AI Art Prints

AI Art Prints — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Digital art

    Digital art

    Digital art, or the digital arts, is artistic work that uses digital technology as part of the creative or presentational process. It can also refer to computational art that uses and engages with digital media. Since the 1960s, various names have been used to describe digital art, including computer art, electronic art, multimedia art, and new media art. Digital art includes pieces stored on physical media, such as with digital painting, as well as digital galleries on websites. Digital art also extends to the field of visual computing. == History == In the early 1960s, John Whitney developed the first computer-generated art using mathematical operations. In 1963, Ivan Sutherland invented the first user interactive computer-graphics interface known as Sketchpad. Between 1974 and 1977, Salvador Dalí created two big canvases of Gala Contemplating the Mediterranean Sea which at a distance of 20 meters is transformed into the portrait of Abraham Lincoln (Homage to Rothko) and prints of Lincoln in Dalivision based on a portrait of Abraham Lincoln processed on a computer by Leon Harmon published in "The Recognition of Faces". The technique is similar to what later became known as photographic mosaics. Andy Warhol created digital art using an Amiga where the computer was publicly introduced at the Lincoln Center in July 1985. An image of Debbie Harry was captured in monochrome from a video camera and digitized into a graphics program called ProPaint. Warhol manipulated the image by adding color using flood fills. == Art made for digital media == Artwork that is highly computational, presented through digital media, and explicitly engages with digital technologies are categorized as "art made for digital media". This differs from art using digital tools, which incorporate digital technology in the creation process but may exist outside the digital world. Digital art historian Christiane Paul writes that it "is highly problematic to classify all art that makes use of digital technologies somewhere in its production and dissemination process as digital art since it makes it almost impossible to arrive at any unifying statement about the art form". == Art that uses digital tools == Digital art can be purely computer-generated (such as fractals and algorithmic art) or taken from other sources, such as a scanned photograph or an image drawn using vector graphics software using a mouse or graphics tablet. Artworks are considered digital paintings when created similarly to non-digital paintings but using software on a computer platform and digitally outputting the resulting image as painted on canvas. Despite differing viewpoints on digital technology's impact on the arts, a consensus exists within the digital art community about its significant contribution to expanding the creative domain, i.e., that it has greatly broadened the creative opportunities available to professional and non-professional artists alike. == Art theorists and art historians == Notable art theorists and historians in this field include: Oliver Grau, Jon Ippolito, Christiane Paul, Frank Popper, Jasia Reichardt, Mario Costa, Christine Buci-Glucksmann, Dominique Moulon, Roy Ascott, Catherine Perret, Margot Lovejoy, Edmond Couchot, Tina Rivers Ryan, Fred Forest and Edward A. Shanken. === Digital painting === Digital painting is either a physical painting made with the use of digital electronics and spray paint robotics within the digital art fine art context or pictorial art imagery made with pixels on a computer screen that mimics artworks from the traditional histories of painting and illustration. === Artificial intelligence art === Artists have used artificial intelligence to create artwork since at least the 1960s. Since their design in 2014, some artists have created artwork using a generative adversarial network (GAN), which is a machine learning framework that allows two "algorithms" to compete with each other and iterate. It can be used to generate pictures that have visual effects similar to traditional fine art. The essential idea of image generators is that people can use text descriptions to let AI convert their text into visual picture content. Anyone can turn their language into a painting through a picture generator. == Digital art education == Digital art education has become more common with the advancement of digital hardware and software. From hardware such as graphics tablets, styluses, tablets, 3D scanners, virtual reality headsets, and digital cameras; to software such as digital art software, 3D modeling software, 3D rendering, digital sculpting, 2D graphics software, digital painting, 3D terrain generation, 2D animation software, 3D animation software, raster graphics editors, vector graphics editors, mathematical art software, and video editing software. == Scholarship and archives == In addition to the creation of original art, research methods that utilize AI have been generated to quantitatively analyze digital art collections. This has been made possible due to the large-scale digitization of artwork in the past few decades. Although the main goal of digitization was to allow for accessibility and exploration of these collections, the use of AI in analyzing them has brought about new research perspectives. Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art. Close reading focuses on specific visual aspects of one piece. Some tasks performed by machines in close reading methods include computational artist authentication and analysis of brushstrokes or texture properties. In contrast, through distant viewing methods, the similarity across an entire collection for a specific feature can be statistically visualized. Common tasks relating to this method include automatic classification, object detection, multimodal tasks, knowledge discovery in art history, and computational aesthetics. Whereas distant viewing includes the analysis of large collections, close reading involves one piece of artwork. Whilst 2D and 3D digital art is beneficial as it allows the preservation of history that would otherwise have been destroyed by events like natural disasters and war, there is the issue of who should own these 3D scans – i.e., who should own the digital copyrights. === Computer demos === Computer demos are based on computer programs, usually non-interactive. It produces audiovisual presentations. They are a novel form of art, which emerged as a consequence of the home computer revolution in the early 1980s. In the classification of digital art, they can be best described as real-time procedurally generated animated audio-visuals. This form of art does not concentrate only on the aesthetics of the final presentation, but also on the complexities and skills involved in creating the presentation. As such, it can be fully enjoyed only by persons with a relatively high knowledge level of relevant computer technologies. An example is that, as said by Hua Jin and Jie Yang, Using computer-aided design software to present the class content in art design teaching," is not to advocate computer-aided design instead of hand-drawn performance, but to make it serve the profession earlier through a more reasonable course arrangement." On the other hand, many of the created pieces of art are primarily aesthetic or amusing, and those can be enjoyed by the general public. === Digital installation art === Digital installation art constitutes a broad field of artistic practices and a variety of forms. Some resemble video installations, especially large-scale works involving projections and live video capture. By using projection techniques that enhance an audience's impression of sensory envelopment, many digital installations attempt to create immersive environments. While others go even further and attempt to facilitate a complete immersion in virtual realms. This type of installation is generally site-specific, scalable, and without fixed dimensionality, meaning it can be reconfigured to accommodate different presentation spaces. Scott Snibbe's "Boundary Functions" is an example of augmented reality digital installation art, which responds to people who enter the installation by drawing lines between people, indicating their personal space.Noah Wardrip-Fruin's "Screen"(2003) utilizes a Cave Automatic Virtual Environment (CAVE) to create an interactive, text-based digital experience that engages the viewer in a multi-sensory interaction. === Internet art and net.art === Internet art is digital art that uses the specific characteristics of the Internet and is exhibited on the Internet. The term "internet art" is included by "net art" for which artists assume that network will be refreshed through history. So the term "post-internet art" is used to exclude artworks outside of the internet media. A representative example is Protocols for Achievements, which is a digital photo frame that confronts the aestheti

