AI Chatbot No Filter No Limit

AI Chatbot No Filter No Limit — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • 80 Million Tiny Images

    80 Million Tiny Images

    80 Million Tiny Images is a dataset intended for training machine-learning systems constructed by Antonio Torralba, Rob Fergus, and William T. Freeman in a collaboration between MIT and New York University. It was published in 2008. The dataset has size 760 GB. It contains 79,302,017 32×32-pixel color images, scaled down from images scraped from the World Wide Web over 8 months. The images are classified into 75,062 classes. Each class is a non-abstract noun in WordNet. Images may appear in more than one class. The dataset was motivated by non-parametric models of neural activations in the visual cortex upon seeing images. The CIFAR-10 dataset uses a subset of the images in this dataset, but with independently generated labels, as the original labels were not reliable. The CIFAR-10 set has 6000 examples of each of 10 classes, and the CIFAR-100 set has 600 examples of each of 100 non-overlapping classes. == Construction == It was first reported in a technical report in April 2007, during the middle of the construction process, when there were only 73 million images. The full dataset was published in 2008. They began with all 75,846 non-abstract nouns in WordNet, and then for each of these nouns, they scraped 7 image search engines: Altavista, Ask.com, Flickr, Cydral, Google, Picsearch, and Webshots. After 8 months of scraping, they obtained 97,245,098 images. Since they did not have enough storage, they downsized the images to 32×32 as they were scraped. After gathering, they removed images with zero variance and intra-word duplicate images, resulting in the final dataset. Out of the 75,846 nouns, only 75,062 classes had any results, so the other nouns did not appear in the final dataset. The number of images per noun follows a Zipf-like distribution, with 1056 images per noun on average. To prevent a few nouns taking up too many images, they put an upper bound of at most 3000 images per noun. == Retirement == The 80 Million Tiny Images dataset was retired from use by its creators in 2020, after a paper by researchers Abeba Birhane and Vinay Prabhu found that some of the labeling of several publicly available image datasets, including 80 Million Tiny Images, contained racist and misogynistic slurs which were causing models trained on them to exhibit racial and sexual bias. The dataset also contained offensive images. Following the release of the paper, the dataset's creators removed the dataset from distribution, and requested that other researchers not use it for further research and to delete their copies of the dataset.

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  • Ubiquitous robot

    Ubiquitous robot

    Ubiquitous robot is a term used in an analogous way to ubiquitous computing. Software useful for "integrating robotic technologies with technologies from the fields of ubiquitous and pervasive computing, sensor networks, and ambient intelligence". The emergence of mobile phone, wearable computers and ubiquitous computing makes it likely that human beings will live in a ubiquitous world in which all devices are fully networked. The existence of ubiquitous space resulting from developments in computer and network technology will provide motivations to offer desired services by any IT device at any place and time through user interactions and seamless applications. This shift has hastened the ubiquitous revolution, which has further manifested itself in the new multidisciplinary research area, ubiquitous robotics. It initiates the third generation of robotics following the first generation of the industrial robot and the second generation of the personal robot. Ubiquitous robot (Ubibot) is a robot incorporating three components including virtual software robot or avatar, real-world mobile robot and embedded sensor system in surroundings. Software robot within a virtual world can control a real-world robot as a brain and interact with human beings. Researchers of KAIST, Korea describe these three components as a Sobot (Software robot), Mobot (Mobile robot), and Embot (Embedded robot).

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  • DONE

    DONE

    The Data-based Online Nonlinear Extremumseeker (DONE) algorithm is a black-box optimization algorithm. DONE models the unknown cost function and attempts to find an optimum of the underlying function. The DONE algorithm is suitable for optimizing costly and noisy functions and does not require derivatives. An advantage of DONE over similar algorithms, such as Bayesian optimization, is that the computational cost per iteration is independent of the number of function evaluations. == Methods == The DONE algorithm was first proposed by Hans Verstraete and Sander Wahls in 2015. The algorithm fits a surrogate model based on random Fourier features and then uses a well-known L-BFGS algorithm to find an optimum of the surrogate model. == Applications == DONE was first demonstrated for maximizing the signal in optical coherence tomography measurements, but has since then been applied to various other applications. For example, it was used to help extending the field of view in light sheet fluorescence microscopy.

