Blocknots were random sequences of numbers contained in a book and organized by numbered rows and columns and were used as additives in the reciphering of Soviet Union codes, during World War II. The Blocknot consisted of a booklet of fifty sheets of 5-figure random additive, 100 additive groups to a sheet. No sheet was used more than once, thus the blocknots were in effect a form of one-time pad. The Soviet Unions highest grade ciphers that were used in the East, were the 5-figure codebook enciphered with the Blocknot book, and were generally considered unbreakable. == Technical Description == Blocknots were distributed centrally from an office in Moscow. Every Blocknot contained 5-figure groups in a number of sheets, for the enciphering of 5-figure messages. The encipherment was effected by applying additives taken from the pad, of which 50-100 5-figure groups appeared. Each pad had a 5-figure number and each sheet had a 2-figure number running consecutively. There were 5 different types of Blocknots, in two different categories The Individual in which each table of random numbers was used only once. The General in which each page of the Blocknot was valid for one day. The security of the additive sequence rested on the choice of different starting points for each message. In 5-figure messages, the blocknot was one of the first 10 Groups in the message. Its position changed at long intervals, but was always easy to re-identify. The Russians differentiated between three types of blocks: The 3-block, DRIERBLOCK. I-block for Individual Block: 50 pages, additive read off in one direction only. The messages could be used and read only between 2 wireless telegraphy stations on one net. The 6-block, SECHSERBLOCK. Z-block for Circular Block: 30 pages, additive read off in either direction. The messages could be used and read, between all W/T stations in a net. The 2-block, ZWEIERBLOCK. OS-block. Used only in traffic from lower to higher formations. Two other types were used, in lower echelons. Notblock: Used in an emergency. Blocknot used for passing on traffic. The distribution of Blocknots was carried out centrally from Moscow to Army Groups then to Armies. The Army was responsible for their distribution throughout the lower levels of the army down to company level. Independent units took their cipher material with them. Occasionally the same blocknot was distributed to two units on different parts of the front, which enabled Depth to be established. Records of all Blocknots used were kept in Berlin and when a repeat was noticed a BLOCKNOT ANGEBOT message was sent out to all German Signals units, to indicate that it may have been possible to break the code using it. There was no certainty in this. A cryptanalyst with the General der Nachrichtenaufklärung stated while being interrogated by TICOM: It seems that depths of up to 8 were established at the beginning of the Russian Campaign but that no 5-figure code was broken after May 1943 German cryptanalysts who were prisoners of war stated under interrogation, that each of the figures 0 to 9 were placed en clair usually within the first ten groups of the text or sometimes at the end. One indicator was the Blocknot number and the consisted of two random figures, the figure representing the type, and the remaining two, the page of the Blocknot being used. In long messages, 000000 was placed in the message when the end of a page had been reached. == Chi number == The Chi-number was the serial numbering of all 5-figure messages passing through the hands of the Cipher Officer, starting on the first of January and ending on thirty-first December of the current year. It always appeared as the last group in an intercepted message, e.g. 00001 on the 1st January, or when the unit was newly set up. The progression of Chi-numbers was carefully observed and recorded in the form of a graph. A Russian corps had about 10 5-figure messages per day, and Army about 20-30 and a Front about 60–100. After only a relatively short time, the individual curves separated sharply and the type of formation could be recognized by the height of the Chi-number alone. == Monitoring == Blocknots were tracked in a card index, that was maintained by the Signal Intelligence Evaluation Centre (NAAS). The NAAS functionality included evaluation and traffic analysis, cryptanalysis, collation and dissemination of intelligence. The card index, which was one amongst several Card Indexes. A careful recording and study of blocks provided the positive clues in the identification and tracking of formations using 5-figure ciphers. The index was subdivided into two files: Search card index, contained all blocknots and chi-numbers whether or not they were known. Unit card index, contained only known Block and Chi-numbers. Inspector Berger, who was the chief cryptanalyst of NAAS 1 stated that the two files formed: The most important and surest instruments for identifying Russian radio nets, known to him. The Blocknots were also used in the Stationary Intercept Company (Feste), the military unit that were designed to work at a lower level to the NAAS, at the Army level and were semi-motorized, and closer to the front. The Feste used the Blocknot value along with several other parameters