). The FAIR principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in the volume, complexity, and rate of production of data. The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capable open license. == FAIR principles published by GO FAIR == Findable The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata (defined by R1 below) F3. Metadata clearly and explicitly include the identifier of the data they describe F4. (Meta)data are registered or indexed in a searchable resource Accessible Once the user finds the required data, they need to know how they can be accessed, possibly including authentication and authorisation. A1. (Meta)data are retrievable by their identifier using a standardised communications protocol A1.1 The protocol is open, free, and universally implementable A1.2 The protocol allows for an authentication and authorisation procedure, where necessary A2. Metadata are accessible, even when the data are no longer available Interoperable The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation I2. (Meta)data use vocabularies that follow FAIR principles I3. (Meta)data include qualified references to other (meta)data Reusable The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings. R1. (Meta)data are richly described with a plurality of accurate and relevant attributes R1.1. (Meta)data are released with a clear and accessible data usage license R1.2. (Meta)data are associated with detailed provenance R1.3. (Meta)data meet domain-relevant community standards The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component). === Acceptance and implementation === Before FAIR, a 2007 OECD report was the most influential paper discussing similar ideas related to data accessibility. In January 2014, the Lorentz Centre at Leiden University hosted a workshop entitled "Jointly designing a data FAIRPORT" where the participants first formulated the FAIR principles. After further discussions, they were published in the March 2016 issue of Scientific Data. At the 2016 G20 Hangzhou summit, the G20 leaders issued a statement endorsing the application of FAIR principles to research. Also in 2016, a group of Australian organisations developed a Statement on FAIR Access to Australia's Research Outputs, which aimed to extend the principles to research outputs more generally. In 2017, Germany, Netherlands and France agreed to establish an international office to support the FAIR initiative, the GO FAIR International Support and Coordination Office. Other international organisations active in the research data ecosystem, such as CODATA or Research Data Alliance (RDA) also support FAIR implementations by their communities. FAIR principles implementation assessment is being explored by FAIR Data Maturity Model Working Group of RDA, CODATA's strategic Decadal Programme "Data for Planet: Making data work for cross-domain challenges" mentions FAIR data principles as a fundamental enabler of data driven science. The Association of European Research Libraries recommends the use of FAIR principles. A 2017 paper by advocates of FAIR data reported that awareness of the FAIR concept was increasing among various researchers and institutes, but also, understanding of the concept was becoming confused as different people apply their own differing perspectives to it. Guides on implementing FAIR data practices state that the cost of a data management plan in compliance with FAIR data practices should be 5% of the total research budget. In 2019 the Global Indigenous Data Alliance (GIDA) released the CARE Principles for Indigenous Data Governance as a complementary guide. The CARE principles extend principles outlined in FAIR data to include Collective benefit, Authority to control, Responsibility, and Ethics to ensure data guidelines address historical contexts and power differentials. The CARE Principles for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event, "Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop", held 8 November 2018, in Gaborone, Botswana. The lack of information on how to implement the guidelines have led to inconsistent interpretations of them. In January 2020, representatives of nine groups of universities around the world produced the Sorbonne declaration on research data rights, which included a commitment to FAIR data, and called on governments to provide support to enable it. In 2021, researchers identified the FAIR principles as a conceptual component of data catalog software tools, with the other components being metadata management, business context and data responsibility roles. In April 2022, Matthias Scheffler and colleagues argued in Nature that FAIR principles are "a must" so that data mining and artificial intelligence can extract useful scientific information from the data. There have been moves in the geosciences to establish FAIR data by use of decimal georeferencing However, making data (and research outcomes) FAIR is a challenging task, and it is challenging to assess the FAIRness. In 2020, the FAIR Data Maturity Model Working Group published a set of guidelines for assessing "FAIRness".

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  Artificial intelligence industry in Canada