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  • Query rewriting

    Query rewriting

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

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  • T Layout

    T Layout

    The T-Layout is an architectural and design concept for web applications, specifically tailored to improve the user experience on mobile devices. It features a horizontally scrollable container divided into three distinct sections, each spanning the full width of the screen, and was developed to optimise space usage and streamline navigation. == Background == The T-Layout introduces horizontal scrolling as a complementary method to the conventional pop-up-based navigation system in mobile web applications. In this layout, the central section which is visible by default upon accessing the application, facilitates the main content of a URL address and is flanked by two "helper" sections. This approach minimises the need for extensive user movements, in order to reach navigation controls typically located at the top of the screen. It is aimed at enhancing the user experience on mobile devices by providing an easier way to access essential content such as the main navigation, e-commerce related screens, or user account related information, ensuring that those elements are readily accessible while requiring minimal user effort. The T-Layout was first implemented by E (e-streetwear.com) in their mobile web app layout, and it was inspired by the interfaces of well-tested native mobile apps like Instagram and Revolut. A study titled "Mobile Navigation and User Preferences Survey" indicated a preference among mobile app users for one-handed usage, primarily navigating with their thumb. These insights led to the T-Layout Experiment, which compared the efficiency of using swipe gestures to access navigational elements against reaching traditional navigation controls. == Development history == It was first released as the mobile layout of E in early 2023. It was originally developed based on six principles: user-centric functionality, lightweight filesize, HTML and CSS implementation with minimal or no use of JavaScript required, suitable both for browser and server-rendering architectures, intuitive design, and improved SEO. The development of the T-Layout was driven by the necessity for more ergonomic and user-friendly interfaces in mobile web applications. Its design, reminiscent of the letter 'T', emerged as a solution to several usability challenges mobile device users face, emphasising ease of access and efficient screen space utilisation. In July 2023, E formalised the concept and its technical specifications, introducing it to the web design and development community. In October 2023 the "Mobile Navigation and User Preferences Survey" was conducted, establishing that the vast majority of individuals prefer to use mobile applications by holding the phone in a one-handed grip, utilising only the thumb for gestures when possible. The subsequent "T-Layout Experiment", designed to measure the time in seconds and the distance (user effort) in pixels, required to access navigational elements by traditionally tapping on fixed-positioned controls compared to swiping anywhere on the screen. The results proved that swipe gestures require less time and much less effort. == Styling and features == The main characteristic of the T-Layout is its horizontal scrolling feature, which can improve navigation efficiency while preserving the functionality of traditionally structured user interfaces. Its Implementation can be achieved with a combination of HTML and styling with CSS as well as precompiled Scss and Sass, CSS-in-JS, and styled JSX. It can be either a purely HTML/CSS solution but JavaScript can be utilised as well to add more specific functionalities, while It can be implemented to both existing and new applications. Its application in server-side rendering architectures will ensure that all its underlying principles apply. Although principally each section in the layout has a distinct role and facilitates specific types of content, the T-Layout as a concept is versatile, and it is adaptable allowing modifications in the layout or how it's implemented to cater to the specific needs of different applications.

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  • Friendly artificial intelligence

