AI Content Generator Jobs

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

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  North Atlantic Population Project

  The North Atlantic Population Project (NAPP) is a collaboration of historical demographers in Britain, Canada, Denmark, Germany, Iceland, Norway, and Sweden to produce a massive census microdata collection for the North Atlantic Region in the late-nineteenth century. The database includes complete individual-level census enumerations for each country, and provides information on over 110 million people. This large scale allows detailed analysis of small geographic areas and population subgroups. The NAPP database is designed to be compatible with the Integrated Public Use Microdata Series (IPUMS), and is disseminated through the IPUMS data-access system at the Minnesota Population Center, University of Minnesota. Major collaborators on the project include Lisa Dillon, University of Montreal; Chad Gaffield, University of Ottawa; Ólöf Garðarsdóttir, Statistics Iceland; Marianne Jarnes Erikstad, University of Tromsø; Jan Oldervall University of Bergen; Evan Roberts, University of Minnesota; Steven Ruggles, University of Minnesota; Kevin Schürer, UK Data Archive; Gunnar Thorvaldsen, University of Tromsø; and Matthew Woollard, UK Data Archive. The project is also coordinated by the Minnesota Population Center at the University of Minnesota.

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  General Problem Solver

  General Problem Solver (GPS) is a computer program created in 1957 by Herbert A. Simon, J. C. Shaw, and Allen Newell (RAND Corporation) intended to work as a universal problem solver machine. In contrast to the former Logic Theorist project, the GPS works with means–ends analysis. == Overview == Any problem that can be expressed as a set of well-formed formulas (WFFs) or Horn clauses, and that constitutes a directed graph with one or more sources (that is, hypotheses) and sinks (that is, desired conclusions), can be solved, in principle, by GPS. Proofs in the predicate logic and Euclidean geometry problem spaces are prime examples of the domain of applicability of GPS. It was based on Simon and Newell's theoretical work on logic machines. GPS was the first computer program that separated its knowledge of problems (rules represented as input data) from its strategy of how to solve problems (a generic solver engine). GPS was implemented in the third-order programming language, IPL. While GPS solved simple problems such as the Towers of Hanoi that could be sufficiently formalized, it could not solve any real-world problems because the search was easily lost in the combinatorial explosion. Put another way, the number of "walks" through the inferential digraph became computationally untenable. (In practice, even a straightforward state space search such as the Towers of Hanoi can become computationally infeasible, albeit judicious prunings of the state space can be achieved by such elementary AI techniques as A and IDA). The user defined objects and operations that could be done on the objects, and GPS generated heuristics by means–ends analysis in order to solve problems. It focused on the available operations, finding what inputs were acceptable and what outputs were generated. It then created subgoals to get closer and closer to the goal. The GPS paradigm eventually evolved into the Soar architecture for artificial intelligence.

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  VoID

  The Vocabulary of Interlinked Datasets (VoID) is a vocabulary for providing concise summaries (metadata) of Resource Description Framework (RDF) datasets—meaningful collections of semantic triples—using the syntax of RDF Schema. It can be used for general metadata (such as information about the license of the dataset), access metadata (information about how to access the dataset), structural metadata (information about how the dataset is structured), and linking metadata (information about links between datasets). A linked dataset is a collection of data, published and maintained by a single provider, available as RDF on the Web, where at least some of the resources in the dataset are identified by dereferencable Uniform Resource Identifiers (URIs). VoID is used to provide metadata on RDF datasets to facilitate query processing on a graph of interlinked datasets in the Semantic Web.

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  OntoCAPE

  OntoCAPE is a large-scale ontology for the domain of Computer-Aided Process Engineering (CAPE). It can be downloaded free of charge via the OntoCAPE Homepage. OntoCAPE is partitioned into 62 sub-ontologies, which can be used individually or as an integrated suite. The sub-ontologies are organized across different abstraction layers, which separate general knowledge from knowledge about particular domains and applications. The upper layers have the character of an upper ontology, covering general topics such as mereotopology, systems theory, quantities and units. The lower layers conceptualize the domain of chemical process engineering, covering domain-specific topics such as materials, chemical reactions, or unit operations.