  The artificial intelligence industry in Canada is a rapidly expanding sector. Although Canada held a pioneering role in the early development of artificial intelligence, transforming research excellence into broad commercial adoption has proven challenging. Despite globally recognized scientific achievements and a deep pool of skilled experts, by June 2024, Canada recorded the lowest rate of AI integration among OECD countries, with only 12% of firms implementing AI in their products or services. However, AI adoption has shown significant momentum—doubling from mid-2024 to mid-2025, rising from 6.1% to 12.2%. As of September 2025, Statistics Canada indicated that while about one-third of Canadian businesses had no plans to adopt artificial intelligence in the next year, 14.5% reported intentions to begin using AI for producing goods or delivering services. The primary reasons for not moving forward with AI were lack of relevance, insufficient knowledge, and privacy concerns. According to Public Works Canada (PwC), the pace of AI adoption in Canada is roughly three-quarters of the United States rate, highlighting a notable gap between the two countries in business integration of this technology. British-Canadian computer scientist Geoffrey Hinton stated in 2025 that Canadian companies are adopting artificial intelligence at a slower pace, which may result in the loss of the country's early advantages in the field. At the "All In AI" conference held in Montreal in September 2025, the Minister of Artificial Intelligence and Digital Innovation Evan Solomon, described "Building digital sovereignty" as the most pressing democratic issue of the time. He introduced a 26-person task force focused on updating Canada's AI strategy. In their 2024 report " "Learning Together for Responsible Artificial Intelligence" report, the Innovation, Science, and Economic Development Canada stressed that public awareness, trust, and AI literacy are essential for the responsible adoption and governance of AI in Canada. Montreal workshops in 2021 expanded the OECD's 2019 definition of AI as "the set of computer techniques that enable a machine (e.g., a computer or telephone) to perform tasks that typically require intelligence, such as reasoning or learning. It is also referred to as the automation of intelligent tasks. Scientific developments in AI, such as deep-learning techniques, have made it possible to design access to huge amounts of data and ever-increasing computing power. These new techniques have been rapidly deployed on a large scale in all areas of social life, in transport, education, culture and health." == Federal investments and policy == The 2025 federal budget allocates over $1 billion over the next five years to bolster Canada's artificial intelligence and quantum computing ecosystem. == Industry landscape or research hubs == AlexNet, an influential deep convolutional neural network developed at the University of Toronto by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, marked a pivotal turning point in modern artificial intelligence. In 2012, it achieved a dramatic reduction in error rates for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), showcasing the practical power of deep learning and GPU acceleration. The success of AlexNet helped cement Canada’s reputation for AI leadership and inspired rapid adoption of deep learning across the technology sector, with ongoing impact in both academic and commercial domains. In healthcare, AlexNet has been adapted for medical imaging to assist with analyzing radiographs, mammograms, and other scans, including identifying abnormalities and supporting clinical diagnosis. In 2015, the Ottawa-based start-up Advanced Symbolics Inc. (ASI) began developing Polly, an artificial intelligence system designed to analyze and anticipate how target audiences behave—enabling more effective communication strategies and advertising campaigns. Polly was named after its first assignment analyzing the politics of Brexit. The AI gained widespread attention in 2016 for accurately forecasting both the Brexit referendum and the 2016 U.S. presidential election won by Donald Trump. The company states that Polly is used by organizations in diverse sectors—including healthcare, politics, entertainment, and mental health research—to support decision-making based on predictive analytics. Chartwatch, an AI tool developed in Canada, has been shown to reduce unexpected hospital deaths by 26% according to a 2024 study. The system analyzes patient data to detect subtle signs of deterioration, supporting healthcare teams in providing timely interventions. === Notable figures in AI in Canada === Geoffrey Hinton's decades-long work eventually formed the foundation of artificial intelligence, which earned him the Nobel Prize for physics in 2024. Yoshua Bengio, who won the Turing Award in 2018 for his pioneering work in deep learning, founded what would become Mila in 1993. Mila, is currently a collaboration between four Montreal-based academic partners.—the Pan-Canadian Artificial Intelligence Strategy includes Alberta's Amii, Toronto's Vector Institute, and Mila. Fakhreddine Karray's work on operational AI has had tangible impact across several Canadian-relevant sectors, notably intelligent transportation systems, virtual healthcare, and driver safety. === AI in the oil and gas industry === According to a 2020 Ernst & Young report the oil and gas industry in Canada is using AI in automating routine, repetitive, and dangerous tasks with technologies like robotic process automation and machine learning; optimizing production and processing; enhancing transportation logistics; improving equipment operation and monitoring; and enabling preventative maintenance. AI is also deployed for data analysis to improve prediction and decision-making, and is expected to automate up to 50% of job competencies in upstream oil and gas by 2040. Oilsands giant Suncor Energy operates a large fleet of autonomous trucks and has started using AI in its dispatch system at the Mildred Lake mine. As of 2024, AI manages routine tasks such as allocating trucks to dump stations and sending them to refuelling locations. === Indigenous and Inuit Innovation in AI === Indigenous organizations have been working on the creation of new technologies for language revitalization in partnership with National Research Council of Canada since the mid-2010s. In 2025, Inuit researchers and technology partners launched an AI-powered initiative to support the revitalization and preservation of Inuktitut, demonstrating how artificial intelligence can be adapted for Indigenous language and cultural priorities. A 2025 CBC article notes that, while AI can help revitalize Inuktitut, Inuit leaders emphasize concerns about data sovereignty, information ownership, and the need for Indigenous leadership to ensure transparency, privacy, and accountability in AI development. == Regulation == Canada's Artificial Intelligence and Data Act (AIDA) was proposed in November 2022, as part of the Digital Charter Implementation Act (Bill C-27). As well voluntary codes, such as the September 2023 Code of Conduct for Generative AI, and landmark investments in advanced computing infrastructure and the Canadian Artificial Intelligence Safety Institute (CAISI) reflect Canada's commitment to both safety and global competitiveness. == AI infrastructure == Canada has undertaken efforts to expand its AI computing infrastructure at both provincial and federal levels. The federal government's Canadian Sovereign AI Compute Strategy, allocated up to C$2 billion in Budget 2024, aims to enhance computing capacity to support domestic AI industry growth and AI adoption across the economy, with up to C$700 million designated to mobilize private sector investment in new or expanded data centres. Alberta has introduced an AI Data Centres Strategy to position itself as a leading North American destination for data centre investment, targeting C$100 billion worth of AI data centres under development by 2030. One major project under Alberta's strategy is the Wonder Valley AI Data Centre Park near Grande Prairie, which was exempted from provincial environmental impact assessment in April 2026 but still requires permits demonstrating safe construction and operation. According to Statista, as of April 2026, Canada has 287 data centres.