    Friendly artificial intelligence

    Friendly artificial intelligence (friendly AI or FAI) is hypothetical artificial general intelligence (AGI) that would have a positive (benign) effect on humanity or at least align with human interests such as fostering the improvement of the human species. It is a part of the ethics of artificial intelligence and is closely related to machine ethics. While machine ethics is concerned with how an artificially intelligent agent should behave, friendly artificial intelligence research is focused on how to practically bring about this behavior and ensuring it is adequately constrained. == Etymology and usage == The term was coined by Eliezer Yudkowsky, who is best known for popularizing the idea, to discuss superintelligent artificial agents that reliably implement human values. Stuart J. Russell and Peter Norvig's leading artificial intelligence textbook, Artificial Intelligence: A Modern Approach, describes the idea: Yudkowsky (2008) goes into more detail about how to design a Friendly AI. He asserts that friendliness (a desire not to harm humans) should be designed in from the start, but that the designers should recognize both that their own designs may be flawed, and that the robot will learn and evolve over time. Thus the challenge is one of mechanism design—to define a mechanism for evolving AI systems under a system of checks and balances, and to give the systems utility functions that will remain friendly in the face of such changes. "Friendly" is used in this context as technical terminology, and picks out agents that are safe and useful, not necessarily ones that are "friendly" in the colloquial sense. The concept is primarily invoked in the context of discussions of recursively self-improving artificial agents that rapidly explode in intelligence, on the grounds that this hypothetical technology would have a large, rapid, and difficult-to-control impact on human society. == Risks of unfriendly AI == The roots of concern about artificial intelligence are very old. Kevin LaGrandeur showed that the dangers specific to AI can be seen in ancient literature concerning artificial humanoid servants such as the golem, or the proto-robots of Gerbert of Aurillac and Roger Bacon. In those stories, the extreme intelligence and power of these humanoid creations clash with their status as slaves (which by nature are seen as sub-human), and cause disastrous conflict. By 1942 these themes prompted Isaac Asimov to create the "Three Laws of Robotics"—principles hard-wired into all the robots in his fiction, intended to prevent them from turning on their creators, or allowing them to come to harm. In modern times as the prospect of superintelligent AI looms nearer, philosopher Nick Bostrom has said that superintelligent AI systems with goals that are not aligned with human ethics are intrinsically dangerous unless extreme measures are taken to ensure the safety of humanity. He put it this way: Basically we should assume that a 'superintelligence' would be able to achieve whatever goals it has. Therefore, it is extremely important that the goals we endow it with, and its entire motivation system, is 'human friendly.' In 2008, Eliezer Yudkowsky called for the creation of "friendly AI" to mitigate existential risk from advanced artificial intelligence. He explains: "The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else." Steve Omohundro says that a sufficiently advanced AI system will, unless explicitly counteracted, exhibit a number of basic "drives", such as resource acquisition, self-preservation, and continuous self-improvement, because of the intrinsic nature of any goal-driven systems and that these drives will, "without special precautions", cause the AI to exhibit undesired behavior. Alexander Wissner-Gross says that AIs driven to maximize their future freedom of action (or causal path entropy) might be considered friendly if their planning horizon is longer than a certain threshold, and unfriendly if their planning horizon is shorter than that threshold. Luke Muehlhauser, writing for the Machine Intelligence Research Institute, recommends that machine ethics researchers adopt what Bruce Schneier has called the "security mindset": Rather than thinking about how a system will work, imagine how it could fail. For instance, he suggests even an AI that only makes accurate predictions and communicates via a text interface might cause unintended harm. In 2014, Luke Muehlhauser and Nick Bostrom underlined the need for 'friendly AI'; nonetheless, the difficulties in designing a 'friendly' superintelligence, for instance via programming counterfactual moral thinking, are considerable. == Coherent extrapolated volition == Yudkowsky advances the Coherent Extrapolated Volition (CEV) model. According to him, our coherent extrapolated volition is "our wish if we knew more, thought faster, were more the people we wished we were, had grown up farther together; where the extrapolation converges rather than diverges, where our wishes cohere rather than interfere; extrapolated as we wish that extrapolated, interpreted as we wish that interpreted". Rather than a Friendly AI being designed directly by human programmers, it is to be designed by a "seed AI" programmed to first study human nature and then produce the AI that humanity would want, given sufficient time and insight, to arrive at a satisfactory answer. The appeal to an objective through contingent human nature (perhaps expressed, for mathematical purposes, in the form of a utility function or other decision-theoretic formalism), as providing the ultimate criterion of "Friendliness", is an answer to the meta-ethical problem of defining an objective morality; extrapolated volition is intended to be what humanity objectively would want, all things considered, but it can only be defined relative to the psychological and cognitive qualities of present-day, unextrapolated humanity. == Other approaches == Steve Omohundro has proposed a "scaffolding" approach to AI safety, in which one provably safe AI generation helps build the next provably safe generation. Seth Baum argues that the development of safe, socially beneficial artificial intelligence or artificial general intelligence is a function of the social psychology of AI research communities and so can be constrained by extrinsic measures and motivated by intrinsic measures. Intrinsic motivations can be strengthened when messages resonate with AI developers; Baum argues that, in contrast, "existing messages about beneficial AI are not always framed well". Baum advocates for "cooperative relationships, and positive framing of AI researchers" and cautions against characterizing AI researchers as "not want(ing) to pursue beneficial designs". In his book Human Compatible, AI researcher Stuart J. Russell lists three principles to guide the development of beneficial machines. He emphasizes that these principles are not meant to be explicitly coded into the machines; rather, they are intended for the human developers. The principles are as follows: The machine's only objective is to maximize the realization of human preferences. The machine is initially uncertain about what those preferences are. The ultimate source of information about human preferences is human behavior. The "preferences" Russell refers to "are all-encompassing; they cover everything you might care about, arbitrarily far into the future." Similarly, "behavior" includes any choice between options, and the uncertainty is such that some probability, which may be quite small, must be assigned to every logically possible human preference. == Public policy == James Barrat, author of Our Final Invention, suggested that "a public-private partnership has to be created to bring A.I.-makers together to share ideas about security—something like the International Atomic Energy Agency, but in partnership with corporations." He urges AI researchers to convene a meeting similar to the Asilomar Conference on Recombinant DNA, which discussed risks of biotechnology. John McGinnis encourages governments to accelerate friendly AI research. Because the goalposts of friendly AI are not necessarily eminent, he suggests a model similar to the National Institutes of Health, where "Peer review panels of computer and cognitive scientists would sift through projects and choose those that are designed both to advance AI and assure that such advances would be accompanied by appropriate safeguards." McGinnis feels that peer review is better "than regulation to address technical issues that are not possible to capture through bureaucratic mandates". McGinnis notes that his proposal stands in contrast to that of the Machine Intelligence Research Institute, which generally aims to avoid government involvement in friendly AI. == Criticism == Some critics believe that both human-level AI and superintelligence are unlikely and that, therefore, friendly AI is unlik

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  • World Congress of Universal Documentation