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  Domain adaptation

  Domain adaptation is a field associated with machine learning and transfer learning. It addresses the challenge of training a model on one data distribution (the source domain) and applying it to a related but different data distribution (the target domain). A common example is spam filtering, where a model trained on emails from one user (source domain) is adapted to handle emails for another user with significantly different patterns (target domain). Domain adaptation techniques can also leverage unrelated data sources to improve learning. When multiple source distributions are involved, the problem extends to multi-source domain adaptation. Domain adaptation is a specific type of transfer learning. According to the taxonomy laid out by Pan and Yang (2010), it falls into the category of transductive transfer learning. In this setting, the source and target tasks are the same (e.g., both are object recognition), but the domains differ (different marginal distributions). This distinguishes it from inductive transfer learning (where labeled data is available for the target task) and unsupervised transfer learning (where labels are unavailable in both domains). == Classification of domain adaptation problems == Domain adaptation setups are classified in two different ways: according to the distribution shift between the domains, and according to the available data from the target domain. === Distribution shifts === Common distribution shifts are classified as follows: Covariate Shift occurs when the input distributions of the source and destination change, but the relationship between inputs and labels remains unchanged. The above-mentioned spam filtering example typically falls in this category. Namely, the distributions (patterns) of emails may differ between the domains, but emails labeled as spam in the one domain should similarly be labeled in another. Prior Shift (Label Shift) occurs when the label distribution differs between the source and target datasets, while the conditional distribution of features given labels remains the same. An example is a classifier of hair color in images from Italy (source domain) and Norway (target domain). The proportions of hair colors (labels) differ, but images within classes like blond and black-haired populations remain consistent across domains. A classifier for the Norway population can exploit this prior knowledge of class proportions to improve its estimates. Concept Shift (Conditional Shift) refers to changes in the relationship between features and labels, even if the input distribution remains the same. For instance, in medical diagnosis, the same symptoms (inputs) may indicate entirely different diseases (labels) in different populations (domains). === Data available during training === Domain adaptation problems typically assume that some data from the target domain is available during training. Problems can be classified according to the type of this available data: Unsupervised: Unlabeled data from the target domain is available, but no labeled data. In the above-mentioned example of spam filtering, this corresponds to the case where emails from the target domain (user) are available, but they are not labeled as spam. Domain adaptation methods can benefit from such unlabeled data, by comparing its distribution (patterns) with the labeled source domain data. Semi-supervised: Most data that is available from the target domain is unlabelled, but some labeled data is also available. In the above-mentioned case of spam filter design, this corresponds to the case that the target user has labeled some emails as being spam or not. Supervised: All data that is available from the target domain is labeled. In this case, domain adaptation reduces to refinement of the source domain predictor. In the above-mentioned example classification of hair-color from images, this could correspond to the refinement of a network already trained on a large dataset of labeled images from Italy, using newly available labeled images from Norway. == Formalization == Let X {\displaystyle X} be the input space (or description space) and let Y {\displaystyle Y} be the output space (or label space). The objective of a machine learning algorithm is to learn a mathematical model (a hypothesis) h : X → Y {\displaystyle h:X\to Y} able to attach a label from Y {\displaystyle Y} to an example from X {\displaystyle X} . This model is learned from a learning sample S = { ( x i , y i ) ∈ ( X × Y ) } i = 1 m {\displaystyle S=\{(x_{i},y_{i})\in (X\times Y)\}_{i=1}^{m}} . Usually in supervised learning (without domain adaptation), we suppose that the examples ( x i , y i ) ∈ S {\displaystyle (x_{i},y_{i})\in S} are drawn i.i.d. from a distribution D S {\displaystyle D_{S}} of support X × Y {\displaystyle X\times Y} (unknown and fixed). The objective is then to learn h {\displaystyle h} (from S {\displaystyle S} ) such that it commits the least error possible for labelling new examples coming from the distribution D S {\displaystyle D_{S}} . The main difference between supervised learning and domain adaptation is that in the latter situation we study two different (but related) distributions D S {\displaystyle D_{S}} and D T {\displaystyle D_{T}} on X × Y {\displaystyle X\times Y} . The domain adaptation task then consists of the transfer of knowledge from the source domain D S {\displaystyle D_{S}} to the target one D T {\displaystyle D_{T}} . The goal is then to learn h {\displaystyle h} (from labeled or unlabelled samples coming from the two domains) such that it commits as little error as possible on the target domain D T {\displaystyle D_{T}} . The major issue is the following: if a model is learned from a source domain, what is its capacity to correctly label data coming from the target domain? == Four algorithmic principles == === Reweighting algorithms === The objective is to reweight the source labeled sample such that it "looks like" the target sample (in terms of the error measure considered). === Iterative algorithms === A method for adapting consists in iteratively "auto-labeling" the target examples. The principle is simple: a model h {\displaystyle h} is learned from the labeled examples; h {\displaystyle h} automatically labels some target examples; a new model is learned from the new labeled examples. Note that there exist other iterative approaches, but they usually need target labeled examples. === Search of a common representation space === The goal is to find or construct a common representation space for the two domains. The objective is to obtain a space in which the domains are close to each other while keeping good performances on the source labeling task. This can be achieved through the use of Adversarial machine learning techniques where feature representations from samples in different domains are encouraged to be indistinguishable. === Hierarchical Bayesian Model === The goal is to construct a Bayesian hierarchical model p ( n ) {\displaystyle p(n)} , which is essentially a factorization model for counts n {\displaystyle n} , to derive domain-dependent latent representations allowing both domain-specific and globally shared latent factors. == Software packages == Several compilations of domain adaptation and transfer learning algorithms have been implemented over the past decades: SKADA (Python) ADAPT (Python) TLlib (Python) Domain-Adaptation-Toolbox (MATLAB)

