AI For Business Analytics

AI For Business Analytics — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  WeChat

  WeChat or Weixin in Chinese (Chinese: 微信; pinyin: Wēixìn ; lit. 'micro-message') is an instant messaging, social media, and mobile payment app developed by Tencent. First released in 2011, it became the world's largest standalone mobile app in 2018 with over 1 billion monthly active users. The Chinese version of WeChat, Weixin, has been described as China's "app for everything" and a super-app because of its wide range of functions. WeChat provides text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video conferencing, video games, mobile payment, sharing of photographs and videos and location sharing. It has been described as having "an almost indispensable part of life in China". Accounts registered using Chinese phone numbers are managed under the Weixin brand, and their data is stored in mainland China and subject to Weixin's terms of service and privacy policy. Non-Chinese numbers are registered under WeChat, and WeChat users are subject to a more liberal terms of service and better privacy policy, and their data is stored in the Netherlands for users in the European Union, and in Singapore for other users. User activity on Weixin, the Chinese version of the app, is analyzed, tracked and shared with Chinese authorities upon request as part of the mass surveillance network in China. Chinese-registered Weixin accounts censor politically sensitive topics, and the software license agreement for Weixin (but not WeChat) explicitly forbids content which "[en]danger[s] national security, divulge[s] state secrets, subvert[s] state power and undermine[s] national unity", as well as other types of content such as content that "[u]ndermine[s] national religious policies" and content that is "[i]nciting illegal assembly, association, procession, demonstrations and gatherings disrupting the social order". Due to its central part of Chinese life, a Chinese person having their WeChat account banned can cause a significant disruption to their life. Any interactions between Weixin and WeChat users are subject to the terms of service and privacy policies of both services. == History == By 2010, Tencent had already attained a massive user base with their desktop messenger app QQ. Recognizing smart phones were likely to disrupt this status quo, CEO Pony Ma sought to proactively invest in alternatives to their own QQ messenger app. WeChat began as a project at Tencent Guangzhou Research and Project center in October 2010. The original version of the app was created by Allen Zhang, named "Weixin" (微信) by Pony Ma, and launched in 2011. The user adoption of WeChat was initially very slow, with users wondering why key features were missing; however, after the release of the Walkie-talkie-like voice messaging feature in May of that year, growth surged. By 2012, when the number of users reached 100 million, Weixin was re-branded "WeChat" by President Martin Lau for the international market. During a period of government support of e-commerce development—for example in the 12th five-year plan (2011–2015)—WeChat also saw new features enabling payments and commerce in 2013, which saw massive adoption after their virtual Red envelope promotion for Chinese New Year 2014. WeChat had over 889 million monthly active users by 2016, and as of 2019 WeChat's monthly active users had risen to an estimate of one billion. As of January 2022, it was reported that WeChat has more than 1.2 billion users. After the launch of WeChat payment in 2013, its users reached 400 million the next year, 90 percent of whom were in China. By comparison, Facebook Messenger and WhatsApp had about one billion monthly active users in 2016 but did not offer most of the other services available on WeChat. For example, in Q2 2017, WeChat's revenues from social media advertising were about US$0.9 billion (RMB6 billion) compared with Facebook's total revenues of US$9.3 billion, 98% of which were from social media advertising. WeChat's revenues from its value-added services were US$5.5 billion. By 2018, WeChat had been used by 93.5% of Chinese internet users. In that year, it became the world's largest standalone mobile app in 2018 with over 1 billion monthly active users. In response to a border dispute between India and China, WeChat was banned in India in June 2020 along with several other Chinese apps, including TikTok. U.S. president Donald Trump sought to ban U.S. "transactions" with WeChat through an executive order but was blocked by a preliminary injunction issued in the United States District Court for the Northern District of California in September 2020. Joe Biden officially dropped Trump's efforts to ban WeChat in the U.S. in June 2021. == Features == WeChat, has been described as China's "app for everything" and a super-app because of its wide range of functions. WeChat provides text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video conferencing, video games, mobile payment, sharing of photographs and videos and location sharing. It has been described as having "an almost indispensable part of life in China". Due to its central part of Chinese life, a Chinese person having their WeChat account banned can cause a significant disruption to their life. === Messaging === WeChat provides a variety of features including text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video calls and conferencing, video games, photograph and video sharing, as well as location sharing. WeChat also allows users to exchange contacts with people nearby via Bluetooth, as well as providing various features for contacting people at random if desired (if people are open to it). It can also integrate with other social networking services such as Facebook and Tencent QQ. Photographs may also be embellished with filters and captions, and automatic translation service is available and could also translate the conversation during messaging. WeChat supports different instant messaging methods, including text messages, voice messages, walkie talkie, and stickers. Users can send previously saved or live pictures and videos, profiles of other users, coupons, lucky money packages, or current GPS locations with friends either individually or in a group chat. WeChat also provides a message recall feature to allow users to recall and withdraw information (e.g. images, documents) that are sent within 2 minutes in a conversation. WeChat also provides a voice-to-text feature that brings convenience when it is not convenient to listen to voice messages, as well as the basic ability to recognize emojis based on different tones of voice. A distance sensing feature is implemented in WeChat. It has the ability to activate the receivers' hold-to-talk function when the phone was brought in close proximity to the ear. After the receiver was held at a certain distance from the ear, the sensor would then proceed to automatically disable the phone speakers. This feature eliminates the risk of the user's voice messages being inadvertently broadcast to the general public. === Public accounts === WeChat users can register as a public account (公众号), which enables them to push feeds to subscribers, interact with subscribers, and provide subscribers with services. Users can also create an official account, which fall under service, subscription, or enterprise accounts. Once users as individuals or organizations set up a type of account, they cannot change it to another type. By the end of 2014, the number of WeChat official accounts had reached 8 million. Official accounts of organizations can apply to be verified (cost 300 RMB or about US$45). Official accounts can be used as a platform for services such as hospital pre-registrations, or credit card service. To create an official account, the applicant must register with Chinese authorities, which discourages "foreign companies". In April 2022, WeChat announced that it will start displaying the location of users in China every time they post on a public account. Meanwhile, overseas users on public accounts will also display the country based on their IP address. === Moments === "Moments" (朋友圈) is WeChat's brand name for its social feed of friends' updates. "Moments" is an interactive platform that allows users to post images, text, and short videos taken by users. It also allows users to share articles and music (associated with QQ Music or other web-based music services). Friends in the contact list can like the content and leave comments, functioning similarly to a private social network. In 2017 WeChat had a policy of a maximum of two advertisements per day per Moments user. Privacy in WeChat works by groups of friends: only the friends from the user's contact are able to view their Moments' contents and comments. The friends of the user will only be able to see the likes and comments from other users only if they are in a mutual friend group. For example, friends from high school are not able to