    World Congress of Universal Documentation

    The World Congress of Universal Documentation was held from 16 to 21 August 1937 in Paris, France. Delegates from 45 countries met to discuss means by which all of the world's information, in print, in manuscript, and in other forms, could be efficiently organized and made accessible. == The Congress in the history of information science == The Congress, held at the Trocadéro under "the auspices" of the Institut International de Bibliographie, was "the apotheosis" of a general movement in the 1930s towards the classification of the growing mass of information and the improvement of access to that information. For the first time in the history of information science, technological means were beginning to catch up with theoretical ends, and the discussions at the conference reflected that fact. Its participation in the Congress was one of the first projects of the American Documentation Institute (ADI). Participants in the conference discussed what has been more recently called "a continuously updated hypertext encyclopedia." Joseph Reagle sees many of the ideas considered at the conference as forerunners of some of the key goals and norms of Wikipedia. == Microfilm == The main resolution adopted by the congress proposed that microfilm be used to make information universally available. Watson Davis, chairman of the American delegation and president of the ADI, stated that the volume of information being produced created difficult problems of access and preservation, but that these could be solved by the use of microfilm. In his address to the Congress, Davis said: Most immediate and practical to put into operation is the microfilming of material in libraries upon demand. It will become fashionable and economical to send a potential book borrower a little strip of microfilm for his permanent possession instead of the book and then badgering him to return it before he has had a chance to use it effectively. I believe that reading machines for microfilm will become as common as typewriters in studies and laboratories. If the principal libraries and information centers of the world will cooperate in such "bibliofilm services," as they are called, if they exchange orders and have essentially uniform methods, forms for ordering, standard microfilm format and production methods and comparable if not uniform prices, the resources of any library will be placed at the disposal of any scholar or scientist anywhere in the world. All the libraries cooperating will merge into one world library without loss of identity or individuality. The world's documentation will become available to even the most isolated and individualistic scholar. The Congress included two separate exhibits on microfilm. One was of the equipment used at the Bibliothèque nationale de France and the other, coordinated by Herman H. Fussler of the University of Chicago, consisting of "an entire microfilm laboratory," complete with cameras, a darkroom, and various kinds of reading machines. Emanuel Goldberg presented a paper on an early copying camera he had invented. Other resolutions passed by the Congress concerned uniform standards for the preparation of articles, for classifying books and other documents, for indexing newspapers and periodicals, and for cooperation between libraries. == H. G. Wells == In his address to the Congress, H. G. Wells said that he thought that his idea of the "world brain" was a precursor to the ideas other delegates were proposing, and explicitly linked the projects being discussed to the work of the encyclopédistes: I am speaking of a process of mental organization throughout the world which I believe to be as inevitable as anything can be in human affairs. All the distresses and horrors of the present time are fundamentally intellectual. The world has to pull its mind together, and this [Congress] is the beginning of its efforts. Civilization is a Phoenix. It perishes in flames and even as it dies it is born again. This synthesis of knowledge upon which you are working is the necessary beginning of a new world. It is good to be meeting here in Paris where the first encyclopedia of power was made. It would be impossible to overrate our debt to Diderot and his associates. == Other participants == Participants in the Congress included authors, librarians, scholars, archivists, scientists, and editors. Some of the notable people in attendance not mentioned above were:

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  • Voiceverse NFT plagiarism scandal

    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

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  • Colloquis

    Colloquis

    Colloquis, previously known as ActiveBuddy and Conversagent, was a company that created conversation-based interactive agents originally distributed via instant messaging platforms. The company had offices in New York, New York, and Sunnyvale, California. == History == Founded in 2000, the company was the brainchild of Robert Hoffer, Timothy Kay, and Peter Levitan. The idea for interactive agents (also known as Internet bots) came from the team's vision to add functionality to increasingly popular instant messaging services. The original implementation took shape as a word-based adventure game but quickly grew to include a wide range of database applications, including access to news, weather, stock information, movie times, Yellow Pages listings, and detailed sports data, as well as a variety of tools (calculators, translator, etc.). These various applications were bundled into one entity and launched as SmarterChild in 2001. SmarterChild acted as a showcase for the quick data access and possibilities for fun conversation that the company planned to turn into customized, niche-specific products. The rapid success of SmarterChild led to targeted promotional products for Radiohead, Austin Powers, The Sporting News, and others. ActiveBuddy sought to strengthen its hold on the interactive agent market for the future by filing for, and receiving, a controversial patent on their creation in 2002. The company also released the BuddyScript SDK, a free developer kit that allow programmers to design and launch their own interactive agents using ActiveBuddy's proprietary scripting language, in 2002. Ultimately, however, the decline in ad spending in 2001 and 2002 led to a shift in corporate strategy towards business focused Automated Service Agents, building products for clients including Cingular, Comcast and Cox Communications. The company subsequently changed its name from ActiveBuddy to Conversagent in 2003, and then again to Colloquis in 2006. Colloquis was purchased by Microsoft in October 2006.

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  • Information flow

    Information flow

    In discourse-based grammatical theory, information flow is any tracking of referential information by speakers. Information may be new, i.e., just introduced into the conversation; given, i.e., already active in the speakers' consciousness; or old, i.e., no longer active. The various types of activation, and how these are defined, are model-dependent. Information flow affects grammatical structures such as: Word order (topic, focus, and afterthought constructions). Active, passive, or middle voice. Choice of deixis, such as articles; "medial" deictics such as Spanish ese and Japanese sore are generally determined by the familiarity of a referent rather than by physical distance. Overtness of information, such as whether an argument of a verb is indicated by a lexical noun phrase, a pronoun, or not mentioned at all. Clefting: Splitting a single clause into two clauses, each with its own verb, e.g. ‘The chicken turtles tasted like chicken.’ becomes ‘It was the chicken turtle | that tasted like chicken.’ In this case, clefting is used to shift the focus of the sentence to the subject, the chicken turtle. Front focus: Placing at the start (front) of a sentence information that would normally occur later in the sentence, to give it extra prominence. For example, in pop culture, Yoda's speech often utilizes such syntactic construction, such as when he says 'much to learn you still have' to Luke Skywalker. End focus (or end weight): Given or familiar information followed by new information. This gives prominence to the final part of the sentences and can enable suspense to build, e.g. ‘Through the door came a gigantic wolf’.(Umer Prince)