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  Microsoft Query

  Microsoft Query is a visual method of creating database queries using examples based on a text string, the name of a document or a list of documents. The QBE system converts the user input into a formal database query using Structured Query Language (SQL) on the backend, allowing the user to perform powerful searches without having to explicitly compose them in SQL, and without even needing to know SQL. It is derived from Moshé M. Zloof's original Query by Example (QBE) implemented in the mid-1970s at IBM's Research Centre in Yorktown, New York. In the context of Microsoft Access, QBE is used for introducing students to database querying, and as a user-friendly database management system for small businesses. Microsoft Excel allows results of QBE queries to be embedded in spreadsheets.

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  Tango (platform)

  Tango (named Project Tango while in testing) was an augmented reality computing platform, developed and authored by the Advanced Technology and Projects (ATAP), a skunkworks division of Google. It used computer vision to enable mobile devices, such as smartphones and tablets, to detect their position relative to the world around them without using GPS or other external signals. This allowed application developers to create user experiences that include indoor navigation, 3D mapping, physical space measurement, environmental recognition, augmented reality, and windows into a virtual world. The first product to emerge from ATAP, Tango was developed by a team led by computer scientist Johnny Lee, a core contributor to Microsoft's Kinect. In an interview in June 2015, Lee said, "We're developing the hardware and software technologies to help everything and everyone understand precisely where they are, anywhere." Google produced two devices to demonstrate the Tango technology: the Peanut phone and the Yellowstone 7-inch tablet. More than 3,000 of these devices had been sold as of June 2015, chiefly to researchers and software developers interested in building applications for the platform. In the summer of 2015, Qualcomm and Intel both announced that they were developing Tango reference devices as models for device manufacturers who use their mobile chipsets. At CES, in January 2016, Google announced a partnership with Lenovo to release a consumer smartphone during the summer of 2016 to feature Tango technology marketed at consumers, noting a less than $500 price-point and a small form factor below 6.5 inches. At the same time, both companies also announced an application incubator to get applications developed to be on the device on launch. On 15 December 2017, Google announced that they would be ending support for Tango on March 1, 2018, in favor of ARCore. == Overview == Tango was different from other contemporary 3D-sensing computer vision products, in that it was designed to run on a standalone mobile phone or tablet and was chiefly concerned with determining the device's position and orientation within the environment. The software worked by integrating three types of functionality: Motion-tracking: using visual features of the environment, in combination with accelerometer and gyroscope data, to closely track the device's movements in space Area learning: storing environment data in a map that can be re-used later, shared with other Tango devices, and enhanced with metadata such as notes, instructions, or points of interest Depth perception: detecting distances, sizes, and surfaces in the environment Together, these generate data about the device in "six degrees of freedom" (3 axes of orientation plus 3 axes of position) and detailed three-dimensional information about the environment. Project Tango was also the first project to graduate from Google X in 2012 Applications on mobile devices use Tango's C and Java APIs to access this data in real time. In addition, an API was also provided for integrating Tango with the Unity game engine; this enabled the conversion or creation of games that allow the user to interact and navigate in the game space by moving and rotating a Tango device in real space. These APIs were documented on the Google developer website. == Applications == Tango enabled apps to track a device's position and orientation within a detailed 3D environment, and to recognize known environments. This allowed the creations of applications such as in-store navigation, visual measurement and mapping utilities, presentation and design tools, and a variety of immersive games. At Augmented World Expo 2015, Johnny Lee demonstrated a construction game that builds a virtual structure in real space, an AR showroom app that allows users to view a full-size virtual automobile and customize its features, a hybrid Nerf gun with mounted Tango screen for dodging and shooting AR monsters superimposed on reality, and a multiplayer VR app that lets multiple players converse in a virtual space where their avatar movements match their real-life movements. Tango apps are distributed through Play. Google has encouraged the development of more apps with hackathons, an app contest, and promotional discounts on the development tablet. == Devices == As a platform for software developers and a model for device manufacturers, Google created two Tango devices. === The Peanut phone === "Peanut" was the first production Tango device, released in the first quarter of 2014. It was a small Android phone with a Qualcomm MSM8974 quad-core processor and additional special hardware including a fisheye motion camera, "RGB-IR" camera for color image and infrared depth detection, and Movidius Vision processing units. A high-performance accelerometer and gyroscope were added after testing several competing models in the MARS lab at the University of Minnesota. Several hundred Peanut devices were distributed to early-access partners including university researchers in computer vision and robotics, as well as application developers and technology startups. Google stopped supporting the Peanut device in September 2015, as by then the Tango software stack had evolved beyond the versions of Android that run on the device. === The Yellowstone tablet === "Yellowstone" was a 7-inch tablet with full Tango functionality, released in June 2014, and sold as the Project Tango Tablet Development Kit. It featured a 2.3 GHz quad-core Nvidia Tegra K1 processor, 128GB flash memory, 1920x1200-pixel touchscreen, 4MP color camera, fisheye-lens (motion-tracking) camera, an IR projector with RGB-IR camera for integrated depth sensing, and 4G LTE connectivity. As of May 27, 2017, the Tango tablet is considered officially unsupported by Google. ==== Testing by NASA ==== In May 2014, two Peanut phones were delivered to the International Space Station to be part of a NASA project to develop autonomous robots that navigate in a variety of environments, including outer space. The soccer-ball-sized, 18-sided polyhedral SPHERES robots were developed at the NASA Ames Research Center, adjacent to the Google campus in Mountain View, California. Andres Martinez, SPHERES manager at NASA, said "We are researching how effective [Tango's] vision-based navigation abilities are for performing localization and navigation of a mobile free flyer on ISS. === Intel RealSense smartphone === Announced at Intel's Developer Forum in August 2015, and offered to public through a Developer Kit since January 2016. It incorporated a RealSense ZR300 camera which had optical features required for Tango, such as the fisheye camera. === Lenovo Phab 2 Pro === Lenovo Phab 2 Pro was the first commercial smartphone with the Tango Technology, the device was announced at the beginning of 2016, launched in August, and available for purchase in the US in November. The Phab 2 Pro had a 6.4 inch screen, a Snapdragon 652 processor, and 64 GB of internal storage, with a rear facing 16 Megapixels camera and 8 MP front camera. === Asus Zenfone AR === Asus Zenfone AR, announced at CES 2017, was the second commercial smartphone with the Tango Technology. It ran Tango AR & Daydream VR on Snapdragon 821, with 6GB or 8GB of RAM and 128 or 256GB of internal memory depending on the configuration.

