AI Email Letter Generator

AI Email Letter Generator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Artificial intelligence arms race

  A military artificial intelligence arms race is a technological, economic, and military competition between two or more states to develop and deploy advanced AI technologies and lethal autonomous weapons systems (LAWS). The goal is to gain a strategic or tactical advantage over rivals, similar to previous arms races involving nuclear or conventional military technologies. Since the mid-2010s, many analysts have noted the emergence of such an arms race between superpowers for better AI technology and military AI, driven by increasing geopolitical and military tensions. An AI arms race is sometimes placed in the context of an AI Cold War between the United States and China. Several influential figures and publications have emphasized that whoever develops artificial general intelligence (AGI) first could dominate global affairs in the 21st century. Russian President Vladimir Putin stated that the leader in AI will "rule the world." Researchers and experts, such as Leopold Aschenbrenner and Adrian Pecotic respectively, warn that the AGI race between major powers like the U.S. and China could reshape geopolitical power. This includes AI for surveillance, autonomous weapons, decision-making systems, cyber operations, and more. == Terminology == Lethal autonomous weapons systems use artificial intelligence to identify and kill human targets without human intervention. LAWS have colloquially been called "slaughterbots" or "killer robots". Broadly, any competition for superior AI is sometimes framed as an "arms race". Advantages in military AI overlap with advantages in other sectors, as countries pursue both economic and military advantages, as per previous arms races throughout history. == History == In 2014, AI specialist Steve Omohundro warned that "An autonomous weapons arms race is already taking place". According to Siemens, worldwide military spending on robotics was US$5.1 billion in 2010 and US$7.5 billion in 2015. China became a top player in artificial intelligence research in the 2010s. According to the Financial Times, in 2016, for the first time, China published more AI research papers than the entire European Union. When restricted to number of AI papers in the top 5% of cited papers, China overtook the United States in 2016 but lagged behind the European Union. 23% of the researchers presenting at the 2017 American Association for the Advancement of Artificial Intelligence (AAAI) conference were Chinese. Eric Schmidt, the former chairman and chief executive officer of Alphabet, has predicted China will be the leading country in AI by 2025. == Risks == One risk concerns the AI race itself, whether or not the race is won by any one group. There are strong incentives for development teams to cut corners with regard to the safety of the system, increasing the risk of critical failures and unintended consequences. This is in part due to the perceived advantage of being the first to develop advanced AI technology. One team appearing to be on the brink of a breakthrough can encourage other teams to take shortcuts, ignore precautions and deploy a system that is less ready. Some argue that using "race" terminology at all in this context can exacerbate this effect. Another potential danger of an AI arms race is the possibility of losing control of the AI systems; the risk is compounded in the case of a race to artificial general intelligence, which may present an existential risk. In 2023, a United States Air Force official reportedly said that during a computer test, a simulated AI drone killed the human character operating it. The USAF later said the official had misspoken and that it never conducted such simulations. A third risk of an AI arms race is whether or not the race is actually won by one group. The concern is regarding the consolidation of power and technological advantage in the hands of one group. A US government report argued that "AI-enabled capabilities could be used to threaten critical infrastructure, amplify disinformation campaigns, and wage war":1, and that "global stability and nuclear deterrence could be undermined".:11 == By nation == === United States === In 2014, former Secretary of Defense Chuck Hagel posited the "Third Offset Strategy" that rapid advances in artificial intelligence will define the next generation of warfare. According to data science and analytics firm Govini, the U.S. Department of Defense (DoD) increased investment in artificial intelligence, big data and cloud computing from $5.6 billion in 2011 to $7.4 billion in 2016. However, the civilian NSF budget for AI saw no increase in 2017. Japan Times reported in 2018 that the United States private investment is around $70 billion per year. The November 2019 'Interim Report' of the United States' National Security Commission on Artificial Intelligence confirmed that AI is critical to US technological military superiority. The U.S. has many military AI combat programs, such as the Sea Hunter autonomous warship, which is designed to operate for extended periods at sea without a single crew member, and to even guide itself in and out of port. From 2017, a temporary US Department of Defense directive requires a human operator to be kept in the loop when it comes to the taking of human life by autonomous weapons systems. On October 31, 2019, the United States Department of Defense's Defense Innovation Board published the draft of a report recommending principles for the ethical use of artificial intelligence by the Department of Defense that would ensure a human operator would always be able to look into the 'black box' and understand the kill-chain process. However, a major concern is how the report will be implemented. The Joint Artificial Intelligence Center (JAIC) (pronounced "jake") is an American organization on exploring the usage of AI (particularly edge computing), Network of Networks, and AI-enhanced communication, for use in actual combat. It is a subdivision of the United States Armed Forces and was created in June 2018. The organization's stated objective is to "transform the US Department of Defense by accelerating the delivery and adoption of AI to achieve mission impact at scale. The goal is to use AI to solve large and complex problem sets that span multiple combat systems; then, ensure the combat Systems and Components have real-time access to ever-improving libraries of data sets and tools." In 2023, Microsoft pitched the DoD to use DALL-E models to train its battlefield management system. OpenAI, the developer of DALL-E, removed the blanket ban on military and warfare use from its usage policies in January 2024. The Biden administration imposed restrictions on the export of advanced NVIDIA chips and GPUs to China in an effort to limit China's progress in artificial intelligence and high-performance computing. The policy aimed to prevent the use of cutting-edge U.S. technology in military or surveillance applications and to maintain a strategic advantage in the global AI race. In 2025, under the second Trump administration, the United States began a broad deregulation campaign aimed at accelerating growth in sectors critical to artificial intelligence, including nuclear energy, infrastructure, and high-performance computing. The goal was to remove regulatory barriers and attract private investment to boost domestic AI capabilities. This included easing restrictions on data usage, speeding up approvals for AI-related infrastructure projects, and incentivizing innovation in cloud computing and semiconductors. Companies like NVIDIA, Oracle, and Cisco played a central role in these efforts, expanding their AI research, data center capacity, and partnerships to help position the U.S. as a global leader in AI development. ==== Project Maven ==== Project Maven is a Pentagon project involving using machine learning and engineering talent to distinguish people and objects in drone videos, apparently giving the government real-time battlefield command and control, and the ability to track, tag and spy on targets without human involvement. Initially the effort was led by Robert O. Work who was concerned about China's military use of the emerging technology. Reportedly, Pentagon development stops short of acting as an AI weapons system capable of firing on self-designated targets. The project was established in a memo by the U.S. Deputy Secretary of Defense on 26 April 2017. Also known as the Algorithmic Warfare Cross Functional Team, it is, according to Lt. Gen. of the United States Air Force Jack Shanahan in November 2017, a project "designed to be that pilot project, that pathfinder, that spark that kindles the flame front of artificial intelligence across the rest of the [Defense] Department". Its chief, U.S. Marine Corps Col. Drew Cukor, said: "People and computers will work symbiotically to increase the ability of weapon systems to detect objects." Project Maven has been noted by allies, such as Australia's Ian Langford, for the

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  Blocking of Twitter in Nigeria