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  • Five safes

    Five safes

    The Five Safes is a framework for helping make decisions about making effective use of data which is confidential or sensitive. It is mainly used to describe or design research access to statistical data held by government and health agencies, and by data archives such as the UK Data Service. It is not an internationally accepted standard. Two of the Five Safes refer to statistical disclosure control, and so the Five Safes is usually used to contrast statistical and non-statistical controls when comparing data management options. == Concept == The Five Safes proposes that data management decisions be considered as solving problems in five 'dimensions': projects, people, settings, data and outputs. The combination of the controls leads to 'safe use'. These are most commonly expressed as questions, for example: These dimensions are scales, not limits. That is, solutions can have a mix of more or fewer controls in each dimension, but the overall aim of 'safe use' independent of the particular mix. For example, a public use file available for open download cannot control who uses it, where or for what purpose, and so all the control (protection) must be in the data itself. In contrast, a file which is only accessed through a secure environment with certified users can contain very sensitive information: the non-statistical controls allow the data to be 'unsafe'. One academic likened the process to a graphic equalizer, where bass and treble can be combined independently to produce a sound the listener likes, which has proven to be a very useful metaphor. This 2023 Data Foundation webinar is an expert discussion of how the elements interact, including an excellent introductory representation. There is no 'order' to the Five Safes, in that one is necessarily more important than the others. However, Ritchie argued that the 'managerial' controls (projects, people, setting) should be addressed before the 'statistical' controls (data, output). The Five Safes concept is associated with other topics which developed from the same programme at ONS, although these are not necessarily implemented. Safe people is associated with 'active researcher management', while safe outputs is linked with principles-based output statistical disclosure control. The Five Safes is a positive framework, describing what is and is not. The EDRU ('evidence-based, default-open, risk-managed, user-centred') attitudinal model is sometimes used to give a normative context == The 'data access spectrum' == From 2003 the Five Safes was also represented in a simpler form as a 'Data Access Spectrum'. The non-data controls (project, people, setting, outputs) tend to work together, in that organisations often see these as a complementary set of restrictions on access. These can then be contrasted with choices about data anonymisation to present a linear representation of data access options. This presentation is consistent with the idea of 'data as a residual', as well as data protection laws of the time which often characterised data simply as anonymous or not anonymous. A similar idea had already been developed independently in 2001 by Chuck Humphrey of the Canadian RDC network, the 'continuum of access'. More recently, The Open Data Institute has developed a 'Data Spectrum toolkit' which includes industry-specific examples. == History and terminology == The Five Safes was devised in the winter of 2002/2003 by Felix Ritchie at the UK Office for National Statistics (ONS) to describe its secure remote-access Virtual Microdata Laboratory (VML). It was described at this time as the 'VML Security Model'. This was adopted by the NORC data enclave, and more widely in the US, as the 'portfolio model' (although this is now also used to refer to a slightly different legal/statistical/educational breakdown). In 2012 the framework as was still being referred to as the 'VML security model', but its increasing use among non-UK organisations led to the adoption of the more general and informative phrase 'Five Safes'. The original framework only had four safes (projects, people, settings and outputs): the framework was used to describe highly detailed data access through a secure environment, and so the 'data' dimension was irrelevant. From 2007 onwards, 'safe data' was included as the framework was used to a describe a wider range of ONS activities. As the US version was based upon the 2005 specification, some US iterations uses have the original four dimensions (eg). Some discussions, such as the OECD, use the term 'secure' instead 'safe'. However, the use of both these terms can cause presentational problems: less control in a particular dimension could be seen to imply 'unsafe users' or 'insecure settings', for example, which distracts from the main message. Hence, the Australian government uses the term "five data sharing principles". The 'Anonymisation Decision-Making Framework' uses a framework based on the Five Safes but relabelling "projects", "people", and "settings" as "governance", "agency" and "infrastructure", respectively; "Output" is omitted, and "safe use" becomes "functional anonymisation". There is no reference to the Five Safes or any associated literature. The Australian version was required to include references to the Five Safes, and presented it as an alternative without comment. == Application == The framework has had three uses: pedagogical, descriptive, and design. Since 2016, it has also been used, directly and indirectly in legislation. See for more detailed examples. === Pedagogy === The first significant use of the framework, other than internal administrative use, was to structure researcher training courses at the UK Office for National Statistics from 2003. UK Data Archive, Administrative Data Research Network, Eurostat, Statistics New Zealand, the Mexican National Institute of Statistics and Geography, NORC, Statistics Canada and the Australian Bureau of Statistics, amongst others, have also used this framework. Most of these courses are for researchers using restricted-access facilities; the Eurostat courses are unusual in that they are designed for all users of sensitive data. === Description === The framework is often used to describe existing data access solutions (e.g. UK HMRC Data Lab, UK Data Service, Statistics New Zealand) or planned/conceptualised ones (e.g. Eurostat in 2011). An early use was to help identify areas where ONS' still had 'irreducible risks' in its provision of secure remote access. The framework is mostly used for confidential social science data. To date it appears to have made little impact on medical research planning, although it is now included in the revised guidelines on implementing HIPAA regulations in the US, and by Cancer Research UK and the Health Foundation in the UK. It has also been used to describe a security model for the Scottish Health Informatics Programme. === Design === In general the Five Safes has been used to describe solutions post-factum, and to explain/justify choices made, but an increasing number of organisations have used the framework to design data access solutions. For example, the Hellenic Statistical Agency developed a data strategy built around the Five Safes in 2016; the UK Health Foundation used the Five Safes to design its data management and training programmes. Use in the private sector is less common but some organisations have incorporated the Five Safes into consulting services. In 2015 the UK Data Service organized a workshop to encourage data users from the academic and private sectors to think about how to manage confidential research data, using the Five Safes to demonstrate alternative options and best practice. Early adopters for strategic design use were in Australia: both the Australian Bureau of Statistics and the Australian Department of Social Service used the Five Safes as an ex ante design tool. In 2017 the Australian Productivity Commission recommended adopting a version of the framework to support cross-government data sharing and re-use. This underwent extensive consultation and culminated in the DAT Act 2022. Since 2020 the Five Safes has been the overriding framework for the design of new secure facilities and data sharing arrangements in the UK for public health and social sciences. This has been promoted by the Office for Statistics Regulation, the UK Statistics Authority, NHS DIgital, and the research funding bodies Administrative Data Research UK and DARE UK. === Regulation and legislation === Three laws have incorporated the Fives Safes. They are explicit in the South Australian Public Sector (Data Sharing) Act 2016, and implicit in the research provisions of the UK Digital Economy Act 2017. The Australian Data Availability and Transparency Act 2022 renames the Five Safes as the Five Data Sharing Principles.A 2025 statutory review of the DAT Act 2022 found "that the DAT Act has not been effective in achieving its objectives.". The review includes specific referen