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  Query rewriting

  Query rewriting is a typically automatic transformation that takes a set of database tables, views, and/or queries, usually indices, often gathered data and query statistics, and other metadata, and yields a set of different queries, which produce the same results but execute with better performance (for example, faster, or with lower memory use). Query rewriting can be based on relational algebra or an extension thereof (e.g. multiset relational algebra with sorting, aggregation and three-valued predicates i.e. NULLs as in the case of SQL). The equivalence rules of relational algebra are exploited, in other words, different query structures and orderings can be mathematically proven to yield the same result. For example, filtering on fields A and B, or cross joining R and S can be done in any order, but there can be a performance difference. Multiple operations may be combined, and operation orders may be altered. The result of query rewriting may not be at the same abstraction level or application programming interface (API) as the original set of queries (though often is). For example, the input queries may be in relational algebra or SQL, and the rewritten queries may be closer to the physical representation of the data, e.g. array operations. Query rewriting can also involve materialization of views and other subqueries; operations that may or may not be available to the API user. The query rewriting transformation can be aided by creating indices from which the optimizer can choose (some database systems create their own indexes if deemed useful), mandating the use of specific indices, creating materialized and/or denormalized views, or helping a database system gather statistics on the data and query use, as the optimality depends on patterns in data and typical query usage. Query rewriting may be rule based or optimizer based. Some sources discuss query rewriting as a distinct step prior to optimization, operating at the level of the user accessible algebra API (e.g. SQL). There are other, largely unrelated concepts also named similarly, for example, query rewriting by search engines.

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  Virtual data room

  A virtual data room (sometimes called a VDR or Deal Room) is an online repository of information that is used for the storing and distribution of documents. In many cases, a virtual data room is used to facilitate the due diligence process during an M&A transaction, loan syndication, or private equity and venture capital transactions. This due diligence process has traditionally used a physical data room to accomplish the disclosure of documents. For reasons of cost, efficiency and security, virtual data rooms have widely replaced the more traditional physical data room. A virtual data room is an extranet to which the bidders and their advisers are given access via the internet. An extranet is essentially a website with limited controlled access, using a secure log-on supplied by the vendor, which can be disabled at any time, by the vendor, if a bidder withdraws. Much of the information released is confidential and restrictions are applied to the viewer's ability to release this to third parties (by means of forwarding, copying or printing). This can be effectively applied to protect the data using digital rights management. The virtual data room provides access to secure documents for authorized users through a dedicated web site, or through secure agent applications. In the process of mergers and acquisitions the data room is set up as part of the central repository of data relating to companies or divisions being acquired or sold. The data room enables the interested parties to view information relating to the business in a controlled environment where confidentiality can be preserved. Conventionally this was achieved by establishing a supervised, physical data room in secure premises with controlled access. In most cases, with a physical data room, only one bidder team can access the room at a time. A virtual data room is designed to have the same advantages as a conventional data room (controlling access, viewing, copying and printing, etc.) with fewer disadvantages. Due to their increased efficiency, many businesses and industries have moved to using virtual data rooms instead of physical data rooms. In 2006, a spokesperson for a company which sets up virtual deal rooms was reported claiming that the process reduced the bidding process by about thirty days compared to physical data rooms. In the process of startup fundraising, a virtual data room is set up to be a central location for key data, documents, and financials. These are shared with venture capital and angel investors and allows them to streamline due diligence. == Application == Any business dealing with private data can apply VDRs when secure transaction processing is required. This includes financial institutions that need to negotiate confidential customer information without involving third parties. VDRs have traditionally been used for IPOs and real estate asset management. Technology companies may use them to exchange and review code or confidential data needed for operations. The same is true for clients, who entrust their valuable code only to the most qualified people in the organisation. The code is not something that can be printed out and brought in a folder. It resides on a computer and must be used together. VDR can find application in any business that manages data in the form of documents, especially law firms, financial advisers or the B2B sector. The latter work with documents that must always be handled and controlled confidentially, and it is difficult to store them securely when they are on a server that other people can access. In addition, in B2B, it is important to close the deal as quickly as possible: the average sales cycle is one to three months. VDR can be compared to a locked filing cabinet where all those folders and documents are kept. It automates the mathematics of pricing to prevent revenue leakage, and initially integrates CRM to ensure accurate synchronisation of all account data, which is important for B2B in particular and sales in general. While virtual data rooms offer many advantages, they are not suitable for every industry. For example, some governments may decide to continue using physical data rooms for highly confidential information sharing. The damage from potential cyberattacks and data breaches exceeds the benefits offered by virtual data rooms. In such cases, the use of VDRs is not considered. Data breaches have particularly affected the US healthcare system from March 2021 to March 2022 - according to IBM Security the cost of the breach was a record high of $10.1 million.