  Twitter was blocked in Nigeria from 5 June 2021 to 13 January 2022. The government imposed a ban on the social network after it deleted tweets made by, and temporarily suspended, the Nigerian president Muhammadu Buhari, warning the southeastern people of Nigeria, predominantly Igbo people, of a potential repeat of the 1967 Nigerian Civil War due to the ongoing insurgency in Southeastern Nigeria. The Nigerian government claimed that the deletion of the president's tweets factored into their decision, but it was ultimately based on "a litany of problems with the social media platform in Nigeria, where misinformation and fake news spread through it have had real world violent consequences", citing the persistent use of the platform for activities that are capable of undermining Nigeria's corporate existence. In January 2022, Nigeria lifted its blocking of Twitter after the platform agreed to establish a legal entity within the country sometime in the first quarter of 2022. == Background == On 1 June 2021, Nigerian President Muhammadu Buhari posted a tweet threatening a crackdown on regional separatists "in the language they understand". The next day, Twitter deleted the tweet, claiming it was in violation of Twitter rules, but gave no further details. Nigeria's Information Minister Lai Mohammed said that Twitter's actions were part of an unfair double standard, as Twitter had not banned incitement tweets from other groups. During the Nigerian Civil War a majority of deaths resulted from the blockade of Biafra which caused the deaths of millions of civilians from starvation, a fact that was not alluded to in the tweet. The Nigerian government has long held concerns over the use of Twitter in the country. The ongoing local End SARS protest began on Twitter and got amplified in 2020 when it had 48 million tweets in ten days. Buhari's government floated the idea of social media regulation on different occasions prior to banning Twitter. Attempts to pass an anti-social media bill in the past have failed majorly due to massive outcry on Twitter. Days before the ban, the country's minister of information called Twitter's activities in Nigeria suspicious, citing its influence on the End SARS protests. == Aftermath == Three days after Twitter was suspended, it was reported that the move had cost the country over 6 billion naira and would also contribute to the worsening unemployment in the country. ExpressVPN reported an over 200 percent increase in web traffic and searches for VPN spiked across the country. In response, Nigeria's Minister of Justice and Attorney General of the Federation Abubakar Malami at first openly threatened to prosecute citizens who bypass the ban using a VPN but then denied saying so after a screenshot of a Twitter deactivation notification he shared on Facebook showed a VPN logo. Nigeria's cultural minister Lai Mohammed stated the ban would be lifted once Twitter submitted to locally licensing, registration and conditions. "It will be licensed by the broadcasting commission, and must agree not to allow its platform to be used by those who are promoting activities that are inimical to the corporate existence of Nigeria." In late June 2021, Twitter announced it would enter talks with the Nigerian government over the platform's suspension. The talks began in July 2021. On 15 September 2021, Mohammed said the Nigerian government will lift the ban on Twitter in a "few days." The Minister said Twitter gave a progress report of their talks with them, adding that it has been productive and quite respectful. On 1 October 2021, President Muhammadu Buhari in his Independence Day broadcast said Twitter must meet the Nigerian government's five conditions before the suspension of the social media platform will be lifted. The conditions are: Respect for national security and cohesion; registration, physical presence and representation in Nigeria; fair taxation; dispute resolution; local content. == Reactions == The ban was condemned by Amnesty International, the British, Canadian and Swedish diplomatic missions to Nigeria, as well as the United States and the European Union in a joint statement. Two domestic organizations, the Socio-Economic Rights and Accountability Project (SERAP) and the Nigerian Bar Association, indicated intent to challenge the ban in court. Twitter itself called the ban "deeply concerning". Former U.S. President Donald Trump, who was permanently suspended from Twitter following the United States Capitol attack in January, praised the ban, stating "Congratulations to the country of Nigeria, who just banned Twitter because they banned their President", and also called on other countries to ban Twitter and Facebook due to "not allowing free and open speech." == Lifting of the ban == On 12 January 2022, the Nigerian Government lifted the ban after Twitter agreed to pay an "applicable tax" and establish "a legal entity in Nigeria during the first quarter of 2022".

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  Media Auxiliary Memory

  Media Auxiliary Memory or Medium Auxiliary Memory (MAM) refers to a chip embedded into a digital media device (usually a tape cartridge) that stores a small amount of data or metadata that a computer can read without having to read the actual tape. MAMs can be used by the tape driver to increase efficiency, or by custom software to store & retrieve custom data. Some examples of MAM's are Cartridge Memory (HP/Seagate/IBM LTO) and MIC (Sony AIT).

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  Social media use by the Islamic State

  The Islamic State is widely known for its posting of disturbing content, such as beheading videos, on the internet. This propaganda is disseminated through websites and many social media platforms such as Twitter, Facebook, Telegram, and YouTube. By utilizing social media, the organization has garnered a strong following and successfully recruited tens of thousands of followers from around the world. In response to its successful use of social media, many websites and social media platforms have banned accounts and removed content promoting the Islamic State from their platforms. == Background == The Islamic State is a Jihadist militant group and a former unrecognised proto-state. The group sophisticatedly utilizes social media as a tool for spreading its message and for international recruitment. == Target audience == IS targets a variety of different groups both in the Middle East and Western Countries. There are a wide variety of motives for why fighters may be prompted to join IS. Researchers from Quantum cite nine attributes characteristic of a fighter looking to join IS: status seeking, identity seeking, revenge, redemption, thrill, ideology, justice, and death. The standard IS recruit, both from the Middle East and Western countries, is relatively young. The average age of IS fighters is around 26 years old, with 86% of recruits being male. Middle Eastern recruits come from economically disadvantaged backgrounds in Northern Iraq. Recent destruction in the Iraq War and Syrian Civil War has created hatred of Western Powers in the region. By 2025, researchers identified a significant shift toward targeting minors and adolescents, a phenomenon dubbed the "Alt-Jihad." This younger demographic is targeted not through theological arguments, but through a "victimhood-revenge" narrative that blends extremist ideology with pop-culture aesthetics in gaming environments like Roblox and Minecraft. In 2024 alone, 42 minors were arrested in Europe for involvement in IS-related plotting or propaganda. Western recruits are often second or third-generation immigrants. Computer scientists Zeeshan ul-hassan Usmani also found that the majority of the Western recruits do not feel "at home" in their home country. As a result, these fighters often have desires to go abroad and escape conditions in their home country. In addition to recruitment, IS's social media presence is also meant to intimidate and spread terror around the world. IS's posting of beheadings and other execution videos primarily target the Western world. == Content and messages == IS produces propaganda videos that range from video executions to full-length documentaries. The videos have a high production quality and incorporate montages, slow motion scenes, and are often accompanied by a short dialogue. IS has a dedicated team of over 100 media insurgents dedicated to recording these videos. While the group previously relied on glossy magazines like Dabiq, post-territorial strategies have shifted focus to the weekly newsletter Al-Naba. Unlike previous publications designed for recruitment, Al-Naba serves as a "central pillar" of the group's media strategy, focusing on bureaucratic reporting and military statistics to project a narrative of endurance and maintain internal cohesion among dispersed fighters. The IS executions typically consist of beheadings or mass shootings in retaliation to western intervention in IS territory. The particular videos that IS often post include executions of "enemies of the Caliphate," which often consist of westerners or Jordanian nationals. Most infamously, an executioner nicknamed Jihadi John was seen in many of these videos prior to his death in 2015. Jihadi John is notorious for executing many US, UK, and Japanese citizens such as Steven Sotloff, David Haines, and Alan Henning. In many of the videos and materials produced by IS, there is the theme of inclusion and brotherhood. Additionally, the videos also focus on three main messages: Convey narrative of global war and ultimate victory Radicalize populations globally Encourage international lone state actor and small cell attacks in support of IS These messages can be seen throughout all content produced by the Islamic State such as war documentaries, execution videos, and Rumiyah (magazine). == Social media usage == From 2013 to 2014, the organization primarily used mainstream platforms such as Twitter, Facebook, and YouTube. In 2014, these large social media platforms removed IS content. Since then, IS has chosen to utilize social media platforms that either protect their content or allow for content to quickly be reposted. These platforms of choice are Telegram, Justpaste.it, and Surespot, until the latter's shutdown in 2022. By 2025, the group had further diversified into decentralized platforms like Rocket.Chat and TamTam to evade moderation. IS also implements marketing initiatives like “Jihadist Follow Friday,” which encourages users to follow new IS-related accounts each Friday. This specific hashtag mirrors commonly used hashtags such as #motivation monday or #throwbackthursday. To augment their online presence and popularity, the organization encourages their followers to use a plethora of Arabic hashtags, which translate to #theFridayofSupportingISIS, and #CalamityWillBefalltheUS. This allows them to gain followers each week while promoting their community and message on a weekly basis. === Twitter === During 2014, there were an estimated 46,000 to 90,000 Twitter accounts that advocated for IS or were run by supporters of the group. In 2015, Twitter reported that it banned 125,000 IS sympathetic accounts. In 2016, it published an update of 325,000 deleted accounts. Though many accounts have been suspended, IS supporters often create new accounts. Twitter defines those who recreate accounts as “resurgents” and explains that these are often difficult accounts to remove completely, since they tend to pop back up in alternate forms. It is estimated that approximately 20% of all IS affiliated Twitter accounts can be traced back to fake accounts created by the same user. Many of these accounts are traced back to the “Baqiya family,” which is an online network of thousands of IS followers. Many of these accounts are active during important IS military victories. During the IS march on Mosul, there were about 42,000 tweets on Twitter supporting the invasion. === Telegram === During 2014, IS became very active on Telegram after many major social media platforms banned IS content and sympathetic accounts. Telegram is an encrypted messaging application. The platform by nature is created as an end-to-end user encryption platform. Further, it also has special features such as the self-destruct timer which erase all evidence and messages. The app has a user data protection policy because violating this policy could potentially damage the app’s brand of customer privacy. Government agencies have been unable to break Telegram's encryption technology. On Telegram, IS often uses the hashtag #KhilafahNews to attract their users. Telegram is used by IS to plan social media campaigns on alternate platforms. The organization also uses Telegram as an anchor platform to connect with their user base when their other accounts are banned on Twitter and Facebook. On 28 February 2016 a video was uploaded threatening to expose the najaasah and shoot the hesitates. Produced by Ibn-Altayb and distributed by Al-Hayat, the video shows footage of Bruxelles attacks and the victims. In July 2017, Telegram came under scrutiny from the media and news media outlets. It has been documented that IS gunmen have used this app to maintain contact with IS leaders in Raqqa days before terror attacks in Turkey, Berlin, and St. Petersburg. Despite concerns from Western media, there has been little to no action taken against IS accounts on Telegram. In April 2019 a video was uploaded in which they urged lone wolves to attempt to attack during the Holy Week in Sevilla and Málaga. In Sevilla, a jihadist who intended to perform a lone wolf attack was arrested. === TikTok === In October 2019, it was reported that IS recruitment content was discovered on TikTok. Approximately two dozen accounts were subsequently shut down in response. By 2025, TikTok had evolved into a "low-threshold" gateway for extremist recruitment, characterized by researchers as part of a "Virtual Caliphate Complex." Nearly 93 unofficial IS support groups, known as "feeder groups," were found to be repackaging official IS content into short-form videos with pink hearts, catchy music, and internet memes to evade detection and appeal to the "TikTok generation." This content often promotes a "victimhood-revenge" narrative rather than complex theology, specifically designed to radicalize minors. === Justpaste.it === Justpaste.it, an anonymous photo and text sharing website, has also been utilized heavily. With the option to lock images, the website allows anonymous