to build a network diagram. The network diagram was studied extensively, as part of a 6-stage process, that involved several departments within the Feste. The outcome was a metric which determined the most interesting circuit for traffic monitoring, and least interesting, where monitoring of traffic should cease. == Analysis == Johannes Marquart was a mathematician and cryptanalyst who initially worked for Inspectorate 7/VI and later led Referat Ia of Group IV of the General der Nachrichtenaufklärung. Marquart was assigned the study of the Soviet Union Blocknot traffic. Marquart and his unit conducted extensive research in an attempt to discover the method by which they were produced. All the counts which they made, however, failed to reveal any non-random characteristics in the design of the tables, and while they thought the Blocknots must have been generated by machine, they were never able to draw any concrete deductions as a result of their research. == Example == The Soviet 3rd Guard Tank Army transmits a 5-figure message with the Blocknot of 37581 (one of the first 10 groups in the message). On the same day the Block 37582 was used by the same formation. The next day 37583 appeared. Thereafter, for a period, the Army was not heard by German Wireless telegraphy intercept operators, as it was maintaining wireless silence. After a few days, an unidentified net with the Blocknot 37588 is picked up. This message net is claimed, because of the proximity of the blocks (88/83) to be the 3rd Guard Tank Army. The missing Blocknots 84-87 were presumably used in telegraphic, telephonic or courier communications. The Chi number provides confirmation of the first assumption, based on proximity of blocknots in most cases.
Rendering equation
In computer graphics, the rendering equation is an integral equation that expresses the amount of light leaving a point on a surface as the sum of emitted light and reflected light. It was independently introduced into computer graphics by David Immel et al. and James Kajiya in 1986. The equation is important in the theory of physically based rendering, describing the relationships between the bidirectional reflectance distribution function (BRDF) and the radiometric quantities used in rendering. The rendering equation is defined at every point on every surface in the scene being rendered, including points hidden from the camera. The incoming light quantities on the right side of the equation usually come from the left (outgoing) side at other points in the scene (ray casting can be used to find these other points). The radiosity rendering method solves a discrete approximation of this system of equations. In distributed ray tracing, the integral on the right side of the equation may be evaluated using Monte Carlo integration by randomly sampling possible incoming light directions. Path tracing improves and simplifies this method. The rendering equation can be extended to handle effects such as fluorescence (in which some absorbed energy is re-emitted at different wavelengths) and can support transparent and translucent materials by using a bidirectional scattering distribution function (BSDF) in place of a BRDF. The theory of path tracing sometimes uses a path integral (integral over possible paths from a light source to a point) instead of the integral over possible incoming directions. == Equation form == The rendering equation may be written in the form L o ( x , ω o , λ , t ) = L e ( x , ω o , λ , t ) + L r ( x , ω o , λ , t ) {\displaystyle L_{\text{o}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)=L_{\text{e}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)+L_{\text{r}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)} L r ( x , ω o , λ , t ) = ∫ Ω f r ( x , ω i , ω o , λ , t ) L i ( x , ω i , λ , t ) ( ω i ⋅ n ) d ω i {\displaystyle L_{\text{r}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)=\int _{\Omega }f_{\text{r}}(\mathbf {x} ,\omega _{\text{i}},\omega _{\text{o}},\lambda ,t)L_{\text{i}}(\mathbf {x} ,\omega _{\text{i}},\lambda ,t)(\omega _{\text{i}}\cdot \mathbf {n} )\operatorname {d} \omega _{\text{i}}} where L o ( x , ω o , λ , t ) {\displaystyle L_{\text{o}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)} is the total spectral radiance of wavelength λ {\displaystyle \lambda } directed outward along direction ω o {\displaystyle \omega _{\text{o}}} at time t {\displaystyle t} , from a particular position x {\displaystyle \mathbf {x} } x {\displaystyle \mathbf {x} } is the location in space ω o {\displaystyle \omega _{\text{o}}} is the direction of the outgoing light λ {\displaystyle \lambda } is a particular wavelength of light t {\displaystyle t} is time L e ( x , ω o , λ , t ) {\displaystyle L_{\text{e}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)} is emitted spectral radiance L r ( x , ω o , λ , t ) {\displaystyle L_{\text{r}}(\mathbf {x} ,\omega _{\text{o}},\lambda ,t)} is reflected spectral radiance ∫ Ω … d ω i {\displaystyle \int _{\Omega }\dots \operatorname {d} \omega _{\text{i}}} is an integral over Ω {\displaystyle \Omega } Ω {\displaystyle \Omega } is the unit hemisphere centered around n {\displaystyle \mathbf {n} } containing all possible values for ω i {\displaystyle \omega _{\text{i}}} where ω i ⋅ n > 0 {\displaystyle \omega _{\text{i}}\cdot \mathbf {n} >0} f r ( x , ω i , ω o , λ , t ) {\displaystyle f_{\text{r}}(\mathbf {x} ,\omega _{\text{i}},\omega _{\text{o}},\lambda ,t)} is the bidirectional reflectance distribution function, the proportion of light reflected from ω i {\displaystyle \omega _{\text{i}}} to ω o {\displaystyle \omega _{\text{o}}} at position x {\displaystyle \mathbf {x} } , time t {\displaystyle t} , and at wavelength λ {\displaystyle \lambda } ω i {\displaystyle \omega _{\text{i}}} is the negative direction of the incoming light L i ( x , ω i , λ , t ) {\displaystyle L_{\text{i}}(\mathbf {x} ,\omega _{\text{i}},\lambda ,t)} is spectral radiance of wavelength λ {\displaystyle \lambda } coming inward toward x {\displaystyle \mathbf {x} } from direction ω i {\displaystyle \omega _{\text{i}}} at time t {\displaystyle t} n {\displaystyle \mathbf {n} } is the surface normal at x {\displaystyle \mathbf {x} } ω i ⋅ n {\displaystyle \omega _{\text{i}}\cdot \mathbf {n} } is the weakening factor of outward irradiance due to incident angle, as the light flux is smeared across a surface whose area is larger than the projected area perpendicular to the ray. This is often written as cos θ i {\displaystyle \cos \theta _{i}} . Two noteworthy features are: its linearity—it is composed only of multiplications and additions, and its spatial homogeneity—it is the same in all positions and orientations. These mean a wide range of factorings and rearrangements of the equation are possible. It is a Fredholm integral equation of the second kind, similar to those that arise in quantum field theory. Note this equation's spectral and time dependence — L o {\displaystyle L_{\text{o}}} may be sampled at or integrated over sections of the visible spectrum to obtain, for example, a trichromatic color sample. A pixel value for a single frame in an animation may be obtained by fixing t ; {\displaystyle t;} motion blur can be produced by averaging L o {\displaystyle L_{\text{o}}} over some given time interval (by integrating over the time interval and dividing by the length of the interval). Note that a solution to the rendering equation is the function L o {\displaystyle L_{\text{o}}} . The function L i {\displaystyle L_{\text{i}}} is related to L o {\displaystyle L_{\text{o}}} via a ray-tracing operation: The incoming radiance from some direction at one point is the outgoing radiance at some other point in the opposite direction. == Applications == Solving the rendering equation for any given scene is the primary challenge in realistic rendering. One approach to solving the equation is based on finite element methods, leading to the radiosity algorithm. Another approach using Monte Carlo methods has led to many different algorithms including path tracing, photon mapping, and Metropolis light transport, among others. == Limitations == Although the equation is very general, it does not capture every aspect of light reflection. Some missing aspects include the following: Transmission, which occurs when light is transmitted through the surface, such as when it hits a glass object or a water surface, Subsurface scattering, where the spatial locations for incoming and departing light are different. Surfaces rendered without accounting for subsurface scattering may appear unnaturally opaque — however, it is not necessary to account for this if transmission is included in the equation, since that will effectively include also light scattered under the surface, Polarization, where different light polarizations will sometimes have different reflection distributions, for example when light bounces at a water surface, Phosphorescence, which occurs when light or other electromagnetic radiation is absorbed at one moment and emitted at a later moment, usually with a longer wavelength (unless the absorbed electromagnetic radiation is very intense), Interference, where the wave properties of light are exhibited, Fluorescence, where the absorbed and emitted light have different wavelengths, Non-linear effects, where very intense light can increase the energy level of an electron with more energy than that of a single photon (this can occur if the electron is hit by two photons at the same time), and emission of light with higher frequency than the frequency of the light that hit the surface suddenly becomes possible, and Doppler effect, where light that bounces off an object moving at a very high speed will get its wavelength changed: if the light bounces off an object that is moving towards it, the light will be blueshifted and the photons will be packed more closely so the photon flux will be increased; if it bounces off an object moving away from it, it will be redshifted and the photon flux will be decreased. This effect becomes apparent only at speeds comparable to the speed of light, which is not the case for most rendering applications. For scenes that are either not composed of simple surfaces in a vacuum or for which the travel time for light is an important factor, researchers have generalized the rendering equation to produce a volume rendering equation suitable for volume rendering and a transient rendering equation for use with data from a time-of-flight camera.