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  Zassenhaus algorithm

  In mathematics, the Zassenhaus algorithm is a method to calculate a basis for the intersection and sum of two subspaces of a vector space. It is named after Hans Zassenhaus, but no publication of this algorithm by him is known. It is used in computer algebra systems. == Algorithm == === Input === Let V be a vector space and U, W two finite-dimensional subspaces of V with the following spanning sets: U = ⟨ u 1 , … , u n ⟩ {\displaystyle U=\langle u_{1},\ldots ,u_{n}\rangle } and W = ⟨ w 1 , … , w k ⟩ . {\displaystyle W=\langle w_{1},\ldots ,w_{k}\rangle .} Finally, let B 1 , … , B m {\displaystyle B_{1},\ldots ,B_{m}} be linearly independent vectors so that u i {\displaystyle u_{i}} and w i {\displaystyle w_{i}} can be written as u i = ∑ j = 1 m a i , j B j {\displaystyle u_{i}=\sum _{j=1}^{m}a_{i,j}B_{j}} and w i = ∑ j = 1 m b i , j B j . {\displaystyle w_{i}=\sum _{j=1}^{m}b_{i,j}B_{j}.} === Output === The algorithm computes the base of the sum U + W {\displaystyle U+W} and a base of the intersection U ∩ W {\displaystyle U\cap W} . === Algorithm === The algorithm creates the following block matrix of size ( ( n + k ) × ( 2 m ) ) {\displaystyle ((n+k)\times (2m))} : ( a 1 , 1 a 1 , 2 ⋯ a 1 , m a 1 , 1 a 1 , 2 ⋯ a 1 , m ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ a n , 1 a n , 2 ⋯ a n , m a n , 1 a n , 2 ⋯ a n , m b 1 , 1 b 1 , 2 ⋯ b 1 , m 0 0 ⋯ 0 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ b k , 1 b k , 2 ⋯ b k , m 0 0 ⋯ 0 ) {\displaystyle {\begin{pmatrix}a_{1,1}&a_{1,2}&\cdots &a_{1,m}&a_{1,1}&a_{1,2}&\cdots &a_{1,m}\\\vdots &\vdots &&\vdots &\vdots &\vdots &&\vdots \\a_{n,1}&a_{n,2}&\cdots &a_{n,m}&a_{n,1}&a_{n,2}&\cdots &a_{n,m}\\b_{1,1}&b_{1,2}&\cdots &b_{1,m}&0&0&\cdots &0\\\vdots &\vdots &&\vdots &\vdots &\vdots &&\vdots \\b_{k,1}&b_{k,2}&\cdots &b_{k,m}&0&0&\cdots &0\end{pmatrix}}} Using elementary row operations, this matrix is transformed to the row echelon form. Then, it has the following shape: ( c 1 , 1 c 1 , 2 ⋯ c 1 , m ∙ ∙ ⋯ ∙ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ c q , 1 c q , 2 ⋯ c q , m ∙ ∙ ⋯ ∙ 0 0 ⋯ 0 d 1 , 1 d 1 , 2 ⋯ d 1 , m ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 0 0 ⋯ 0 d ℓ , 1 d ℓ , 2 ⋯ d ℓ , m 0 0 ⋯ 0 0 0 ⋯ 0 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 0 0 ⋯ 0 0 0 ⋯ 0 ) {\displaystyle {\begin{pmatrix}c_{1,1}&c_{1,2}&\cdots &c_{1,m}&\bullet &\bullet &\cdots &\bullet \\\vdots &\vdots &&\vdots &\vdots &\vdots &&\vdots \\c_{q,1}&c_{q,2}&\cdots &c_{q,m}&\bullet &\bullet &\cdots &\bullet \\0&0&\cdots &0&d_{1,1}&d_{1,2}&\cdots &d_{1,m}\\\vdots &\vdots &&\vdots &\vdots &\vdots &&\vdots \\0&0&\cdots &0&d_{\ell ,1}&d_{\ell ,2}&\cdots &d_{\ell ,m}\\0&0&\cdots &0&0&0&\cdots &0\\\vdots &\vdots &&\vdots &\vdots &\vdots &&\vdots \\0&0&\cdots &0&0&0&\cdots &0\end{pmatrix}}} Here, ∙ {\displaystyle \bullet } stands for arbitrary numbers, and the vectors ( c p , 1 , c p , 2 , … , c p , m ) {\displaystyle (c_{p,1},c_{p,2},\ldots ,c_{p,m})} for every p ∈ { 1 , … , q } {\displaystyle p\in \{1,\ldots ,q\}} and ( d p , 1 , … , d p , m ) {\displaystyle (d_{p,1},\ldots ,d_{p,m})} for every p ∈ { 1 , … , ℓ } {\displaystyle p\in \{1,\ldots ,\ell \}} are nonzero. Then ( y 1 , … , y q ) {\displaystyle (y_{1},\ldots ,y_{q})} with y i := ∑ j = 1 m c i , j B j {\displaystyle y_{i}:=\sum _{j=1}^{m}c_{i,j}B_{j}} is a basis of U + W {\displaystyle U+W} and ( z 1 , … , z ℓ ) {\displaystyle (z_{1},\ldots ,z_{\ell })} with z i := ∑ j = 1 m d i , j B j {\displaystyle z_{i}:=\sum _{j=1}^{m}d_{i,j}B_{j}} is a basis of U ∩ W {\displaystyle U\cap W} . === Proof of correctness === First, we define π 1 : V × V → V , ( a , b ) ↦ a {\displaystyle \pi _{1}:V\times V\to V,(a,b)\mapsto a} to be the projection to the first component. Let H := { ( u , u ) ∣ u ∈ U } + { ( w , 0 ) ∣ w ∈ W } ⊆ V × V . {\displaystyle H:=\{(u,u)\mid u\in U\}+\{(w,0)\mid w\in W\}\subseteq V\times V.