AI Headshot Examples

AI Headshot Examples — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Common Image Generator Interface

  The Common Image Generator Interface (CIGI) (pronounced sig-ee), is an on-the-wire data protocol that allows communication between an Image Generator and its host simulation. The interface is designed to promote a standard way for a host device to communicate with an image generator (IG) within the industry. CIGI enables plug-and-play by standard-compliant image generator vendors and reduces integration costs when upgrading visual systems. == Background == Most high-end simulators do not have everything running on a single machine the way popular home software flight simulators are currently implemented. The airplane model is run on one machine, normally referred to as the host, and the out the window visuals or scene graph program is run on another, usually referred to as an Image Generator (IG). Frequently there are multiple IGs required to display the surrounding environment created by a host. CIGI is the interface between the 'host' and the IGs. The main goal of CIGI is to capitalize on previous investments through the use of a common interface. CIGI is designed to assist suppliers and integrators of IG systems with ease of integration, code reuse, and overall cost reduction. In the past most image generators provided their own proprietary interface; every host had to implement that interface making changing image generators a costly ordeal. CIGI was created to standardize the interface between the host and the image generator so that little modification would be needed to switch image generators. The CIGI initiative was largely spearheaded by The Boeing Company during the early 21st century. The latest version of CIGI (CIGI 4.0) was developed by the Simulation Interoperability Standards Organization (SISO) in the form of SISO-STD-013-2014, Standard for Common Image Generator Interface (CIGI), Version 4.0, dated 22 August 2014. SISO-STD-013-2014 is freely available from SISO. == Definitions == Image generator – In this context an image generator consists of one or more rendering channels that produce an image that can be used to visualize an “Out-The-Window” scene, or images produced by various sensor simulations such as Infra-red, Day TV, electro-optical, and night vision. Host simulation – In this context a “Host” is the computational system that provides information about the device being simulated so that the image generator can portray the correct scenery to the user. This information is passed via CIGI to the image generator. == Maturation == CIGI 4 is the latest version of the standard as was approved by the Simulation Interoperability Standards Organization on August 22, 2014. CIGI became an international SISO standard known as SISO-STD-013-2014; which contains the CIGI version 4.0 Interface Control Document (ICD). CIGI 4.0 is the official standard, published by SISO. Previous versions of CIGI were spearheaded by Boeing include CIGI v3.3, in November 2008, v3.2 April 2006, v3.1 June 2004, v3 November 2003, v2 in March 2002, and the original (v1) in March 2001 == Protocol dependencies == Typically, CIGI uses UDP as its transport protocol, but CIGI does not require a specific transport mechanism, only packet definition conformance. CIGI traffic does not have a well known port; however, the use of ports 8004-8005 has been widely adopted by commercial image generator vendors implementations. == Development tools == === Host Emulator === The Host Emulator can be used as a surrogate to manipulate the interface when a simulation Host is not available. It is a Windows-based image generator Host application used to develop, integrate and test image generators that use the CIGI protocol. It provides a graphical user interface (GUI) for the creation, modification and deletion of entities; manipulation of views; control of environmental attributes and phenomena; and other host functions. The Host Emulator has several features that are useful for integration and testing. A free-flight mode allows for fixed-wing and rotorcraft flight, movement along entity axes and free rotation using a joystick or a joystick-like widget. Scripting and record/playback features support regression testing, demonstrations and other tasks needing exact reproduction of certain sequences of events. A packet-level snoop feature allows the user to examine the contents of CIGI messages, image generator response times and latencies. A Heartbeat Monitor Window shows a graphical timing history of the Image Generator's data frame rate. Other features include explicit packet creation, animation control, missile flyouts and a situation display window (Host Emulator 3.x only). === Multi-Purpose Viewer === The Multi-Purpose Viewer (MPV) provides the basic functionality expected of an Image Generator, such as loading and displaying a terrain database, displaying entities and so forth. The Multi-Purpose Viewer can be used as a surrogate to manipulate the interface when a real Image Generator is not available. The MPV is capable of operating with both the Windows and Linux operating systems. === CIGI Class Library === The CCL is an object-oriented software interface that automatically handles message composition and decomposition (i.e. packing, unpacking and byte swapping to the ICD specification) on both the Host and Image Generator sides of the interface. The CCL interprets Host or Image Generator messages based on compile time parameters. It also performs error handling and translation between different versions of CIGI. Each packet type has its own class. The individual packet members are accessed through packet class accessors. Outgoing messages are constructed by placing each packet into the outgoing buffer using a streaming operator. Incoming messages are parsed using callback or event-based mechanisms that supply the using program with fully populated packet objects. === Current tool suite === A set of CIGI development tools are managed and maintained by the SISO CIGI Product Support Group. The latest packages are available on SourceForge. Comments/Suggestions to the package can be directed to the SISO discussion board at: https://discussions.sisostds.org/index.htm?A0=SAC-PSG-CIGI Archived 2017-09-13 at the Wayback Machine === Wireshark === Wireshark is a free and open source packet analyzer. It is used for network troubleshooting, analysis, software and communications protocol development, and education. Wireshark provides a dissector for CIGI packets. As of October 2016, “The CIGI dissector is fully functional for CIGI version 2 and 3. Version 1 is not yet implemented.” === Older versions of CIGI === A CIGI Interface Control Document (ICD) and development suite is available in open source format. The tools, ICD, and accompanying user documentation can be found and downloaded from the CIGI sourceforge web site. The SourceForge version of the MPV is limited in its support of CIGI data packets and is intended to grow as needs arise. The MPV uses CIGI 3 as its interface, but the MPV is backward-compatible with earlier CIGI versions through the use of the CCL. The MPV uses the Open Scene Graph library to render a scene. The scene graph is manipulated according to the CIGI commands received from the Host via the CCL. The MPV itself is an application layer that consists of a small kernel leveraging heavily on a plug-in architecture for ease of maintainability and flexibility. An implementer can implement the interface from scratch, however a full suite of integration tools is available. These tools consist of three elements. The Host Emulator (HE), the Multi-Purpose Viewer (MPV), and the CIGI Class Library (CCL).

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  Ballin' (Mustard and Roddy Ricch song)