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  CoDi

  CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state. In a growth phase a neural network is grown in the CA-space based on an underlying chromosome. There are four types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase. Signals are distributed from the neuron bodies via their axon tree and collected from connection dendrites. These two basic interactions cover every case, and they can be expressed simply, using a small number of rules. == Cell interaction during signaling == The neuron body cells collect neural signals from the surrounding dendritic cells and apply an internally defined function to the collected data. In the CoDi model the neurons sum the incoming signal values and fire after a threshold is reached. This behavior of the neuron bodies can be modified easily to suit a given problem. The output of the neuron bodies is passed on to its surrounding axon cells. Axonal cells distribute data originating from the neuron body. Dendritic cells collect data and eventually pass it to the neuron body. These two types of cell-to-cell interaction cover all kinds of cell encounters. Every cell has a gate, which is interpreted differently depending on the type of the cell. A neuron cell uses this gate to store its orientation, i.e. the direction in which the axon is pointing. In an axon cell, the gate points to the neighbor from which the neural signals are received. An axon cell accepts input only from this neighbor, but makes its own output available to all its neighbors. In this way axon cells distribute information. The source of information is always a neuron cell. Dendritic cells collect information by accepting information from any neighbor. They give their output, (e.g. a Boolean OR operation on the binary inputs) only to the neighbor specified by their own gate. In this way, dendritic cells collect and sum neural signals, until the final sum of collected neural signals reaches the neuron cell. Each axonal and dendritic cell belongs to exactly one neuron cell. This configuration of the CA-space is guaranteed by the preceding growth phase. == Synapses == The CoDi model does not use explicit synapses, because dendrite cells that are in contact with an axonal trail (i.e. have an axon cell as neighbor) collect the neural signals directly from the axonal trail. This results from the behavior of axon cells, which distribute to every neighbor, and from the behavior of the dendrite cells, which collect from any neighbor. The strength of a neuron-neuron connection (a synapse) is represented by the number of their neighboring axon and dendrite cells. The exact structure of the network and the position of the axon-dendrite neighbor pairs determine the time delay and strength (weight) of a neuron-neuron connection. This principle infers that a single neuron-neuron connection can consist of several synapse with different time delays with independent weights. == Genetic encoding and growth of the network == The chromosome is initially distributed throughout the CA-space, so that every cell in the CA-space contains one instruction of the chromosome, i.e. one growth instruction, so that the chromosome belongs to the network as a whole. The distributed chromosome technique of the CoDi model makes maximum use of the available CA-space and enables the growth of any type of network connectivity. The local connection of the grown circuitry to its chromosome, allows local learning to be combined with the evolution of grown neural networks. Growth signals are passed to the direct neighbors of the neuron cell according to its chromosome information. The blank neighbors, which receive a neural growth signal, turn into either an axon cell or a dendrite cell. The growth signals include information containing the cell type of the cell that is to be grown from the signal. To decide in which directions axonal or dendritic trails should grow, the grown cells consult their chromosome information which encodes the growth instructions. These growth instructions can have an absolute or a relative directional encoding. An absolute encoding masks the six neighbors (i.e. directions) of a 3D cell with six bits. After a cell is grown, it accepts growth signals only from the direction from which it received its first signal. This reception direction information is stored in the gate position of each cell's state. == Implementation as a partitioned CA == The states of our CAs have two parts, which are treated in different ways. The first part of the cell-state contains the cell's type and activity level and the second part serves as an interface to the cell's neighborhood by containing the input signals from the neighbors. Characteristic of our CA is that only part of the state of a cell is passed to its neighbors, namely the signal and then only to those neighbors specified in the fixed part of the cell state. This CA is called partitioned, because the state is partitioned into two parts, the first being fixed and the second is variable for each cell. The advantage of this partitioning-technique is that the amount of information that defines the new state of a CA cell is kept to a minimum, due to its avoidance of redundant information exchange. == Implementation in hardware == Since CAs are only locally connected, they are ideal for implementation on purely parallel hardware. When designing the CoDi CA-based neural networks model, the objective was to implement them directly in hardware (FPGAs). Therefore, the CA was kept as simple as possible, by having a small number of bits to specify the state, keeping the CA rules few in number, and having few cellular neighbors. The CoDi model was implemented in the FPGA based CAM-Brain Machine (CBM) by Korkin. == History == CoDi was introduced by Gers et al. in 1998. A specialized parallel machine based on FPGA Hardware (CAM) to run the CoDi model on a large scale was developed by Korkin et al. De Garis conducted a series of experiments on the CAM-machine evaluating the CoDi model. The original model, where learning is based on evolutionary algorithms, has been augmented with a local learning rule via feedback from dendritic spikes by Schwarzer.

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  Colossus (supercomputer)