} Then π 1 ( H ) = U + W {\displaystyle \pi _{1}(H)=U+W} and H ∩ ( 0 × V ) = 0 × ( U ∩ W ) {\displaystyle H\cap (0\times V)=0\times (U\cap W)} . Also, H ∩ ( 0 × V ) {\displaystyle H\cap (0\times V)} is the kernel of π 1 | H {\displaystyle {\pi _{1}|}_{H}} , the projection restricted to H. Therefore, dim ⁡ ( H ) = dim ⁡ ( U + W ) + dim ⁡ ( U ∩ W ) {\displaystyle \dim(H)=\dim(U+W)+\dim(U\cap W)} . The Zassenhaus algorithm calculates a basis of H. In the first m columns of this matrix, there is a basis y i {\displaystyle y_{i}} of U + W {\displaystyle U+W} . The rows of the form ( 0 , z i ) {\displaystyle (0,z_{i})} (with z i ≠ 0 {\displaystyle z_{i}\neq 0} ) are obviously in H ∩ ( 0 × V ) {\displaystyle H\cap (0\times V)} . Because the matrix is in row echelon form, they are also linearly independent. All rows which are different from zero ( ( y i , ∙ ) {\displaystyle (y_{i},\bullet )} and ( 0 , z i ) {\displaystyle (0,z_{i})} ) are a basis of H, so there are dim ⁡ ( U ∩ W ) {\displaystyle \dim(U\cap W)} such z i {\displaystyle z_{i}} s. Therefore, the z i {\displaystyle z_{i}} s form a basis of U ∩ W {\displaystyle U\cap W} . == Example == Consider the two subspaces U = ⟨ ( 1 − 1 0 1 ) , ( 0 0 1 − 1 ) ⟩ {\displaystyle U=\left\langle \left({\begin{array}{r}1\\-1\\0\\1\end{array}}\right),\left({\begin{array}{r}0\\0\\1\\-1\end{array}}\right)\right\rangle } and W = ⟨ ( 5 0 − 3 3 ) , ( 0 5 − 3 − 2 ) ⟩ {\displaystyle W=\left\langle \left({\begin{array}{r}5\\0\\-3\\3\end{array}}\right),\left({\begin{array}{r}0\\5\\-3\\-2\end{array}}\right)\right\rangle } of the vector space R 4 {\displaystyle \mathbb {R} ^{4}} . Using the standard basis, we create the following matrix of dimension ( 2 + 2 ) × ( 2 ⋅ 4 ) {\displaystyle (2+2)\times (2\cdot 4)} : ( 1 − 1 0 1 1 − 1 0 1 0 0 1 − 1 0 0 1 − 1 5 0 − 3 3 0 0 0 0 0 5 − 3 − 2 0 0 0 0 ) . {\displaystyle \left({\begin{array}{rrrrrrrr}1&-1&0&1&&1&-1&0&1\\0&0&1&-1&&0&0&1&-1\\\\5&0&-3&3&&0&0&0&0\\0&5&-3&-2&&0&0&0&0\end{array}}\right).} Using elementary row operations, we transform this matrix into the following matrix: ( 1 0 0 0 ∙ ∙ ∙ ∙ 0 1 0 − 1 ∙ ∙ ∙ ∙ 0 0 1 − 1 ∙ ∙ ∙ ∙ 0 0 0 0 1 − 1 0 1 ) {\displaystyle \left({\begin{array}{rrrrrrrrr}1&0&0&0&&\bullet &\bullet &\bullet &\bullet \\0&1&0&-1&&\bullet &\bullet &\bullet &\bullet \\0&0&1&-1&&\bullet &\bullet &\bullet &\bullet \\\\0&0&0&0&&1&-1&0&1\end{array}}\right)} (Some entries have been replaced by " ∙ {\displaystyle \bullet } " because they are irrelevant to the result.) Therefore ( ( 1 0 0 0 ) , ( 0 1 0 − 1 ) , ( 0 0 1 − 1 ) ) {\displaystyle \left(\left({\begin{array}{r}1\\0\\0\\0\end{array}}\right),\left({\begin{array}{r}0\\1\\0\\-1\end{array}}\right),\left({\begin{array}{r}0\\0\\1\\-1\end{array}}\right)\right)} is a basis of U + W {\displaystyle U+W} , and ( ( 1 − 1 0 1 ) ) {\displaystyle \left(\left({\begin{array}{r}1\\-1\\0\\1\end{array}}\right)\right)} is a basis of U ∩ W {\displaystyle U\cap W} .