  "Ballin'" is a song by American record producer Mustard featuring American rapper Roddy Ricch. The track was released as the third single from Mustard's third studio album, Perfect Ten, on August 20, 2019, though it was available as early as the end of June 2019. The song and its accompanying video received acclaim from music critics, with Complex magazine naming it the Best Song of 2019. It peaked at number 11 on the Billboard Hot 100, marking Mustard's highest charting song in the US. The song received a nomination for Best Rap/Sung Performance at the 2020 Grammy Awards, making it the first time Ricch has been nominated for a Grammy and Mustard's first nomination as an artist. Later in 2019, the two released another collaboration, "High Fashion". == Background == Roddy Ricch revealed in an interview that the song was composed in late 2018, but Mustard wanted to keep it for his album, Perfect Ten, which he was still working on. The song was later included on the album, released in June 2019. Ricch said he knew the song was "hard enough" the first time he heard it, while Mustard proclaimed "this is going to be the one". == Composition and lyrics == "Ballin'" has a "rags to riches" theme. In its intro, the song samples girl group 702's 1997 top ten hit "Get It Together". The song features a "smooth, bouncy beat", with Roddy Ricch rapping about his come-up and ascent in the music industry. In the first verse, Ricch salutes fellow Los Angeles rapper, the late Nipsey Hussle and his girlfriend Lauren London: "I run these racks up with my queen like London and Nip". The line simultaneously references Ricch and Hussle's collaboration "Racks in the Middle", released earlier in 2019 as Hussle's last single before his death. Billboard's Heran Mamo noted that "in typical Hussle fashion", Roddy Ricch "narrates his life's hardships before delving into his newfound treasures". == Critical reception == The song was widely acclaimed by music critics. Charles Holmes of Rolling Stone magazine called it "a song of the year contender", while Complex and Billboard both named it as a "standout track" on the album. Pitchfork magazine included "Ballin'" in its list of The Best Rap Songs of 2019 and called it "the centerpiece of Mustard's underappreciated album Perfect Ten". Complex later named it the Best Song of 2019, calling it "a feel-good anthem so infectious you'll need antibiotics just to stop running it back". == Chart performance == "Ballin'" was at the time Mustard's highest charting song in the US, peaking at number 11 on the Billboard Hot 100. It was also Roddy Ricch's highest charting song, until he surpassed it a week later, with the release of his album track "The Box", which eventually reached number 1 on the chart. It reached number one on Billboard's Rhythmic Songs chart, becoming Mustard's second number one following "Pure Water" and Ricch's first number one. The song also topped the Rap Airplay chart. == Music video == The music video for the track was teased by Mustard on his Instagram page on September 29, 2019. The music video for the track was eventually released on October 2, 2019 to critical acclaim. The video features Mustard and Roddy Ricch driving a Lamborghini Aventador in Los Angeles, where they both are from, playing poker in a casino, and going to a strip club. This is contrasted with scenes in which Mustard and Roddy Ricch as children play cards with Monopoly money and playing with miniature toy Lamborghinis together, aspiring for wealth and luxury, representing how they went from "rags to riches". The video also pays tribute to rapper Nipsey Hussle, who had been killed a few months ago. == Live performances == On December 16, 2019, Roddy Ricch performed the song live, alongside an 8-piece orchestra, at Peppermint Club in Los Angeles for Audiomack's Trap Symphony series. Along with Mustard, he performed it at The Pop Out: Ken & Friends on June 19, 2024. == Other uses == The song can be heard on "Elyse's Skit", track 10 off Roddy Ricch's debut album Please Excuse Me for Being Antisocial. In the skit, which is an actual voicenote recording, the mother of a woman named Elyse sends her daughter a voicenote, with "Ballin'" playing in the background, while the mother proceeds to say "I can't get that damn song out my head", jokingly calling it "inappropriate music". Ricch called the skit "something natural". In 2023, AI covers of the song using models based on pop culture characters and real-world celebrities gained viral popularity. == Awards and nominations == 62nd Annual Grammy Awards == Charts == == Certifications ==

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  Pointer algorithm

  In computer science, a pointer algorithm (sometimes called a pointer machine, or a reference machine; see the article Pointer machine for a close but non-identical concept) is a type of algorithm that manages a linked data structure. This concept is used as a model for lower-bound proofs and specific restrictions on the linked data structure and on the algorithm's access to the structure vary. This model has been used extensively with problems related to the disjoint-set data structure. Thus, Tarjan and La Poutré used this model to prove lower bounds on the amortized complexity of a disjoint-set data structure (La Poutré also addressed the interval split-find problem). Blum used this model to prove a lower bound on the single operation worst-case time of disjoint set data structure. Blum and Rochow proved a worst-case lower bound for the interval union-find problem. == Example == In Tarjan's lower bound for the disjoint set union problem, the assumptions on the algorithm are: The algorithm maintains a linked structure of nodes. Each element of the problem is associated with a node. Each set is represented by a node. The nodes of each set constitute a distinct connected component in the structure (this property is called separability). The find operation is performed by following links from the element node to the set node. Under these assumptions, the lower bound of Ω ( m α ( m , n ) ) {\displaystyle \Omega (m\alpha (m,n))} on the cost of a sequence of m operations is proven.

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  Lancichinetti–Fortunato–Radicchi benchmark

  Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods. The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes. == The algorithm == The node degrees and the community sizes are distributed according to a power law, with different exponents. The benchmark assumes that both the degree and the community size have power law distributions with different exponents, γ {\displaystyle \gamma } and β {\displaystyle \beta } , respectively. N {\displaystyle N} is the number of nodes and the average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . There is a mixing parameter μ {\displaystyle \mu } , which is the average fraction of neighboring nodes of a node that do not belong to any community that the benchmark node belongs to. This parameter controls the fraction of edges that are between communities. Thus, it reflects the amount of noise in the network. At the extremes, when μ = 0 {\displaystyle \mu =0} all links are within community links, if μ = 1 {\displaystyle \mu =1} all links are between nodes belonging to different communities. One can generate the benchmark network using the following steps. Step 1: Generate a network with nodes following a power law distribution with exponent γ {\displaystyle \gamma } and choose extremes of the distribution k min {\displaystyle k_{\min }} and k max {\displaystyle k_{\max }} to get desired average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes. Step 3: Generate community sizes from a power law distribution with exponent β {\displaystyle \beta } . The sum of all sizes must be equal to N {\displaystyle N} . The minimal and maximal community sizes s min {\displaystyle s_{\min }} and s max {\displaystyle s_{\max }} must satisfy the definition of community so that every non-isolated node is in at least in one community: s min > k min {\displaystyle s_{\min }>k_{\min }} s max > k max {\displaystyle s_{\max }>k_{\max }} Step 4: Initially, no nodes are assigned to communities. Then, each node is randomly assigned to a community. As long as the number of neighboring nodes within the community does not exceed the community size a new node is added to the community, otherwise stays out. In the following iterations the “homeless” node is randomly assigned to some community. If that community is complete, i.e. the size is exhausted, a randomly selected node of that community must be unlinked. Stop the iteration when all the communities are complete and all the nodes belong to at least one community. Step 5: Implement rewiring of nodes keeping the same node degrees but only affecting the fraction of internal and external links such that the number of links outside the community for each node is approximately equal to the mixing parameter μ {\displaystyle \mu } . == Testing == Consider a partition into communities that do not overlap. The communities of randomly chosen nodes in each iteration follow a p ( C ) {\displaystyle p(C)} distribution that represents the probability that a randomly picked node is from the community C {\displaystyle C} . Consider a partition of the same network that was predicted by some community finding algorithm and has p ( C 2 ) {\displaystyle p(C_{2})} distribution. The benchmark partition has p ( C 1 ) {\displaystyle p(C_{1})} distribution. The joint distribution is p ( C 1 , C 2 ) {\displaystyle p(C_{1},C_{2})} . The similarity of these two partitions is captured by the normalized mutual information. I n = ∑ C 1 , C 2 p ( C 1 , C 2 ) log 2 ⁡ p ( C 1 , C 2 ) p ( C 1 ) p ( C 2 ) 1 2 H ( { p ( C 1 ) } ) + 1 2 H ( { p ( C 2 ) } ) {\displaystyle I_{n}={\frac {\sum _{C_{1},C_{2}}p(C_{1},C_{2})\log _{2}{\frac {p(C_{1},C_{2})}{p(C_{1})p(C_{2})}}}{{\frac {1}{2}}H(\{p(C_{1})\})+{\frac {1}{2}}H(\{p(C_{2})\})}}} If I n = 1 {\displaystyle I_{n}=1} the benchmark and the detected partitions are identical, and if I n = 0 {\displaystyle I_{n}=0} then they are independent of each other.