Voiceverse NFT plagiarism scandal
In January 2022, 15—the pseudonymous Massachusetts Institute of Technology (MIT) artificial intelligence researcher and creator of the non-commercial generative artificial intelligence voice synthesis research project 15.ai—discovered that the blockchain-based technology company Voiceverse had plagiarized from their platform. Voiceverse marketed itself as a service that offered AI voice cloning technology that could be purchased and traded as non-fungible tokens (NFTs). Amid heightened controversy over NFTs in the gaming industry, voice actor Troy Baker (who has been described as one of the most famous voice actors in video games) announced his partnership with Voiceverse on January 14, 2022, triggering immediate backlash over concerns about the environmental impact of NFTs, potential for fraud, predatory monetization in video games, and the potential of AI displacing jobs for human voice actors. Later that same day, 15 revealed through server logs that Voiceverse had generated voice lines using 15's free text-to-speech platform, pitch-shifted the audio to make them unrecognizable, and falsely marketed the samples as their own technology before selling them as NFTs. Within an hour of being confronted with evidence, Voiceverse confessed and stated that their marketing team had used 15.ai without proper attribution while rushing to create a technology demo to coincide with Baker's partnership announcement, further exacerbating the already negative reception to the original announcement. In response, 15 replied "Go fuck yourself"; the interaction went viral and garnered a large amount of support for the developer. News publications universally characterized this incident as Voiceverse having "stolen" from 15.ai. The next day, Baker appeared on a podcast and stated that his motivation had been to help independent creators who were unable to afford professional voice actors. Following continued backlash and the plagiarism revelation, Baker ended his partnership with Voiceverse on January 31, 2022. Subsequently, the incident was documented in multiple AI ethics databases, criticisms of predatory monetization in video games, and retrospectives as one of the earliest instances of plagiarism and theft stemming from artificial intelligence during the AI boom. == Background == === Troy Baker === Troy Baker is a prominent voice actor in the video game industry best known for his performances as Joel Miller in The Last of Us franchise. Baker has been described as "ubiquitous" by Polygon, "one of the most high-profile and prolific voice actors in video games" by Eurogamer, and "arguably the most famous voice actor in the gaming industry" by GameGuru. His other prominent roles include voicing Agent John "Jonesy" Jones in Fortnite, Booker DeWitt in BioShock Infinite, and both Batman and Joker in multiple Batman video games. As of October 2025, Baker holds the record for the most acting nominations at the BAFTA Games Awards, with five between 2013 and 2021. === Voiceverse === Voiceverse is a blockchain-based startup founded by the Bored Ape Yacht Club that marketed itself as offering AI voice cloning technology in the form of NFTs. Prior to the announcement of their partnership with Baker, Voiceverse had partnered with LOVO, Inc., an AI voice platform that, according to LOVO, could generate human-like voices. Voiceverse stated that any user who purchases a voice NFT would have unlimited and perpetual access to the voice model, which could be used to create content such as audiobooks, YouTube videos, podcasts, e-learning materials, in-game voice chat, and Zoom calls. Voiceverse promised that buyers would "OWN [sic] all of the IP" of content they created using these voices. Voiceverse's roadmap included plans to release 8,888 initial voice NFTs, a feature to add emotions to existing voices, and the ability for users to mint their own voices as NFTs. Prior to Baker's partnership, Voiceverse had also partnered with voice actors Charlet Chung, who voices D.Va in Overwatch, and Andy Milonakis of The Andy Milonakis Show. === 15.ai === 15.ai is a free web application launched in 2020 that uses artificial intelligence to generate text-to-speech voices of fictional characters from popular media. Created by a pseudonymous artificial intelligence researcher known as 15, who began developing the technology as a freshman during their undergraduate research at MIT, it was an early example of an application of generative artificial intelligence during the initial stages of the AI boom. The platform showed that deep neural networks could generate emotionally expressive speech with only 15 seconds of speech; the name "15.ai" references the creator's statement that a voice can be convincingly cloned with just 15 seconds of audio, as opposed to the tens of hours of data previously required. 