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  Hyperparameter optimization

  In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. The objective function takes a set of hyperparameters and returns the associated loss. Cross-validation is often used to estimate this generalization performance, and therefore choose the set of values for hyperparameters that maximize it. == Approaches == === Grid search === The traditional method for hyperparameter optimization has been grid search, or a parameter sweep, which is simply an exhaustive searching through a manually specified subset of the hyperparameter space of a learning algorithm. A grid search algorithm must be guided by some performance metric, typically measured by cross-validation on the training set or evaluation on a hold-out validation set. Since the parameter space of a machine learner may include real-valued or unbounded value spaces for certain parameters, manually set bounds and discretization may be necessary before applying grid search. For example, a typical soft-margin SVM classifier equipped with an RBF kernel has at least two hyperparameters that need to be tuned for good performance on unseen data: a regularization constant C and a kernel hyperparameter γ. Both parameters are continuous, so to perform grid search, one selects a finite set of "reasonable" values for each, say C ∈ { 10 , 100 , 1000 } {\displaystyle C\in \{10,100,1000\}} γ ∈ { 0.1 , 0.2 , 0.5 , 1.0 } {\displaystyle \gamma \in \{0.1,0.2,0.5,1.0\}} Grid search then trains an SVM with each pair (C, γ) in the Cartesian product of these two sets and evaluates their performance on a held-out validation set (or by internal cross-validation on the training set, in which case multiple SVMs are trained per pair). Finally, the grid search algorithm outputs the settings that achieved the highest score in the validation procedure. Grid search suffers from the curse of dimensionality, but is often embarrassingly parallel because the hyperparameter settings it evaluates are typically independent of each other. === Random search === Random Search replaces the exhaustive enumeration of all combinations by selecting them randomly. This can be simply applied to the discrete setting described above, but also generalizes to continuous and mixed spaces. A benefit over grid search is that random search can explore many more values than grid search could for continuous hyperparameters. It can outperform Grid search, especially when only a small number of hyperparameters affects the final performance of the machine learning algorithm. In this case, the optimization problem is said to have a low intrinsic dimensionality. Random Search is also embarrassingly parallel, and additionally allows the inclusion of prior knowledge by specifying the distribution from which to sample. Despite its simplicity, random search remains one of the important base-lines against which to compare the performance of new hyperparameter optimization methods. === Bayesian optimization === Bayesian optimization is a global optimization method for noisy black-box functions. Applied to hyperparameter optimization, Bayesian optimization builds a probabilistic model of the function mapping from hyperparameter values to the objective evaluated on a validation set. By iteratively evaluating a promising hyperparameter configuration based on the current model, and then updating it, Bayesian optimization aims to gather observations revealing as much information as possible about this function and, in particular, the location of the optimum. It tries to balance exploration (hyperparameters for which the outcome is most uncertain) and exploitation (hyperparameters expected close to the optimum). In practice, Bayesian optimization has been shown to obtain better results in fewer evaluations compared to grid search and random search, due to the ability to reason about the quality of experiments before they are run. === Gradient-based optimization === For specific learning algorithms, it is possible to compute the gradient with respect to hyperparameters and then optimize the hyperparameters using gradient descent. The first usage of these techniques was focused on neural networks. Since then, these methods have been extended to other models such as support vector machines or logistic regression. A different approach in order to obtain a gradient with respect to hyperparameters consists in differentiating the steps of an iterative optimization algorithm using automatic differentiation. A more recent work along this direction uses the implicit function theorem to calculate hypergradients and proposes a stable approximation of the inverse Hessian. The method scales to millions of hyperparameters and requires constant memory. In a different approach, a hypernetwork is trained to approximate the best response function. One of the advantages of this method is that it can handle discrete hyperparameters as well. Self-tuning networks offer a memory efficient version of this approach by choosing a compact representation for the hypernetwork. More recently, Δ-STN has improved this method further by a slight reparameterization of the hypernetwork which speeds up training. Δ-STN also yields a better approximation of the best-response Jacobian by linearizing the network in the weights, hence removing unnecessary nonlinear effects of large changes in the weights. Apart from hypernetwork approaches, gradient-based methods can be used to optimize discrete hyperparameters also by adopting a continuous relaxation of the parameters. Such methods have been extensively used for the optimization of architecture hyperparameters in neural architecture search. === Evolutionary optimization === Evolutionary optimization is a methodology for the global optimization of noisy black-box functions. In hyperparameter optimization, evolutionary optimization uses evolutionary algorithms to search the space of hyperparameters for a given algorithm. Evolutionary hyperparameter optimization follows a process inspired by the biological concept of evolution: Create an initial population of random solutions (i.e., randomly generate tuples of hyperparameters, typically 100+) Evaluate the hyperparameter tuples and acquire their fitness function (e.g., 10-fold cross-validation accuracy of the machine learning algorithm with those hyperparameters) Rank the hyperparameter tuples by their relative fitness Replace the worst-performing hyperparameter tuples with new ones generated via crossover and mutation Repeat steps 2-4 until satisfactory algorithm performance is reached or is no longer improving. Evolutionary optimization has been used in hyperparameter optimization for statistical machine learning algorithms, automated machine learning, typical neural network and deep neural network architecture search, as well as training of the weights in deep neural networks. === Population-based === Population Based Training (PBT) learns both hyperparameter values and network weights. Multiple learning processes operate independently, using different hyperparameters. As with evolutionary methods, poorly performing models are iteratively replaced with models that adopt modified hyperparameter values and weights based on the better performers. This replacement model warm starting is the primary differentiator between PBT and other evolutionary methods. PBT thus allows the hyperparameters to evolve and eliminates the need for manual hypertuning. The process makes no assumptions regarding model architecture, loss functions or training procedures. PBT and its variants are adaptive methods: they update hyperparameters during the training of the models. On the contrary, non-adaptive methods have the sub-optimal strategy to assign a constant set of hyperparameters for the whole training. === Early stopping-based === A class of early stopping-based hyperparameter optimization algorithms is purpose-built for large search spaces of continuous and discrete hyperparameters, particularly when the computational cost to evaluate the performance of a set of hyperparameters is high. Irace implements the iterated racing algorithm, that focuses the search around the most promising configurations, using statistical tests to discard the ones that perform poorly. Another early stopping hyperparameter optimization algorithm is successive halving (SHA), which begins as a random search but periodically prunes low-performing models, thereby focusing computational resources on more promising models. Asynchronous successive halving (ASHA) further improves upon SHA's resource utilization profile by removing the need to synchronously evaluate a