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  • A.I.s

    A.I.s

    A.I.s is a themed anthology of science fiction short works edited by American writers Jack Dann and Gardner Dozois. It was first published in paperback by Ace Books in December 2004. It was reissued as an ebook by Baen Books in June 2013. The book collects ten novelettes and short stories by various science fiction authors, together with a preface by the editors. == Contents == "Preface" (Jack Dann and Gardner Dozois) "Antibodies" (Charles Stross) "Trojan Horse" (Michael Swanwick) "Birth Day" (Robert Reed) "The Hydrogen Wall" (Gregory Benford) "The Turing Test" (Chris Beckett) "Dante Dreams" (Stephen Baxter) "The Names of All the Spirits" (J. R. Dunn) "From the Corner of My Eye" (Alexander Glass) "Halfjack" (Roger Zelazny) "Computer Virus" (Nancy Kress)

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  • Computer-assisted proof

    Computer-assisted proof

    A computer-assisted proof is a mathematical proof that has been at least partially generated by computer. Most computer-aided proofs to date have been implementations of large proofs-by-exhaustion of a mathematical theorem. The idea is to use a computer program to perform lengthy computations, and to provide a proof that the result of these computations implies the given theorem. In 1976, the four color theorem was the first major theorem to be verified using a computer program. Attempts have also been made in the area of artificial intelligence research to create smaller, explicit, new proofs of mathematical theorems from the bottom up using automated reasoning techniques such as heuristic search. Such automated theorem provers have proved a number of new results and found new proofs for known theorems. Additionally, interactive proof assistants allow mathematicians to develop human-readable proofs which are nonetheless formally verified for correctness. Since these proofs are generally human-surveyable (albeit with difficulty, as with the proof of the Robbins conjecture) they do not share the controversial implications of computer-aided proofs-by-exhaustion. == Methods == One method for using computers in mathematical proofs is by means of so-called validated numerics or rigorous numerics. This means computing numerically yet with mathematical rigour. One uses set-valued arithmetic and inclusion principle in order to ensure that the set-valued output of a numerical program encloses the solution of the original mathematical problem. This is done by controlling, enclosing and propagating round-off and truncation errors using for example interval arithmetic. More precisely, one reduces the computation to a sequence of elementary operations, say ( + , − , × , / ) {\displaystyle (+,-,\times ,/)} . In a computer, the result of each elementary operation is rounded off by the computer precision. However, one can construct an interval provided by upper and lower bounds on the result of an elementary operation. Then one proceeds by replacing numbers with intervals and performing elementary operations between such intervals of representable numbers. == Philosophical objections == Computer-assisted proofs are the subject of some controversy in the mathematical world, with Thomas Tymoczko first to articulate objections. Those who adhere to Tymoczko's arguments believe that lengthy computer-assisted proofs are not, in some sense, 'real' mathematical proofs because they involve so many logical steps that they are not practically verifiable by human beings, and that mathematicians are effectively being asked to replace logical deduction from assumed axioms with trust in an empirical computational process, which is potentially affected by errors in the computer program, as well as defects in the runtime environment and hardware. Other mathematicians believe that lengthy computer-assisted proofs should be regarded as calculations, rather than proofs: the proof algorithm itself should be proved valid, so that its use can then be regarded as a mere "verification". Arguments that computer-assisted proofs are subject to errors in their source programs, compilers, and hardware can be resolved by providing a formal proof of correctness for the computer program (an approach which was successfully applied to the four color theorem in 2005) as well as replicating the result using different programming languages, different compilers, and different computer hardware. Another possible way of verifying computer-aided proofs is to generate their reasoning steps in a machine readable form, and then use a proof checker program to demonstrate their correctness. Since validating a given proof is much easier than finding a proof, the checker program is simpler than the original assistant program, and it is correspondingly easier to gain confidence into its correctness. However, this approach of using a computer program to prove the output of another program correct does not appeal to computer proof skeptics, who see it as adding another layer of complexity without addressing the perceived need for human understanding. Another argument against computer-aided proofs is that they lack mathematical elegance—that they provide no insights or new and useful concepts. In fact, this is an argument that could be advanced against any lengthy proof by exhaustion. An additional philosophical issue raised by computer-aided proofs is whether they make mathematics into a quasi-empirical science, where the scientific method becomes more important than the application of pure reason in the area of abstract mathematical concepts. This directly relates to the argument within mathematics as to whether mathematics is based on ideas, or "merely" an exercise in formal symbol manipulation. It also raises the question whether, if according to the Platonist view, all possible mathematical objects in some sense "already exist", whether computer-aided mathematics is an observational science like astronomy, rather than an experimental one like physics or chemistry. This controversy within mathematics is occurring at the same time as questions are being asked in the physics community about whether twenty-first century theoretical physics is becoming too mathematical, and leaving behind its experimental roots. The emerging field of experimental mathematics is confronting this debate head-on by focusing on numerical experiments as its main tool for mathematical exploration. == Theorems proved with the help of computer programs == Inclusion in this list does not imply that a formal computer-checked proof exists, but rather, that a computer program has been involved in some way. See the main articles for details.