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  Australian Geoscience Data Cube

  The Australian Geoscience Data Cube (AGDC) is an approach to storing, processing and analyzing large collections of Earth observation data. The technology is designed to meet challenges of national interest by being agile and flexible with vast amounts of layered grid data. The AGDC reduces processing time of traditional image analysis by calibrating, pre-computing known extents, pixel alignment and storing metadata in a cell lattice structure. The temporal-pixel aligned data can often be analysed faster across space and time dimensions than previous scene based techniques. This allows the AGDC to be flexible in tackling future challenges and improve analysis times on every-increasing data repositories of earth observation. The AGDC has also been used internationally to allow countries to maintain ecologically sustainable programs and reduce the difficulty curve of utilizing Remote Sensing data. == Background == The AGDC was originally conceived by Geoscience Australia but is now maintained in a partnership between Geoscience Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO) and National Computational Infrastructure National Facility (Australia) (NCI). This is made possible by the funding from the partnership and a number of organisations such as National Collaborative Research Infrastructure Strategy (NCRIS). == Analysis ready data, ingestion and indexing == The data processed in the cube is made analysis ready before being ingested and indexed into the AGDC. Analysis ready data is pre-processed data that has applied corrections for instrument calibration (gains and offsets), geolocation (spatial alignment) and radiometry (solar illumination, incidence angle, topography, atmospheric interference). The ingestion process manages the translation of datasets into the storage units while maintaining a database index. The data within the storage and index can be accessed via API calls often compiled within code such as Python (programming language). Example: s2a_l1c = dc.load(product='s2a_level1c_granule',x=(147.36, 147.41), y=(-35.1, -35.15), measurements=['04','03','02'], output_crs='EPSG:4326', resolution=(-0.00025,0.00025)) === Datasets currently stored === Geoscience Australia Landsat Surface Reflectance (1987 to present) Landsat Pixel Quality Landsat Fractional Cover Landsat NDVI === Datasets that have been piloted === USGS Landsat Surface Reflectance SRTM DEM Himawari 8 MODIS Sentinel-2 L1C / S2A Australian Gridded Climate Data == Open source == The AGDC code base is situated in GitHub as an open repository. The core code base moved to the Open Data Cube in early 2017 as part of an international collaboration. Whilst the code base is the Open Data Cube, individual cubes exist as their own right such as the AGDC on the National Computational Infrastructure National Facility (Australia) (NCI) using the High-Performance Computing Cluster HPCC. The core code can be installed on personal computers or public computers (using git) and has many unit tests. Documentation for the code base exists on Read the Docs. == Challenges of the AGDC == The AGDC is designed to meet nationally significant challenges such as the following. Sustainability Environment Water resource management Disaster assist Policy development Community planning Forest preservation Carbon measurement == International awards == The AGDC won the 2016 Content Platform of the Year award from Geospatial World Forum.