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  Randomized benchmarking

  Randomized benchmarking is an experimental method for measuring the average error rates of quantum computing hardware platforms. The protocol estimates the average error rates by implementing long sequences of randomly sampled quantum gate operations. Randomized benchmarking is the industry-standard protocol used by quantum hardware developers such as IBM and Google to test the performance of the quantum operations. The original theory of randomized benchmarking, proposed by Joseph Emerson and collaborators, considered the implementation of sequences of Haar-random operations, but this had several practical limitations. The now-standard protocol for randomized benchmarking (RB) relies on uniformly random Clifford operations, as proposed in 2006 by Dankert et al. as an application of the theory of unitary t-designs. In current usage randomized benchmarking sometimes refers to the broader family of generalizations of the 2005 protocol involving different random gate sets that can identify various features of the strength and type of errors affecting the elementary quantum gate operations. Randomized benchmarking protocols are an important means of verifying and validating quantum operations and are also routinely used for the optimization of quantum control procedures. == Overview == Randomized benchmarking offers several key advantages over alternative approaches to error characterization. For example, the number of experimental procedures required for full characterization of errors (called tomography) grows exponentially with the number of quantum bits (called qubits). This makes tomographic methods impractical for even small systems of just 3 or 4 qubits. In contrast, randomized benchmarking protocols are the only known approaches to error characterization that scale efficiently as number of qubits in the system increases. Thus RB can be applied in practice to characterize errors in arbitrarily large quantum processors. Additionally, in experimental quantum computing, procedures for state preparation and measurement (SPAM) are also error-prone, and thus quantum process tomography is unable to distinguish errors associated with gate operations from errors associated with SPAM. In contrast, RB protocols are robust to state-preparation and measurement errors Randomized benchmarking protocols estimate key features of the errors that affect a set of quantum operations by examining how the observed fidelity of the final quantum state decreases as the length of the random sequence increases. If the set of operations satisfies certain mathematical properties, such as comprising a sequence of twirls with unitary two-designs, then the measured decay can be shown to be an invariant exponential with a rate fixed uniquely by features of the error model. == History == Randomized benchmarking was proposed in Scalable noise estimation with random unitary operators, where it was shown that long sequences of quantum gates sampled uniformly at random from the Haar measure on the group SU(d) would lead to an exponential decay at a rate that was uniquely fixed by the error model. Emerson, Alicki and Zyczkowski also showed, under the assumption of gate-independent errors, that the measured decay rate is directly related to an important figure of merit, the average gate fidelity and independent of the choice of initial state and any errors in the initial state, as well as the specific random sequences of quantum gates. This protocol applied for arbitrary dimension d and an arbitrary number n of qubits, where d=2n. The SU(d) RB protocol had two important limitations that were overcome in a modified protocol proposed by Dankert et al., who proposed sampling the gate operations uniformly at random from any unitary two-design, such as the Clifford group. They proved that this would produce the same exponential decay rate as the random SU(d) version of the protocol proposed in Emerson et al.. This follows from the observation that a random sequence of gates is equivalent to an independent sequence of twirls under that group, as conjectured in and later proven in. This Clifford-group approach to Randomized Benchmarking is the now standard method for assessing error rates in quantum computers. A variation of this protocol was proposed by NIST in 2008 for the first experimental implementation of an RB-type for single qubit gates. However, the sampling of random gates in the NIST protocol was later proven not to reproduce any unitary two-design. The NIST RB protocol was later shown to also produce an exponential fidelity decay, albeit with a rate that depends on non-invariant features of the error model In recent years a rigorous theoretical framework has been developed for Clifford-group RB protocols to show that they work reliably under very broad experimental conditions. In 2011 and 2012, Magesan et al. proved that the exponential decay rate is fully robust to arbitrary state preparation and measurement errors (SPAM). They also proved a connection between the average gate fidelity and diamond norm metric of error that is relevant to the fault-tolerant threshold. They also provided evidence that the observed decay was exponential and related to the average gate fidelity even if the error model varied across the gate operations, so-called gate-dependent errors, which is the experimentally realistic situation. In 2018, Wallman and Dugas et al., showed that, despite concerns raised in, even under very strong gate-dependence errors the standard RB protocols produces an exponential decay at a rate that precisely measures the average gate-fidelity of the experimentally relevant errors. The results of Wallman. in particular proved that the RB error rate is so robust to gate-dependent errors models that it provides an extremely sensitive tool for detecting non-Markovian errors. This follows because under a standard RB experiment only non-Markovian errors (including time-dependent Markovian errors) can produce a statistically significant deviation from an exponential decay The standard RB protocol was first implemented for single qubit gate operations in 2012 at Yale on a superconducting qubit. A variation of this standard protocol that is only defined for single qubit operations was implemented by NIST in 2008 on a trapped ion. The first implementation of the standard RB protocol for two-qubit gates was performed in 2012 at NIST for a system of two trapped ions

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  WebGL

  WebGL (short for Web Graphics Library) is a JavaScript API for rendering interactive 2D and 3D graphics within any compatible web browser without the use of plug-ins. WebGL is fully integrated with other web standards, allowing GPU-accelerated usage of physics, image processing, and effects in the HTML canvas. WebGL elements can be mixed with other HTML elements and composited with other parts of the page or page background. WebGL programs consist of control code written in JavaScript, and shader code written in OpenGL ES Shading Language (GLSL ES, sometimes referred to as ESSL), a language similar to C or C++. WebGL code is executed on a computer's GPU. WebGL is designed and maintained by the non-profit Khronos Group. On February 9, 2022, Khronos Group announced WebGL 2.0 support from all major browsers. From 2024, a new graphics API, WebGPU, is being developed to supersede WebGL. WebGPU provides extended capabilities, a more modern interface, and direct GPU access, which is useful for demanding graphics as well as AI applications. == Design == WebGL 1.0 is based on OpenGL ES 2.0 and provides an API for 3D graphics. It uses the HTML5 canvas element and is accessed using Document Object Model (DOM) interfaces. WebGL 2.0 is based on OpenGL ES 3.0. It guarantees the availability of many optional extensions of WebGL 1.0, and exposes new APIs. Automatic memory management is provided implicitly by JavaScript. Like OpenGL ES 2.0, WebGL lacks the fixed-function APIs introduced in OpenGL 1.0 and deprecated in OpenGL 3.0. This functionality, if required, has to be implemented by the developer using shader code and JavaScript. Shaders in WebGL are written in GLSL and passed to the WebGL API as text strings. The WebGL implementation compiles these strings to GPU code. This code is executed for each vertex sent through the API and for each pixel rasterized to the screen. == History == WebGL evolved out of the Canvas 3D experiments started by Vladimir Vukićević at Mozilla. Vukićević first demonstrated a Canvas 3D prototype in 2006. By the end of 2007, both Mozilla and Opera had made their own separate implementations. In early 2009, the non-profit technology consortium Khronos Group started the WebGL Working Group, with initial participation from Apple, Google, Mozilla, Opera, and others. Version 1.0 of the WebGL specification was released March 2011. An early application of WebGL was Zygote Body. In November 2012 Autodesk announced that they ported most of their applications to the cloud running on local WebGL clients. These applications included Autodesk Fusion and AutoCAD. Development of the WebGL 2 specification started in 2013 and finished in January 2017. The specification is based on OpenGL ES 3.0. First implementations are in Firefox 51, Chrome 56 and Opera 43. == Implementations == === Almost Native Graphics Layer Engine === Almost Native Graphics Layer Engine (ANGLE) is an open source graphic engine which implements WebGL 1.0 (2.0 which closely conforms to ES 3.0) and OpenGL ES 2.0 and 3.0 standards. It is a default backend for both Google Chrome and Mozilla Firefox on Windows platforms and works by translating WebGL and OpenGL calls to available platform-specific APIs. ANGLE currently provides access to OpenGL ES 2.0 and 3.0 to desktop OpenGL, OpenGL ES, Direct3D 9, and Direct3D 11 APIs. ″[Google] Chrome uses ANGLE for all graphics rendering on Windows, including the accelerated Canvas2D implementation and the Native Client sandbox environment.″ == Software == WebGL is widely supported by modern browsers. However, its availability depends on other factors, too, like whether the GPU supports it. The official WebGL website offers a simple test page. More detailed information (like what renderer the browser uses, and what extensions are available) can be found at third-party websites. === Desktop browsers === Source: Google Chrome – WebGL 1.0 has been enabled on all platforms that have a capable graphics card with updated drivers since version 9, released in February 2011. By default on Windows, Chrome uses the ANGLE (Almost Native Graphics Layer Engine) renderer to translate OpenGL ES to Direct X 9.0c or 11.0, which have better driver support. However, on Linux and Mac OS X, the default renderer is OpenGL. It is also possible to force OpenGL as the renderer on Windows. Since September 2013, Chrome also has a newer Direct3D 11 renderer, which requires a newer graphics card. Chrome 56+ supports WebGL 2.0. Firefox – WebGL 1.0 has been enabled on all platforms that have a capable graphics card with updated drivers since version 4.0. Since 2013 Firefox also uses DirectX on the Windows platform via ANGLE. Firefox 51+ supports WebGL 2.0. Safari – Safari 6.0 and newer versions installed on OS X Mountain Lion, Mac OS X Lion and Safari 5.1 on Mac OS X Snow Leopard implemented support for WebGL 1.0, which was disabled by default before Safari 8.0. Safari version 12 (available in MacOS Mojave) has available support for WebGL 2.0 as an "Experimental" feature. Safari 15 enables WebGL 2.0 for all users. Opera – WebGL 1.0 has been implemented in Opera 11 and 12, but was disabled by default in 2014. Opera 43+ supports WebGL 2.0. Internet Explorer – WebGL 1.0 is partially supported in Internet Explorer 11. Internet Explorer initially failed most of the official WebGL conformance tests, but Microsoft later released several updates. The latest 0.94 WebGL engine currently passes ≈97% of Khronos tests. WebGL support can also be manually added to earlier versions of Internet Explorer using third-party plugins such as IEWebGL. Microsoft Edge – For Microsoft Edge Legacy, the initial stable release supports WebGL version 0.95 (context name: "experimental-webgl") with an open source GLSL to HLSL transpiler. Version 10240+ supports WebGL 1.0 as prefixed. Latest Chromium-based Edge supports WebGL 2.0. === Mobile browsers === Google Chrome – WebGL 1.0 is supported on Android as of Chrome 25. WebGL 2.0 is supported on Android as of Chrome 58. Chrome is used for the Android system webview as of Android 5. Firefox for mobile – WebGL 1.0 is available for Android devices since Firefox 4. Safari on iOS – WebGL 1.0 is available for mobile Safari in iOS 8. WebGL 2.0 is available for mobile Safari in iOS 15. Microsoft Edge – Prefixed WebGL 1.0 was available on Windows 10 Mobile.. Latest Chromium-based Edge supports WebGL 2.0. Opera Mobile – Opera Mobile 12 supports WebGL 1.0 (on Android only). Sailfish OS – WebGL 1.0 is supported in the default Sailfish browser. Tizen – WebGL 1.0 is supported == Tools and ecosystem == === Utilities === The low-level nature of the WebGL API, which provides little on its own to quickly create desirable 3D graphics, motivated the creation of higher-level libraries that abstract common operations (e.g. loading scene graphs and 3D objects in certain formats; applying linear transformations to shaders or view frustums). Some such libraries were ported to JavaScript from other languages. Examples of libraries that provide high-level features include A-Frame (VR), BabylonJS, PlayCanvas, three.js, OSG.JS, Google’s model-viewer and CopperLicht. Web3D also made a project called X3DOM to make X3D and VRML content run on WebGL. === Games === There has been an emergence of 2D and 3D game engines for WebGL, such as Unreal Engine 4 and Unity. The Stage3D/Flash-based Away3D high-level library also has a port to WebGL via TypeScript. A more light-weight utility library that provides just the vector and matrix math utilities for shaders is sylvester.js. It is sometimes used in conjunction with a WebGL specific extension called glUtils.js. There are also some 2D libraries built atop WebGL, like Cocos2d-x or Pixi.js, which were implemented this way for performance reasons in a move that parallels what happened with the Starling Framework over Stage3D in the Flash world. The WebGL-based 2D libraries fall back to HTML5 canvas when WebGL is not available. Removing the rendering bottleneck by giving almost direct access to the GPU has exposed performance limitations in the JavaScript implementations. Some were addressed by asm.js and WebAssembly (similarly, the introduction of Stage3D exposed performance problems within ActionScript, which were addressed by projects like CrossBridge). === Content creation === As with any other graphics API, creating content for WebGL scenes requires using a 3D content creation tool and exporting the scene to a format that is readable by the viewer or helper library. Desktop 3D authoring software such as Blender, Autodesk Maya or SimLab Composer can be used for this purpose. In particular, Blend4Web allows a WebGL scene to be authored entirely in Blender and exported to a browser with a single click, even as a standalone web page. There are also some WebGL-specific software such as CopperCube and the online WebGL-based editor Clara.io. Online platforms such as Sketchfab and Clara.io allow users to directly upload their 3D models