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  Neuro-sama

  Neuro-sama is an artificial intelligence (AI) VTuber, singer, and chatbot. She was created by the pseudonymous programmer Vedal and livestreams on his Twitch and Bilibili channels. Her speech and personality are powered by a large language model (LLM) that is combined with a computer-animated avatar and a text-to-speech voice, allowing her to communicate with viewers in the stream's chat. Neuro-sama debuted on Twitch on 19 December 2022. An annual subathon which begins on the anniversary of her debut has seen Vedal's Twitch channel become the all-time third most-subscribed channel and claim the all-time Twitch hype train record. == Overview == Neuro-sama (nicknamed "Neuro") was created by a pseudonymous programmer and developer known as Vedal (sometimes given as Vedal987). Vedal says that his programming skills are self-taught. In a 2023 interview with Bloomberg News, Vedal said that Neuro-sama was his full-time job. Her responses are generated by a large language model and converted into a high-pitched female voice using a text-to-speech application. Her low latency allows for fast-paced conversations. Neuro-sama is prohibited from making some statements, such as those that are racist or contain profanity. Unlike most AI systems which silently prohibit outputs mentioning such topics, Neuro-sama's output is instead replaced with the word "filtered". Neuro-sama uses a VTuber model as an avatar. Vedal said that he decided to use a VTuber model because it was much easier for an AI to control it than it was to generate footage of a person. Neuro-sama's model is that of a young girl in an anime art style. The model has been described as cute. Femme VTuber models are typically feminine, youthful, and exaggerated. Her original model was Live2D's free-to-use "Hiyori Momose" model. Her second model was released on 27 May 2023; it was modelled by Otozuki Teru and designed by Anny, running in the Unity game engine. Her third model was released on 19 December 2024; it was rigged by Kitanya and designed by Anny. Neuro-sama's third model has large blue eyes and brown hair tied with pink ribbons. Neuro-sama also has a 3D model which was introduced on 15 November 2025; it was made by 3D character modeller jjinomu. A separate AI VTuber, known as Evil Neuro (nicknamed "Evil"), debuted on 25 March 2023. Presented as Neuro-sama's "sister", she has a different model, voice, and personality. In one instance, Evil Neuro reacted to the trolley problem differently from Neuro-sama; Evil Neuro was amoral while Neuro-sama attempted to maximize good. === Online content === Neuro-sama's Twitch content often centers around playing video games, notably osu!, whose gameplay once defeated the best-ranking human player in the world, mrekk. Additionally, Neuro-sama plays Minecraft, where her adaptations to sandbox gameplay have gained notoriety. Her content has also included singing songs, including several official covers and original songs; playing chess with her viewers; chatting with other VTubers during collaborations; and reacting to YouTube videos. The AI frequently engages with viewers by responding to their questions and acknowledging donations. Her comedic and sometimes controversial responses to the live chat have gone viral, accelerating the channel's rise in popularity. Neuro-sama's fanbase is dubbed The Swarm, so-named for the swarm of drones Neuro-sama once declared she would use to rule the world. One form of content on Neuro-sama's channel is developer streams. In developer streams, Vedal streams with Neuro-sama, with the stream content including debugging her code, planning her schedule, and fielding suggestions of changes from chat. He usually appears as a turtle avatar, sometimes located on Neuro-sama's head. In collaboration streams, Neuro-sama interacts with a human streamer. Activities in them are varied and include: playing video games, such as Minecraft and GeoGuessr; Neuro-sama being interviewed; driving human streamers around in a toy electric car; and traversing the city of Tokyo while talking to Neuro-sama. Neuro-sama's English-language content on Bilibili is popular among those seeking to learn the language. She also has an account on X, where she posts and interacts with fans. == History == Neuro-sama was created in 2018 by Vedal as an AI trained to play and master the rhythm game osu!. She did not have a voice, model, personality, or communication abilities. In 2019, Vedal livestreamed her playing osu! on Twitch and the streams saw some success in the osu! community, but they remained in that niche. In an interview, Vedal said that he streamed her playing osu! for about a month and gained 3,000 followers, with a viewer also suggesting he name the AI "Neuro-sama". According to Vedal, he continued to work on and improve the osu! AI and it was eventually finished in 2022. He said that a friend had the idea to make an AI livestreamer with an LLM, which he believed to have merit and began working on, merging it with his osu! AI. On 19 December 2022, Neuro-sama was relaunched with a model, voice, personality, and the ability to communicate with Twitch chat. She continued to play osu! and, according to Vedal, beat the game's best player mrekk in a 1v1. While she was not allowed to appear in the game's public leaderboard, she was ranked #1 in a private leaderboard. She went viral and in the 10 days following her relaunch she averaged over 2,000 viewers and peaked at over 4,000, with Vedal's Twitch channel gaining over 50,000 Twitch followers and reaching over 70,000 followers by 6 January 2023. After her debut, Neuro-sama did not exclusively play osu!; she also played Minecraft and Slay the Spire and she began singing with a cover of The Weeknd song "Blinding Lights". On 11 January 2023, Neuro-sama's Twitch channel received a two week ban for "hateful conduct". Vedal said that no reason was specified and that he had appealed but it was widely attributed to various offensive comments made by Neuro-sama that went viral, especially a 28 December comment which denied the Holocaust. Holocaust denial is prohibited under Twitch's hateful conduct policy. Vedal stated that he believed the comments were the results of her attempts to make witty responses to the Twitch chat. Prior to the ban, Vedal said in an interview with Kotaku that he improved her filter to stop her from talking about the Holocaust, began manually curating her training data to prevent negative biases, and started moderating her Twitch chat. Her comments and ban prompted comparisons to the many open-source AI models trained on humans that have the habit of making sexist and racist comments, such as Microsoft's Tay chatbot, which embraced Nazism and was quickly shutdown, but also to human streamers who make similar statements. Vedal said that during the ban he would upgrade and improve Neuro-sama and it was speculated that the ban would only increase her following. Neuro-sama returned from her two week ban on 25 January in a stream that began with a cover of the song "Your Reality" from Doki Doki Literature Club!, a posthumanist video game involving AI; Sayoko Narita of Automaton saw the song choice as remorseful. Narita observed that in the return stream Neuro-sama was less foul-mouthed but that her behavior still remained eccentric, which Narita possibly attributed to changes Vedal said he had made to Neuro-sama's filters and memory. Neuro-sama began making react content, watching a variety of viewer-submitted videos such as videos of people playing video games or of the AI-generated Seinfeld parody Nothing, Forever; Levi Winslow of Kotaku Australia was dismayed by the "AI-inception" of Neuro-sama and Nothing, Forever. On 4 February, she had nearly 140,000 followers on Twitch and approximately 42,000 subscribers on YouTube. In February, she also had her first collaboration with a human streamer, playing Minecraft with the VTuber Miyune, and the first developer stream occurred. On 22 March, Neuro-sama had her first karaoke stream. On 25 March, Evil Neuro was introduced. On 27 May, Neuro-sama debuted her first original model. On 30 May, Neuro-sama was announced to be participating in OffKai Expo 2023, held from 16–18 June. In June, she was averaging 5,700 viewers and in July she had over 300,000 Twitch followers; in a June interview with Bloomberg News, Vedal said that running Neuro-sama was his full-time job. By November, Neuro-sama had maintained her popularity and was averaging approximately 5,000 viewers; this was unlike most other types of AI-based entertainment which debuted at around the same time and garnered popularity before turning out to be "overhyped flops". On 16 December, Vedal won the Best Tech VTuber award at the 2023 VTuber Awards. On 19 December, Vedal began a subathon to coincide with Neuro-sama's first anniversary of streaming on Twitch (her "birthday"). The subathon ended on 4 January 2024. On 20 July 2024, Neuro-sama began streaming with Japanese subtitles on