15.ai became an Internet phenomenon in early 2021 when content utilizing it went viral on social media and quickly gained widespread use among various Internet fandoms. 15 has emphasized that it remain free and non-commercial; it only requires users to give proper credit when using the service for content creation. === NFTs in the video game industry === By early 2022, NFTs had become highly controversial within the gaming industry. Critics raised concerns about their environmental impact due to the significant energy consumption of blockchain technology. In addition, the prevalence of scams, fraud, and potential money laundering associated with NFT sales, as well as fears that NFTs were a new form of predatory monetization following the increasing frequency of loot boxes, caused vocal pushback from the gaming community. Several major gaming companies had begun exploring NFT integration into their products, though fan backlash had already forced some projects to be cancelled. On December 16, 2021, the developers of S.T.A.L.K.E.R. 2: Heart of Chernobyl announced that they would be including NFTs in the game, but cancelled within an hour of the announcement due to immediate universal backlash. Simultaneously, the rise of AI voice technology raised concerns among voice actors about potential job displacement and the devaluation of their work amidst the voice acting industry's ongoing struggles for better compensation and working conditions. == Partnership announcement and backlash == On January 14, 2022, 1:02 a.m. EST, Baker announced on Twitter that he was partnering with Voiceverse "to explore ways where together we might bring new tools to new creators to make new things, and allow everyone a chance to own & invest in the IP's they create." The announcement concluded with the statement "You can hate. Or you can create." Baker's specific role with Voiceverse remained unclear at the time of the announcement. Along with Baker's announcement, Voiceverse promoted their supposed voice AI technology on Twitter by posting animated videos that featured a cat character created by NFT firm Chubbiverse. The videos concluded with text that read "The Voice Powered By Voiceverse"; Voiceverse stated on Twitter that the voices in the animations had been generated using their own AI voice synthesis technology and presented the videos as a technology demonstration of their voice NFT capabilities. The announcement provoked immediate and widespread backlash from the gaming community. Baker's tweet received thousands of replies and quote retweets (the vast majority of which were negative), far more than the number of likes; Michael McWhertor of Polygon described it as a "textbook example of being ratioed" and commented that reactions had been amplified by the final part of Baker's announcement. Michael Beckwith of Metro called Baker's approach "bizarrely aggressive". Later that day, Baker responded to the backlash by apologizing for his choice of words. He said he appreciated people's thoughts and acknowledged that the "hate/create part might have been a bit antagonistic," calling it a "bad attempt to bring levity". Despite the apology, Baker and his fellow voice actors did not distance themselves from Voiceverse at this point. At the same time, Voiceverse attempted to address the criticisms, stating that they were working to move to more environmentally friendly blockchain technology and that voice actors would receive royalties from NFT sales, with actors benefiting from any increase in NFT value. == Plagiarism revelation == On December 13, 2021, amidst the increasingly negative reactions toward NFTs among the general public, the creator of 15.ai (known pseudonymously as 15) announced that they had "no interest in incorporating NFTs into any aspect of [their] work." On January 14, 2022, 11:17 a.m. EST (10 hours after Baker's initial announcement), 15 commented on the Voiceverse venture, stating that it "sounds like a scam". Two hours later, at 1:20 p.m., 15 explicitly accused Voiceverse of "actively attempting to appropriate [15's] work for [Voiceverse's] own benefit." 15 provided evidence through
KiSAO
The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. KiSAO is part of the BioModels.net project and of the COMBINE initiative. == Structure == KiSAO consists of three main branches: simulation algorithm simulation algorithm characteristic simulation algorithm parameter The elements of each algorithm branch are linked to characteristic and parameter branches using has characteristic and has parameter relationships accordingly. The algorithm branch itself is hierarchically structured using relationships which denote that the descendant algorithms were derived from, or specify, more general ancestors.
