Telligent Community is a community and collaboration software platform developed by Telligent Systems and was first released in 2004. Telligent Community is built on the Telligent Evolution platform, with a variety of core applications running on top of it such as blogs, forums, media galleries, and wikis. Additional applications from third parties using the API's and REST stack can be installed or integrated with the platform. Telligent Community is built with ASP.NET, C#, and Microsoft SQL Server. It is available as downloadable software that can be installed on a web server or via hosting providers. The current version is Verint Community 12.0 which was released February 2012. The product used to be named Community Server before being rebranded as part of the 5.0 release. == History == Telligent Systems was founded by Rob Howard in 2004, who was previously part of Microsoft's ASP.NET team. Telligent introduced its first product, Community Server, in the fall of 2004. Community Server was one of the first integrated community platforms that brought together blogs, photo galleries, wikis, forums, user profiles and more. Community Server was based on the merger of three then-widely used open source ASP.NET projects: the ASP.NET Forums, nGallery photo gallery, and .Text blog engine. The people behind those projects (Scott Watermasysk, Jason Alexander, and Rob Howard) joined together as Telligent Systems and along with several other software developers created Community Server 1.0. Between 2004 and 2009 Community Server steadily grew in scope, features, and capabilities. In 2008 Telligent Systems released a second version of Community Server that targeted as an Enterprise Social Software platform used to create and manage internal employee communities and intranets. Originally branded as Community Server Evolution this was later renamed Telligent Enterprise. Telligent also announced a new Enterprise Reporting platform at its first Community Server Developers Conference in 2008, which was later renamed Harvest. It was one of the first analytics suites for enterprise collaboration software, and provides social analytics including sentiment analysis, social fingerprints, and buzz analysis on social networking sites such as Twitter. Telligent rebranded all of its products on June 23, 2009 at the Enterprise 2.0 conference when it launched its new Evolution platform product suite. Community Server became known as Telligent Community, Community Server Evolution became known as Telligent Enterprise and the underlying platform that both run on is now referred to as Telligent Evolution. The Social Analytics suite was renamed Telligent Analytics.
KoalaPad
The KoalaPad is a graphics tablet, released in 1983 by US company Koala Technologies Corporation, for the Apple II, TRS-80 Color Computer (as the TRS-80 Touch Pad), Atari 8-bit computers, Commodore 64, and IBM PC compatibles. Originally designed by Dr. David Thornburg as a low-cost computer drawing tool for schools, the Koala Pad and the bundled drawing program, KoalaPainter, was popular with home users as well. KoalaPainter was called KoalaPaint in some versions for the Apple II, and PC Design for the IBM PC. A program called Graphics Exhibitor was included for creating slideshow presentations from KoalaPainter drawings. == Description == The pad was four inches square (i.e. roughly 10×10 cm) and mounted on a slightly inclined base with the back of the pad higher than the front. At the top, "behind" the pad, were two buttons. The pad hooked into the computer using the analog signals of the joystick ports (the so-called paddle inputs), which meant that it had a low resolution and tended to jostle the cursor if moved during use. As an alternative to the drawing stylus, the pad could as easily be operated by the user's fingers for tasks that demanded less precision, such as selecting between menu items (thus using the pad as a kind of "indirect touch screen"). The top-mounted buttons tended to be somewhat frustrating to use, as the user had to "reach around" the stylus to push the buttons in order to start or stop drawing. A similar tablet from Atari, the Atari CX77 Touch Tablet, addressed this with a built-in button on the stylus, which some enterprising users adapted for use with their KoalaPad. == KoalaPainter == The pad shipped with a simple bitmap graphics editor developed by Audio Light called KoalaPainter, PC Design or Micro Illustrator depending on the target machine (see release history). Although bundled with the pad, KoalaPainter could also be operated using an ordinary digital joystick. One unique feature of the program, for its time, was that it held two pictures in the computer's memory, allowing the user to flip from one to the other—a function commonly used in order to study the differences between an original and a modified picture, and to copy and paste between two different pictures. Some third-party bitmap editors could also be used with the KoalaPad, such as Broderbund's Dazzle Draw for the Apple II. === Release history === KoalaPainter for Commodore 64 (1983) and Atari 8-bit computers (1983) PC Design for the IBM PC (1983) Micro Illustrator for the Apple II (1983), Atari 8-bit computers (1983) and Commodore Plus/4 (1984) KoalaPainter II for Commodore 64 (1984) === Reception === Ahoy! called KoalaPainter "a very powerful and effective color drawing package", and concluded that it and the KoalaPad were "excellent in ease of use, a fine choice for a beginner as well as young children". BYTE's reviewer stated in December 1984 that he made far fewer errors when using an Apple Mouse with MousePaint than with a KoalaPad and its software. He found that MousePaint was easier to use and more efficient, predicting that the mouse would receive more software support than the pad. Cassie Stahl