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  DiscoVision

  DiscoVision is the name of several things related to the video LaserDisc format. It was the original name of the "Reflective Optical Videodisc System" format later known as "LaserVision" or LaserDisc. == Description == MCA DiscoVision, Inc. was a division of entertainment giant MCA (Music Corporation of America), established in 1969 to develop and sell an optical videodisc system. MCA released discs pressed in Carson and Costa Mesa, California on the DiscoVision label from the format's Atlanta, Georgia launch in 1978 to 1982 and the release of the film The Four Seasons. DiscoVision titles included films from Universal Pictures, Paramount Pictures, Warner Bros. Pictures, and Disney content. Agreements were made with Columbia Pictures and United Artists, though no discs were released on the DiscoVision label from either studio. Most of these companies later established their own labels for the format, the first being Paramount with a dozen movies released on the Paramount Home Video label in the summer of 1981. The successor to MCA DiscoVision, DiscoVision Associates (DVA), was the result of a partnership between IBM and MCA. It was hoped that the merger would provide the basis for improvement of the quality of DiscoVision pressings, but no appreciable improvement ever took hold. In 1981, responsibility for the laser videodisc was sold to Pioneer Electronic Corporation, after MCA Discovision had previously started a partnership in 1977 with Pioneer, Universal Pioneer, to produce the Pioneer PR-7820 player (the first industrial model of DiscoVision player from 1978), as well as establishing disc pressing plants in Japan. As part of the partnership, Pioneer, in association with MCA, had a disc replication facility in Kofu, Japan that produced discs. Some of the last DiscoVision label discs were manufactured by Pioneer in Japan. In the same year, MCA discontinued their DiscoVision branding, due to the sale of the technology to Pioneer (who then rebranded the format as LaserDisc) and in turn rebranded their laserdisc releases, now fabricated by Pioneer, under the MCA Videodisc banner; this was changed to the "MCA Home Video" name for both its VHS and videodisc releases. Some of DiscoVision's technical staff went on to form MCA Video Games, in an effort to produce video game cartridges. DiscoVision Associates later evolved into a patent holding company which manages and licenses intellectual property related to LaserDisc, Compact Disc, and optical disc technologies, as well as other non-disc related fields. In 1989, Pioneer acquired DiscoVision Associates where it continues to license its technologies independently. As the portfolio of patent expired, the presence of DiscoVision became less visible. However, it established the success of a patent holding company, which other companies are stimulated to generate royalty income from their own patent portfolio.