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  • Agents of S.H.I.E.L.D. season 4

    Agents of S.H.I.E.L.D. season 4

    The fourth season of the American television series Agents of S.H.I.E.L.D., based on the Marvel Comics spy organization S.H.I.E.L.D., follows Phil Coulson and other S.H.I.E.L.D. agents and allies after the signing of the Sokovia Accords. It is set in the Marvel Cinematic Universe (MCU) and acknowledges the continuity of the franchise's films. The season was produced by ABC Studios, Marvel Television, and Mutant Enemy Productions, with Jed Whedon, Maurissa Tancharoen, and Jeffrey Bell serving as showrunners. Clark Gregg reprises his role as Coulson from the film series, starring alongside the returning series regulars Ming-Na Wen, Chloe Bennet, Iain De Caestecker, Elizabeth Henstridge, and Henry Simmons. They are joined by John Hannah who was promoted from his recurring guest role in the third season. The fourth season was ordered in March 2016, with production taking place from that July until the following April. Due to its broadcast schedule, the season was split into three "pods": Ghost Rider for the first eight episodes, featuring recurring guest star Gabriel Luna as the supernatural Robbie Reyes / Ghost Rider and exploring mysticism in the MCU alongside the film Doctor Strange (2016); LMD, referring to the new Life Model Decoy program, for the next seven episodes which focus on recurring guest star Mallory Jansen as the LMD Aida; and Agents of Hydra for the final seven episodes, partly set in a "what if" virtual reality that allowed the return of former series regular Brett Dalton as Grant Ward. The season is also affected by the events of the film Captain America: Civil War (2016), and continues storylines established in the canceled series Agent Carter. The first episode premiered at a screening on September 19, 2016, with the season then airing for 22 episodes on ABC, from September 20, 2016, until May 16, 2017. The premiere debuted to 3.58 million viewers, down from previous season premieres but average for the series. Critical response to the season was positive, with many feeling that each pod was better than the last and in particular praising the visual effects and tone of Ghost Rider, the writing and acting of LMD, and the character development and political commentary explored during Agents of Hydra. The season saw series low viewership, but was still considered to have solved ABC's problem during its new Tuesday night timeslot, and the series was renewed for a fifth season in May 2017. == Episodes == == Cast and characters == == Production == === Development === Agents of S.H.I.E.L.D. was renewed for a fourth season on March 3, 2016, earlier than usual for the series. Executive producer Jed Whedon said on this, "We're thrilled to know going into the end of [season three] with certainty that we will be returning, because we can build our story accordingly." Executive producer Maurissa Tancharoen also noted that logistics for hiring directors for the season in advance would be easier, "which is a very nice privilege to have...that's a luxury". The end of the episode "What If..." features an onscreen tribute to Bill Paxton, who died in February 2017 and had portrayed John Garrett in the series' first season. The series paid additional tribute to Paxton in "All the Madame's Men" with promos during The Bakshi Report news segment showcasing John Garrett as a fallen American hero. The end of "World's End" features a similar onscreen tribute to Powers Boothe, who died in May 2017 and had portrayed Gideon Malick in the series' third season. === Writing === The season shifted to the later 10 pm timeslot, allowing it to take on a darker, more mature tone than previous seasons. According to Tancharoen, "The whole tagline for this year is 'Agents of S.H.I.E.L.D. After Dark'". The timeslot gave the series the opportunity to present an increased level of violence and partial nudity, as well as take more risks and present edgier themes. Following the third-season finale, Tancharoen stated that the fourth season would explore the guilt Daisy Johnson has over Lincoln Campbell's death. Executive producer Jeffrey Bell noted the writers tried to continue the tradition of "finding new combinations and new conflicts" between different sets of characters, given "a lot of procedurals [see] the same people doing the same thing for five years". Pairings that would be explored included Coulson and Mack, continuing from the end of season three, who have a mutual respect for one another due to their relationships with Daisy, and Leo Fitz and Holden Radcliffe, who work together. The Fitz-Simmons relationship was also explored more, examining the new challenges it presented for the two "working together, loving each other and living together". Following the third season's dealing with the themes of Captain America: Civil War (2016), such as the opposing reactions to the Inhumans, Whedon said that the question of "How do you deal with a war with powered people at that level, a government level?" was one that they wanted to answer in the fourth season. Tancharoen called the Inhumans "a permanent part of our universe now", with Whedon adding, "we have a quick-fire way of introducing people with powers. It gives us a lot of leeway in our world, and it lets us explore the metaphors of what it is like to be different. We will never close that chapter." With the Inhumans film being removed from Marvel Studios' release schedule, the series had "a little more freedom" and were "able to do a little bit more" with the species, including the potential of introducing some of the "classic" Inhumans, though the series would focus less on Inhumans than the third season which saw "a real significant Inhuman agenda story". It was not intended to be a spin-off of Agents of S.H.I.E.L.D. On the evolution of S.H.I.E.L.D. to featuring so many powered characters, Whedon said "the dynamic in the world has changed. There was one person with powers, and then by The Avengers there were maybe six total ... now they're much more prevalent, so there's reaction from the public based on that." The season is structured into three "pods" based on its airing schedule: the first eight episodes, subtitled Ghost Rider; LMD (Life Model Decoy) for the subsequent seven episodes; and a third pod for the final seven episodes called Agents of Hydra. Elements and characters cross over between the different pods, but the sections "definitely have a different feel" from one another, as Bell explained that 22 episodes "is a long time to hold a big bad or a single plot line, especially for an audience", and for the past two seasons, the series was able to have two separated halves that "allows us to introduce a big bad. And then, something happens and we rise somebody new ... Now, there's three of those." "Financial considerations" were also taken into account in creating the pods for the season, as using LMDs does not "cost as much as setting a guy's head on fire via CGI". In terms of writing the "complicated season", Whedon said the writers were "aware that our fans are our fans and have spent some time with these characters and are clever and see things coming sometimes ... Part of our job is to create not just what we are presenting on plot, but letting the audience be one step ahead of us and being one step ahead of that." He added that the writers knew that they wanted to tell a Ghost Rider story, an LMD story, and a "what if" scenario, and the hardest part was making each pod still fit together as a single season. The major connection ultimately became the Darkhold, which leads from the magic of Ghost Rider to the advanced science of LMD and then the Framework in Agents of Hydra. Ghost Rider also reappears in the final episode of the season, "World's End", as an additional connection. ==== Ghost Rider ==== While planning the fourth season, Marvel suggested that the series introduce Ghost Rider, after the character's film rights had returned to Marvel from Sony in May 2013. Loeb felt that this made the season unquestionably "the series' biggest" with the "most ambitious story yet". He added that "one of the things that we talked about is, S.H.I.E.L.D. always looked out for the weird, the unusual, the things that were and could be a problem for the public", and Marvel realized that Ghost Rider's abilities, which are more mystical than anything seen in the series to date, opened up "a quarter of the universe that we haven't really spent a lot of time exploring ... what happens if our very real, our very grounded agents who are very much a family have to take on something that is as bizarre and powerful and unique as Ghost Rider." Bell added that the producers would have been willing to give an entire season of the show to a Ghost Rider arc if the season was 13 episodes or less, but 22 episodes seemed too long to "feel like one flavor". The Robbie Reyes version of Ghost Rider was chosen over other versions of the character from the comics because of his relationship with his brother Gabe, w