in InfoWorld's Essential Guide to Atari Computers praised the tablet and its documentation, rating it "Excellent" among all categories and stating that "Playing with the KoalaPad becomes addictive. It does everything it claims to, and it does it well". She also liked Micro Illustrator, rating it "Excellent" except for "Good" for Performance. While criticizing the limited erase function, Stahl reported an undocumented feature enabling exporting pictures to other software. === File format === The Commodore 64 version of KoalaPainter used a fairly simple file format corresponding directly to the way bitmapped graphics are handled on the computer: A two-byte load address, followed immediately by 8,000 bytes of raw bitmap data, 1,000 bytes of raw "Video Matrix" data, 1,000 bytes of raw "Color RAM" data, and a one-byte Background Color field. == KoalaWare == Koala Technologies offered more software beyond the bundled KoalaPainter and Graphics Exhibitor for use with the pad. Among these applications, marketed under the moniker KoalaWare (like KoalaPainter itself), was educational software for use with customized keypads and overlays, such as spelling tools, music programs, and mathematics instruction software, as well as software for "translating" graphical designs into Logo programs.
Action model learning
Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with the creation and modification of a software agent's knowledge about the effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in a logic-based action description language and used as input for automated planners. Learning action models is important when goals change. When an agent acted for a while, it can use its accumulated knowledge about actions in the domain to make better decisions. Thus, learning action models differs from reinforcement learning. It enables reasoning about actions instead of expensive trials in the world. Action model learning is a form of inductive reasoning, where new knowledge is generated based on the agent's observations. The usual motivation for action model learning is the fact that manual specification of action models for planners is often a difficult, time-consuming, and error-prone task (especially in complex environments). == Action models == Given a training set E {\displaystyle E} consisting of examples e = ( s , a , s ′ ) {\displaystyle e=(s,a,s')} , where s , s ′ {\displaystyle s,s'} are observations of a world state from two consecutive time steps t , t ′ {\displaystyle t,t'} and a {\displaystyle a} is an action instance observed in time step t {\displaystyle t} , the goal of action model learning in general is to construct an action model ⟨ D , P ⟩ {\displaystyle \langle D,P\rangle } , where D {\displaystyle D} is a description of domain dynamics in action description formalism like STRIPS, ADL or PDDL and P {\displaystyle P} is a probability function defined over the elements of D {\displaystyle D} . However, many state of the art action learning methods assume determinism and do not induce P {\displaystyle P} . In addition to determinism, individual methods differ in how they deal with other attributes of domain (e.g. partial observability or sensoric noise). == Action learning methods == === State of the art === Recent action learning methods take various approaches and employ a wide variety of tools from different areas of artificial intelligence and computational logic. As an example of a method based on propositional logic, we can mention SLAF (Simultaneous Learning and Filtering) algorithm, which uses agent's observations to construct a long propositional formula over time and subsequently interprets it using a satisfiability (SAT) solver. Another technique, in which learning is converted into a satisfiability problem (weighted MAX-SAT in this case) and SAT solvers are used, is implemented in ARMS (Action-Relation Modeling System). Two mutually similar, fully declarative approaches to action learning were based on logic programming paradigm Answer Set Programming (ASP) and its extension, Reactive ASP. In another example, bottom-up inductive logic programming approach was employed. Several different solutions are not directly logic-based. For example, the action model learning using a perceptron algorithm or the multi level greedy search over the space of possible action models. In the older paper from 1992, the action model learning was studied as an extension of reinforcement learning. Nonetheless, further algorithms can be found that operate under different assumptions: FAMA can work even when some observations are missing, and it produces a general (lifted) planning model. It treats learning an action model like a planning problem, making sure the learned model matches the observations given. NOLAM can learn general action models even from noisy or imperfect data. LOCM focuses only on the order of actions in the data, ignoring any details about the states between those actions. The family of safe action model (SAM) learning methods create models that guarantee any plans made with them will actually work in the real world. There's also an extension called N-SAM that can learn action models with numeric conditions and effects. Additionally, numeric action models like N-SAM can be used to improve reinforcement learning (RL) performance through the RAMP algorithm. === Literature === Most action learning research papers are published in journals and conferences focused on artificial intelligence in general (e.g. Journal of Artificial Intelligence Research (JAIR), Artificial Intelligence, Applied Artificial Intelligence (AAI) or AAAI conferences). Despite mutual relevance of the topics, action model learning is usually not addressed in planning conferences like the International Conference on Automated Planning and Scheduling (ICAPS).