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  Outline of databases

  The following is provided as an overview of and topical guide to databases: Database – organized collection of data, today typically in digital form. The data are typically organized to model relevant aspects of reality (for example, the availability of rooms in hotels), in a way that supports processes requiring this information (for example, finding a hotel with vacancies). == What type of things are databases? == Databases can be described as all of the following: Information – sequence of symbols that can be interpreted as a message. Information can be recorded as signs, or transmitted as signals. Data – values of qualitative or quantitative variables, belonging to a set of items. Data in computing (or data processing) are often represented by a combination of items organized in rows and multiple variables organized in columns. Data are typically the results of measurements and can be visualised using graphs or images. Computer data – information in a form suitable for use with a computer. Data is often distinguished from programs. A program is a sequence of instructions that detail a task for the computer to perform. In this sense, data is everything in software that is not program code. == Types of databases == Active database – Database with event driven features Animation database – Database for storing and reusing animation fragments or motion capture data Back-end database – Organized collection of data in computingPages displaying short descriptions of redirect targets Bibliographic database – database of bibliographic records, an organized digital collection of references to published literature, including journal and newspaper articles, conference proceedings, reports, government and legal publications, patents, books, etc. Centralized database – database located and maintained in one location, unlike a distributed database. Cloud database – Database running on a cloud computing platform Collection database – collection catalog of a museum or archive implemented using a computerized database, in which the institution's objects or material are catalogued. Collective Optimization Database – open repository to enable sharing of benchmarks, data sets and optimization cases from the community, provide web services and Plug-in (computing)|plugins to analyze optimization data and predict program transformations or better hardware designs for multi-objective optimizations based on statistical and machine learning techniques provided there is enough information collected in the repository from multiple users. Configuration management database – Database used to store info on hardware and software assets Cooperative database – holds information on customers and their transactions. Current database – conventional database that stores data that is valid now. Directory – repository or database of information which is optimized for reading, under the assumption that data updates are very rare compared to data reads. Commonly, a directory supports search and browsing in addition to simple lookups. Distributed database – database in which storage devices are not all attached to a common CPU. Document-oriented database – computer program designed for storing, retrieving, and managing document-oriented, or Semi-structured model|semi structured data, information. EDA database – database specialized for the purpose of electronic design automation. Endgame tablebase – computerized database that contains precalculated exhaustive analysis of a chess endgame position. Food composition database (FCDB) – provides detailed information on the nutritional composition of foods. Full-text database – database that contains the complete text of books, dissertations, journals, magazines, newspapers or other kinds of textual documents. Also called a "complete-text database". Government database – collects personal information for various reasons (mass surveillance, Schengen Information System in the European Union, social security, statistics, etc.). Graph database – uses graph structures with nodes, edges, and properties to represent and store data. Knowledge base – special kind of database for knowledge management. A knowledge base provides a means for information to be collected, organised, shared, searched and utilised. Mobile database – can be connected to by a mobile computing device over a mobile network. Navigational database – database in which objects (or records) in it are found primarily by following references from other objects. Non-native speech database – speech database of non-native pronunciations of English. Online database – database accessible from a network, including from the Internet. Operational database – accessed by an Operational System to carry out regular operations of an organization. Parallel database – improves performance through parallelization of various operations, such as loading data, building indexes and evaluating queries. Probabilistic database – uncertain database in which the possible worlds have associated probabilities. Real-time database – processing system designed to handle workloads whose state is constantly changing (Buchmann). Relational database – collection of data items organized as a set of formally described tables from which data can be accessed easily. Spatial database – database that is optimized to store and query data that is related to objects in space, including points, lines and polygons. Temporal database – database with built-in time aspects, for example a temporal data model and a temporal version of Structured Query Language (SQL). Time series database – a time series is an associative array of numbers indexed by a datetime or a datetime range. These time series are often called profiles or curves, depending upon the market. A time series of stock prices might be called a price curve, or a time series of energy consumption might be called a load profile. Despite the disparate naming, the operations performed on them are sufficiently common as to demand special database treatment. Triplestore – purpose-built database for the storage and retrieval of triples, a triple being a data entity composed of subject-predicate-object, like "Bob is 35" or "Bob knows Fred". Very large database (VLDB) – contains an extremely high number of tuples (database rows), or occupies an extremely large physical filesystem storage space. Vulnerability database – platform aimed at collecting, maintaining, and disseminating information about discovered vulnerabilities targeting real computer systems. XLDB – Stands for "eXtremely Large Data Base". XML database – data stored in XML format, where it can be queried, exported and serialized into the desired format. == History of databases == History of databases – History of database management systems –: == Database use == Database usage requirements – Database theory – encapsulates a broad range of topics related to the study and research of the theoretical realm of databases and database management systems. Database machine – or is a computer or special hardware that stores and retrieves data from a database. Also called a "back end processor" Database server – computer program that provides database services to other computer programs or computers, as defined by the client-server model. Database application – computer program whose primary purpose is entering and retrieving information from a computer-managed database. Database management system (DBMS) – software package with computer programs that control the creation, maintenance, and use of a database. Database connection – facility in computer science that allows client software to communicate with database server software, whether on the same machine or not. Datasource – name given to the connection set up to a database from a server. The name is commonly used when creating a query to the database. The Database Source Name (DSN) does not have to be the same as the filename for the database. For example, a database file named "friends.mdb" could be set up with a DSN of "school". Then DSN "school" would then be used to refer to the database when performing a query. Data Source Name (DSN) – are data structures used to describe a connection to a data source. Sometimes known as a database source name though data sources are not limited to databases. Database administrator (DBA) – is a person responsible for the installation, configuration, upgrade, administration, monitoring and maintenance of physical databases. Lock – Comparison of database tools – (provides tables for comparing general and technical information for a number of available database administrator tools.) Database-centric architecture – software architectures in which databases play a crucial role. Also called "data-centric architecture". Intelligent database – was put forward as a system that manages information (rather than data) in a way that appears natural to users and which goes beyond simple record keeping. Two-phase locking (2PL) – is a

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  Cultural technology

  Cultural technology (Korean: 문화기술; Hanja: 文化技術; RR: munhwagisul) is a system used by South Korean talent agencies to promote K-pop culture throughout the world as part of the Korean Wave. The system was developed by Lee Soo-man, founder of talent agency and record company SM Entertainment. == History == === Coinage === During a speech at the Stanford Graduate School of Business in 2011, Lee said he coined the term "cultural technology" as a system about fourteen years prior, when S.M. Entertainment decided to promote its K-pop artists to all of Asia. In the late 1990s, Lee and his colleagues created a manual on cultural technology, which specified the steps needed to popularize K-pop artists outside South Korea. "The manual, which all S.M. employees are instructed to learn, explains when to bring in foreign composers, producers, and choreographers; what chord progressions to use in what country; the precise color of eyeshadow a performer should wear in a particular country; the exact hand gestures he or she should make; and the camera angles to be used in the videos (a three-hundred-and-sixty-degree group shot to open the video, followed by a montage of individual closeups)," according to The New Yorker. The term "cultural technology," apart from Lee's systemized definition, can be traced back to the lectures of Michael White, an Australian social worker, educator, and therapeutic theorist and his works Narrative Means to Therapeutic Ends (1990) and Maps of Narrative Practice (2007). Its usage may also date further back to French philosopher Michel Foucault (1977). South Korean computer scientist Kwangyun Wohn said he coined the term "culture technology" in 1994. Cultural technology has also been one of six technology initiatives of the South Korean government since 2001. In regards to cultural technology, the Korean Wave is considered one of the most successful outcomes of government support of exporting Korean entertainment products. === The Four Core Stages === The cultural technology system originally employed by SM Entertainment since the 1990s existed in four stages: Casting, Training, Producing, and Marketing/Managing. Each of these four stages were curated to help spread the Hallyu wave through the development of its artists, and are present in the strategies of many other South Korean talent agencies when creating, debuting, and marketing groups. ==== Casting ==== While the majority of K-pop idols are from South Korea, some are from Japan, China, or Thailand. Many of Korea's entertainment companies, such as SM's Global Auditions, Bighit's Hit It auditions, and YG's Next Generation, host worldwide auditions. Scouting and streetcasting are also common, with members like BTS's Jin recruited for their looks or other surface reasons. Sometimes, casting agents go to dance schools to recruit the top dancers to be trained further at the entertainment company. ==== Training ==== Idols train extensively before debut. They receive training in dance, vocal activities, presentation, and other areas that will benefit them in the industry. Oftentimes, this training will last for years at a time, and trainees are in the proverbial dungeon. Before debut, idols and groups attempt to gain fans through pre-debut activities. SM Entertainment has a system in place called SM Rookies, which is a pre-debut team that hosts concerts and releases videos that strengthen the fanbase of the group even before their first single is released. Other forms of pre-debut activities include featuring in other, more seasoned idols' videos—like Nu'est in Orange Caramel or Exo in Girls' Generation-TTS Twinkle or BTS in Jo Kwon. One particular method of pre-debut training is coupled with casting in production shows, like Sixteen and Produce 101, in which members for a final group are selected and trained. ==== Producing ==== The production of music is integral in culture technology. For cultural technology, production of music helps create differentiated content to set trends in the K-pop world—trends that vary from music to also costume, choreography, and music videos. SM in particular focuses heavily on the expansion globally. Some companies also outsource production to more internationally famed parties, like Cube Entertainment's partnership with Skrillex for 4minute's Act. 7. ==== Marketing/Managing ==== In the marketing and management stage, talent agencies seek to broaden their reach. Often, idols have potential for being actors and actresses in dramas, or perhaps hosts/permanent members of variety shows like Kim Hee-chul in Knowing Bros. This so-called omnidirectional marketing lineup ranges over lifestyle and seeks to reach many aspects of living, like music, TV, drama, entertainment, sports, and fashion. This is also where older groups find new life, like Super Junior. Companies are not complacent but experiment constantly to develop the best marketing for the best management system. Marketing also aspires to branch out to international audiences, sometimes via the implementation of variety shows. Despite being primarily in Korean, these variety shows are accessible to all due to the simplistic, easily understood nature of shows—game-oriented shows like Run BTS! or consistently subbed shows like Weekly Idol are popular in showing the fun-loving side of idols. == Evolution into New Culture Technology == In February 2016, SM hosted a press conference discussing the future of SM and its cultural technology. Lee Soo-man announced the implementation of New Culture Technology, an SM-specific system. While SM's cultural technology in the past relied on local, Korean artists like Rain and BoA, the updated model tries to embed more and more foreign singers from strategic markets into larger girl or boy bands. These imported singers are then used to promote their acts back in their respective home countries. New Culture Technology is five projects—SM Station, EDM, Digital Platforms, Rookies Entertainment, and MCN—and one experimental group, NCT. It is a convergence and expansion of SM's four core culture technologies developed and deals heavily with interaction and the desire to innovate through communication. === SM Station === SM announced their intention of creating a new song every week for 52 weeks. Through this constant output of music, they intend to stray away from conventional forms of music and show active movement in digital music market and physical album market through freely and continuously releasing music. Additionally, this SM Station will feature collaborations between artists, producers, composers, and company brands outside the SM label. The name of SM Station is both derived from the radio station and the metaphorical train station. === NCT === Neo Culture Technology (NCT) introduced the idea of "Interactive". SM company tried to connect the targeting market, customers and artist, in order to lead the K-pop culture. NCT (Neo Culture Technology) is the new artist group formed by SM that embodies the concepts of cultural technology. With the seemingly limitless combinations and groups, SM aspires to make the whole world a stage for NCT. Since 2023, there are six NCT groups, who debuted on the digital song sales: NCT U, NCT 127, NCT Dream, WayV, NCT DoJaeJung, and NCT Wish. As of October 2023, the group consists of 25 members: Johnny, Taeyong, Yuta, Kun, Doyoung, Ten, Jaehyun, Winwin, Jungwoo, Mark, Xiaojun, Hendery, Renjun, Jeno, Haechan, Jaemin, Yangyang, Chenle, Jisung, Sion, Riku, Yushi, Daeyoung, Ryo, and Sakuya. ScreaM Records ScreaM Records has been released by SM Entertainment as an EDM label since 2016 for "SM TOWN: New Culture Technology". ScreaM Records is made for "performances made to be enjoyed". It collaborates with inside and outside Korean well-known EDM DJs. ScreaM Records has first launched collaborated song "Wave" E-Mart's home electronics store, Electro Mart. "Our goal is to provide opportunities to producers who have yet to be discovered and produce world famous DJs from the Asian scene." a ScreaM Records representative said. == Three stages of globalization == According to Lee, there are three stages necessary to popularize Korean culture outside South Korea: exporting the product, collaborating with international companies to expand the product's presence abroad, and finally creating a joint venture with international companies. As part of their joint ventures with international companies, South Korean talent agencies may hire foreign composers, producers, and choreographers to ensure K-pop songs feel "local" to foreign countries.