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  • Corpus of Linguistic Acceptability

    Corpus of Linguistic Acceptability

    Corpus of Linguistic Acceptability (CoLA) is a dataset the primary purpose of which is to serve as a benchmark for evaluating the ability of artificial neural networks, including large language models, to judge the grammatical correctness of sentences. It consists of 10,657 English sentences from published linguistics literature that were manually labeled either as grammatical or ungrammatical. == Public version == The publicly available version of CoLA contains 9,594 sentences that belong to training and development sets. It excludes 1,063 sentences reserved for a held-out test set.

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  • Akoma Ntoso

    Akoma Ntoso

    Akoma Ntoso (Architecture for Knowledge-Oriented Management of African Normative Texts using Open Standards and Ontologies, AKN) is an international technical standard for representing legal documents (executive, legislative, and judiciary) in a structured manner using a domain specific, legal XML vocabulary. The term akoma ntoso means "linked hearts" in the Akan language of West Africa. Akoma Ntoso is a legal document standard designed to serve as a basis for modern machine-readable and fully digital legislative and judicial processes. This is achieved by providing a coherent syntax and well-defined semantics to represent legal documents in a digital format. It is designed to be suitable as a common exchange format in all parliamentary, legal and judicial systems around the world. Taking advantage of the shared heritage present in all legal systems, Akoma Ntoso has been developed to have ample flexibility to respond to all the differences in texts, languages, and legal practices. Aiming to expand on certain common practices, the standard therefore has a broad scope. It includes a common extensible model for data (the document content) and metadata (such as bibliographic information and annotations). Specifically, as a common legal document standard for the interchange of legal documents it is designed to be highly flexible in its support of documents and functionalities, maintaining a large set of both structural and semantic building blocks (over 500 entities in version 3.0) for representing this wide variety of document types of virtually all legal traditions. It is extensible in order to allow for modifications to address the individual criteria of organizations or unique aspects of various legal practices and languages without sacrificing interoperability with other systems. Akoma Ntoso is as such part of a wider approach to developing open, non-proprietary technical standards for structuring legal documents and information under the name of Legal XML, which also includes formats and standards for, e.g., eContracts, eNotarization, electronic court filings, the technical representation of legal norms and rules (LegalRuleML) or technical standards for the interfaces of, e.g., litigant portal exchange platforms. Akoma Ntoso allows machine-driven processes to operate on the syntactic and semantic components of digital parliamentary, judicial and legislative documents, thus facilitating the development of high-quality information resources. It can substantially enhance the performance, accountability, quality and openness of parliamentary and legislative operations based on best practices and guidance through machine-assisted drafting and machine-assisted (legal) analysis. Embedded in the environment of the semantic web, it forms the basis for a heterogenous yet interoperable ecosystem, with which these tools can operate and communicate, as well as for future applications and use cases based on digital law or rule representation. == Definition == The Akoma Ntoso standard defines a set of machine readable electronic representations in XML format of the building blocks of parliamentary, legislative and judiciary documents. As official self-description, the standard (...) defines a set of simple, technology-neutral electronic representations of parliamentary, legislative and judiciary documents for e-services in a worldwide context and provides an enabling framework for the effective exchange of "machine readable" parliamentary, legislative and judiciary documents such as legislation, debate record, minutes, judgements, etc. Providing access to primary legal materials, parliamentary works and judiciaries documents is not just a matter of giving physical or on-line access to them. "Open access" requires the information to be described and classified in a uniform and organized way so that content is structured into meaningful elements that can be read and understood by software applications, so that the content is made "machine readable" and more sophisticated applications than on-screen display are made possible. The standard is composed of: an XML vocabulary that defines the mapping between the structure of legal documents and their equivalent in XML; specifications of an XML schema that defines the structure of legal documents in XML. They provide rich possibilities of description for several types of parliamentary, legislative and judiciary document, such as bills, acts and parliamentary records, judgments, or gazettes; a recommended naming convention for providing unique identifiers to legal sources based on FRBR model; a MIME type definition. == History and adoption == Akoma Ntoso started as an UNDESA project in 2004 within the initiative "Strengthening Parliaments' Information Systems in Africa". Its core vocabulary was created mostly by Monica Palmirani and Fabio Vitali, two professors from the Centre for Research in the History, Philosophy, and Sociology of Law and in Computer Science and Law (CIRSFID) of the University of Bologna. A first legislative text editor supporting Akoma Ntoso was developed in 2007 on the base of OpenOffice. In 2010 European Parliament developed an open source web-based application called AT4AM based on Akoma Ntoso for facilitating the production and the management of legislative amendments. Thanks to this project, the application of Akoma Ntoso could be extended to new type of documents (e.g. legislative proposal, transcript) and to other scenarios (e.g., multilingual translation process). Akoma Ntoso also was explicitly designed to be compliant with CEN Metalex, one of the other popular legal standards, which is used in the legislation.gov.uk. In 2012, the Akoma Ntoso specifications became the main working base for the activities of the LegalDocML Technical Committee within the LegalXML member section of OASIS. The "United States Legislative Markup" (USLM) schema for the United States Code (the US codified laws), developed in 2013, and the LexML Brasil XML schema for Brazilian legislative and judiciary documents, developed before, in 2008, were both designed to be consistent with Akoma Ntoso. The United States Library of Congress created the Markup of US Legislation in Akoma Ntoso challenge in July 2013 to create representations of selected US bills using the most recent Akoma Ntoso standard within a couple months for a $5000 prize, and the Legislative XML Data Mapping challenge in September 2013 to produce a data map for US bill XML and UK bill XML to the most recent Akoma Ntoso schema within a couple months for a $10000 prize. The National Archives of UK converted all the legislation in AKN in 2014. The availability of bulk legislation "moved the UK's ranking from fourth to first, in the 2014 Global Open Data Index, for legislation". The Senate of Italian Republic provides, since July 2016, all the bills in Akoma Ntoso as bulk in open data repository. The German Federal Ministry of the Interior started the project Elektronische Gesetzgebung ("Electronic Legislation") in 2015/2016 and published Version 1.0 of the German application profile "LegalDocML.de" in March 2020. The projects aim is to digitalize the entire legislative lifecycle from drafting to publication. Germany decided to adopt a model-driven development approach to creating and providing a subschema-based application profile in order to ensure interoperability among organizationally independent actors, each with their respective IT landscapes and tools. In this initial version LegalDocML.de covers draft bills in the form of laws, regulations and general administrative directives. As part of an ongoing development process, the standard could incrementally be expanded in future stages to include all relevant document types of parliamentary, legislative and promulgation processes and tools. The High-Level Committee on Management (HLCM), part of the United Nations System Chief Executives Board for Coordination, set up a Working Group on Document Standards that approved in April 2017 to adopt Akoma Ntoso as standard for modeling its documentation. Akoma Ntoso in its version 1.0 is finally adopted as OASIS standard in the frame of LegalDocML in August 2018.