.ai
.ai is the Internet country code top-level domain (ccTLD) for Anguilla, a British Overseas Territory in the Caribbean. It is administered by the government of Anguilla. It is a popular domain hack with companies and projects related to the artificial intelligence industry (AI). Google's ad targeting treats .ai as a generic top-level domain (gTLD) because "users and website owners frequently see [the domain] as being more generic than country-targeted." In 2021, Google Search analyst Gary Illyes announced that ".ai" had been added to Google’s list of generic country-code top-level domains, meaning that Google would no longer infer Anguilla-specific targeting from the ccTLD. Identity Digital began managing the domain as of January 2025. == Second and third level registrations == Registrations within off.ai, com.ai, net.ai, and org.ai are available worldwide without restriction. From 15 September 2009, second level registrations within .ai are available to everyone worldwide. == Registration == The minimum registration term allowed for .ai domains is 2 through 10 years for registration and renewal, and a 2-year renewal for domain transfer. Identity Digital is the authority in charge of managing this extension. Registrations began on 16 February 1995. The limits on the number of characters used for the domain name are, at a minimum, from 1 to 3, depending on the registrar, and always at most 63 characters. The character set supported for .ai domain names includes A–Z, a–z, 0–9, and hyphen. As of November 2022, .ai domains cannot accommodate IDN characters. There are no requirements for registering a domain, including local and foreign residents. A .ai domain can be suspended or revoked, if the domain is involved in illegal activity such as violating trademarks or copyrights. Usage must not violate the laws of Anguilla. Anguilla uses the UDRP. Filing a UDRP challenge requires using one of the ICANN Approved Dispute Resolution Service Providers. If the domain is with an ICANN accredited registrar, they should work with the arbitrator. Usually this means either doing nothing or transferring a domain. .ai domains are transferable to any desired registrars as the registration of domain is done maintaining EPP. There used to be a whois.ai-based platform of expired domains in which those could be procured and auctioned every ten days through a standard online process. The last auctions of such kind closed there in December 2024; the platform had been scheduled for shutdown on 30 June 2025, but remained online in the months following that date. == Valuation == Domains cost depends on the registrar, with yearly fees ranging from US$140 (the base fee, as established by Anguilla) to $200. As of July 2025, the highest-valued .ai domain is an undisclosed one sold on 8 November 2023, on Escrow.com, for US$1,500,000—months after an initial $300,000 sale to the same buyer. Among the publicly disclosed ones, the most valued, fin.ai, was sold for $1,000,000 in March 2025. On 16 December 2017, the .ai registry started supporting the Extensible Provisioning Protocol (EPP) and migrated all of its domains onto an EPP system. Consequently, many registrars are allowed to sell .ai domains. Since that date, the .ai ccTLD has also been popular with artificial intelligence companies and organisations. Though such trends are primarily seen among new AI based companies or startups, many established AI and Tech companies preferred not to opt for .ai domains. For example, DeepMind has its domain retained at .com; Meta has redirected its facebook.ai domain to ai.meta.com. == Impact on Anguilla's economy == The registration fees earned from the .ai domains go to the treasury of the Government of Anguilla. As per a 2018 New York Times report, the total revenue generated out of selling .ai domains was $2.9 million. In 2023, Anguilla's government made about US$32 million from fees collected for registering .ai domains; that amounted to over 10% of gross domestic product for the territory. "In the years before the real breakthrough of AI, revenue from .ai domains made up less than 1% of our state income, by 2025 it will be around 47%," explained Jose Vanterpool, Minister of Infrastructure and Communications (MICUHITES), in an interview with BBC. The high 90% renewal rate of .ai domains and the 2025 renewal wave of domains registered in 2023 are driving another surge in state revenues, according to Domaintechnik.