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  Style sheet (web development)

  A web style sheet is a form of separation of content and presentation for web design in which the markup (i.e., HTML or XHTML) of a webpage contains the page's semantic content and structure, but does not define its visual layout (style). Instead, the style is defined in an external style sheet file using a style sheet language such as CSS or XSLT. This design approach is identified as a "separation" because it largely supersedes the antecedent methodology in which a page's markup defined both style and structure. The philosophy underlying this methodology is a specific case of separation of concerns. == Benefits == Separation of style and content has advantages, but has only become practical after improvements in popular web browsers' CSS implementations. === Speed === Overall, users experience of a site utilising style sheets will generally be quicker than sites that do not use the technology. ‘Overall’ as the first page will probably load more slowly – because the style sheet AND the content will need to be transferred. Subsequent pages will load faster because no style information will need to be downloaded – the CSS file will already be in the browser’s cache. === Maintainability === Holding all the presentation styles in one file can reduce the maintenance time and reduces the chance of error, thereby improving presentation consistency. For example, the font color associated with a type of text element may be specified — and therefore easily modified — throughout an entire website simply by changing one short string of characters in a single file. The alternative approach, using styles embedded in each individual page, would require a cumbersome, time consuming, and error-prone edit of every file. === Accessibility === Sites that use CSS with either XHTML or HTML are easier to tweak so that they appear similar in different browsers (Chrome, Internet Explorer, Mozilla Firefox, Opera, Safari, etc.). Sites using CSS "degrade gracefully" in browsers unable to display graphical content, such as Lynx, or those so very old that they cannot use CSS. Browsers ignore CSS that they do not understand, such as CSS 3 statements. This enables a wide variety of user agents to be able to access the content of a site even if they cannot render the style sheet or are not designed with graphical capability in mind. For example, a browser using a refreshable braille display for output could disregard layout information entirely, and the user would still have access to all page content. === Customization === If a page's layout information is stored externally, a user can decide to disable the layout information entirely, leaving the site's bare content still in a readable form. Site authors may also offer multiple style sheets, which can be used to completely change the appearance of the site without altering any of its content. Most modern web browsers also allow the user to define their own style sheet, which can include rules that override the author's layout rules. This allows users, for example, to bold every hyperlink on every page they visit. Browser extensions like Stylish and Stylus have been created to facilitate management of such user style sheets. === Consistency === Because the semantic file contains only the meanings an author intends to convey, the styling of the various elements of the document's content is very consistent. For example, headings, emphasized text, lists and mathematical expressions all receive consistently applied style properties from the external style sheet. Authors need not concern themselves with the style properties at the time of composition. These presentational details can be deferred until the moment of presentation. === Portability === The deferment of presentational details until the time of presentation means that a document can be easily re-purposed for an entirely different presentation medium with merely the application of a new style sheet already prepared for the new medium and consistent with elemental or structural vocabulary of the semantic document. A carefully authored document for a web page can easily be printed to a hard-bound volume complete with headers and footers, page numbers and a generated table of contents simply by applying a new style sheet.

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  Digital first

  Digital first is a communication theory that publishers should release content into new media channels in preference to old media. The premise behind the theory is that after the advent of Internet, most established media organizations continued to give priority to traditional media. Over time, those organizations faced a choice to either publish first in digital media or traditional media. A "digital first" decision occurs when a publisher chooses to distribute information online in preference to or at the expense of traditional media like print publishing. Many employers and employees find it challenging to imagine using digital first practices. Distributing content digital first introduces new practices, including a need to manage the data which tracks readership. Many paper print publishers feel intimidated by the idea of publishing content online before publishing it in paper media. Comedian John Oliver in the show Last Week Tonight criticized digital first practices as a cause of lower standards in journalism. == Digital-First Transformation in Business and Education == The classical perspective of an information system is that it represents and reflects physical reality. However, it is increasingly evident that digital technologies not only represent reality but also actively shape it, as, in many instances, the digital version is created first, and the physical version follows. Gradually, digital infrastructures are integrated in people's work and life, shaping a digital environment through technologies such as 5G, sensors, and blockchain. The Digital First Framework, developed by Professor Youngjin Yoo, is a conceptual approach that helps the physical companies in the integration of digital technologies into the core of product and service design. The shift from traditional cars, where the physical vehicle precedes its digital representation on Google maps, to autonomous vehicles, where the digital representation (the blue dot) is created first, emphasizes the digital-first mindset in the design and operation of systems. In today's business environment, it's critical for organizations to embrace a digital-first strategy. Companies built on digital platforms will significantly diverge from traditional, hierarchical business structures that typically focus on a single product or market. These digitally-centered enterprises will offer products and services that are tailored to individual requirements, utilizing algorithms to assess needs based on specific situations, and relying on external partners to provide these solutions. This highlights the need to transform traditional R&D practices. It's essential for R&D teams to move beyond their laboratories and immerse themselves in the environments of their users. Understanding the context of use is fundamental to creating a relevant platform. As an illustration, the concept of Digital-first, as defined by Rohm et al. (2019), involves the integration of digital projects within educational courses, exemplified by institutions like M-School. The program adopts a programmatic approach, where successive courses progressively build upon one another, adopting an all-encompassing perspective that regards all aspects of marketing as inherently digital. Students actively participate in real-world projects, including campaigns for community improvement, and are tasked with generating content for diverse platforms. Through hands-on collaboration with live clients and the utilization of tools such as Google AdWords and Facebook Advertising, students acquire practical experience in the realms of digital marketing and analytics. == vBook == A vBook is an eBook that is digital first media with embedded video, images, graphs, tables, text, and other media.