  Colossus is a supercomputer developed by xAI. Construction began in 2024 in Memphis, Tennessee; the system became operational in July 2024. It is currently the world's largest AI supercomputer. Colossus's primary purpose is to train the company's chatbot, Grok. In addition, Colossus provides computing support to the social-media platform X and to other projects of Elon Musk, such as SpaceX. In 2025, it expanded to neighboring Southaven, Mississippi across the Tennessee–Mississippi border. As of May 6, 2026, Anthropic has agreed to rent all compute capacity at the Colossus 1 data center. == Background == Colossus was launched in September 2024 at a former Electrolux site in South Memphis to train the AI language model Grok. Within 19 days of the project's conception, xAI was ready to begin construction. The site was chosen because the abandoned Electrolux building could be repurposed to expedite construction and its proximity to a nearby wastewater treatment facility provided a water source. As of February 2025, xAI plans to build an $80 million facility to process additional wastewater for use at the supercomputer. === xAI === Musk incorporated xAI in March 2023 with the stated purpose of understanding the "nature of the universe". The team includes former members of OpenAI, DeepMind, Microsoft, and Tesla. Musk was one of the founding members of the company OpenAI, investing up to US$45 million in 2015. He left OpenAI in 2018, reportedly to avoid conflicts of interest with Tesla. It has also been reported that he had made a bid for leadership at OpenAI and left when his proposal was rejected. The exact reasons for his departure from the company are unclear. Both Dell Technologies and Supermicro partnered with xAI to build the supercomputer. It was originally powered by 100,000 Nvidia graphics processing units (GPUs) and was constructed in 122 days. 3 months after the first 100,000 GPUs were deployed, xAI announced that they had increased the system to 200,000 GPUs and that they intended to continue increasing the computer's processing power to 1 million GPUs. As of April 2025, xAI claimed Colossus was the largest AI training platform in the world. == Choice of location == xAI selected Memphis, in southwestern Tennessee, as the site for Colossus in part because an existing industrial facility allowed the project to proceed more quickly than constructing a new data center. Elon Musk was initially told that building a data center would take 18–24 months. The company instead searched for a vacant facility and selected the former Electrolux factory in Memphis. Electrolux opened the facility in 2012 and operated it for about eight years before closing it in 2020 after relocating operations to Springfield, Tennessee. The building covered 785,000 sq ft (72,900 m2) and had been purchased by Phoenix Investors in December 2023 for $35 million . Because the structure was already in place, work on the supercomputer could begin immediately rather than waiting for a new facility to be constructed. According to Forbes, xAI considered seven or eight other sites before selecting Memphis, and Musk finalized the decision to build in Memphis in about a week. The decision was finalized in March 2024, after which construction began. xAI publicly announced in June 2024 that Colossus would be built in Memphis. The building itself was not the only reason xAI selected Memphis. According to the Greater Memphis Chamber, the company chose the city because of its "reliable power grid, ability to create a water recycling facility, proximity to the Mississippi River and ample land". The city was also able to provide the large amounts of electricity and water needed to operate the supercomputer. At full capacity, the system was expected to require 150 megawatts of electricity and millions of gallons of water per day. The project also relied on partnerships with local and regional organizations including Memphis Light, Gas and Water (MLGW), Tennessee Valley Authority (TVA), the City of Memphis, and Shelby County. The city also provided financial incentives for the project. == Environmental impact == AI data centers consume large amounts of energy. At the site of Colossus in South Memphis, the grid connection was only 8 MW, so xAI applied to temporarily set up more than a dozen gas turbines (Voltagrid’s 2.5 MW units and Solar Turbines’ 16 MW SMT-130s) which would steadily burn methane gas from a 16-inch natural gas main. Aerial imagery in April 2025 showed 35 gas turbines had been set up at a combined 422 MW. These turbines have been estimated to generate about "72 megawatts, which is approximately 3% of the (TVA) power grid". The higher number of gas turbines and the subsequent emissions requires xAI to have a major source permit. In Memphis, xAI was able to avoid some environmental rules in the construction of Colossus, such as operating without permits for the on-site methane gas turbines because they are "portable". The Shelby County Health Department told NPR that "it only regulates gas-burning generators if they're in the same location for more than 364 days". However, in a January 2026 ruling, the EPA revised its New Source Performance Standard and announced that large methane gas turbines require permits even for temporary operations. In November 2024, the grid connection was upgraded to 150 MW, and some turbines were removed. Along with high electricity needs, the expected water demand is over five million gallons of water per day. While xAI has stated they plan to work with MLGW on a wastewater treatment facility and the installation of 50 megawatts of large battery storage facilities, there are currently no concrete plans in place aside from a one-page factsheet shared by MLGW. == Community response == The plan to build Colossus in Memphis was unknown to residents, City Council members, and environmental agencies. Many did not find out about the project until the day before, or the day of, as they watched the announcement on the local news. Keshaun Pearson, president of Memphis Community Against Pollution, stated that there is a historical lack of transparency and communication surrounding environmental issues in Memphis. Some community members in Memphis have expressed concern about the potential for additional air and water pollution caused by the supercomputer. In a letter to the Shelby County Health Department, the Southern Environmental Law Center stated the emissions from the turbines make the facility "...likely the largest industrial emitter of NOx in Memphis..." This is due to data supplied by the manufacturer showing that "...xAI emits between 1,200 and 2,000 tons of smog-forming nitrogen oxides (NOx)..." At a public Shelby County Commissioner's hearing on April 9, 2025, residents living near the site of Colossus voiced complaints about air quality, noting that they have chronic respiratory issues related to living in a polluted section of Memphis. One woman said she smells "everything but the right thing and the right thing is the clean air." Other residents voiced frustration that Brent Mayo, the senior xAI official responsible for building out xAI's infrastructure, did not attend the meeting to discuss community concerns. Keshaun Pearson also stated that "We're getting more and more days a year where it is unhealthy for us to go outside." People living near the site of Colossus have said they were not offered the opportunity for a public review of the plans, nor were they provided with information on how their community could potentially benefit. The community is also concerned about the strain on the power grid. Memphis's peak demand is around 3 GW. In November 2024, TVA approved xAI's request for access to more than 100 megawatts of power to Colossus which is supplied by MLGW. In December 2022, MLGW imposed (then rescinded) rolling blackouts during several days of extreme cold, straining the power grid. In a letter to the TVA, the SELC "urged the agency to 'prioritize Memphis families' access to reliable power over the 'secondary purpose' of serving xAI". == Current progress == In early December 2024, Ted Townsend detailed how the power of Colossus doubled in its processing capability. When it first went online in September 2024, it was using "100,000 Nvidia H100 processing chips". This initial launch demonstrated Colossus to be the largest supercomputer globally. The maximum power consumption increased from 150 to 250 MW. As of June 2025, the supercomputer consists of 150,000 H100 GPUs, 50,000 H200 GPUs, and 30,000 GB200 GPUs. Another 110,000 GB200 GPUs are to be brought online at a second data center, also in the Memphis area. The expansion of this supercomputer has already been discussed and will be the second phase of the project. xAI also plans to increase Colossus to 1 million GPUs. Because the supercomputer currently utilizes gas turbines for power, alongside 168 Tesla Megapack battery storage units. xAI is also looking to add more

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  Civitai

  Civitai is an online platform and marketplace for generative artificial intelligence (Gen AI) content, primarily focused on AI-generated images and models, and AI-generated videos. == History == Civitai was founded in 2022 by Justin Maier. By January 2023, the site reached 100,000 registered users and 3 million by November. In November 2023, Civitai secured funding from venture capital firm Andreessen Horowitz. By April 2024, Civitai had 23.2 million monthly accesses. The company is headquartered in Boise, Idaho. == Platform == Civitai allows users to share and download AI models, particularly those used for image generation. The platform supports various AI models, including Stable Diffusion and Flux, and provides a space for users to showcase and monetize their AI-generated content. Users have profile pages and can comment on other users' models and images. The website also features a virtual currency called Buzz that can be used to generate images on Civitai's servers. Buzz can be bought or earned by engaging with the site. The platform is open source. == Controversies == In 2023, 404 Media reported that Civitai began a "Bounties" marketplace where users could commission deepfakes, of real or fake people. Users are rewarded with Buzz for completing Bounties. In December 2023, AI provider OctoML announced it had ended its business relationship with Civitai after concerns were raised users were generating images that “could be categorized as child pornography.”