Artificial reproduction
Artificial reproduction is the re-creation of life brought about by means other than natural ones. It is new life built by human plans and projects. Examples include artificial selection, artificial insemination, in vitro fertilization, artificial womb, artificial cloning, and kinematic replication. Artificial reproduction is one aspect of artificial life. Artificial reproduction can be categorized into one of two classes according to its capacity to be self-sufficient: non-assisted reproductive technology and assisted reproductive technology. Cutting plants' stems and placing them in compost is a form of assisted artificial reproduction, xenobots are an example of a more autonomous type of reproduction, while the artificial womb presented in the movie the Matrix illustrates a non assisted hypothetical technology. The idea of artificial reproduction has led to various technologies. == Theology == Humans have aspired to create life since immemorial times. Most theologies and religions have conceived this possibility as exclusive of deities. Christian religions consider the possibility of artificial reproduction, in most cases, as heretical and sinful. == Philosophy == Although ancient Greek philosophy raised the concept that man could imitate the creative capacity of nature, classic Greeks thought that if possible, human beings would reproduce things as nature does, and vice versa, nature would do the things that man does in the same way. Aristotle, for example, wrote that if nature made tables, it would make them just as men do. In other words, Aristotle said that if nature were to create a table, such table will look like a human-made table. Correspondingly, Descartes envisioned the human body, and nature, as a machine. Cartesian philosophy does not stop seeing a perfect mirror between nature and the artificial. However, Kant revolutionized this old idea by criticizing such naturalism. Kant pedagogically wrote: "Reason, in order to be taught by nature, must approach nature with its principles in one hand, according to which the agreement among appearances can count as laws, and, in the other hand, the experiment thought out in accord with these principles—in order to be instructed by nature not like a pupil, who has recited to him whatever the teacher wants to say, but like an appointed judge who compels witnesses to answer the questions he puts to them.". Humans are not instructed by nature but rather use nature as raw material to invent. Humans find alternatives to the natural restrictions imposed by natural laws thus, nature is not necessarily mirrored. In accordance with Kant (and contrary to what Aristotle thought) Karl Marx, Alfred Whitehead, Jaques Derrida and Juan David García Bacca noticed that nature is incapable of reproducing tables; or airplanes, or submarines, or computers. If nature tried to create airplanes, it would produce birds. If nature tried to create submarines, it would get fishes. If nature tried to create computers, brains would grow. And if nature tried to create man, modern man, monkeys will be evolved. According to Whitehead, if we look for something natural in artificial life, in the most elaborate cases, if anything, only atoms remain natural. Juan David Garcia Bacca summarized, “It will not come out from wood, it will not be born, a galley; from clay, a vessel; from linen, a dress; from iron, a lever,...From natural, artificial. In the artificial, the natural is reduced to a simple raw material, even though it is perfectly specified with natural specification. The artificial is the real, positive, and original negation of the natural: of species, of genus and of essence. Thus, its ontology is superior to natural ontology. And for this very reason Marx did not attach any importance to Darwin, whose evolutionism is confined to the natural order: to changes, at most, from variety to variety, from species to species... natural. For the same reason, nature has no dialectics, even though continuous evolution and selection can occur. The dialectic cannot emerge from the natural, for deeper reasons than, using today's terms, from a bird, an airplane cannot emerge; from fish, a submarine; from ears, a telephone; from eyes, a television; from a brain, a digital computer; from feet, a car; from hands, an engine; from Euclid, Descartes; from Aristotle, Newton; from Plato, Marx.” According to García Bacca, the major difference between natural causes and artificial causes is that nature does not have plans and projects, while humans design things following plans and projects. In contrast, other influential authors such as Michael Behe have depicted the concept and promoted the idea of intelligent design, a notion that has aroused several doubts and heated controversies, as it reframe natural causes in accordance with a natural plan. Previous ideas that have also provided a positive 'sense' to natural reproduction, are orthogenesis, syntropy, orgone and morphic resonance, among others. Although, these ideas have been historically marginalized