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  • AI@50

    AI@50

    AI@50, formally known as the "Dartmouth Artificial Intelligence Conference: The Next Fifty Years" (July 13–15, 2006), was a conference organized by James H. Moor, commemorating the 50th anniversary of the Dartmouth workshop which effectively inaugurated the history of artificial intelligence. Five of the original ten attendees were present: Marvin Minsky, Ray Solomonoff, Oliver Selfridge, Trenchard More, and John McCarthy. While sponsored by Dartmouth College, General Electric, and the Frederick Whittemore Foundation, a $200,000 grant from the Defense Advanced Research Projects Agency (DARPA) called for a report of the proceedings that would: Analyze progress on AI's original challenges during the first 50 years, and assess whether the challenges were "easier" or "harder" than originally thought and why Document what the AI@50 participants believe are the major research and development challenges facing this field over the next 50 years, and identify what breakthroughs will be needed to meet those challenges Relate those challenges and breakthroughs against developments and trends in other areas such as control theory, signal processing, information theory, statistics, and optimization theory. A summary report by the conference director, James H. Moor, was published in AI Magazine. == Conference Program and links to published papers == James H. Moor, conference Director, Introduction Carol Folt and Barry Scherr, Welcome Carey Heckman, Tonypandy and the Origins of Science === AI: Past, Present, Future === John McCarthy, What Was Expected, What We Did, and AI Today Marvin Minsky, The Emotion Machine === The Future Model of Thinking === Ron Brachman and Hector Levesque, A Large Part of Human Thought David Mumford, What is the Right Model for 'Thought'? Stuart Russell, The Approach of Modern AI === The Future of Network Models === Geoffrey Hinton & Simon Osindero, From Pandemonium to Graphical Models and Back Again Rick Granger, From Brain Circuits to Mind Manufacture === The Future of Learning & Search === Oliver Selfridge, Learning and Education for Software: New Approaches in Machine Learning Ray Solomonoff, Machine Learning — Past and Future Leslie Pack Kaelbling, Learning to be Intelligent Peter Norvig, Web Search as a Product of and Catalyst for AI === The Future of AI === Rod Brooks, Intelligence and Bodies Nils Nilsson, Routes to the Summit Eric Horvitz, In Pursuit of Artificial Intelligence: Reflections on Challenges and Trajectories === The Future of Vision === Eric Grimson, Intelligent Medical Image Analysis: Computer Assisted Surgery and Disease Monitoring Takeo Kanade, Artificial Intelligence Vision: Progress and Non-Progress Terry Sejnowski, A Critique of Pure Vision === The Future of Reasoning === Alan Bundy, Constructing, Selecting and Repairing Representations of Knowledge Edwina Rissland, The Exquisite Centrality of Examples Bart Selman, The Challenge and Promise of Automated Reasoning === The Future of Language and Cognition === Trenchard More The Birth of Array Theory and Nial Eugene Charniak, Why Natural Language Processing is Now Statistical Natural Language Processing Pat Langley, Intelligent Behavior in Humans and Machines === The Future of the Future === Ray Kurzweil, Why We Can Be Confident of Turing Test Capability Within a Quarter Century George Cybenko, The Future Trajectory of AI Charles J. Holland, DARPA's Perspective === AI and Games === Jonathan Schaeffer, Games as a Test-bed for Artificial Intelligence Research Danny Kopec, Chess and AI Shay Bushinsky, Principle Positions in Deep Junior's Development === Future Interactions with Intelligent Machines === Daniela Rus, Making Bodies Smart Sherry Turkle, From Building Intelligences to Nurturing Sensibilities === Selected Submitted Papers: Future Strategies for AI === J. Storrs Hall, Self-improving AI: An Analysis Selmer Bringsjord, The Logicist Manifesto Vincent C. Müller, Is There a Future for AI Without Representation? Kristinn R. Thórisson, Integrated A.I. Systems === Selected Submitted Papers: Future Possibilities for AI === Eric Steinhart, Survival as a Digital Ghost Colin T. A. Schmidt, Did You Leave That 'Contraption' Alone With Your Little Sister? Michael Anderson & Susan Leigh Anderson, The Status of Machine Ethics Marcello Guarini, Computation, Coherence, and Ethical Reasoning