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  Edge inference

  Edge inference is the process of running machine learning or deep learning models on local devices (edge devices) such as smartphones, IoT devices, embedded systems, and edge servers instead of centralized cloud computing infrastructure. A key feature of edge computing is edge inference, which allows for real-time data processing, low latency, and improved privacy by reducing the amount of data sent to remote servers.

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  Golden record (informatics)

  In informatics, a golden record is the valid version of a data element (record) in a single source of truth system. It may refer to a database, specific table or data field, or any unit of information used. A golden copy is a consolidated data set, and is supposed to provide a single source of truth and a "well-defined version of all the data entities in an organizational ecosystem". Other names sometimes used include master source or master version. The term has been used in conjunction with data quality, master data management, and similar topics. (Different technical solutions exist, see master data management). == Master data == In master data management (MDM), the golden copy refers to the master data (master version) of the reference data which works as an authoritative source for the "truth" for all applications in a given IT landscape.

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  BioCreative

  BioCreAtIvE (A critical assessment of text mining methods in molecular biology) consists in a community-wide effort for evaluating information extraction and text mining developments in the biological domain. It was preceded by the Knowledge Discovery and Data Mining (KDD) Challenge Cup for detection of gene mentions. == Community Challenges == === First edition (2004-2005) === Three main tasks were posed at the first BioCreAtIvE challenge: the entity extraction task, the gene name normalization task, and the functional annotation of gene products task. The data sets produced by this contest serve as a Gold Standard training and test set to evaluate and train Bio-NER tools and annotation extraction tools. === Second edition (2006-2007) === The second BioCreAtIvE challenge (2006-2007) had also 3 tasks: detection of gene mentions, extraction of unique idenfiers for genes and extraction information related to physical protein-protein interactions. It counted with participation of 44 teams from 13 countries. === Third edition (2011-2012) === The third edition of BioCreative included for the first time the InterActive Task (IAT), designed to evaluate the practical usability of text mining tools in real-world biocuration tasks. === Fifth edition (2016) === BioCreative V had 5 different tracks, including an interactive task (IAT) for usability of text mining systems and a track using the BioC format for curating information for BioGRID.

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  Virtual facility

  A Virtual Facility (VF) is a highly realistic digital representation of a data center, used to model all relevant aspects of a physical data center with a high degree of precision. The term "virtual" in Virtual Facility refers to its use of virtual reality, rather than the abstraction of computer resources as seen in platform virtualization. The VF mirrors the characteristics of a physical facility over time and allows for detailed analysis and modeling. == VF Model features == A standard VF model includes: Three-dimensional physical facility layout Network connectivity of facility equipment Full inventory of facility equipment, including electronics and electrical systems such as power distribution units (PDUs) and uninterruptible power supplies (UPSs) Full air conditioning system (ACUs) and controls within the room The term Virtual Facility was introduced to address the emerging environmental problems facing modern Mission Critical Facilities (MCFs). This concept combines virtual reality (VR), computer simulation, and expert systems applied to the domain of facilities. The VF type of computer simulation allows for detailed analysis and prototyping of airflow in the data center using computational fluid dynamics (CFD) techniques. This enables the visualization and numerical analysis of airflow and temperatures within the facility, helping to predict real-world outcomes. == VF applications == The VF model can be used to assist with the following: Greenfield design Asset management Troubleshooting existing data centers Making existing data centers more resilient Making existing data centers more energy efficient Cost prediction Staff training Capacity planning Load growth management Many organizations use VF models to virtually assess scenarios before committing resources to physical changes. This allows for better decision-making regarding the addition or modification of equipment, helping to avoid logistical or thermal problems.

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  Art Recognition

  Art Recognition is a Swiss technology company headquartered in Adliswil, within the Zurich metropolitan area, Switzerland. Art Recognition specializes in the application of artificial intelligence (AI) for art authentication and the detection of art forgeries. == Overview == Art Recognition was established in 2019 by Dr. Carina Popovici and Christiane Hoppe-Oehl. Art Recognition employs a combination of machine learning techniques, computer vision algorithms, and deep neural networks to assess the authenticity of artworks. The company's technology undergoes a process of data collection, dataset preparation, and training. === Academic partnerships and grants === Art Recognition has established a relationship with Innosuisse, a Swiss innovation agency, to expand its research and development initiatives. It has also formed a strategic collaboration with Nils Büttner, an art historian and professor at the State Academy of Fine Arts Stuttgart (ABK Stuttgart). === Notable developments === In May 2024, Art Recognition played a key role in identifying counterfeit artworks, including alleged Monets and Renoirs, being sold on eBay. Germann Auction in November 2024 became the first auction house to successfully conduct a sale of artwork authenticated entirely by artificial intelligence. As of January 2025, Art Recognition has appointed art crime expert and Pulitzer Prize finalist Noah Charney as an advisor. === Recognition and debates === The company was featured on the front page of The Wall Street Journal for its involvement in the authentication case of the Flaget Madonna, believed to have been partly painted by Raphael. A broadcast by the Swiss public television SRF covered how the algorithm can be used to detect art forgeries with high accuracy. The technology developed by Art Recognition has been recognized for its role in providing a technology-based art authentication solution, compared to traditional methods. == Controversial cases == Art Recognition's AI algorithm has been applied to several high-profile and controversial artworks, sparking significant interest and debate in the art world. Samson and Delilah at the National Gallery in London: The National Gallery's "Samson and Delilah", traditionally attributed to the artist Rubens, has also been examined using Art Recognition's AI, which has assessed the painting as non-authentic. De Brecy Tondo Madonna. A research team from Bradford University and the University of Nottingham initially attributed the painting to Raphael, employing an AI face recognition software, while the AI developed at Art Recognition returned a negative result. The Bradford group's AI was trained on 49 images, whereas Art Recognition employed a larger dataset of over 100 images. Lucian Freud Painting Controversy: Featured in The New Yorker, a painting attributed to Lucian Freud became a subject of dispute. Art Recognition's AI analysis played a big role in examining the painting's authenticity. Titian at Kunsthaus Zürich: A painting attributed to Titian, housed at Kunsthaus Zürich, has been a topic of debate among art experts. The application of Art Recognition's technology offered a new perspective. Following this debate, Kunsthaus Zürich has announced plans to initiate a comprehensive project aimed at resolving the authenticity questions surrounding the painting. Art Recognition has contributed to the authentication debate surrounding The Polish Rider, a painting traditionally attributed to Rembrandt but subject to scholarly debate.