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  AI nationalism

  AI nationalism is the idea that nations should develop and control their own artificial intelligence technologies to advance their own interests and ensure technological sovereignty. This concept is gaining traction globally, leading countries to implement new laws, form strategic alliances, and invest significantly in domestic AI capabilities. == Global trends and national strategies == In 2018, British technology investor Ian Hogarth published an influential essay titled AI Nationalism. He argued that as AI gains more power and its economic and military significance expands, governments will take measures to bolster their own domestic AI industries, and predicted that the advancement of machine learning systems would lead to what he termed "AI nationalism." He anticipated that this rise in AI would accelerate a global arms race, resulting in more closed economies, restrictions on foreign acquisitions, and limitations on the movement of talent. Hogarth predicted that AI policy would become a central focus of government agendas. He also criticized Britain’s approach to AI strategy, citing the sale of London-based DeepMind—one of the leading AI laboratories, acquired by Google for a relatively modest £400 million in 2014—as a significant misstep. AI nationalism is chiefly reflected in the escalating rhetoric of an artificial intelligence arms race, portraying AI development as a zero-sum game where the winner gains significant economic, political, and military advantages. This mindset, as highlighted in a 2017 Pentagon report, warns that sharing AI technology could erode technological supremacy and enhance rivals' capabilities. The winner-takes-all mentality of AI nationalism poses risks including unsafe AI development, increased geopolitical tension, and potential military aggression (such as cyberattacks or targeting AI professionals). Several countries, including Canada, France, and India, have formulated national strategies to advance their positions in AI. In the United States, a leading player in the global AI arena, trade policies have been enacted to restrict China's access to critical microchips, reflecting a strategic effort to maintain a technological edge. The United States’ National Security Commission on Artificial Intelligence (NSCAI) frames AI development as a critical aspect of a broader technology competition crucial for national success. It emphasizes the need to outpace China in AI to maintain strategic advantage, reflecting AI nationalism by linking geopolitical power directly to advancements in AI. France has seen notable governmental support for local AI startups, particularly those specializing in language technologies that cater to French and other non-English languages. In Saudi Arabia, Crown Prince Mohammed bin Salman is investing billions in AI research and development. The country has actively collaborated with major technology firms such as Amazon, IBM, and Microsoft to establish itself as a prominent AI hub. == Historical and cultural context == AI nationalism is seen as deeply connected to historical racism and imperialism. It is viewed not merely as a technological competition but as a contest over racial and civilizational superiority. Historically, technological achievements were often used to justify colonialism and racial hierarchies, with Western societies perceiving their advancements as evidence of superiority. In the context of AI, this historical context continues to shape views on intelligence and development. Some argue that AI nationalism reinforces the idea of fundamental civilizational divides, especially between the Western world and China. This perspective often frames China's progress in AI as a direct challenge to Western values, presenting the AI competition as a struggle over values. AI nationalism is said to draw from long-standing anti-Asian stereotypes, such as the "Yellow Peril," which portray Asian nations as threats to Western civilization. This viewpoint links Asian technological advances with dehumanization and artificiality, reflecting persistent anxieties about China's growing role in the global tech landscape. == Implications == AI nationalism is seen as a component of a broader trend towards the fragmentation of the internet, where digital services are increasingly influenced by local regulations and national interests. This shift is creating a new technological landscape in which the impact of artificial intelligence on individuals' lives can vary significantly depending on their geographic location. J. Paul Goode argues that AI nationalism may exacerbate existing societal divisions by promoting the development of systems that embed cultural biases, thereby privileging certain groups while disadvantaging others.

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  Xinhua–Sogou AI news anchor

  Xinhua News Agency and Sogou of China developed an artificial intelligence (AI) for news reporting purposes. The AI was unveiled in 2018. It is touted to be the "world's first AI news anchor". == History == The AI was unveiled at the 2018 World Internet Conference in Wuzhen, Zhejiang, China. The AI devises avatars patterned after real life Xinhua anchors. The AI patterned after Qiu Hao spoke in Chinese, while the one derived from the likeness of Zhang Zhao speaks in English. The unveiling of the AI raised concerns of its impact on employment. Xinhua and Sogou unveiled Xin Xiaomeng, an AI with a female avatar in 2019. People's Daily followed suit by unveiling its own AI newscaster in 2023.

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  ProVisual Engine

  The ProVisual Engine is an AI-powered imaging system developed by Samsung Electronics for mobile devices. It was introduced in 2024 with the Galaxy S24 series as a component of Samsung's Galaxy AI ecosystem, providing advanced image processing to enhance image quality in photography and videography. == Overview == The ProVisual Engine processes images using adaptive scene recognition, real-time optimization, and advanced image processing. It adjusts color accuracy, dynamic range, and noise levels, providing both automated and manual controls to accommodate various user preferences. == Features == The ProVisual Engine encompasses several features. === Quad Tele System === The Quad Tele System features 2x, 3x, 5x, and 10x optical zoom, supported by digital processing to enhance zoom clarity and detail. It incorporates Image Signal Processing (ISP) to refine detail retention, reduce noise, and enhance image clarity at different zoom levels while minimizing distortion. === Nightography === Nightography utilizes noise reduction techniques and advanced sensor technology to enhance low-light photography. By adjusting exposure and minimizing motion blur, the system helps produce more precise and more detailed images in dark environments for both photos and videos. === Generative Edit === Generative Edit allows for object removal, background expansion, and intelligent resizing. It reconstructs missing areas by filling backgrounds and completing cut-off objects, adjusting composition while preserving image integrity and refinement. === Expert RAW === Expert RAW allows users to capture RAW images directly from the camera app for advanced shooting and editing. It includes HDR (High Dynamic Range) support to enhance detail and dynamic range. The ProVisual Engine utilizes multi-frame processing to generate RAW images with increased clarity and depth for post-processing. === Enhance-X and Camera Shift === Enhance-X is an AI-based image processing tool that applies upscaling, noise reduction, and sharpening. Its Camera Shift feature adjusts the perceived camera height by modifying framing and proportions. A recent update extended support to human and pet images. == Compatible devices == As of 2025, the ProVisual Engine is available on the following devices: === Galaxy S series === Galaxy S26 Series (Galaxy S26, S26+. S26 Ultra) Galaxy S25 Series (Galaxy S25, S25+, S25 Edge, S25 Ultra, S25 FE) Galaxy S24 Series (Galaxy S24, S24+, S24 Ultra) === Galaxy Z series === Galaxy Z Fold 7 Galaxy Z Flip 7, Z Flip 7 FE Galaxy Z Fold 6 Galaxy Z Flip 6 === Galaxy Tab S series === Galaxy Tab S10 series (Tab S10+, Tab S10 Ultra) Galaxy Tab S9 series (Tab S9, Tab S9+, Tab S9 Ultra) === Galaxy Z series === Galaxy Z Fold 7, Z Flip 7, Z Flip 7 FE Galaxy Z Fold 6, Z Flip 6 === Galaxy Tab S series === Galaxy Tab S10 series (Tab S10+, Tab S10 Ultra) Galaxy Tab S9 series (Tab S9, Tab S9+, Tab S9 Ultra) Note: Quad Tele System refers to the multi-telephoto setup (2×, 3×, 5×, 10×) available only on the Ultra models (S24 Ultra and S25 Ultra). Note: On Galaxy Tab models, only Enhance-X editing features are supported; the Expert RAW camera app is not available.