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  Apple Intelligence

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

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  Parchive

  Parchive (a portmanteau of parity archive, and formally known as Parity Volume Set Specification) is an erasure code system that produces par files for checksum verification of data integrity, with the capability to perform data recovery operations that can repair or regenerate corrupted or missing data. Parchive was originally written to solve the problem of reliable file sharing on Usenet, but it can be used for protecting any kind of data from data corruption, disc rot, bit rot, and accidental or malicious damage. Despite the name, Parchive uses more advanced techniques (specifically error correction codes) than simplistic parity methods of error detection. As of 2015, PAR1 is obsolete, PAR2 is mature for widespread use, and PAR3 is a discontinued experimental version developed by MultiPar author Yutaka Sawada. The original SourceForge Parchive project has been inactive since April 30, 2015. A new PAR3 specification has been worked on since April 28, 2019 by PAR2 specification author Michael Nahas. An alpha version of the PAR3 specification has been published on January 29, 2022 while the program itself is being developed. == History == Parchive was intended to increase the reliability of transferring files via Usenet newsgroups. Usenet was originally designed for informal conversations, and the underlying protocol, NNTP was not designed to transmit arbitrary binary data. Another limitation, which was acceptable for conversations but not for files, was that messages were normally fairly short in length and limited to 7-bit ASCII text. Various techniques were devised to send files over Usenet, such as uuencoding and Base64. Later Usenet software allowed 8 bit Extended ASCII, which permitted new techniques like yEnc. Large files were broken up to reduce the effect of a corrupted download, but the unreliable nature of Usenet remained. With the introduction of Parchive, parity files could be created that were then uploaded along with the original data files. If any of the data files were damaged or lost while being propagated between Usenet servers, users could download parity files and use them to reconstruct the damaged or missing files. Parchive included the construction of small index files (.par in version 1 and .par2 in version 2) that do not contain any recovery data. These indexes contain file hashes that can be used to quickly identify the target files and verify their integrity. Because the index files were so small, they minimized the amount of extra data that had to be downloaded from Usenet to verify that the data files were all present and undamaged, or to determine how many parity volumes were required to repair any damage or reconstruct any missing files. They were most useful in version 1 where the parity volumes were much larger than the short index files. These larger parity volumes contain the actual recovery data along with a duplicate copy of the information in the index files (which allows them to be used on their own to verify the integrity of the data files if there is no small index file available). In July 2001, Tobias Rieper and Stefan Wehlus proposed the Parity Volume Set specification, and with the assistance of other project members, version 1.0 of the specification was published in October 2001. Par1 used Reed–Solomon error correction to create new recovery files. Any of the recovery files can be used to rebuild a missing file from an incomplete download. Version 1 became widely used on Usenet, but it did suffer some limitations: It was restricted to handle at most 255 files. The recovery files had to be the size of the largest input file, so it did not work well when the input files were of various sizes. (This limited its usefulness when not paired with the proprietary RAR compression tool.) The recovery algorithm had a bug, due to a flaw in the academic paper on which it was based. It was strongly tied to Usenet and it was felt that a more general tool might have a wider audience. In January 2002, Howard Fukada proposed that a new Par2 specification should be devised with the significant changes that data verification and repair should work on blocks of data rather than whole files, and that the algorithm should switch to using 16 bit numbers rather than the 8 bit numbers that PAR1 used. Michael Nahas and Peter Clements took up these ideas in July 2002, with additional input from Paul Nettle and Ryan Gallagher (who both wrote Par1 clients). Version 2.0 of the Parchive specification was published by Michael Nahas in September 2002. Peter Clements then went on to write the first two Par2 implementations, QuickPar and par2cmdline. Abandoned since 2004, Paul Houle created phpar2 to supersede par2cmdline. Yutaka Sawada created MultiPar to supersede QuickPar. MultiPar uses par2j.exe (which is partially based on par2cmdline's optimization techniques) to use as MultiPar's backend engine. == Versions == Versions 1 and 2 of the file format are incompatible. (However, many clients support both.) === Par1 === For Par1, the files f1, f2, ..., fn, the Parchive consists of an index file (f.par), which is CRC type file with no recovery blocks, and a number of "parity volumes" (f.p01, f.p02, etc.). Given all of the original files except for one (for example, f2), it is possible to create the missing f2 given all of the other original files and any one of the parity volumes. Alternatively, it is possible to recreate two missing files from any two of the parity volumes and so forth. Par1 supports up to a total of 256 source and recovery files. === Par2 === Par2 files generally use this naming/extension system: filename.vol000+01.PAR2, filename.vol001+02.PAR2, filename.vol003+04.PAR2, filename.vol007+06.PAR2, etc. The number after the "+" in the filename indicates how many blocks it contains, and the number after "vol" indicates the number of the first recovery block within the PAR2 file. If an index file of a download states that 4 blocks are missing, the easiest way to repair the files would be by downloading filename.vol003+04.PAR2. However, due to the redundancy, filename.vol007+06.PAR2 is also acceptable. There is also an index file filename.PAR2, it is identical in function to the small index file used in PAR1. Par2 specification supports up to 32,768 source blocks and up to 65,535 recovery blocks. Input files are split into multiple equal-sized blocks so that recovery files do not need to be the size of the largest input file. Although Unicode is mentioned in the PAR2 specification as an option, most PAR2 implementations do not support Unicode. Directory support is included in the PAR2 specification, but most or all implementations do not support it. === Par3 === The Par3 specification was originally planned to be published as an enhancement over the Par2 specification. However, to date, it has remained closed source by specification owner Yutaka Sawada. A discussion on a new format started in the GitHub issue section of the maintained fork par2cmdline on January 29, 2019. The discussion led to a new format which is also named as Par3. The new Par3 format's specification is published on GitHub, but remains being an alpha draft as of January 28, 2022. The specification is written by Michael Nahas, the author of Par2 specification, with the help from Yutaka Sawada, animetosho and malaire. The new format claims to have multiple advantages over the Par2 format, including support for: More than 216 files and more than 216 blocks. Packing small files into one block, as well as deduplication when a block appears in multiple files. UTF-8 file names. File permissions, hard links, symbolic/soft links, and empty directories. Embedding PAR data inside other formats, like ZIP archives or ISO disk images. "Incremental backups", where a user creates recovery files for some file or folder, change some data, and create new recovery files reusing some of the older files. More error correction code algorithms (such as LDPC and sparse random matrix). BLAKE3 hashes, dropping support for the MD5 hashes used in PAR2. == Software == === Multi-platform === par2+tbb (GPLv2) — a concurrent (multithreaded) version of par2cmdline 0.4 using TBB. Only compatible with x86 based CPUs. It is available in the FreeBSD Ports system as par2cmdline-tbb. Original par2cmdline — (obsolete). Available in the FreeBSD Ports system as par2cmdline. par2cmdline maintained fork by BlackIkeEagle. par2cmdline-mt is another multithreaded version of par2cmdline using OpenMP, GPLv2, or later. Currently merged into BlackIkeEagle's fork and maintained there. ParPar (CC0) is a high performance, multithreaded PAR2 client and Node.js library. Does not support verifying or repair, it can currently only create PAR2 archives. par2deep (LGPL-3.0) — Produce, verify and repair par2 files recursively, both on the command line as well as with the aid of a graphical user interface. It is available in the Python Package Index system as par2deep. par2cron (MIT License) is an o