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  Ontology (information science)

  In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology. Every academic discipline or field, in creating its terminology, thereby lays the groundwork for an ontology. Each uses ontological assumptions to frame explicit theories, research and applications. Improved ontologies may improve problem solving within that domain, interoperability of data systems, and discoverability of data. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages. For instance, the definition and ontology of economics is a primary concern in Marxist economics, but also in other subfields of economics. An example of economics relying on information science occurs in cases where a simulation or model is intended to enable economic decisions, such as determining what capital assets are at risk and by how much (see risk management). What ontologies in both information science and philosophy have in common is the attempt to represent entities, including both objects and events, with all their interdependent properties and relations, according to a system of categories. In both fields, there is considerable work on problems of ontology engineering (e.g., Quine and Kripke in philosophy, Sowa and Guarino in information science), and debates concerning to what extent normative ontology is possible (e.g., foundationalism and coherentism in philosophy, BFO and Cyc in artificial intelligence). Applied ontology is considered by some as a successor to prior work in philosophy. However many current efforts are more concerned with establishing controlled vocabularies of narrow domains than with philosophical first principles, or with questions such as the mode of existence of fixed essences or whether enduring objects (e.g., perdurantism and endurantism) may be ontologically more primary than processes. Artificial intelligence has retained considerable attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields, including biomedical informatics and industry. Such efforts often use ontology editing tools such as Protégé. == Ontology in philosophy == Ontology is a branch of philosophy and intersects areas such as metaphysics, epistemology, and philosophy of language, as it considers how knowledge, language, and perception relate to the nature of reality. Metaphysics deals with questions like "what exists?" and "what is the nature of reality?". One of five traditional branches of philosophy, metaphysics is concerned with exploring existence through properties, entities and relations such as those between particulars and universals, intrinsic and extrinsic properties, or essence and existence. Metaphysics has been an ongoing topic of discussion since recorded history. == Etymology == The compound word ontology combines onto-, from the Greek ὄν, on (gen. ὄντος, ontos), i.e. "being; that which is", which is the present participle of the verb εἰμί, eimí, i.e. "to be, I am", and -λογία, -logia, i.e. "logical discourse", see classical compounds for this type of word formation. While the etymology is Greek, the oldest extant record of the word itself, the Neo-Latin form ontologia, appeared in 1606 in the work Ogdoas Scholastica by Jacob Lorhard (Lorhardus) and in 1613 in the Lexicon philosophicum by Rudolf Göckel (Goclenius). The first occurrence in English of ontology as recorded by the OED (Oxford English Dictionary, online edition, 2008) came in Archeologia Philosophica Nova or New Principles of Philosophy (1663) by Gideon Harvey. == Formal ontology == Since the mid-1970s, researchers in the field of artificial intelligence (AI) have recognized that knowledge engineering is the key to building large and powerful AI systems. AI researchers argued that they could create new ontologies as computational models that enable certain kinds of automated reasoning, which was only marginally successful. In the 1980s, the AI community began to use the term ontology to refer to both a theory of a modeled world and a component of knowledge-based systems. In particular, David Powers introduced the word ontology to AI to refer to real world or robotic grounding, publishing in 1990 literature reviews emphasizing grounded ontology in association with the call for papers for a AAAI Summer Symposium Machine Learning of Natural Language and Ontology, with an expanded version published in SIGART Bulletin and included as a preface to the proceedings. Some researchers, drawing inspiration from philosophical ontologies, viewed computational ontology as a kind of applied philosophy. In 1993, the widely cited web page and paper "Toward Principles for the Design of Ontologies Used for Knowledge Sharing" by Tom Gruber used ontology as a technical term in computer science closely related to earlier idea of semantic networks and taxonomies. Gruber introduced the term as a specification of a conceptualization: An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. And it is a different sense of the word than its use in philosophy. Attempting to distance ontologies from taxonomies and similar efforts in knowledge modeling that rely on classes and inheritance, Gruber stated (1993): Ontologies are often equated with taxonomic hierarchies of classes, class definitions, and the subsumption relation, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972). To specify a conceptualization, one needs to state axioms that do constrain the possible interpretations for the defined terms. Recent experimental ontology frameworks have also explored resonance-based AI-human co-evolution structures, such as IAMF (Illumination AI Matrix Framework), OntoMotoOS (a meta-operating system concept for ethical and ontological AI–human co-evolution), and PSRT (Phase-Structural Reality Theory across multi-scale ontological layers). Though not yet widely adopted in academic discourse, such models propose phased approaches to ethical harmonization and structural emergence. As refinement of Gruber's definition Feilmayr and Wöß (2016) stated: "An ontology is a formal, explicit specification of a shared conceptualization that is characterized by high semantic expressiveness required for increased complexity." == Formal ontology components == Contemporary ontologies share many structural similarities, regardless of the language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes and relations. === Types === ==== Domain ontology ==== A domain ontology (or domain-specific ontology) represents concepts which belong to a realm of the world, such as biology or politics. Each domain ontology typically models domain-specific definitions of terms. For example, the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punched card" and "video card" meanings. Since domain ontologies are written by different people, they represent concepts in very specific and unique ways, and are often incompatible within the same project. As systems that rely on domain ontologies expand, they often need to merge domain ontologies by hand-tuning each entity or using a combination of software merging and hand-tuning. This presents a challenge to the ontology designer. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.). At present, merging ontologies that are not developed from a common upper ontology is a largely manual process and therefore time-consuming and expensive. Domain ontologies that use the same upper ontology to provide a set of basic elements with which to specify the meanings of the domain ontology entities can be merged with less effo

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  Investigative Data Warehouse