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  Grid-oriented storage

  Grid-oriented Storage (GOS) was a term used for data storage by a university project during the era when the term grid computing was popular. == Description == GOS was a successor of the term network-attached storage (NAS). GOS systems contained hard disks, often RAIDs (redundant arrays of independent disks), like traditional file servers. GOS was designed to deal with long-distance, cross-domain and single-image file operations, which is typical in Grid environments. GOS behaves like a file server via the file-based GOS-FS protocol to any entity on the grid. Similar to GridFTP, GOS-FS integrates a parallel stream engine and Grid Security Infrastructure (GSI). Conforming to the universal VFS (Virtual Filesystem Switch), GOS-FS can be pervasively used as an underlying platform to best utilize the increased transfer bandwidth and accelerate the NFS/CIFS-based applications. GOS can also run over SCSI, Fibre Channel or iSCSI, which does not affect the acceleration performance, offering both file level protocols and block level protocols for storage area network (SAN) from the same system. In a grid infrastructure, resources may be geographically distant from each other, produced by differing manufacturers, and have differing access control policies. This makes access to grid resources dynamic and conditional upon local constraints. Centralized management techniques for these resources are limited in their scalability both in terms of execution efficiency and fault tolerance. Provision of services across such platforms requires a distributed resource management mechanism and the peer-to-peer clustered GOS appliances allow a single storage image to continue to expand, even if a single GOS appliance reaches its capacity limitations. The cluster shares a common, aggregate presentation of the data stored on all participating GOS appliances. Each GOS appliance manages its own internal storage space. The major benefit of this aggregation is that clustered GOS storage can be accessed by users as a single mount point. GOS products fit the thin-server categorization. Compared with traditional “fat server”-based storage architectures, thin-server GOS appliances deliver numerous advantages, such as the alleviation of potential network/grid bottle-necks, CPU and OS optimized for I/O only, ease of installation, remote management and minimal maintenance, low cost and Plug and Play, etc. Examples of similar innovations include NAS, printers, fax machines, routers and switches. An Apache server has been installed in the GOS operating system, ensuring an HTTPS-based communication between the GOS server and an administrator via a Web browser. Remote management and monitoring makes it easy to set up, manage, and monitor GOS systems. == History == Frank Zhigang Wang and Na Helian proposed a funding proposal to the UK government titled “Grid-Oriented Storage (GOS): Next Generation Data Storage System Architecture for the Grid Computing Era” in 2003. The proposal was approved and granted one million pounds in 2004. The first prototype was constructed in 2005 at Centre for Grid Computing, Cambridge-Cranfield High Performance Computing Facility. The first conference presentation was at IEEE Symposium on Cluster Computing and Grid (CCGrid), 9–12 May 2005, Cardiff, UK. As one of the five best work-in-progress, it was included in the IEEE Distributed Systems Online. In 2006, the GOS architecture and its implementations was published in IEEE Transactions on Computers, titled “Grid-oriented Storage: A Single-Image, Cross-Domain, High-Bandwidth Architecture”. Starting in January 2007, demonstrations were presented at Princeton University, Cambridge University Computer Lab and others. By 2013, the Cranfield Centre still used future tense for the project. Peer-to-peer file sharings use similar techniques.

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  Energy informatics

  Energy informatics is a research field covering the use of information and communication technology to address energy utilization and management challenges. Methods used for "smart" implementations often combine IoT sensors with artificial intelligence and machine learning. Energy Informatics is founded on flow networks that are the major suppliers and consumers of energy. Their efficiency can be improved by collecting and analyzing information. == Application areas == The field among other consider application areas within: Smart Buildings by developing ICT-centred solutions for improving the energy-efficiency of buildings. Smart Cities by investigating the synergies between demand patterns and supply availability of energy flows in cities and communities to improve energy efficiency, increase integration of renewable sources, and provide resilience towards system faults caused by extreme situations, like hurricanes and flooding. Smart Industries including the development of ICT-centred solutions for improving the energy efficiency and predictability of energy intensive industrial processes, without compromising process and product quality. Smart Energy Networks by developing ICT-centred solutions for coordinating the supply and demand in environmentally sustainable energy networks.