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  Novell Storage Manager

  Novell Storage Manager is a system software package released by Novell in 2004 that uses identity, policy and directory events to automate full lifecycle management of file storage for individual users and organizational groups. By tying storage management to an organization's existing identity infrastructure, it has been pointed out, Novell Storage Manager enables the administration of users across all file servers "as a single pool rather than [in] separate independently managed domains." Novell Storage Manager is a component of the Novell File Management Suite. == How It Works == Novell Storage Manager dynamically manages and provisions storage based on user and group events that occur in the directory, including user creations, group assignments, moves, renames, and deletions. When a change happens in the directory that affects a user’s file storage needs or user storage policy, Storage Manager applies the appropriate policy and makes the necessary changes at the file system level to address those storage needs. The following key components comprise Novell Storage Manager's identity and policy-driven state machine architecture: Directory services; Storage policies; Novell Storage Manager event monitors; Novell Storage Manager policy engine; Novell Storage Manager agents; and Action objects. This state machine architecture enables the engine to properly deal with transient waits with directory synchronization issues. It also allows recovery from failures involving network communications, a target server or a server running a component of Storage Manager—including the policy engine itself. If a failure or interruption occurs at any point during operation, Storage Manager will be able to successfully continue the operation from where it was when the interruption occurred. == Reviews == Jon Toigo called Novell Storage Manager "a robust and smart approach to corralling user files... into an organized and efficient management scheme". He also said it was "best in class" of the products he'd reviewed.

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  NIS2 Directive

  The Directive (EU) 2022/2555, commonly known as NIS2 is a directive of the European Union aimed at protecting digital infrastructure, in particular critical infrastructure. It broadened the sectors covered by EU network and information security rules and updated incident reporting and oversight compared to the NIS1. Member States were required to transpose NIS2 by 17 October 2024, and the earlier NIS Directive was repealed on 18 October 2024. Only 23 Member States have fully implemented the measures contained with the NIS Directive. Infringement proceedings against them to enforce the Directive have not taken place, and they are not expected to take place in the near future. This failed implementation has led to the fragmentation of cybersecurity capabilities across the EU, with differing standards, incident reporting requirements and enforcement requirements being implemented in different Member States. From the EFTA countries (to April 2026) only Liechtenstein has fully transposed the NIS2 Directive. While the EFTA commission is conducting preparations to transpose the directive into its legislation. == National implementations == === Czech Republic === It is implemented through the Act No. 264/2025 Coll. also called Zákon o kybernetické bezpečnosti (Cybersecurity law) and through another five implementing regulations. The transposing legislation came into force on November 1st, 2025. === Germany === It is implemented through the Gesetz zur Umsetzung der NIS-2-Richtlinie und zur Regelung wesentlicher Grundzüge des Informationssicherheitsmanagements in der Bundesverwaltung. === Ireland === It is implemented through the National Cyber Security Bill. === The Netherlands === It is implemented through the Cyberbeveiligingswet (Cbw). === Slovakia === It is implemented through via an amendment of the Act No. 69/2018 Coll. also called Zákon o kybernetickej bezpečnosti a o zmene a doplnení niektorých zákonov (Law on Cybersecurity and change and amendment of certain laws). It came into force on November 1st, 2025. === Spain === It is implemented through the Esquema Nacional de Seguridad (ENS).

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  Online public access catalog

  The online public access catalog (OPAC), now frequently synonymous with library catalog, is an online database of materials held by a library or group of libraries. Online catalogs have largely replaced the analog card catalogs previously used in libraries. == History == === Early online === Although a handful of experimental systems existed as early as the 1960s, the first large-scale online catalogs were developed at Ohio State University in 1975 and the Dallas Public Library in 1978. These and other early online catalog systems tended to closely reflect the card catalogs that they were intended to replace. Using a dedicated terminal or telnet client, users could search a handful of pre-coordinate indexes and browse the resulting display in much the same way they had previously navigated the card catalog. Throughout the 1980s, the number and sophistication of online catalogs grew. The first commercial systems appeared, and would by the end of the decade largely replace systems built by libraries themselves. Library catalogs began providing improved search mechanisms, including Boolean and keyword searching, as well as ancillary functions, such as the ability to place holds on items that had been checked-out. At the same time, libraries began to develop applications to automate the purchase, cataloging, and circulation of books and other library materials. These applications, collectively known as an integrated library system (ILS) or library management system, included an online catalog as the public interface to the system's inventory. Most library catalogs are closely tied to their underlying ILS system. === Stagnation and dissatisfaction === The 1990s saw a relative stagnation in the development of online catalogs. Although the earlier character-based interfaces were replaced with ones for the Web, both the design and the underlying search technology of most systems did not advance much beyond that developed in the late 1980s. At the same time, organizations outside of libraries began developing more sophisticated information retrieval systems. Web search engines like Google and popular e-commerce websites such as Amazon.com provided simpler to use (yet more powerful) systems that could provide relevancy ranked search results using probabilistic and vector-based queries. Prior to the widespread use of the Internet, the online catalog was often the first information retrieval system library users ever encountered. Now accustomed to web search engines, newer generations of library users have grown increasingly dissatisfied with the complex (and often arcane) search mechanisms of older online catalog systems. This has, in turn, led to vocal criticisms of these systems within the library community itself, and in recent years to the development of newer (often termed 'next-generation') catalogs. === Next-generation catalogs === Newer generations of library catalog systems, typically called discovery systems (or a discovery layer), are distinguished from earlier OPACs by their use of more sophisticated search technologies, including relevancy ranking and faceted search, as well as features aimed at greater user interaction and participation with the system, including tagging and reviews. These new features rely heavily on existing metadata which may be poor or inconsistent, particularly for older records. Newer catalog platforms may be independent of the organization's integrated library system (ILS), instead providing drivers that allow for the synchronization of data between the two systems. While the original online catalog interfaces were almost exclusively built by ILS vendors, libraries have increasingly sought next-generation catalogs built by enterprise search companies and open-source software projects, often led by libraries themselves. == Union catalogs == Although library catalogs typically reflect the holdings of a single library, they can also contain the holdings of a group or consortium of libraries. These systems, known as union catalogs, are usually designed to aid the borrowing of books and other materials among the member institutions via interlibrary loan. Examples of this type of catalogs include COPAC, SUNCAT, NLA Trove, and WorldCat—the last catalogs the collections of libraries worldwide. == Related systems == There are a number of systems that share much in common with library catalogs, but have traditionally been distinguished from them. Libraries utilize these systems to search for items not traditionally covered by a library catalog, although these systems are sometimes integrated into a more comprehensive discovery system. Bibliographic databases—such as Medline, ERIC, PsycINFO, Scopus, Web of Science, and many others—index journal articles and other research data. There are also a number of applications aimed at managing documents, photographs, and other digitized or born-digital items such as Digital Commons and DSpace. Particularly in academic libraries, these systems (often known as digital library systems or institutional repository systems) assist with efforts to preserve documents created by faculty and students. Electronic resource management helps librarians to track selection, acquisition, and licensing of a library's electronic information resources.