and often called pseudoscience, recently Bio-semioticians are reconsidering some of them under symbolic approaches. Current metaphysics of science actually recognizes that the artificial ways of reproduction are diverse from nature, i.e., unnatural, anti-natural or supernatural. Because Biosemiotics does not focus on the function of life but on its meaning, it has a better understanding of the artificial than classic biology. == Science == Biology, being the study of cellular life, addresses reproduction in terms of growth and cellular division (i.e., binary fission, mitosis and meiosis); however, the science of artificial reproduction is not restricted by the mirroring of these natural processes.The science of artificial reproduction is actually transcending the natural forms, and natural rules, of reproduction. For example, xenobots have redefined the classical conception of reproduction. Although xenobots are made of eukariotic cells they do not reproduce by mitosis, but rather by kinematic replication. Such constructive replication does not involve growing but rather building. == Assisted reproductive technologies == Assisted reproductive technology (ART)'s purpose is to assist the development of a human embryo, commonly because of medical concerns due to fertility limitations. == Non-assisted reproductive technologies == Non-assisted reproductive technologies (NART) could have medical motivations but are mostly driven by a wider heterotopic ambition. Although, NARTs are initially designed by humans, they are programed to become independent of humans to a relative or absolute extent. James Lovelock proposed that such novelties could overcome humans. === Artificial cloning === Cloning is the cellular reproductive processes where two or more genetically identical organisms are created, either by natural or artificial means. Artificial cloning normally involves editing the genetic code, somatic cell nuclear transfer and 3D bioprinting. === Non-assisted artificial womb === A non-assisted artificial womb or artificial uterus is a device that allow for ectogenesis or extracorporeal pregnancy by growing an embryonic form outside the body of an organism (that would normally carry the embryo to term) without any human assistance. The aspect of non-assistance is the key distinction between the current artificial womb technology (AWT) in modern medical research, which still relies on human assistance. With this non-assisted hypothetical technology, a zygote or stem cells are used to create an embryo that is then incubated and monitored by artificial intelligence (AI) within a chamber composed of biocompatible material. The AI maintains the necessary conditions for the embryo to develop and thrive, proceeding to mimic organic labor and childbirth in order to best help the embryo adjust to the outside world. Ectogenesis—gestation, depicted in the science fiction movie The Matrix, is a fast approaching reality. This type of innovation presupposes that vertebrate wombs are not the only way for bearing humans or other similar forms of life. === Kinematic replication === Self-replication without binary fission, meiosis, mitosis (or any other form of cellular reproduction that involves division and growing) can be achieved. Xenobots are an example of kinematic replication. They are biobots, named after the African clawed frog (Xenopus laevis). Xenobots are cellular life forms designed by using artificial intelligence to build more of themselves by combining frog cells in a liquid medium. The term kinematic replication is usually reserved for biomolecules (e.g. DNA, RNA, prions, etc.) and artificially designed cellular forms (e.g. xenobots). === Machine constructive replication === Machine constructive replication mimics human traditional manufacturing but is entirely self-automated. Such constructive replication is a more general form of kinematic replication, which does not necessarily
Content Threat Removal
Content Threat Removal (CTR) is a cybersecurity technology intended to defeat the threat posed by handling digital content in the cyberspace. Unlike other defenses, including antivirus software and sandboxed execution, CTR does not rely on being able to detect threats. Similar to Content Disarm and Reconstruction, CTR is designed to remove the threat without knowing whether it has done so and acts without knowing if data contains a threat or not. Detection strategies work by detecting unsafe content, and then blocking or removing that content. Content that is deemed safe is delivered to its destination. In contrast, Content Threat Removal assumes all data is hostile and delivers none of it to the destination, regardless of whether it is actually hostile. Although no data is delivered, the business information carried by the data is delivered using new data created for the purpose. == Threat == Advanced attacks continuously defeat defenses that are based on detection. These are often referred to as zero-day attacks, because as soon as they are discovered attack detection mechanisms must be