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  Human–AI interaction

  Human–AI interaction is a developing field of research and a sub-field of human–computer interaction (HCI). HCI is a field of research that explores the interactions between humans and computer-based technology, focusing on design implementation, user experience, and psychological factors. With the proliferation of artificial intelligence (AI), there has developed a sub-section of HCI research dedicated specifically to artificial intelligence and how people interact with and are impacted by it. This is human–AI interaction, abbreviated either as HAX or HAII. == Introduction == Artificial intelligence (AI), in general, has fluid definitions and varied research applications, but in brief can be applied to mechanizing tasks that would require human intelligence to complete. AI are tools designed to replicate the human abilities of navigating uncertainty, active learning, and processing information in different contexts. Within the context of HCI and HAX research, artificial intelligence can be broken into two sub-fields, natural language processing (NLP) and computer vision (CV). AI technologies notably include machine-learning, deep-learning and neural networks, and large-language models (LLMs). As a new and rapidly developing technology, AI is changing how computers work and therefore changing how humans interact with computers. Unlike the traditional human-computer interaction, where a human directs a machine, human-AI interaction is characterized by a more collaborative relationship between the computer program (the AI) and the human user, as AI is perceived as an active agent rather than a tool. This changing dynamic creates new questions and necessitates new research methods that are not present in traditional HCI research. According to a scoping review on the state of the discipline, the HAX field comprises research on the "design, development, and evaluation of AI systems" and encompasses the themes of human-AI collaboration, human-AI competition, human-AI conflict, and human-AI symbiosis. == Design == Machine learning and artificial intelligence have been used for decades in targeted advertising and to recommend content in social media. Ethical Guidelines (Framework for ethical AI development) == User Experience (UX) == This section should handle research on how users interact with tools. What techniques do they use, do they develop habits, what types of programs and devices are they using to access these tools, what do they use these tools to do exactly. === Cognitive Frameworks in AI Tool Users === AI has been viewed with various expectations, attributions, and often misconceptions. Many people exclusively understand AI as the LLM chatbots they interact with, like ChatGPT or Claude, or other generative AI programs. [Insert section: discuss how people interact with these specific AI tools as a connection to the following paragraphs] Most fundamentally, humans have a mental model of understanding AI's reasoning and motivation for its decision recommendations, and building a holistic and precise mental model of AI helps people create prompts to receive more valuable responses from AI. However, these mental models are not whole because people can only gain more information about AI through their limited interaction with it; more interaction with AI builds a better mental model that a person may build to produce better prompt outcomes. Research on human-AI interaction has emphasized that users develop mental models of AI systems and revise those models through repeated use, feedback, and explanation, while design research has stressed the importance of communicating capabilities and limitations early and supporting trust calibration through explanation and correction. In a 2025 SSRN working paper, John DeVadoss proposed "Hypothetico-Deductive Interaction" (HDI), a framework that describes human-AI interaction as a mutual process of conjecture and refutation in which users test assumptions about an AI system's capabilities while the system infers and updates assumptions about user goals through its responses and clarifying questions. DeVadoss argued that this framing helps explain prompt iteration, weak capability awareness, and trust miscalibration, and suggested design responses such as clearer communication of uncertainty, easier correction, actionable explanations, and safer failure modes. == Research themes == === Human-AI collaboration === Human-AI collaboration occurs when the human and AI supervise the task on the same level and extent to achieve the same goal. Some collaboration occurs in the form of augmenting human capability. AI may help human ability in analysis and decision-making through providing and weighing a volume of information, and learning to defer to the human decision when it recognizes its unreliability. It is especially beneficial when the human can detect a task that AI can be trusted to make few errors so that there is not a lot of excessive checking process required on the human's end. Some findings show signs of human-AI augmentation, or human–AI symbiosis, in which AI enhances human ability in a way that co-working on a task with AI produces better outcomes than a human working alone. For example: the quality and speed of customer service tasks increase when a human agent collaborates with AI, training on specific models allows AI to improve diagnoses in clinical settings, and AI with human-intervention can improve creativity of artwork while fully AI-generated haikus were rated negatively. Human-AI synergy, a concept in which human-AI collaboration would produce more optimal outcomes than either human or AI working alone could explain why AI does not always help with performance. Some AI features and development may accelerate human-AI synergy, while others may stagnate it. For example, when AI updates for better performance, it sometimes worsens the team performance with human and AI by reducing the compatibility with the new model and the mental model a user has developed on the previous version. Research has found that AI often supports human capabilities in the form of human-AI augmentation and not human-AI synergy, potentially because people rely too much on AI and stop thinking on their own. Prompting people to actively engage in analysis and think when to follow AI recommendations reduces their over-reliance, especially for individuals with higher need for cognition. === Human-AI competition === Robots and computers have substituted routine tasks historically completed by humans, but agentic AI has made it possible to also replace cognitive tasks including taking phone calls for appointments and driving a car. At the point of 2016, research has estimated that 45% of paid activities could be replaced by AI by 2030. Perceived autonomy of robots is known to increase people's negative attitude toward them, and worry about the technology taking over leads people to reject it. There has been a consistent tendency of algorithm aversion in which people prefer human advice over AI advice. However, people are not always able to tell apart tasks completed by AI or other humans. See AI takeover for more information. It is also notable that this sentiment is more prominent in the Western cultures as Westerners tend to show less positive views about AI compared to East Asians. == Research on the psychological impacts of AI == === Perception on others who use AI === As much as people perceive and make judgment about AI itself, they also form impressions of themselves and others who use AI. In the workplace, employees who disclose the use of AI in their tasks are more likely to receive feedback that they are not as hardworking as those who are in the same job who receive non-AI help to complete the same tasks. AI use disclosure diminishes the perceived legitimacy in the employee's task and decision making which ultimately leads observers to distrust people who use AI. Although these negative effects of AI use disclosure are weakened by the observers who use AI frequently themselves, the effect is still not attenuated by the observers' positive attitude towards AI. === Bias, AI, and human === Although AI provides a wide range of information and suggestions to its users, AI itself is not free of biases and stereotypes, and it does not always help people reduce their cognitive errors and biases. People are prone to such errors by failing to see other potential ideas and cases that are not listed by AI responses and committing to a decision suggested by AI that directly contradicts the correct information and directions that they are already aware of. Gender bias is also reflected as the female gendering of AI technologies which conceptualizes females as a helpful assistant. == Emotional connection with AI == Human-AI interaction has been theorized in the context of interpersonal relationships mainly in social psychology, communications and media studies, and as a technology interface through the lens of hu

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  Nanonetwork

  A nanonetwork or nanoscale network is a set of interconnected nanomachines (devices a few hundred nanometers or a few micrometers at most in size) which are able to perform only very simple tasks such as computing, data storing, sensing and actuation. Nanonetworks are expected to expand the capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information. Nanonetworks enable new applications of nanotechnology in the biomedical field, environmental research, military technology and industrial and consumer goods applications. Nanoscale communication is defined in IEEE P1906.1. == Communication approaches == Classical communication paradigms need to be revised for the nanoscale. The two main alternatives for communication in the nanoscale are based either on electromagnetic communication or on molecular communication. === Electromagnetic === This is defined as the transmission and reception of electromagnetic radiation from components based on novel nanomaterials. Recent advancements in carbon and molecular electronics have opened the door to a new generation of electronic nanoscale components such as nanobatteries, nanoscale energy harvesting systems, nano-memories, logical circuitry in the nanoscale and even nano-antennas. From a communication perspective, the unique properties observed in nanomaterials will decide on the specific bandwidths for emission of electromagnetic radiation, the time lag of the emission, or the magnitude of the emitted power for a given input energy, amongst others. For the time being, two main alternatives for electromagnetic communication in the nanoscale have been envisioned. First, it has been experimentally demonstrated that is possible to receive and demodulate an electromagnetic wave by means of a nanoradio, i.e., an electromechanically resonating carbon nanotube which is able to decode an amplitude or frequency modulated wave. Second, graphene-based nano-antennas have been analyzed as potential electromagnetic radiators in the terahertz band. === Molecular === Molecular communication is defined as the transmission and reception of information by means of molecules. The different molecular communication techniques can be classified according to the type of molecule propagation in walkaway-based, flow-based or diffusion-based communication. In walkway-based molecular communication, the molecules propagate through pre-defined pathways by using carrier substances, such as molecular motors. This type of molecular communication can also be achieved by using E. coli bacteria as chemotaxis. In flow-based molecular communication, the molecules propagate through diffusion in a fluidic medium whose flow and turbulence are guided and predictable. The hormonal communication through blood streams inside the human body is an example of this type of propagation. The flow-based propagation can also be realized by using carrier entities whose motion can be constrained on the average along specific paths, despite showing a random component. A good example of this case is given by pheromonal long range molecular communications. In diffusion-based molecular communication, the molecules propagate through spontaneous diffusion in a fluidic medium. In this case, the molecules can be subject solely to the laws of diffusion or can also be affected by non-predictable turbulence present in the fluidic medium. Pheromonal communication, when pheromones are released into a fluidic medium, such as air or water, is an example of diffusion-based architecture. Other examples of this kind of transport include calcium signaling among cells, as well as quorum sensing among bacteria. Based on the macroscopic theory of ideal (free) diffusion the impulse response of a unicast molecular communication channel was reported in a paper that identified that the impulse response of the ideal diffusion based molecular communication channel experiences temporal spreading. Such temporal spreading has a deep impact in the performance of the system, for example in creating the intersymbol interference (ISI) at the receiving nanomachine. In order to detect the concentration-encoded molecular signal two detection methods named sampling-based detection (SD) and energy-based detection (ED) have been proposed. While the SD approach is based on the concentration amplitude of only one sample taken at a suitable time instant during the symbol duration, the ED approach is based on the total accumulated number of molecules received during the entire symbol duration. In order to reduce the impact of ISI a controlled pulse-width based molecular communication scheme has been analysed. The work presented in showed that it is possible to realize multilevel amplitude modulation based on ideal diffusion. A comprehensive study of pulse-based binary and sinus-based, concentration-encoded molecular communication system have also been investigated.

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  Photonically Optimized Embedded Microprocessors

  The Photonically Optimized Embedded Microprocessors (POEM) is DARPA program. It should demonstrate photonic technologies that can be integrated within embedded microprocessors and enable energy-efficient high-capacity communications between the microprocessor and DRAM. For realizing POEM technology CMOS and DRAM-compatible photonic links should operate at high bit-rates with very low power dissipation. == Current research == Currently research in this field is at University of Colorado, Berkley University, and Nanophotonic Systems Laboratory ( Ultra-Efficient CMOS-Compatible Grating Coupler Design).

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