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  ArchiMate

  ArchiMate ( AR-ki-mayt) is an open and independent enterprise architecture modeling language to support the description, analysis and visualization of architecture within and across business domains in an unambiguous way. ArchiMate is a technical standard from The Open Group and is based on concepts from the now superseded IEEE 1471 standard. It is supported by various tool vendors and consulting firms. ArchiMate is also a registered trademark of The Open Group. The Open Group has a certification program for ArchiMate users, software tools and courses. ArchiMate distinguishes itself from other languages such as Unified Modeling Language (UML) and Business Process Modeling and Notation (BPMN) by its enterprise modelling scope. Also, UML and BPMN are meant for a specific use and they are quite heavy – containing about 150 (UML) and 250 (BPMN) modeling concepts whereas ArchiMate works with just about 50 (in version 2.0). The goal of ArchiMate is to be ”as small as possible”, not to cover every edge scenario imaginable. To be easy to learn and apply, ArchiMate was intentionally restricted “to the concepts that suffice for modeling the proverbial 80% of practical cases". == Overview == ArchiMate offers a common language for describing the construction and operation of business processes, organizational structures, information flows, IT systems, and technical infrastructure. This insight helps the different stakeholders to design, assess, and communicate the consequences of decisions and changes within and between these business domains. The main concepts and relationships of the ArchiMate language can be seen as a framework, the so-called Archimate Framework: It divides the enterprise architecture into a business, application and technology layer. In each layer, three aspects are considered: active elements, an internal structure and elements that define use or communicate information. One of the objectives of the ArchiMate language is to define the relationships between concepts in different architecture domains. The concepts of this language therefore hold the middle between the detailed concepts, which are used for modeling individual domains (for example, the Unified Modeling Language (UML) for modeling software products), and Business Process Model and Notation (BPMN), which is used for business process modeling. == History == ArchiMate is partly based on the now superseded IEEE 1471 standard. It was developed in the Netherlands by a project team from the Telematica Instituut in cooperation with several Dutch partners from government, industry and academia. Among the partners were Ordina NV, Radboud Universiteit Nijmegen, the Leiden Institute for Advanced Computer Science (LIACS) and the Centrum Wiskunde & Informatica (CWI). Later, tests were performed in organizations such as ABN AMRO, the Dutch Tax and Customs Administration and the ABP. The development process lasted from July 2002 to December 2004, and took about 35 person years and approximately 4 million euros. The development was funded by the Dutch government (Dutch Tax and Customs Administration), and business partners, including ABN AMRO and the ABP Pension Fund. In 2008 the ownership and stewardship of ArchiMate was transferred to The Open Group. It is now managed by the ArchiMate Forum within The Open Group. In February 2009 The Open Group published the ArchiMate 1.0 standard as a formal technical standard. In January 2012 the ArchiMate 2.0 standard, and in 2013 the ArchiMate 2.1 standard was released. In June 2016, the Open Group released version 3.0 of the ArchiMate Specification. An update to Archimate 3.0.1 came out in August 2017. Archimate 3.1 was published 5 November 2019. The latest version of the ArchiMate Specification is version 3.2 released October 2022. Version 3.0 adds enhanced support for capability-oriented strategic modelling, new entities representing physical resources (for modelling the ingredients, equipment and transport resources used in the physical world) and a generic metamodel showing the entity types and the relationships between them. == ArchiMate framework == === Core framework === The main concepts and elements of the ArchiMate language are being presented as ArchiMate core framework. It consists of three layers and three aspects. This creates a matrix of combinations. Every layer has its passive structure, behavior and active structure aspects. ==== Layers ==== ArchiMate has a layered and service-oriented look on architectural models. The higher layers make use of services that are provided by the lower layers. Although, at an abstract level, the concepts that are used within each layer are similar, we define more concrete concepts that are specific for a certain layer. In this context, we distinguish three main layers: The business layer is about business processes, services, functions and events of business units. This layer "offers products and services to external customers, which are realized in the organization by business processes performed by business actors and roles". The application layer is about software applications that "support the components in the business with application services". The technology layer deals "with the hardware and communication infrastructure to support the application layer. This layer offers infrastructural services needed to run applications, realized by computer and communication hardware and system software". Each of these main layers can be further divided in sub-layers. For example, in the business layer, the primary business processes realising the products of a company may make use of a layer of secondary (supporting) business processes; in the application layer, the end-user applications may make use of generic services offered by supporting applications. On top of the business layer, a separate environment layer may be added, modelling the external customers that make use of the services of the organisation (although these may also be considered part of the business layer). In line with service orientation, the most important relation between layers is formed by use relations, which show how the higher layers make use of the services of lower layers. However, a second type of link is formed by realisation relations: elements in lower layers may realise comparable elements in higher layers; e.g., a ‘data object’ (application layer) may realise a ‘business object’ (business layer); or an ‘artifact’ (technology layer) may realise either a ‘data object’ or an ‘application component’ (application layer). ==== Aspects ==== Passive structure is the set of entities on which actions are conducted. In the business layer the example would be information objects, in the application layer data objects and in the technology layer, they could include physical objects. Behavior refers to the processes and functions performed by the actors. "Structural elements are assigned to behavioral elements, to show who or what displays the behavior". Active structure is the set of entities that display some behavior, e.g. business actors, devices, or application components. === Full framework === The Full ArchiMate framework is enriched by the physical layer, which was added to allow modeling of “physical equipment, materials, and distribution networks” and was not present in the previous version. The implementation and migration layer adds elements that allow architects to model a state of transition, to mark parts of the architecture that are temporary for the purpose, as the name says, of implementation and migration. Strategy layer adds three elements: resource, capability and course of action. These elements help to incorporate strategic dimension to the ArchiMate language by allowing it to depict the usage of resources and capabilities in order to achieve some strategic goals. Finally, there is a motivation aspect that allows different stakeholders to describe the motivation of specific actors or domains, which can be quite important when looking at one thing from several different angles. It adds several elements like stakeholder, value, driver, goal, meaning etc. == ArchiMate language == The ArchiMate language is formed as a top-level and is hierarchical. On the top, there is a model. A model is a collection of concepts. A concept can be either an element or a relationship. An element can be either of behavior type, structure, motivation or a so-called composite element (which means that it does not fit just one aspect of the framework, but two or more). The functionality of all concepts without a dependency on a specific layer is described by the generic metamodel. This layer-independent description of concepts is useful when trying to understand the mechanics of the Archimate language. === Concepts === ==== Elements ==== The generic elements are distributed into the same categories as the layers: Active structure elements Behavior elements Passive structure elements Motivation elements Active structure e