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  • Metadirectory

    Metadirectory

    A metadirectory system provides for the flow of data between one or more directory services and databases in order to maintain synchronization of that data. It is an important part of identity management systems. The data being synchronized typically are collections of entries that contain user profiles and possibly authentication or policy information. Most metadirectory deployments synchronize data into at least one LDAP-based directory server, to ensure that LDAP-based applications such as single sign-on and portal servers have access to recent data, even if the data is mastered in a non-LDAP data source. Metadirectory products support filtering and transformation of data in transit. Most identity management suites from commercial vendors include a metadirectory product, or a user provisioning product.

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  • Ontology learning

    Ontology learning

    Ontology learning (ontology extraction, ontology augmentation generation, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval. As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process. Typically, the process starts by extracting terms and concepts or noun phrases from plain text using linguistic processors such as part-of-speech tagging and phrase chunking. Then statistical or symbolic techniques are used to extract relation signatures, often based on pattern-based or definition-based hypernym extraction techniques. == Procedure == Ontology learning (OL) is used to (semi-)automatically extract whole ontologies from natural language text. The process is usually split into the following eight tasks, which are not all necessarily applied in every ontology learning system. === Domain terminology extraction === During the domain terminology extraction step, domain-specific terms are extracted, which are used in the following step (concept discovery) to derive concepts. Relevant terms can be determined, e.g., by calculation of the TF/IDF values or by application of the C-value / NC-value method. The resulting list of terms has to be filtered by a domain expert. In the subsequent step, similarly to coreference resolution in information extraction, the OL system determines synonyms, because they share the same meaning and therefore correspond to the same concept. The most common methods therefore are clustering and the application of statistical similarity measures. === Concept discovery === In the concept discovery step, terms are grouped to meaning bearing units, which correspond to an abstraction of the world and therefore to concepts. The grouped terms are these domain-specific terms and their synonyms, which were identified in the domain terminology extraction step. === Concept hierarchy derivation === In the concept hierarchy derivation step, the OL system tries to arrange the extracted concepts in a taxonomic structure. This is mostly achieved with unsupervised hierarchical clustering methods. Because the result of such methods is often noisy, a supervision step, e.g., user evaluation, is added. A further method for the derivation of a concept hierarchy exists in the usage of several patterns that should indicate a sub- or supersumption relationship. Patterns like “X, that is a Y” or “X is a Y” indicate that X is a subclass of Y. Such pattern can be analyzed efficiently, but they often occur too infrequently to extract enough sub- or supersumption relationships. Instead, bootstrapping methods are developed, which learn these patterns automatically and therefore ensure broader coverage. === Learning of non-taxonomic relations === In the learning of non-taxonomic relations step, relationships are extracted that do not express any sub- or supersumption. Such relationships are, e.g., works-for or located-in. There are two common approaches to solve this subtask. The first is based upon the extraction of anonymous associations, which are named appropriately in a second step. The second approach extracts verbs, which indicate a relationship between entities, represented by the surrounding words. The result of both approaches need to be evaluated by an ontologist to ensure accuracy. === Rule discovery === During rule discovery, axioms (formal description of concepts) are generated for the extracted concepts. This can be achieved, e.g., by analyzing the syntactic structure of a natural language definition and the application of transformation rules on the resulting dependency tree. The result of this process is a list of axioms, which, afterwards, is comprehended to a concept description. This output is then evaluated by an ontologist. === Ontology population === At this step, the ontology is augmented with instances of concepts and properties. For the augmentation with instances of concepts, methods based on the matching of lexico-syntactic patterns are used. Instances of properties are added through the application of bootstrapping methods, which collect relation tuples. === Concept hierarchy extension === In this step, the OL system tries to extend the taxonomic structure of an existing ontology with further concepts. This can be performed in a supervised manner with a trained classifier or in an unsupervised manner via the application of similarity measures. === Frame and Event detection === During frame/event detection, the OL system tries to extract complex relationships from text, e.g., who departed from where to what place and when. Approaches range from applying SVM with kernel methods to semantic role labeling (SRL) to deep semantic parsing techniques. == Tools == Dog4Dag (Dresden Ontology Generator for Directed Acyclic Graphs) is an ontology generation plugin for Protégé 4.1 and OBOEdit 2.1. It allows for term generation, sibling generation, definition generation, and relationship induction. Integrated into Protégé 4.1 and OBO-Edit 2.1, DOG4DAG allows ontology extension for all common ontology formats (e.g., OWL and OBO). Limited largely to EBI and Bio Portal lookup service extensions.