Artificial intelligence in government
Artificial intelligence (AI) has a range of uses in government. It can be used to further public policy objectives (in areas such as emergency services, health and welfare), as well as assist the public to interact with the government (through the use of virtual assistants, for example). According to the Harvard Business Review, "Applications of artificial intelligence to the public sector are broad and growing, with early experiments taking place around the world." Hila Mehr from the Ash Center for Democratic Governance and Innovation at Harvard University notes that AI in government is not new, with postal services using machine methods in the late 1990s to recognise handwriting on envelopes to automatically route letters. The use of AI in government comes with significant benefits, including efficiencies resulting in cost savings (for instance by reducing the number of front office staff) and reducing the opportunities for corruption. However, it also carries risks (described below). == Uses of AI in government == The potential uses of AI in government are wide and varied, with Deloitte considering that "Cognitive technologies could eventually revolutionize every facet of government operations". Mehr suggests that six types of government problems are appropriate for AI applications: Resource allocation—such as where administrative support is required to complete tasks more quickly. Large datasets—where these are too large for employees to work efficiently and multiple datasets could be combined to provide greater insights. Expert shortage—including where basic questions could be answered and niche issues can be learned. Predictable scenario—historical data makes the situation predictable. Procedural tasks refer to repetitive tasks in which the answers to inputs or outputs are binary. Diverse data—where data takes various forms (such as visual and linguistic) and needs to be summarized regularly. Mehr states that "While applications of AI in government work have not kept pace with the rapid expansion of AI in the private sector, the potential use cases in the public sector mirror common applications in the private sector." Potential and actual uses of AI in government can be divided into three broad categories: those that contribute to public policy objectives, those that assist public interactions with the government, and other uses. === Contributing to public policy objectives === There are a range of examples of where AI can contribute to public policy objectives. These include: Receiving benefits at job loss, retirement, bereavement and child birth almost immediately, in an automated way (thus without requiring any actions from citizens at all) Social insurance service provision Classifying emergency calls based on their urgency (like the system used by the Cincinnati Fire Department in the United States) Detecting and preventing the spread of diseases Assisting public servants in making welfare payments and immigration decisions Adjudicating bail hearings Triaging health care cases Monitoring social media for public feedback on policies Monitoring social media to identify emergency situations Identifying fraudulent benefits claims Predicting a crime and recommending optimal police presence Predicting traffic congestion and car accidents Anticipating road maintenance requirements Identifying breaches of health regulations Providing personalised education to students Marking exam papers Assisting with defence and national security (see Artificial intelligence § Military and Applications of artificial intelligence § Other fields in which AI methods are implemented respectively) Artificial Intelligence in China has been used to drive both political and economic markets. In 2019, Shanghai’s government rolled out 100 billion yuan to assist in funding enterprises that used AI to introduce 22 new policy agendas. Shanghai invested in these enterprises to attract top international talent in order to set up the Shanghai Municipal Big Data Center. City Brain AI is an urban management platform made by Alibaba. China uses City Brain AI to maintain a significant share of capital investment through public and state owned enterprises. The synergy between public and private sectors are more than capital-driven with City Brain AI. The blend of both public and private shareholding is only made out to be through the role of provincial and sub-provincial governments. Both hold control over the direction that City Brain AI makes both socially and economically. === Assisting public interactions with government === AI can be used to assist members of the public to interact with government and access government services, for example by: Answering questions using virtual assistants or chatbots (see below) Directing requests to the appropriate area within government Filling out forms Assisting with searching documents (e.g. IP Australia's trade mark search) Scheduling appointments Various governments, including those of Australia and Estonia, have implemented virtual assistants to aid citizens in navigating services, with applications ranging from tax inquiries to life-event registrations. === Gerrymandering === Gerrymandering is a method of influencing political process by drawing map boundaries in favor of incumbent parties. Academic researchers Wendy Tam Cho and Bruce Cain have proposed partially automating the map-drawing process with an AI system to reduce partisan gerrymandering. Even with this AI system, the process may still be manipulated to favor partisan interests, so the researchers emphasized the importance of transparency and human involvement. === Other uses === Other uses of AI in government include: Translation Language interpretation pioneered by the European Commission's Directorate General for Interpretation and Florika Fink-Hooijer. Drafting documents == Potential benefits == AI offers potential efficiencies and cost savings for the government. For example, Deloitte has estimated that automation could save US Government employees between 96.7 million to 1.2 billion hours a year, resulting in potential savings of between $3.3 billion to $41.1 billion a year. The Harvard Business Review has stated that while this may lead a government to reduce employee numbers, "Governments could instead choose to invest in the quality of its services. They can re-employ workers' time towards more rewarding work that requires lateral thinking, empathy, and creativity—all things at which humans continue to outperform even the most sophisticated AI program." == Risks == Risks associated with the use of AI in government include AI becoming susceptible to bias, a lack of transparency in how an AI application may make decisions, and the accountability for any such decisions. For example, a 2026 lawsuit alleged that the U.S. Department of Government Efficiency used ChatGPT to flag and cancel federal humanities grants, including projects on Jewish history and Israeli culture, over some objections from NEH officials, illustrating how automated decision-making could affect funding outcomes.