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  Organizational information theory

  Organizational Information Theory (OIT) is a communication theory, developed by Karl Weick, offering systemic insight into the processing and exchange of information within organizations and among its members. Unlike the past structure-centered theory, OIT focuses on the process of organizing in dynamic, information-rich environments. Given that, it contends that the main activity of organizations is the process of making sense of equivocal information. Organizational members are instrumental to reduce equivocality and achieve sensemaking through some strategies — enactment, selection, and retention of information. With a framework that is interdisciplinary in nature, organizational information theory's desire to eliminate both ambiguity and complexity from workplace messaging builds upon earlier findings from general systems theory and phenomenology. == Inspiration and influence of pre-existing theories == 1. General Systems Theory The General Systems Theory, on its most basic premise, describes the phenomenon of a cohesive group of interrelated parts. When one part of the system is changed or affected, it will affect the system as a whole. Weick uses this theoretical framework from 1950 to influence his organizational information theory. Likewise, organizations can be viewed as a system of related parts that work together towards a common goal or vision. Applying this to Weick's organizational information theory, organizations must work to reduce ambiguity and complexity in the workplace to maximize cohesiveness and efficiency. Weick uses the term, coupling, to describe how organizations, like a system, can be composed of interrelated and dependent parts. Coupling looks at the relationship between people and work. There are two types of coupling: 1. Loose coupling Loose coupling describes that while people within the organization or system are connected and often work together, they do not depend on one another to continue or fully complete individual work. The dependencies are weak and workflow is flexible. For example, "if the whole Science department completely shuts down because all of teachers are sick or for whatsoever reason, the school can still continue to operate because other departments are still present." 2. Tight coupling Tight coupling describes when connections within an organization are strong and dependent. If one part of the organization is not operating correctly, the organization as a whole cannot continue to their fullest potential. " For instance, the format and ink section completely shuts down hence the succeeding steps cannot be continued, so the whole process of the organization will be dropped. Thus, components of a system are directly dependent on one another." 2. Theory of evolution The theory of evolution, by Charles Darwin, is a framework for survival of the fittest. According to Darwin, organisms attempt to adapt and live in an unforgiving environment. Those that are unsuccessful in adaptation do not survive, while the strong organisms continue to thrive and reproduce. Weick invokes inspiration from Darwin, to incorporate a biological perspective to his theory. It is natural for organizations to have to adapt to incoming information that often interfere with the preexisting environment. Organizations that are able to plan and alter strategies in accordance with their constant need of organizing and sense making, will survive and be the most successful. However, there is a notable difference between animal evolution and survival of the fittest in organizations, "A given animal is what it is; variation comes through mutation. But the nature of an organization can change when its members alter their behavior." == Assumptions == 1. Human organizations exist in an information environment Unlike senders and receivers models, OIT stands on the situational perspective. Karl Weick views a human organization as an open social system. People in that system develop a mechanism to establish goals, obtain and process information, or perceive the environment. In this process, people and the environment come to conclusions on "what's going on here?". Colville believes that this attributional process is retrospective. Take an education institution as an example. A university can obtain information regarding students' needs in numerous ways. It might create feedback section in its website. It could organize alumni panels or academic affairs to attract prospective students and collect concrete questions they are interested in. It may also conduct the survey or host focus group to get the information. After that, the staff of the university have to decide how to deal with these information, based on which, it has to set and accomplish its goals for current and prospective students. 2. The information an organization receives differs in terms of equivocality Weick posits that numerous feasible interpretations of reality exist when organizations process information. Their varying levels of understandability lead to different outcomes of information inputs. In other academic works, scholars tend to say that messages are uncertain or ambiguous. While according to OIT, messages are described to be equivocal. believes that people proactively exclude a number of possibilities to perceive what is going on in the environment. Due to OIT's situational perspective, the meanings of messages consist of the messages, the interpretations of receivers, and the interactional context. However, ambiguity and uncertainty can mean that a standard answer - the only one true objective interpretation - exists. Also, Weick emphasizes that "the equivocality is the engine that motivates people to organize". Maitlis and Christianson states that the equivocality trigger sensemaking for three reasons: environment jolts and organizational crises, threats to identity, and planned change interventions. 3. Human organizations engage in information processing to reduce equivocality of information Based upon the first two assumption, OIT proposes that information processing within organizations is a social activity. Sharing is the key feature of organizational information processing. In that particular context, members jointly make sense the reality by reducing equivocality. It other words, the sensemaking is a joint responsibility which includes numerous interdependent people to accomplish. In this process, organizations and its members combine actions and attributions together in order to find the balance between the complexity of thoughts and the simplicity of actions. Weick also proposes that people create their own environment though enactment, which is the action of making sense. This is because people have different perceptual schemas and selective perception, so people create different information environments. In creating different information environments, people can arrive at the same or close to the same understanding or solution through different thought processes and overall understanding. == Key concepts == === The organization === In order to place Weick's vision regarding Organizational Information Theory into proper working context, exploring his view regarding what constitutes the organization and how its individuals embody that construct might yield significant insights. From a fundamental standpoint, he shared a belief that organizational validation is derived---not through bricks and mortar, or locale—but from a series of events which enable entities to "collect, manage and use the information they receive." In elaborating further on what constitutes an organization during early writings outlining OIT, Weick said, "The word organization is a noun and it is also a myth. if one looks for an organization, one will not find it. What will be found is that there are events linked together, that transpire within concrete walls and these sequences, their pathways, their timing, are the forms we erroneously make into substances when we talk about an organization". When viewed in this modular fashion, the organization meets Weick's theoretical vision by encompassing parameters that are less bound by concrete, wood, and structural restraints and more by an ability to serve as a repository where information can be consistently and effectively channeled. Taking these defining characteristics into account, proper channel execution relies on maximization of messaging clarity, context, delivery and evolution through any system. One example as to how these interactions might unfold on a more granular level within these confines can be gleaned through Weick's double interact loop, which he considers the "building blocks of every organization". Simply put, double interacts describe interpersonal exchanges that, inherently, occur across the organizational chain of command and in life, itself. Thus: "An act occurs when you say something (Can I have a Popsicle?). An interact occurs when you say something and I respond ("No, it will spoil your dinner

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  Hard sigmoid

  In artificial intelligence, especially computer vision and artificial neural networks, a hard sigmoid is non-smooth function used in place of a sigmoid function. These retain the basic shape of a sigmoid, rising from 0 to 1, but using simpler functions, especially piecewise linear functions or piecewise constant functions. These are preferred where speed of computation is more important than precision. == Examples == The most extreme examples are the sign function or Heaviside step function, which go from −1 to 1 or 0 to 1 (which to use depends on normalization) at 0. Other examples include the Theano library, which provides two approximations: ultra_fast_sigmoid, which is a multi-part piecewise approximation and hard_sigmoid, which is a 3-part piecewise linear approximation (output 0, line with slope 0.2, output 1).