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  XLeratorDB

  XLeratorDB is a suite of database function libraries that enable Microsoft SQL Server to perform a wide range of additional (non-native) business intelligence and ad hoc analytics. The libraries, which are embedded and run centrally on the database, include more than 450 individual functions similar to those found in Microsoft Excel spreadsheets. The individual functions are grouped and sold as six separate libraries based on usage: finance, statistics, math, engineering, unit conversions and strings. WestClinTech, the company that developed XLeratorDB, claims it is "the first commercial function package add-in for Microsoft SQL Server." == Company history == WestClinTech (LLC), founded by software industry veterans Charles Flock and Joe Stampf in 2008, is located in Irvington, New York, United States. Flock was a co-founder of The Frustum Group, developer of the OPICS enterprise banking and trading platform, which was acquired by London-based Misys, PLC in 1996. Stampf joined Frustum in 1994 and with Flock remained active with the company after acquisition, helping to develop successive generations of OPICS now employed by over 150 leading financial institutions worldwide. Following a full year of research, development and testing, WestClinTech introduced and recorded its first commercial sale of XLeratorDB in April 2009. In September 2009, XLeratorDB became available to all Federal agencies through NASA's Strategic Enterprise-Wide Procurement (SEWP-IV) program, a government-wide acquisition contract. == Technology == XLeratorDB uses Microsoft SQL CLR(Common Language Runtime) technology. SQL CLR allows managed code to be hosted by, and run in, the Microsoft SQL Server environment. SQL CLR relies on the creation, deployment and registration of .NET Framework assemblies that are physically stored in managed code dynamic-link libraries (DLL). The assemblies may contain .NET namespaces, classes, functions, and properties. Because managed code compiles to native code prior to execution, functions using SQL CLR can achieve significant performance increases versus the equivalent functions written in T-SQL in some scenarios. XLeratorDB requires Microsoft SQL Server 2005 or SQL Server 2005 Express editions, or later (compatibility mode 90 or higher). The product installs with PERMISSION_SET=SAFE. SAFE mode, the most restrictive permission set, is accessible by all users. Code executed by an assembly with SAFE permissions cannot access external system resources such as files, the network, the internet, environment variables, or the registry. == Functions == In computer science, a function is a portion of code within a larger program which performs a specific task and is relatively independent of the remaining code. As used in database and spreadsheet applications these functions generally represent mathematical formulas widely used across a variety of fields. While this code may be user-generated, it is also embedded as a pre-written sub-routine in applications. These functions are typically identified by common nomenclature which corresponds to their underlying operations: e.g. IRR identifies the function which calculates Internal Rate of Return on a series of periodic cash flows. === Function uses === As subroutines, functions can be integrated and used in a variety of ways, and as part of larger, more complicated applications. Within large enterprise applications they may, for example, play an important role in defining business rules or risk management parameters, while remaining virtually invisible to end users. Within database management systems and spreadsheets, however, these kinds of functions also represent discrete sets of tools; they can be accessed directly and utilized on a stand-alone basis, or in more complex, user-defined configurations. In this context, functions can be used for business intelligence and ad hoc analysis of data in fields such as finance, statistics, engineering, math, etc. === Function types === XLeratorDB uses three kinds of functions to perform analytic operations: scalar, aggregate, and a hybrid form which WestClinTech calls Range Queries. Scalar functions take a single value, perform an operation and return a single value. An example of this type of function is LOG, which returns the logarithm of a number to a specified base. Aggregate functions operate on a series of values but return a single, summarizing value. An example of this type of function is AVG, which returns the average of values in a specified group. In XLeratorDB there are some functions which have characteristics of aggregate functions (operating on multiple series of values) but cannot be processed in SQL CLR using single column inputs, such as AVG does. For example, irregular internal rate of return (XIRR), a financial function, operates on a collection of cash flow values from one column, but must also apply variable period lengths from another column and an initial iterative assumption from a third, in order to return a single, summarizing value. WestClinTech documentation notes that Range Queries specify the data to be included in the result set of the function independently of the WHERE clause associated with the T-SQL statement, by incorporating a SELECT statement into the function as a string argument; the function then traps that SELECT statement, executes it internally and processes the result. Some XLeratorDB functions that employ Range Queries are: NPV, XNPV, IRR, XIRR, MIRR, MULTINOMIAL, and SERIESSUM. Within the application these functions are identified by a "_q" naming convention: e.g. NPV_q, IRR_q, etc. == Analytic functions == === SQL Server functions === Microsoft SQL Server is the #3 selling database management system (DBMS), behind Oracle and IBM. (While versions of SQL Server have been on the market since 1987, XLeratorDB is compatible with only the 2005 edition and later.) Like all major DBMS, SQL Server performs a variety of data mining operations by returning or arraying data in different views (also known as drill-down). In addition, SQL Server uses Transact-SQL (T-SQL) to execute four major classes of pre-defined functions in native mode. Functions operating on the DBMS offer several advantages over client layer applications like Excel: they utilize the most up-to-date data available; they can process far larger quantities of data; and, the data is not subject to exporting and transcription errors. SQL Server 2008 includes a total of 58 functions that perform relatively basic aggregation (12), math (23) and string manipulation (23) operations useful for analytics; it includes no native functions that perform more complex operations directly related to finance, statistics or engineering. === Excel functions === Microsoft Excel, a component of Microsoft Office suite, is one of the most widely used spreadsheet applications on the market today. In addition to its inherent utility as a stand-alone desktop application, Excel overlaps and complements the functionality of DBMS in several ways: storing and arraying data in rows and columns; performing certain basic tasks such as pivot table and aggregating values; and facilitating sharing, importing and exporting of database data. Excel's chief limitation relative to a true database is capacity; Excel 2003 is limited to some 65k rows and 256 columns; Excel 2007 extends this capacity to roughly 1million rows and 16k columns. By comparison, SQL Server is able to manage over 500k terabytes of memory. Excel offers, however, an extensive library of specialized pre-written functions which are useful for performing ad hoc analysis on database data. Excel 2007 includes over 300 of these pre-defined functions, although customized functions can also be created by users, or imported from third party developers as add-ons. Excel functions are grouped by type: === Excel business intelligence functions === Operating on the client computing layer Excel plays an important role as a business intelligence tool because it: performs a wide array of complex analytic functions not native to most DBMS software offers far greater ad hoc reporting and analytic flexibility than most enterprise software provides a medium for sharing and collaborating because of its ubiquity throughout the enterprise Microsoft reinforces this positioning with Business Intelligence documentation that positions Excel in a clearly pivotal role. === XLeratorDB vs. Excel functions === While operating within the database environment, XLeratorDB functions utilize the same naming conventions and input formats, and in most cases, return the same calculation results as Excel functions. XLeratorDB, coupled with SQL Server's native capabilities, compares to Excel's function sets as follows:

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  Documentalist

  A documentalist is a professional, trained in documentation science and specializing in assisting researchers in their search for scientific and technical documentation. With the development of bibliographical databases such as MEDLINE, documentalists were professionals who searched such databases on the behalf of users. When the field of documentation changed its name to information science, the terms information specialist or information professional often replaced the term documentalist.

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  Agentic commerce

  Agentic commerce (also referred to as agent-based commerce) describes an emerging form of e-commerce in which autonomous artificial intelligence (AI) agents independently execute purchasing and payment processes on behalf of users or organizations. Unlike conventional digital commerce systems, which require direct human interaction at key decision points, agentic commerce systems are designed to search for products or services, evaluate options, make purchasing decisions, and complete payments without real-time human involvement. An emerging development within the broader fields of e-commerce, fintech, and artificial intelligence; agentic commerce combines advances in generative AI, autonomous agents, application programming interfaces (APIs), and digital payment infrastructures to direct transactions with no direct human interaction. == Characteristics == A defining feature of agentic commerce is the delegation of end-to-end commercial activities to software agents. These agents typically operate according to predefined user preferences, rules, or constraints, such as price limits, quality criteria, delivery times, or preferred payment methods. Based on these parameters, an agent can autonomously perform tasks including product discovery, price comparison, contract selection, order placement, and payment execution. In contrast to decision-support systems, which provide recommendations to human users, agentic commerce systems are designed to act independently. Human involvement may be limited to initial configuration, periodic supervision, or exception handling. == Comparison with traditional and AI-assisted commerce == Traditional e-commerce requires users to manually browse products, select offers, and authorize payments. Generative AI systems used in commerce commonly assist users by answering questions or suggesting options, and do not complete transactions autonomously. Agentic commerce differs in that decision-making authority is partially or fully transferred to AI agents. As a result, the conventional customer journey, characterized by conscious decision points, may be replaced by continuous, automated micro-decisions performed by software. == Applications and business use cases == Potential applications of agentic commerce include recurring purchases, subscription management, business-to-business procurement, inventory replenishment, and price monitoring. In such contexts, transactions are often predictable and standardized, making them suitable for automation. From a business perspective, agentic commerce systems may be used to optimize supply chains, manage inventory levels, negotiate prices algorithmically, or execute transactions across multiple platforms. Enterprises adopting the new technology include retailers Walmart, Home Depot, Wayfair and Urban Outfitters, and ad tech DSPs, including Google Ads, Amazon, and Yahoo. Chinese tech firms are using apps to provide full-service shopping and payment tools. These includes Alibaba, Tencent, and ByteDance who are currently developing AI powered shopping apps. The Qwen AI chatbot allows users to complete transactions directly within its interface. US firms are still leading in developing AI models but integration is slower due to privacy restrictions. == Payments and technical infrastructure == Agentic commerce relies on digital payment systems capable of supporting automated, machine-initiated transactions, including API-based payment processing, tokenization, real-time authorization, and continuous risk monitoring. Typical user interfaces, such as shopping carts, may be replaced by backend integrations between AI agents, merchants, and payment service providers. For example, Iike 2025, Alibaba launched Alipay AI Pay, which grew and began operating as an application for different retailers. In December 2025, Alipay teamed up with Rokid to enable developers to integrate AI payments into AI agents on Rokid's Lingzhu platform. In January 2025, Alipay unveiled the Agentic Commerce Trust Protocol in partnership with Alibaba's consumer AI applications, such as the Qwen App and Taobao Instant Commerce. Qwen adopted the platform first, connecting it to Taobao Instant Commerce