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  • DARPA Grand Challenge

    DARPA Grand Challenge

    The DARPA Grand Challenge is a prize competition for American autonomous vehicles, funded by the Defense Advanced Research Projects Agency, the most prominent research organization of the United States Department of Defense. Congress has authorized DARPA to award cash prizes to further DARPA's mission to sponsor revolutionary, high-payoff research that bridges the gap between fundamental discoveries and military use. The initial DARPA Grand Challenge in 2004 was created to spur the development of technologies needed to create the first fully autonomous ground vehicles capable of completing a substantial off-road course within a limited time. The third event, the DARPA Urban Challenge in 2007, extended the initial Challenge to autonomous operation in a mock urban environment. The 2012 DARPA Robotics Challenge, focused on autonomous emergency-maintenance robots, and new Challenges are still being conceived. The DARPA Subterranean Challenge was tasked with building robotic teams to autonomously map, navigate, and search subterranean environments. Such teams could be useful in exploring hazardous areas and in search and rescue. In addition to the challenges in autonomous technology, DARPA has also conducted prize competitions in other areas of technology. == History and background == Fully autonomous vehicles have been an international pursuit for many years, from endeavors in Japan (starting in 1977), Germany (Ernst Dickmanns and VaMP), Italy (the ARGO Project), the European Union (EUREKA Prometheus Project), the United States of America, and other countries. DARPA funded the development of the first fully autonomous robot beginning in 1966 with the Shakey the robot project at Stanford Research Institute, now SRI International. The first autonomous ground vehicle capable of driving on and off roads was developed by DARPA as part of the Strategic Computing Initiative beginning in 1984 leading to demonstrations of autonomous navigation by the Autonomous Land Vehicle and the Navlab. The Grand Challenge was the first long distance competition for driverless cars in the world; other research efforts in the field of driverless cars take a more traditional commercial or academic approach. The U.S. Congress authorized DARPA to offer prize money ($1 million) for the first Grand Challenge to facilitate robotic development, with the ultimate goal of making one-third of ground military forces autonomous by 2015. Following the 2004 event, Dr. Tony Tether, the director of DARPA, announced that the prize money had been increased to $2 million for the next event, which was claimed on October 9, 2005. The first, second and third places in the 2007 Urban Challenge received $2 million, $1 million, and $500,000, respectively. 14 new teams have qualified in year 2015. The competition was open to teams and organizations from around the world, as long as there was at least one U.S. citizen on the roster. Teams have participated from high schools, universities, businesses and other organizations. More than 100 teams registered in the first year, bringing a wide variety of technological skills to the race. In the second year, 195 teams from 36 U.S. states and 4 foreign countries entered the race. == 2004 Grand Challenge == The first competition of the DARPA Grand Challenge was held on March 13, 2004 in the Mojave Desert region of the United States, along a 150-mile (240 km) route that follows along the path of Interstate 15 from just before Barstow, California to just past the California–Nevada border in Primm. None of the robot vehicles finished the route. Carnegie Mellon University's Red Team and car Sandstorm (a converted Humvee) traveled the farthest distance, completing 11.78 km (7.32 mi) of the course before getting hung up on a rock after making a switchback turn. No winner was declared, and the cash prize was not given. Therefore, a second DARPA Grand Challenge event was scheduled for 2005. == 2005 Grand Challenge == The second competition of the DARPA Grand Challenge began at 6:40 am on October 8, 2005. All but one of the 23 finalists in the 2005 race surpassed the 11.78 km (7.32 mi) distance completed by the best vehicle in the 2004 race. Five vehicles successfully completed the 212 km (132 mi) course: Vehicles in the 2005 race passed through three narrow tunnels and negotiated more than 100 sharp left and right turns. The race concluded through Beer Bottle Pass, a winding mountain pass with a sheer drop-off on one side and a rock face on the other. Although the 2004 course required more elevation gain and some very sharp switchbacks (Daggett Ridge) were required near the beginning of the route, the course had far fewer curves and generally wider roads than the 2005 course. The natural rivalry between the teams from Stanford and Carnegie Mellon (Sebastian Thrun, head of the Stanford team was previously a faculty member at Carnegie Mellon and colleague of Red Whittaker, head of the CMU team) was played out