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  Johns Hopkins Beast

  The Johns Hopkins Beast was a mobile automaton, an early pre-robot, built in the 1960s at the Johns Hopkins University Applied Physics Laboratory. The machine had a rudimentary intelligence and the ability to survive on its own. As it wandered through the white halls of the laboratory, it would seek black wall outlets. When it found one it would plug in and recharge. The robot was cybernetic. It did not use a computer. Its control circuitry consisted of dozens of transistors controlling analog voltages. It used photocell optics and sonar to navigate. The 2N404 transistors were used to create NOR logic gates that implemented the Boolean logic to tell it what to do when a specific sensor was activated. The 2N404 transistors were also used to create timing gates to tell it how long to do something. 2N1040 Power transistors were used to control the power to the motion treads, the boom, and the charging mechanism. The original sensors in Mod I were physical touch only. The wall socket was detected by physical switches on the arm that followed the wall. Once detected, two electrical prongs were extended until they entered the wall socket and made the electrical connection to charge the vehicle. The stairway, doors, and pipes on the hall wall were also detected by physical switches and recognized by appropriate logic. The sonar guidance system was developed for Mod I and improved for Mod II. It used two ultrasonic transducers to determine distance, location within the halls, and obstructions in its path. This provided "The Beast" with bat-like guidance. At this point, it could detect obstructions in the hallway, such as people. Once an obstruction was detected, the Beast would slow down and then decide whether to stop or divert around the obstruction. It could also ultrasonically recognize the stairway and doorways to take appropriate action. An optical guidance system was added to Mod II. This provided, among other capabilities, the ability to optically identify the black wall sockets that contrasted with the white wall. The Hopkins Beast Autonomous Robot Mod II link below was written by Dr. Ronald McConnell, at that time a co-op student and one of the designers for Mod II.

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  Free boundary condition

  In image processing, the free boundary condition is the convention used when applying a convolution kernel to a digital image in which pixel locations that lie outside the image boundaries are interpreted as having a value of zero.[1] The question of what value to assign out-of-bounds pixels may arise, for instance, when applying a 3×3 kernel to the corner pixel in an image.