updated to identify and neutralize the attack, and until they are, all systems are unprotected. These attacks succeed because attackers find new ways of evading detection. Polymorphic code can be used to evade the detection of known unsafe data and sandbox detection allows attacks to evade dynamic analysis. == Method == A Content Threat Removal defence works by intercepting data on its way to its destination. The business information carried by the data is extracted and the data is discarded. Then entirely new, clean and safe data is built to carry the information to its destination. The effect of building new data to carry the business information is that any unsafe elements of the original data are left behind and discarded. This includes executable data, macros, scripts and malformed data that trigger vulnerabilities in applications. While CTR is a form of content transformation, not all transformations provide a complete defence against the content threat. == Applicability == CTR is applicable to user-to-user traffic, such as email and chat, and machine-to-machine traffic, such as web services. Data transfers can be intercepted by in-line application layer proxies and these can transform the way information content is delivered to remove any threat. CTR works by extracting business information from data and it is not possible to extract information from executable code. This means CTR is not directly applicable to web browsing, since most web pages are code. It can, however, be applied to content that is downloaded from, and uploaded to, websites. Although most web pages cannot be transformed to render them safe, web browsing can be isolated and the remote access protocols used to reach the isolated environment can be subjected to CTR. CTR provides a solution to the problem of stegware. It naturally removes detectable steganography and eliminates symbiotic and permutation steganography through normalisation.
Coherent extrapolated volition
Coherent extrapolated volition (CEV) is a theoretical framework in the field of AI alignment describing an approach by which an artificial superintelligence (ASI) would act on a benevolent supposition of what humans would want if they were more knowledgeable, more rational, had more time to think, and had matured together as a society, as opposed to humanity's current individual or collective preferences. It was proposed by Eliezer Yudkowsky in 2004 as part of his work on friendly AI. == Concept == CEV proposes that an advanced AI system should derive its goals by extrapolating the idealized volition of humanity. This means aggregating and projecting human preferences into a coherent utility function that reflects what people would desire under ideal epistemic and moral conditions. The aim is to ensure that AI systems are aligned with humanity's true interests, rather than with transient or poorly informed preferences. In poetic terms, 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. == Debate == Yudkowsky and Nick Bostrom note that CEV has several interesting properties. It is designed to be humane and self-correcting, by capturing the source of human values instead of trying to list them. It avoids the difficulty of laying down an explicit, fixed list of rules. It encapsulates moral growth, preventing flawed current moral beliefs from getting locked in. It limits the influence that a small group of programmers can have on what the ASI would value, thus also reducing the incentives to build ASI first. And it keeps humanity in charge of its destiny. CEV also faces significant theoretical and practical challenges. Bostrom notes that CEV has "a number of free parameters that could be specified in various ways, yielding different versions of the proposal." One such parameter is the extrapolation base (whose extrapolated volition is taken into account). For example, whether it should include people with severe dementia, patients in a vegetative state, foetuses, or embryos. He also notes that if CEV's extrapolation base only includes humans, there is a risk that the result would be ungenerous toward other animals and digital minds. One possible solution would be to include a mechanism to expand CEV's extrapolation base. == Variants and alternatives == A proposed theoretical alternative to CEV is to rely on an artificial superintelligence's superior cognitive capabilities to figure out what is morally right, and let it act accordingly. It is also possible to combine both techniques, for instance with the ASI following CEV except when it is morally impermissible. In another review, a philosophical analysis explores CEV through the lens of social trust in autonomous systems. Drawing on Anthony Giddens' concept of "active trust", the author proposes an evolution of CEV into "Coherent, Extrapolated and Clustered Volition" (CECV). This formulation aims to better reflect the moral preferences of diverse cultural groups, thus offering a more pragmatic ethical framework for designing AI systems that earn public trust while accommodating societal diversity.