during the race. Mechanical problems plagued H1ghlander before it was passed by Stanley. Gray Team's entry was a miracle in itself, as the team from the suburbs of New Orleans was caught in Hurricane Katrina a few short weeks before the race. The fifth finisher, Terramax, a 30,000 pound entry from Oshkosh Truck, finished on the second day. The huge truck spent the night idling on the course, but was particularly nimble in carefully picking its way down the narrow roads of Beer Bottle Pass. == 2007 Urban Challenge == The third competition of the DARPA Grand Challenge, known as the "Urban Challenge", took place on November 3, 2007 at the site of the now-closed George Air Force Base (currently used as Southern California Logistics Airport), in Victorville, California (Google map). The course involved a 96 km (60 mi) urban area course, to be completed in less than 6 hours. Rules included obeying all traffic regulations while negotiating with other traffic and obstacles and merging into traffic. Unlike previous challenges, the 2007 Urban Challenge organizers divided competitors into two "tracks", A and B. All Track A and Track B teams were part of the same competition circuit, but the teams chosen for the Track A program received US $1 million in funding. These 11 teams largely represented major universities and large corporate interests such as CMU teaming with GM as Tartan Racing, Stanford teaming with Volkswagen, Virginia Tech teaming with TORC Robotics as VictorTango, Oshkosh Truck, Honeywell, Raytheon, Caltech, Autonomous Solutions, Cornell University, and MIT. One of the few independent entries in Track A was the Golem Group. DARPA has not publicly explained the rationale behind the selection of Track A teams. Teams were given maps sparsely charting the waypoints that defined the competition courses. At least one team, Tartan Racing, enhanced the maps through the insertion of additional extrapolated waypoints for improved navigation. A debriefing paper published by Team Jefferson illustrates graphically the contrast between the course map it was given by DARPA and the course map used by Tartan Racing. Tartan Racing claimed the $2 million prize with their vehicle "Boss", a Chevy Tahoe. The second-place finisher earning the $1 million prize was the Stanford Racing Team with their entry "Junior", a 2006 Volkswagen Passat. Coming in third place was team VictorTango, winning the $500,000 prize with their 2005 Ford Escape hybrid, "Odin". MIT placed 4th, with Cornell University and University of Pennsylvania/Lehigh University also completing the course. The six teams that successfully finished the entire course: While the 2004 and 2005 events were more physically challenging for the vehicles, the robots operated in isolation and only encountered other vehicles on the course when attempting to pass. The Urban Challenge required designers to build vehicles able to obey all traffic laws while they detect and avoid other robots on the course. This is a particular challenge for vehicle software, as vehicles must make "intelligent" decisions in real time based on the actions of other vehicles. Other than previous autonomous vehicle efforts that focused on structured situations such as highway driving with little interaction between the vehicles, this competition operated in a more cluttered urban environment and required the cars to perform sophisticated interactions with each other, such as maintaining precedence at a 4-way stop intersection. == 2012 Robotics Challenge == The DARPA Robotics Challenge is an ongoing competition focusing on humanoid robotics. The primary goal of the program is to develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments. It launched in October 2012, and hosted the Virtual Robotics Competition in June 2013. Two more competitions are planned: the DRC Trials in December 2013, and the DRC Finals in December 2014. Unlike prior Challenges, the construction of the "vehicles" w

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  • Language Computer Corporation

    Language Computer Corporation

    Language Computer Corporation (LCC) is a natural language processing research company based in Richardson, Texas. The company develops a variety of natural language processing products, including software for question answering, information extraction, and automatic summarization. Since its founding in 1995, the low-profile company has landed significant United States Government contracts, with $8,353,476 in contracts in 2006-2008. While the company has focused primarily on the government software market, LCC has also used its technology to spin off three start-up companies. The first spin-off, known as Lymba Corporation, markets the PowerAnswer question answering product originally developed at LCC. In 2010, LCC's CEO, Andrew Hickl, co-founded two start-ups which made use of the company's technology. These included Swingly, an automatic question answering start-up, and Extractiv, an information extraction service that was founded in partnership with Houston, Texas-based 80legs.