and Alipay AI Pay. Users could use Qwen's agentic feature to place food and drink orders within the application instead of having to click outside to an external browser. For merchants, participation in agentic commerce may require products and services to be presented in structured, machine-readable formats to ensure discoverability and interoperability with autonomous agents. == Universal Commerce Protocol (UCP) == In January 2026, Google announced the Universal Commerce Protocol (UCP), an open-source web standard intended to enable interoperability between AI agents and retail systems across the shopping journey, from discovery and checkout to post-purchase support. UCP makes use of REST, JSON-RPC transports, and support for Agent Payments Protocol (AP2), Agent2Agent (A2A), and Model Context Protocol (MCP). == Legal, regulatory, and security considerations == The use of autonomous agents in commerce raises legal and regulatory questions, particularly regarding authorization, liability, consumer protection, and fraud prevention. Existing payment and contract frameworks are generally based on human decision-makers, and their applicability to autonomous agents remains an area of active discussion. Open issues include responsibility for unauthorized or erroneous transactions, mechanisms for dispute resolution, standards for agent authentication, and compliance with data protection and financial regulations. Continuous, automated transaction patterns may also require new approaches to security and risk assessment. Traditional fraud models centered on identity verification may be insufficient for agentic commerce, and that merchants may need intent-based detection methods using machine learning and behavioral analysis to distinguish legitimate AI agents from malicious automation. === Governance frameworks === The deployment of autonomous AI agents in commercial environments has prompted the development of dedicated governance frameworks. These aim to define operational boundaries, decision authority, oversight mechanisms, and accountability structures for agentic systems. The Agentic Commerce Framework (ACF), created in 2025 by Vincent Dorange, is a governance standard that structures the deployment of autonomous AI agents around four founding principles (Decision Sovereignty, Governance by Design, Ultimate Human Control, Traceable Accountability), four operational layers, and 18 governance KPIs. In January 2026, Singapore's Infocomm Media Development Authority (IMDA) published the Model AI Governance Framework for Agentic AI, extending its existing AI governance guidelines to address agent-specific risks including delegation chains and multi-agent coordination. The Cloud Security Alliance (CSA) has also proposed an Agentic Trust Framework applying zero-trust principles to AI agent governance. == Ecosystem and implementation == The adoption of agentic commerce typically requires changes in commerce architecture, data modeling, identity and permissions, and API-based orchestration of checkout and post-purchase workflows. Management consultancies have identified agentic commerce as a structural evolution of digital commerce, emphasizing the role of AI-driven agents in automating discovery, decision-making, and transaction processes across commerce systems. McKinsey & Company has described agentic commerce as a significant shift in how consumers interact with brands and how enterprises design their commerce operating models. In Europe, this ecosystem also includes digital commerce consultancies specializing in the adoption of agentic commerce. Consulting firms such as Horrea support brands in understanding and implementing the technological and organizational shifts associated with agentic commerce. == Market development and outlook == Agentic commerce is generally regarded as an early-stage development. Industry analysts have projected that AI-driven agents could account for a small but growing share of digital payment transactions within the coming years. Due to the scale of global digital commerce, even limited adoption could represent substantial transaction volumes. Analysts expect that by 2029, AI agents could handle between 1% and 4% of all digital payment transactions. With a projected total transaction volume of over $36 trillion a year, even a small share translates into a market worth up to $1.47 trillion. According to a McKinsey study from October 2025, agentic commerce projects that by 2030, the U.S. business-to-consumer retail market alone could see up to $1 trillion in revenue orchestrated through agentic commerce. On a global scale, the opportunity could range from $3 trillion to $5 trillion. Early experiments and pilot projects have demonstrated both the potential and current limitations of the

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