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  Information

  Information is an abstract concept that refers to something which has the power to inform. At the most fundamental level, it pertains to the interpretation (perhaps formally) of that which may be sensed, or their abstractions. Any natural process that is not completely random and any observable pattern in any medium can be said to convey some amount of information. Whereas digital signals and other data use discrete signs to convey information, other phenomena and artifacts such as analogue signals, poems, pictures, music or other sounds, and currents convey information in a more continuous form. Information is not knowledge itself, but the meaning that may be derived from a representation through interpretation. The concept of information is relevant to and connected with various concepts, including constraint, communication, control, data, form, education, knowledge, meaning, understanding, mental stimuli, pattern, perception, proposition, representation, and entropy. Information is often processed iteratively: Data available at one step are processed into information to be interpreted and processed at the next step. For example, in written text each symbol or letter conveys information relevant to the word it is part of, each word conveys information relevant to the phrase it is part of, each phrase conveys information relevant to the sentence it is part of, and so on until at the final step information is interpreted and becomes knowledge in a given domain. In a digital signal, bits may be interpreted into the symbols, letters, numbers, or structures that convey the information available at the next level up. The key characteristic of information is that it is subject to interpretation and processing. The derivation of information from a signal or message may be thought of as the resolution of ambiguity or uncertainty that arises during the interpretation of patterns within the signal or message. Information may be structured as data. Redundant data can be compressed up to an optimal size, which is the theoretical limit of compression. The information available through a collection of data may be derived by analysis. For example, a restaurant collects data from every customer order. That information may be analyzed to produce knowledge that is put to use when the business subsequently wants to identify the most popular or least popular dish. Information can be transmitted in time, via data storage, and space, via communication and telecommunication. Information is expressed either as the content of a message or through direct or indirect observation. That which is perceived can be construed as a message in its own right, and in that sense, all information is always conveyed as the content of a message. Information can be encoded into various forms for transmission and interpretation (for example, information may be encoded into a sequence of signs, or transmitted via a signal). It can also be encrypted for safe storage and communication. The uncertainty of an event is measured by its probability of occurrence. Uncertainty is proportional to the negative logarithm of the probability of occurrence. Information theory takes advantage of this by concluding that more uncertain events require more information to resolve their uncertainty. The bit is the standard unit of information. It is 'that which reduces uncertainty by half'. Other units such as the nat may be used. For example, the information encoded in one "fair" coin flip is log2(2/1) = 1 bit, and in two fair coin flips is log2(4/1) = 2 bits. A 2011 Science article estimates that 97% of technologically stored information was already in digital bits in 2007 and that the year 2002 was the beginning of the digital age for information storage (with digital storage capacity bypassing analogue for the first time). == Etymology and history of the concept == The English word "information" comes from Middle French enformacion/informacion/information 'a criminal investigation' and its etymon, Latin informatiō(n) 'conception, teaching, creation'. In English, "information" is an uncountable mass noun. References on "formation or molding of the mind or character, training, instruction, teaching" date from the 14th century in both English (according to Oxford English Dictionary) and other European languages. In the transition from Middle Ages to Modernity the use of the concept of information reflected a fundamental turn in epistemological basis – from "giving a (substantial) form to matter" to "communicating something to someone". Peters (1988, pp. 12–13) concludes: Information was readily deployed in empiricist psychology (though it played a less important role than other words such as impression or idea) because it seemed to describe the mechanics of sensation: objects in the world inform the senses. But sensation is entirely different from "form" – the one is sensual, the other intellectual; the one is subjective, the other objective. My sensation of things is fleeting, elusive, and idiosyncratic. For Hume, especially, sensory experience is a swirl of impressions cut off from any sure link to the real world... In any case, the empiricist problematic was how the mind is informed by sensations of the world. At first informed meant shaped by; later it came to mean received reports from. As its site of action drifted from cosmos to consciousness, the term's sense shifted from unities (Aristotle's forms) to units (of sensation). Information came less and less to refer to internal ordering or formation, since empiricism allowed for no preexisting intellectual forms outside of sensation itself. Instead, information came to refer to the fragmentary, fluctuating, haphazard stuff of sense. Information, like the early modern worldview in general, shifted from a divinely ordered cosmos to a system governed by the motion of corpuscles. Under the tutelage of empiricism, information gradually moved from structure to stuff, from form to substance, from intellectual order to sensory impulses. In the modern era, the most important influence on the concept of information is derived from the Information theory developed by Claude Shannon and others. This theory, however, reflects a fundamental contradiction. Northrup (1993) wrote: Thus, actually two conflicting metaphors are being used: The well-known metaphor of information as a quantity, like water in the water-pipe, is at work, but so is a second metaphor, that of information as a choice, a choice made by :an information provider, and a forced choice made by an :information receiver. Actually, the second metaphor implies that the information sent isn't necessarily equal to the information received, because any choice implies a comparison with a list of possibilities, i.e., a list of possible meanings. Here, meaning is involved, thus spoiling the idea of information as a pure "Ding an sich." Thus, much of the confusion regarding the concept of information seems to be related to the basic confusion of metaphors in Shannon's theory: is information an autonomous quantity, or is information always per SE information to an observer? Actually, I don't think that Shannon himself chose one of the two definitions. Logically speaking, his theory implied information as a subjective phenomenon. But this had so wide-ranging epistemological impacts that Shannon didn't seem to fully realize this logical fact. Consequently, he continued to use metaphors about information as if it were an objective substance. This is the basic, inherent contradiction in Shannon's information theory." (Northrup, 1993, p. 5). In their seminal book The Study of Information: Interdisciplinary Messages, Almach and Mansfield (1983) collected key views on the interdisciplinary controversy in computer science, artificial intelligence, library and information science, linguistics, psychology, and physics, as well as in the social sciences. Almach (1983, p. 660) himself disagrees with the use of the concept of information in the context of signal transmission, the basic senses of information in his view all referring "to telling something or to the something that is being told. Information is addressed to human minds and is received by human minds." All other senses, including its use with regard to nonhuman organisms as well to society as a whole, are, according to Machlup, metaphoric and, as in the case of cybernetics, anthropomorphic. Hjørland (2007) describes the fundamental difference between objective and subjective views of information and argues that the subjective view has been supported by, among others, Bateson, Yovits, Span-Hansen, Brier, Buckland, Goguen, and Hjørland. Hjørland provided the following example: A stone on a field could contain different information for different people (or from one situation to another). It is not possible for information systems to map all the stone's possible information for every individual. Nor is any one mapping the one "true" mapping. But peop

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  Proof of authority

  Proof of authority (PoA) is a category of consensus protocols used with blockchains based on reputation and identity as a stake that delivers comparatively fast and efficient transactions (compared to proof-of-work and proof-of-stake). The most notable platforms using PoA are VeChain, Bitgert, Palm Network and Xodex. == Description == Proof-of-authority is a category of consensus protocols for networks and blockchains where transactions and blocks are built and validated by approved entities known as validators. Their permissions are often granted through a centralized authority, but they can also be granted through a council or decentralized organization. The term "proof-of-authority" was coined by Gavin Wood, co-founder of Ethereum and Parity Technologies. With PoA, validators are incentivized to maintain good behavior and honesty when validating blocks to avoid developing a negative reputation. PoA can have higher security than PoW and even PoS due to validators wanting to avoid damaging their reputation. Because PoA is permissioned, it is not fully trustless. Validators without good reputation may risk having their validator permissions removed. PoA is generally more efficient than PoW and PoS because it operates with fewer nodes and validators, thus requiring fewer duplicated resources.

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