  Investigative Data Warehouse (IDW) is a searchable database operated by the FBI. It was created in 2004. Much of the nature and scope of the database is classified. The database is a centralization of multiple federal and state databases, including criminal records from various law enforcement agencies, the U.S. Department of the Treasury's Financial Crimes Enforcement Network (FinCEN), and public records databases. According to Michael Morehart's testimony before the House Committee on Financial Services in 2006, the "IDW is a centralized, web-enabled, closed system repository for intelligence and investigative data. This system, maintained by the FBI, allows appropriately trained and authorized personnel throughout the country to query for information of relevance to investigative and intelligence matters." == Overview == In 2004, according to a government solicitation for bids to manage the project, it was approximately 10TB in size. In 2005, according to one FBI official, the IDW contained approximately 100 million documents. In 2006 it contained more than 560 million documents and was accessible by more than 12,000 individuals. According to the FBI's website, as of August 22, 2007, the database contained 700 million records from 53 databases and was accessible by 13,000 individuals around the world. As of 2007, the FBI was the subject of a lawsuit brought by the EFF (Electronic Frontier Foundation) because of a lack of public notice describing the database and the criteria for including personal information, as required by the Privacy Act of 1974. The lawsuits were a result of two Freedom of Information Act requests filed by the EFF in 2006. It was built in part by Chiliad corporation, the FBI Office of the Chief Technology Officer, and others. Companies listed on the FOIA files include Northrop Grumman . == Purpose == Investigative Data Warehouse–Secret (IDW-S) "provides data and data processing/analysis services to FBI agents and analysts as they perform counter-terrorism, counter-intelligence, and law enforcement missions". The core subsystem supports the Counter-Terrorism Division (CTD), the Special Event Unit, and via DOCLAB-S, the Joint Intelligence Committee Investigation (JICI) and IntelPlus. According to a 2005 email, "IDW will also be used for criminal and other authorized non-CT investigations as it evolves." (CT being counter terrorism) == Subsystems == Within the system, there were subsystems named IDW-S Core, SPT, and DOCLAB-S The special projects team (SPT): allows for the rapid import of new specialized data sources. These data sources are not made available to the general IDW users but instead are provided to a small group of users who have a demonstrated "need-to-know". The SPT System is similar in function to the IDW-S system, with the main difference is a different set of data sources. The SPT System allows its users to access not only the standard IDW Data Store but the specialized SPT Data Store. == Privacy == According to internal emails, the FBI performed several Privacy Impact Assessments (PIAs) of the IDW system. They worked with lawyers from their National Security Law Branch (NSLB) to attempt to make sure their system was complying with various laws regarding sharing of information and secrecy (for example, rule 6e of the Federal Rules of Criminal Procedure, regarding the secrecy of Grand Jury material ). The Information Sharing Policy Group (ISPG) formed a Discretionary Access Control Team (DACT), to work on "approval of data sets" and "access control requirements" for IDW and DataMart, and responding to other Intelligence Community agencies requesting access. The EFF FOIA IDW website states "Despite the vast amount of personal information contained in the IDW, the FBI has never published a Privacy Act notice describing the system or explaining the ways in which the records might be used." There was also a 2005 email from someone on the Office of General Council (OGC) about "preliminary staff musings that maybe we should limit FBI PIA requirements to non-NS systems" (NS being National Security). There was also an email from 2006 saying that 'national security systems are exempt from E-Gov', apparently referring to the E-Government Act of 2002, which has a section that deals with privacy. == Data sources == The IDW used many data sources. The FOIA documents from EFF are heavily redacted, but some of the sources are as follows: FBI Automated Case Support system (ACS), subset of the Electronic Case File (ECF) system Joint Intelligence Committee Investigation documents (JICI), with OCR text "Open Source News" (public websites, such as the Washington Post and others) Secure Automated Messaging Network (SAMNet) Violent Gang and Terrorist Organizing File (VGTOF) DARPA TIDES program ('open source news' that has been organized and collected) IntelPlus Filerooms, with OCR text FBI National Crime Information Center (NCIC) FBI Records Management Division (RMD), Document Laboratory (DocLab), FBIHQ MiTAP (collects data from public sources, websites, etc.) SPT-Specific data sources (partial list, FOIA files have large parts redacted): Unified Name Index (UNI) extracts Financial Center (FinCen), including Bank Secrecy Act data "Various Sources", including the Transportation Security Administration FBI Counterterrorism Division (CTD) Telephone numbers / addresses from ACS Case data from ACS Terrorist Watch List (TWL) "Other NJTTF data" DoS ... Lost/Stolen Passport data No Fly List, from TSA Selectee list, from TSA ACS/ECF with some case types excluded CIA non-TS/non-SCI Technical Discussions (TDs) and Intelligence Information Reports (IIRs) from 1978 to the May 2004 There was also talk of linking the FTTTF "Data Mart" with IDW. The data in IDW is classified at the 'Secret' level or lower. Higher classifications are not allowed, and can be removed

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  Information strategist

  An information strategist analyses the information flow within an organisation and directs its information resources to better serve the organisation's strategic goals. They work with information technology or within a corporate library to direct high quality information from a variety of sources to users, based upon their profiles and needs. In warfare, information strategists not only seek to improve information flows for their own side but also try to disrupt the information flows of the enemy in order to demoralize and deceive them.