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  POSC Caesar

  POSC Caesar Association (PCA) is an international, open and not-for-profit, member organization that promotes the development of open specifications to be used as standards for enabling the interoperability of data, software and related matters. PCA is the initiator of ISO 15926 "Integration of life-cycle data for process plants including oil and gas production facilities" and is committed to its maintenance and enhancement. Nils Sandsmark has been the General Manager of POSC Caesar Association since 1999 and Thore Langeland, Norwegian Oil Industry Association (Norwegian: Oljeindustriens Landsforening, OLF), is the chairman of the board. == History == === Caesar Offshore === The first predecessor of POSC Caesar Association, the Caesar Offshore program, started in 1993. The original focus was on standardizing technical data definitions for capital intensive projects at the handover from the EPC contractor to the owner/operators of onshore and offshore oil and gas production facilities. The program was sponsored by The Research Council of Norway, two EPC contractors (Aker Maritime and Kværner), three owners/operators (Norsk Hydro, Saga Petroleum and Statoil) and DNV as service provider and project owner. === POSC Caesar project === During the period 1994–96, Caesar Offshore Program was defined as a project of Petrotechnical Open Software Corporation (POSC) (now Energistics), and changed its name to the POSC Caesar Project. In 1995 the project was joined by BP, Brown and Root and Elf Aquitaine and in 1997 by Intergraph, IBM, Oracle, Lloyd's, Shell, ABB and UMOE Technologies. During that time, POSC Caesar also became a member of European Process Industries STEP Technical Liaison Executive (EPISTLE) where it collaborates with PISTEP (UK), and USPI-NL (The Netherlands) on the development of ISO 10303, also known as "Standard for the Exchange of Product model data (STEP)". === POSC Caesar Association === In 1997, POSC Caesar Association was founded as an independent, global, non-profit, member organization. POSC Caesar Association serves an international membership and collaborates with other international organizations. It has its main office in Norway. Albeit the name of POSC Caesar Association still hints to its past as a project within the Petrotechnical Open Software Corporation (POSC) (now Energistics), from 1997 onwards, the organization has been independent. Energistics and POSC Caesar Association do collaborate, and are formally member in each other's organization. == Membership == POSC Caesar Association has with its current 36 members from around the world and has established an international footprint (with a strong membership in Norway) that includes a variety of backgrounds, from academia and solution providers to engineering contractors and owners/operators. The members are (subdivided by organization type): Associations: Energistics (USA) and The Norwegian Oil Industry Association (OLF, Norway); Universities and Research Institutes: International Research Institute of Stavanger (IRIS, Norway), Norwegian University of Science and Technology (NTNU, Norway), Korea Advanced Institute of Science and Technology (KAIST, Korea), SINTEF (Norway), University of Bergen (Norway), University of Oslo (Norway), University of Stavanger (Norway), University of Tromsø (Norway) and Western Norway Research Institute (Norway); Oil and Gas Companies: BP (UK), Petronas (Malaysia) and Statoil (Norway); Engineering contractors and consultants: Akvaplan-niva (Norway), Aker Solutions (Norway), Asset Life Cycle Information Management (ALCIM, Malaysia), CAESAR systems (USA), Bechtel (USA), Det Norske Veritas (DNV, Norway), Information Logic (USA) and iXIT Engineering Technology (Germany), Phusion IM Ltd (UK); Solution providers: Aveva (UK), Bentley Systems (USA), Jotne EPM Technology (Norway), Epsis (Norway), Eurostep (Sweden), International Business Machines Corporation (IBM, USA), Siemens - Comos Industry Solutions (before Innotec) (Germany), Intergraph (USA), Invenia (Norway), Keel Solution (Denmark), Noumenon (UK), NRX (Canada), Octaga (Norway) and Tektonisk (Norway). In general, the organization holds three membership meetings a year; one in January / February in North-America (typically USA), one in April / May in Europe (typically Norway) and one in October in Asia (typically Malaysia). == Activities and services == === Initiator and custodian of ISO 15926 === In consultation with the other EPISTLE members and the International Organization for Standardization (ISO), it was decided in 2003 (some say already in 1997) that for modeling-technical reasons it was better to discontinue the development of ISO 10303 and to initiate the development of ISO 15926 "Integration of life-cycle data for process plants including oil and gas production facilities." Over the years, the scope of the standard has increased from the initial capital-intensive projects in the upstream oil and gas industry, to include also relevant terminology for downstream oil and gas industry applications and to deal with real-time data related to the actual oil and gas production. ISO 15926 has also over the years evolved from a dictionary (a list of terms with definitions), over a taxonomy (added hierarchy) to an ontology (a formal representation of a set of concepts within a domain and the relationships between those concepts). ISO 15926 is therefore sometimes nicknamed the "Oil and Gas Ontology", for some considered to be an essential prerequisite together with Semantic Web technologies to get to better interoperability, an optimal use of all available data across boundaries and an increase in efficiency. This is what some call the next generation of Integrated Operations. === Reference data services === Placeholders: Flow scheme of WIP - RDS - ISO and role of SIGs RDS Standards in database pilot (ISO) === Special interest groups === Placeholders: Overview of SIGs Drilling and Completion Reservoir and Production Operations and Maintenance == Projects == There are a number of projects (co-)organized by POSC Caesar Association working on the extension of the ISO 15926 standard in different application areas. === Capital intensive projects application domain === The following projects are running at the moment (August 2009): The ADI Project of FIATECH, to build the tools (which will then be made available in the public domain) The IDS Project of POSC Caesar Association, to define product models required for data sheets A joint collaboration project between FIATECH POSC Caesar Association is the ADI-IDS project is the ISO 15926 WIP === Upstream oil and gas industry application domain === The following projects are currently running (August 2009): The Integrated Operations in the High North (IOHN) project is working on extending ISO 15926 to handle real-time data transmission and (pre-)processing to enable the next generation of Integrated Operations. The Environment Web project to include environmental reporting terms and definitions as used in EPIM's EnvironmentWeb in ISO 15926. Finalised projects include: The Integrated Information Platform (IIP) project working on establishing a real-time information pipeline based on open standards. It worked among others on: Daily Drilling Report (DDR) to including all terms and definitions in ISO 15926. This standard became mandatory on February 1, 2008 for reporting on the Norwegian Continental Shelf by the Norwegian Petroleum Directorate (NPD) and Safety Authority Norway (PSA). NPD says that the quality of the reports has improved considerably since. Daily Production Report (DPR) to including all terms and definitions in ISO 15926. This standard was tested successfully on the Valhall (BP-operated) and Åsgard (StatoilHydro-operated) fields offshore Norway. The terminology and XML schemata developed have also been included in Energistics’ PRODML standard. == Conferences and events == === Semantic Days === === Sogndal academic network meeting === == Collaborations == POSC Caesar is collaborating with a number of standardization bodies, including: Mimosa: collaboration on open information standards for Operations and Maintenance mainly for the downstream oil and gas industry; FIATECH: collaboration on open information standards for life cycle data of capital projects; Energistics: collaboration on information standards for the upstream oil and gas industry, including WITSML and PRODML; OASIS: collaboration on e-business standards; ISO TC184/SC4: the host of the ISO 15926 standard.