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  • Ordered key–value store

    Ordered key–value store

    An ordered key–value store (OKVS) is a type of data storage paradigm that can support multi-model databases. An OKVS is an ordered mapping of bytes to bytes. An OKVS will keep the key–value pairs sorted by the key lexicographic order. OKVS systems provides different set of features and performance trade-offs. Most of them are shipped as a library without network interfaces, in order to be embedded in another process. Most OKVS support ACID guarantees. Some OKVS are distributed databases. Ordered key–value stores found their way into many modern database systems including NewSQL database systems. == History == The origin of ordered key–value store stems from the work of Ken Thompson on dbm in 1979. Later in 1991, Berkeley DB was released that featured a B-Tree backend that allowed the keys to stay sorted. Berkeley DB was said to be very fast and made its way into various commercial product. It was included in Python standard library until 2.7. In 2009, Tokyo Cabinet was released that was superseded by Kyoto Cabinet that support both transaction and ordered keys. In 2011, LMDB was created to replace Berkeley DB in OpenLDAP. There is also Google's LevelDB that was forked by Facebook in 2012 as RocksDB. In 2014, WiredTiger, successor of Berkeley DB was acquired by MongoDB and is since 2019 the primary backend of MongoDB database. Other notable implementation of the OKVS paradigm are Sophia and SQLite3 LSM extension. Another notable use of OKVS paradigm is the multi-model database system called ArangoDB based on RocksDB. Some NewSQL databases are supported by ordered key–value stores. JanusGraph, a property graph database, has both a Berkeley DB backend and FoundationDB backend. == Key concepts == === Lexicographic encoding === There are algorithms that encode basic data types (boolean, string, number) and composition of those data types inside sorted containers (tuple, list, vector) that preserve their natural ordering. It is possible to work with an ordered key–value store without having to work directly with bytes. In FoundationDB, it is called the tuple layer. === Range query === Inside an OKVS, keys are ordered, and because of that it is possible to do range queries. A range query retrieves all keys between two specified keys, ensuring that the fetched keys are returned in a sorted order. === Subspaces === === Key composition === One can construct key spaces to build higher level abstractions. The idea is to construct keys, that takes advantage of the ordered nature of the top level key space. When taking advantage of the ordered nature of the key space, one can query ranges of keys that have particular pattern. === Denormalization === Denormalization, as in, repeating the same piece of data in multiple subspace is common practice. It allows to create secondary representation, also called indices, that will allow to speed up queries. == Higher level abstractions == The following abstraction or databases were built on top ordered key–value stores: Timeseries database, Record Database, also known as Row store databases, they behave similarly to what is dubbed RDBMS, Tuple Stores, also known as Triple Store or Quad Store but also Generic Tuple Store, Document database, that mimics MongoDB API, Full-text search Geographic Information Systems Property Graph Versioned Data Vector space database for Approximate Nearest Neighbor All those abstraction can co-exist with the same OKVS database and when ACID is supported, the operations happens with the guarantees offered by the transaction system. == Feature matrix == == Use-cases == OKVS are useful to implement two strategies: optimize a small feature e.g. to make a 10% improvement in read or write latency; the second strategy is to take advantage of the distributed nature of FoundationDB, and TiKV, for which there is no equivalent at very large scale in resilience. Both users need to re-implement the needed high level abstractions, because there are no portable ready-to-use libraries of high-level abstraction. There is still a complex balance, of complexity, maintainability, fine-tuning, and readily available features that makes it still a choice of experts. Sometime more specialized data-structures can be faster than a high-level abstraction on top of an OKVS. Another interest of OKVS paradigm stems from it simple, and versatile interface, that makes it an interesting target for experimental storage algorithms, and data structures.