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  Dark mode

  A dark mode, dark theme, night mode, or light-on-dark color scheme is a color scheme that uses light-colored text, icons, and graphical user interface elements on a dark background. It is often discussed in terms of computer user interface design and web design. Many modern websites and operating systems offer the user an optional light-on-dark display mode. Some users find dark mode displays more visually appealing, and claim that it can reduce eye strain. Displaying white at full brightness uses roughly six times as much power as pure black on a 2016 Google Pixel, which has an OLED display. However, conventional LED displays may not benefit from reduced power consumption; but if a LED display has the partial dimming features, it still benefits from reduced power consumption. Most modern operating systems support an optional light-on-dark color scheme. == History == Microsoft introduced the high contrast themes in Windows 95. Later, Microsoft introduced a dark theme in the Anniversary Update of Windows 10 in 2016. In 2018, Apple followed in macOS Mojave. In September 2019, iOS 13 and Android 10 both introduced dark modes. Some operating systems provide tools to change the dark mode state automatically at sundown or sunrise. A "prefers-color-scheme" option was created for front-end web developers in 2019, being a CSS property that signals a user's choice for their system to use a light or dark color theme. Firefox and Chromium have optional dark theme for all internal screens. It is also possible for third-party developers to implement their own dark themes. There are also a variety of browser add-ons that can re-theme web sites with dark color schemes, also aligning with system theme. Wikipedia's mobile and desktop versions received a dark mode option in 2024. == Implementation == There is a prefers-color-scheme media query in CSS, to detect if the user has requested light or dark color scheme and serve the requested color scheme. It can be indicated from the user's operating system preference or a user agent. CSS example: JavaScript example: == Energy usage == Light on dark color schemes require less energy to display on OLED displays. This positively impacts battery life and reduces energy consumption. While an OLED will consume around 40% of the power of an LCD displaying an image that is primarily black, it can use more than three times as much power to display an image with a white background, such as a document or web site. This can lead to reduced battery life and higher energy usage unless a light-on-dark color scheme is used. The long-term reduced power usage may also prolong battery life or the useful life of the display and battery. The energy savings that can be achieved using a light-on-dark color scheme are because of how OLED screens work: in an OLED screen, each subpixel generates its own light and it only consumes power when generating light. This is in contrast to how an LCD works: in an LCD, subpixels either block or allow light from an always-on (lit) LED backlight to pass through. "AMOLED Black" color schemes (that use pure black instead of dark gray) do not necessarily save more energy than other light-on-dark color schemes that use dark gray instead of black, as the power consumption on an AMOLED screen decreases proportionately to the average brightness of the displayed pixels. Although it is true that AMOLED black does save more energy than dark gray, the additional energy savings are often negligible; AMOLED black will only give an additional energy saving of less than 1%, for instance, over the dark gray that's used in the dark theme for Google's official Android apps. In November 2018, Google confirmed that dark mode on Android saved battery life. == Web issues == Some argue that a color scheme with light text on a dark background is easier to read on the screen, because the lower overall brightness causes less eyestrain, while others argue to the contrary. Some pages on the web are designed for white backgrounds; Image assets (GIF, PNG, SVG, WOFF, etc) can be used improperly causing visual artifacts if dark mode is forced (instead of designed for) with a plugin like Dark Reader.

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  Causal AI

  Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision. Systems based on causal AI, by identifying the underlying web of causality for a behaviour or event, provide insights that solely predictive AI models might fail to extract from historical data. An analysis of causality may be used to supplement human decisions in situations where understanding the causes behind an outcome is necessary, such as quantifying the impact of different interventions, policy decisions or performing scenario planning. A 2024 paper from Google DeepMind demonstrated mathematically that "Any agent capable of adapting to a sufficiently large set of distributional shifts must have learned a causal model". The paper offers the interpretation that learning to generalise beyond the original training set requires learning a causal model, concluding that causal AI is necessary for artificial general intelligence. == History == The concept of causal AI and the limits of machine learning were raised by Judea Pearl, the Turing Award-winning computer scientist and philosopher, in 2018's The Book of Why: The New Science of Cause and Effect. Pearl asserted: “Machines' lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.” In 2020, Columbia University established a Causal AI Lab under Director Elias Bareinboim. Professor Bareinboim's research focuses on causal and counterfactual inference and their applications to data-driven fields in the health and social sciences as well as artificial intelligence and machine learning. Technological research and consulting firm Gartner for the first time included causal AI in its 2022 Hype Cycle report, citing it as one of five critical technologies in accelerated AI automation. Causal AI is closely related to but distinct from fields such as causal inference, explainable AI and causal reasoning. While causal inference focuses on estimating cause-effect relationships (often from observational data), causal AI emphasises the integration of those causal models into AI systems for prediction, planning and adaptation.

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  Algorithmic transparency

  Algorithmic transparency is the principle that the factors that influence the decisions made by algorithms should be visible, or transparent, to the people who use, regulate, and are affected by systems that employ those algorithms. Although the phrase was coined in 2016 by Nicholas Diakopoulos and Michael Koliska about the role of algorithms in deciding the content of digital journalism services, the underlying principle dates back to the 1970s and the rise of automated systems for scoring consumer credit. The phrases "algorithmic transparency" and "algorithmic accountability" are sometimes used interchangeably – especially since they were coined by the same people – but they have subtly different meanings. Specifically, "algorithmic transparency" states that the inputs to the algorithm and the algorithm's use itself must be known, but they need not be fair. "Algorithmic accountability" implies that the organizations that use algorithms must be accountable for the decisions made by those algorithms, even though the decisions are being made by a machine, and not by a human being. Current research around algorithmic transparency interested in both societal effects of accessing remote services running algorithms, as well as mathematical and computer science approaches that can be used to achieve algorithmic transparency. In the United States, the Federal Trade Commission's Bureau of Consumer Protection studies how algorithms are used by consumers by conducting its own research on algorithmic transparency and by funding external research. In the European Union, the data protection laws that came into effect in May 2018 include a "right to explanation" of decisions made by algorithms, though it is unclear what this means. Furthermore, the European Union founded The European Center for Algorithmic Transparency (ECAT).

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