Belief–desire–intention model
For popular psychology, the belief–desire–intention (BDI) model of human practical reasoning was developed by Michael Bratman as a way of explaining future-directed intention. BDI is fundamentally reliant on folk psychology (the 'theory theory'), which is the notion that our mental models of the world are theories. It was used as a basis for developing the belief–desire–intention software model. == Applications == BDI was part of the inspiration behind the BDI software architecture, which Bratman was also involved in developing. Here, the notion of intention was seen as a way of limiting time spent on deliberating about what to do, by eliminating choices inconsistent with current intentions. BDI has also aroused some interest in psychology. BDI formed the basis for a computational model of childlike reasoning CRIBB.
National Data Repository
A National Data Repository (NDR) is a data bank that seeks to preserve and promote a country's natural resources data, particularly data related to the petroleum exploration and production (E&P) sector. A National Data Repository is normally established by an entity that governs, controls and supports the exchange, capture, transference and distribution of E&P information, with the final target to provide the State with the tools and information to assure the growth, govern-ability, control, independence and sovereignty of the industry. The two fundamental reasons for a country to establish an NDR are to preserve data generated inside the country by the industry, and to promote investments in the country by utilizing data to reduce the exploration, production, and transportation business risks. Countries take different approaches towards preserving and promoting their natural resources data. The approach varies according to a country's natural resources policies, level of openness, and its attitude towards foreign investment. == Data types == NDRs store a vast array of data related to a country's natural resources. This includes wells, well log data, well reports, core samples, seismic surveys, post-stack seismic, field data/tapes, seismic (acquisition/processing) reports, production data, geological maps and reports, license data and geological models. == Funding models == Some NDRs are financed entirely by a country's government. Others are industry-funded. Still some are hybrid systems, funded in part by industry and government. NDRs typically charge fees for data requests and for data loading. The cost differs significantly between countries. In some cases an annual membership is charged to oil companies to store and access the data in the NDR. == Standards body == Energistics is the global energy standards resource center for the upstream oil and gas industry. Energistics National Data Repository Work Group: The standards body is Energistics. === Energistics-standards-directory === Global regulators of upstream oil and natural gas information, including seismic, drilling, production and reservoir data, formed the National Data Repository (NDR) Work Group in 2008 to collaborate on the development of data management standards and to assist emerging nations with hydrocarbon reserves to better collect, maintain and deliver oil and gas data to the public and to the industry. Ten countries, led by the Netherlands, Norway and the United Kingdom, formed NDR to share best practices and to formalize the development and deployment of data management standards for regulatory agencies. The other countries involved in the NDR Work Group's formation are Australia, Canada, India, Kenya, New Zealand, South Africa and the United States. Annual NDR Conference: Approximately every 18 months Energistics organizes a National Data Repository Conference. The purpose is to provide government and regulatory agencies from around the world an opportunity to attend a series of workshops dedicated to developing data exchange standards, improving communications with the oil and gas industry and learning data management techniques for natural resources information. === Society of Exploration Geophysicists and The International Oil and Gas Producers Association === The SEG is the custodian of the SEG standards which are used for the exchange, retention and release of seismic data. They are commonly used by National Data Repositories with the SEGD and SEGY being the field and processed exchange standards respectively. == NDRs around the world == Click here to see a map of the NDRs around the world