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  • Documentation

    Documentation

    Documentation is any communicable material that is used to describe, explain, or instruct regarding some attributes of an object, system, or procedure, such as its parts, assembly, installation, maintenance, and use. As a form of knowledge management and knowledge organization, documentation can be provided on paper, online, or on digital or analog media, such as audio tape or CDs. Examples of such resources include user guides, white papers, online help, and quick-reference guides. Paper or hard-copy documentation has become less common. Contemporary documentation is often distributed through websites, software products, and other online applications. Documentation, understood as a set of instructional materials, should not be confused with documentation science, which is the study of the recording and retrieval of information. == Principles for producing documentation == While associated International Organization for Standardization (ISO) standards are not easily available publicly, a guide from other sources for this topic may serve the purpose. Documentation development may involve document drafting, formatting, submitting, reviewing, approving, distributing, reposting and tracking, etc., and are convened by associated standard operating procedure in a regulatory industry. It could also involve creating content from scratch. Documentation should be easy to read and understand. If it is too long and too wordy, it may be misunderstood or ignored. Clear, concise words should be used, and sentences should be limited to a maximum of 15 words. Documentation intended for a general audience should avoid gender-specific terms and cultural biases. In a series of procedures, steps should be clearly numbered. == Producing documentation == Technical writers and corporate communicators are professionals whose field and work is documentation. Ideally, technical writers have a background in both the subject matter and also in writing, managing content, and information architecture. Technical writers more commonly collaborate with subject-matter experts, such as engineers, technical experts, medical professionals, etc. to define and then create documentation to meet the user's needs. Corporate communications includes other types of written documentation, for example: Market communications (MarCom): MarCom writers endeavor to convey the company's value proposition through a variety of print, electronic, and social media. This area of corporate writing is often engaged in responding to proposals. Technical communication (TechCom): Technical writers document a company's product or service. Technical publications can include user guides, installation and configuration manuals, and troubleshooting and repair procedures. Legal writing: This type of documentation is often prepared by attorneys or paralegals. Compliance documentation: This type of documentation codifies standard operating procedures, for any regulatory compliance needs, as for safety approval, taxation, financing, and technical approval. Healthcare documentation: This field of documentation encompasses the timely recording and validation of events that have occurred during the course of providing health care. == Documentation in computer science == === Types === The following are typical software documentation types: Request for proposal Requirements/statement of work/scope of work Software design and functional specification System design and functional specifications Change management, error and enhancement tracking User acceptance testing Manpages The following are typical hardware and service documentation types: Network diagrams Network maps Datasheet for IT systems (server, switch, e.g.) Service catalog and service portfolio (Information Technology Infrastructure Library) === Software Documentation Folder (SDF) tool === A common type of software document written in the simulation industry is the SDF. When developing software for a simulator, which can range from embedded avionics devices to 3D terrain databases by way of full motion control systems, the engineer keeps a notebook detailing the development "the build" of the project or module. The document can be a wiki page, Microsoft Word document or other environment. They should contain a requirements section, an interface section to detail the communication interface of the software. Often a notes section is used to detail the proof of concept, and then track errors and enhancements. Finally, a testing section to document how the software was tested. This documents conformance to the client's requirements. The result is a detailed description of how the software is designed, how to build and install the software on the target device, and any known defects and workarounds. This build document enables future developers and maintainers to come up to speed on the software in a timely manner, and also provides a roadmap to modifying code or searching for bugs. === Software tools for network inventory and configuration === These software tools can automatically collect data of your network equipment. The data could be for inventory and for configuration information. The Information Technology Infrastructure Library requests to create such a database as a basis for all information for the IT responsible. It is also the basis for IT documentation. Examples include XIA Configuration. == Documentation in criminal justice == "Documentation" is the preferred term for the process of populating criminal databases. Examples include the National Counterterrorism Center's Terrorist Identities Datamart Environment, sex offender registries, and gang databases. == Documentation in early childhood education == Documentation, as it pertains to the early childhood education field, is "when we notice and value children's ideas, thinking, questions, and theories about the world and then collect traces of their work (drawings, photographs of the children in action, and transcripts of their words) to share with a wider community". Thus, documentation is a process, used to link the educator's knowledge and learning of the child/children with the families, other collaborators, and even to the children themselves. Documentation is an integral part of the cycle of inquiry - observing, reflecting, documenting, sharing and responding. Pedagogical documentation, in terms of the teacher documentation, is the "teacher's story of the movement in children's understanding". According to Stephanie Cox Suarez in "Documentation - Transforming our Perspectives", "teachers are considered researchers, and documentation is a research tool to support knowledge building among children and adults". Documentation can take many different styles in the classroom. The following exemplifies ways in which documentation can make the research, or learning, visible: Documentation panels (bulletin-board-like presentation with multiple pictures and descriptions about the project or event). Daily log (a log kept every day that records the play and learning in the classroom) Documentation developed by or with the children (when observing children during documentation, the child's lens of the observation is used in the actual documentation) Individual portfolios (documentation used to track and highlight the development of each child) Electronic documentation (using apps and devices to share documentation with families and collaborators) Transcripts or recordings of conversations (using recording in documentation can bring about deeper reflections for both the educator and the child) Learning stories (a narrative used to "describe learning and help children see themselves as powerful learners") The classroom as documentation (reflections and documentation of the physical environment of a classroom). Documentation is certainly a process in and of itself, and it is also a process within the educator. The following is the development of documentation as it progresses for and in the educator themselves: Develop(s) habits of documentation Become(s) comfortable with going public with recounting of activities Develop(s) visual literacy skills Conceptualize(s) the purpose of documentation as making learning styles visible, and Share(s) visible theories for interpretation purposes and further design of curriculum.

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