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  Versata

  Versata is a privately held software company, one of several business units under the ESW Capital umbrella. Versata acquires underperforming or financially struggling enterprise software companies, integrates them into their portfolio, and makes operational changes to improve the viability and performance of the companies. == History == === Early years (1991–2000) === This company was founded in 1991 with the name Image Innovations; Naren Bakshi was co-founder and president, Kevin Fletcher Tweedy was vice president of technology, and they sold a development tool set named Image Application WorkBench that worked with Plexus Software's imaging platform. In 1997, the company name changed to Vision Software. They sold a small suite of software: Vision Builder for accelerated coding; and Vision StoryBoard Pro for creating software documentation. In 1998, their flagship product was a Java development tool named Vision JADE. In January 2000, the company changed names again, this time to Versata, and their e-business automation system, Versata Logic Suite, had three components: Versata Logic Server to host business rules written in Java, Versata Studio for developing the business rules, and Versata Connectors for connecting the logic server to IBM database servers. === Public company (2000–2006) === They went public in March 2000 during the dot-com bubble, raising about $94 million and reaching a market capitalization of over $2.5 billion despite reporting just $13 million in revenue and a $21 million loss in the prior year. In November 2000, Versata expanded into the business workflow area with the acquisition of Verve, Inc. and its workflow management system by the same name. From early 2001 through mid-2003, Versata's revenues were in quarter-over-quarter decline until Alan Baratz took over as CEO. Five consecutive quarters of growth followed until early 2005, when revenues once again took a downward plunge. In mid-2005, the company was notified by NASDAQ that it no longer met NASDAQ's requirements for continued listing, related to maintenance of a minimum amount of shareholder's equity, market value, or net income. In July 2005, Versata was delisted from NASDAQ and publicly traded on the OTC (also known as the Pink Sheets). == Versata, a business unit of ESW Capital == In January 2006, Austin-based Trilogy, Inc. acquired the company and took it private. Trilogy then proceeded to merge portions of Trilogy, specifically, Trilogy Technology Group, into Versata and began acquiring further companies, reorganizing dramatically and offshoring most technical positions to its office in Bangalore, India. From 2006 to 2008, Versata continued to make acquisitions mostly in US. Most of the employees in the acquired companies were laid -off with the majority work being offshored to its India office in Bangalore. In early 2009, Versata made another major overhaul of its business model when it asked all its employees in India to work as contractors through oDesk for a gDev which is an entity incorporated by Trilogy to manage its outsourcing activities. The only employees left in Versata were the ones in US. == Acquisitions == a Corizon was acquired by Metatomix, while Metatomix was part of Versata. b Infopia was acquired by Everest Software, while Everest Software was part of Versata. c Symphony Commerce was acquired by Quantum Retail, while Quantum Retail was part of Versata. == Legal disputes == === Patent infringement and "poison pill" lawsuits with Selectica === The legal disputes with Selectica began in 2004 (before Trilogy acquired Versata in January 2006) and lasted until 2010. While there were many suits and counter-suits, they largely centered around three issues: 2004–2006: Patent infringement in configure, price, and quote (CPQ) software 2005–2007: Patent infringement in contract lifecycle management (CLM) software 2008–2010: The "poison pill" lawsuit In 2004, Selectica and Trilogy had competing CPQ software: Selectica sold Solutions Advisor and Deal Optimization, while Trilogy sold Selling Chain. In April of that year, Trilogy Software sued Selectica for patent infringement. In 2005, before the court ruling, Trilogy made several offers to buy Selectica, but the board rejected them. In January 2006, the court ordered Selectica to pay Trilogy $7.5 million in damages. Four days after the January 2006 judgment in the first lawsuit, Trilogy announced its acquisition of Versata for an undisclosed amount. In 2005, Selectica had acquired the Determine CLM software platform, which included features that overlapped with some offered by Versata. In October 2006, Versata filed a second patent infringement lawsuit. The case was settled in 2007, with Selectica agreeing to pay Trilogy and Versata $10 million, plus up to $7.5 million in additional contingent payments. In 2008, Versata began acquiring Selectica stock. By December, Selectica's board amended its shareholder rights plan to adopt a "poison pill" with an unusually low trigger threshold: if any shareholder acquired more than 4.99% of company stock, their ownership would be diluted. The board explained that the move was meant to protect Selectica's net operating losses (NOLs), which were tax-deductible if the company returned to profitability. Under IRS Section 382, a significant change in stock ownership could cause those NOLs to be disqualified. Versata intentionally triggered the poison pill and also offered to sell back the stocks at a profit (greenmailing them), which prompted a legal dispute over whether Selectica's board had the authority to set such a low threshold and whether defending NOLs justified triggering shareholder dilution. The case ultimately reached the Delaware Supreme Court, which upheld the poison pill in October 2010, ruling in favor of Selectica. === Intellectual property lawsuit over joint development with Sun Microsystems === In 1998, Sun Microsystems hired Trilogy to help Sun's developers in California create a software configurator (later named the WC5 Configurator) that Sun's customers could use to modify products they wanted to buy, customizing products to have the features they wanted. Trilogy worked on the WC5 Configurator for several years, then Sun transferred the work to Oracle to finish. Trilogy believed that they owned the copyright to the work they'd done for Sun, and in 2006 after the merger with Versata they sued Sun for more than $100 million in damages. In April 2009, a jury ruled in favor of Sun and rejected Versata's claims. === Patent lawsuit and ruling on patents of abstract ideas with SAP === SAP developed Pricing Engine, a component in their enterprise resource planning (ERP) system. It competed with an older Trilogy product called Pricer, which was part of Trilogy's Selling Chain platform in the mid-1990s before they merged with Versata. In April 2007—the year after Trilogy acquired Versata—Versata filed a lawsuit against SAP for patent infringement. In August 2009, the jury agreed with Versata and awarded them $139 million. The court granted a new trial on damages and in September 2011, in the retrial, the jury awarded Versata $345 million. This then went to the US Court of Appeals, which in May 2013 affirmed the $345 million damages award, plus interest that had accumulated. In October 2014, Versata and SAP settled their litigation for an undisclosed amount of money. With the dispute between Versata and SAP settled, in June 2013 the Patent Trial and Appeal Board (PTAB) reviewed the validity of the patent itself, and issued a decision in a Covered Business Method (CBM) review, stating that the disputed items were abstract ideas and thus under the US patent law not patentable. In July 2015, the Federal Circuit agreed with PTAB's decision that the challenged items were not patentable. === Trade secrets and damages dispute with Internet Brands === Internet Brands was formerly known as CarsDirect and AutoData Solutions. Like Trilogy, they made software for automakers that helped customers compare vehicles online. In the late 1990s, Trilogy and Internet Brands tried to combine their products but failed to do so, and after a December 1999 lawsuit they made a settlement agreement in May 2001. In 2008, Versata sued Internet Brands claiming they had violated the settlement agreement by making presentations to potential clients stating they had a license from Versata to use and sell Versata technical solutions; and doing so had cost Versata business with Chrysler. Internet Brands' countersuit argued that Versata had misappropriated trade secrets and asked the jury to use Versata's business relationship with Toyota—including revenue from Toyota contracts—as a benchmark to calculate damages. The jury agreed and used that data to determine a $2 million damages award in favor of Internet Brands’ subsidiary, AutoData Solutions. Versata appealed the decision, and in January 2014 the court upheld the $2 million award to Internet Brands. === Patent challenges a

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