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  • Content-based image retrieval

    Content-based image retrieval

    Content-based image retrieval, also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey for a scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing). "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness. == Comparison with metadata searching == An image meta search requires humans to have manually annotated images by entering keywords or metadata in a large database, which can be time-consuming and may not capture the keywords desired to describe the image. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. In the same regard, CBIR systems have similar challenges in defining success. "Keywords also limit the scope of queries to the set of predetermined criteria." and, "having been set up" are less reliable than using the content itself. == History == The term "content-based image retrieval" seems to have originated in 1992 when it was used by Japanese Electrotechnical Laboratory engineer Toshikazu Kato to describe experiments into automatic retrieval of images from a database, based on the colors and shapes present. Since then, the term has been used to describe the process of retrieving desired images from a large collection on the basis of syntactical image features. The techniques, tools, and algorithms that are used originate from fields such as statistics, pattern recognition, signal processing, and computer vision. === QBIC - Query By Image Content === The earliest commercial CBIR system was developed by IBM and was called QBIC (Query By Image Content). Recent network- and graph-based approaches have presented a simple and attractive alternative to existing methods. While the storing of multiple images as part of a single entity preceded the term BLOB (Binary Large OBject), the ability to fully search by content, rather than by description, had to await IBM's QBIC. === VisualRank === == Technical progress == The interest in CBIR has grown because of the limitations inherent in metadata-based systems, as well as the large range of possible uses for efficient image retrieval. Textual information about images can be easily searched using existing technology, but this requires humans to manually describe each image in the database. This can be impractical for very large databases or for images that are generated automatically, e.g. those from surveillance cameras. It is also possible to miss images that use different synonyms in their descriptions. Systems based on categorizing images in semantic classes like "cat" as a subclass of "animal" can avoid the miscategorization problem, but will require more effort by a user to find images that might be "cats", but are only classified as an "animal". Many standards have been developed to categorize images, but all still face scaling and miscategorization issues. Initial CBIR systems were developed to search databases based on image color, texture, and shape properties. After these systems were developed, the need for user-friendly interfaces became apparent. Therefore, efforts in the CBIR field started to include human-centered design that tried to meet the needs of the user performing the search. This typically means inclusion of: query methods that may allow descriptive semantics, queries that may involve user feedback, systems that may include machine learning, and systems that may understand user satisfaction levels. == Techniques == Many CBIR systems have been developed, but as of 2006, the problem of retrieving images on the basis of their pixel content remains largely unsolved. Different query techniques and implementations of CBIR make use of different types of user queries. === Query By Example === QBE (Query By Example) is a query technique that involves providing the CBIR system with an example image that it will then base its search upon. The underlying search algorithms may vary depending on the application, but result images should all share common elements with the provided example. Options for providing example images to the system include: A preexisting image may be supplied by the user or chosen from a random set. The user draws a rough approximation of the image they are looking for, for example with blobs of color or general shapes. This query technique removes the difficulties that can arise when trying to describe images with words. === Semantic retrieval === Semantic retrieval starts with a user making a request like "find pictures of Abraham Lincoln". This type of open-ended task is very difficult for computers to perform - Lincoln may not always be facing the camera or in the same pose. Many CBIR systems therefore generally make use of lower-level features like texture, color, and shape. These features are either used in combination with interfaces that allow easier input of the criteria or with databases that have already been trained to match features (such as faces, fingerprints, or shape matching). However, in general, image retrieval requires human feedback in order to identify higher-level concepts. === Relevance feedback (human interaction) === Combining CBIR search techniques available with the wide range of potential users and their intent can be a difficult task. An aspect of making CBIR successful relies entirely on the ability to understand the user intent. CBIR systems can make use of relevance feedback, where the user progressively refines the search results by marking images in the results as "relevant", "not relevant", or "neutral" to the search query, then repeating the search with the new information. Examples of this type of interface have been developed. === Iterative/machine learning === Machine learning and application of iterative techniques are becoming more common in CBIR. === Other query methods === Other query methods include browsing for example images, navigating customized/hierarchical categories, querying by image region (rather than the entire image), querying by multiple example images, querying by visual sketch, querying by direct specification of image features, and multimodal queries (e.g. combining touch, voice, etc.) == Content comparison using image distance measures == The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others. For example, a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images. Search results then can be sorted based on their distance to the queried image. Many measures of image distance (Similarity Models) have been developed. === Color === Computing distance measures based on color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an image holding specific values. Examining images based on the colors they contain is one of the most widely used techniques because it can be completed without regard to image size or orientation. However, research has also attempted to segment color proportion by region and by spatial relationship among several color regions. === Texture === Texture measures look for visual patterns in images and how they are spatially defined. Textures are represented by texels which are then placed into a number of sets, depending on how many textures are detected in the image. These sets not only define the texture, but also where in the image the texture is located. Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as a two-dimensional gray level variation. The relative brightness of pairs of pixels is computed such that degree of contrast, regularity, coarseness and directionality may be estimated. The problem is in identifying patterns of co-pixel variation and associating them with particular classes of textures such as silky, or rough. Other methods of classifying textures include: Co-occurrence matrix Laws texture energy Wavelet transform Orthogonal transforms (discrete Chebyshev moments) =

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  • IJCAI Computers and Thought Award

    IJCAI Computers and Thought Award

    The IJCAI Computers and Thought Award is presented every two years by the International Joint Conference on Artificial Intelligence (IJCAI), recognizing outstanding young scientists in artificial intelligence. It was originally funded with royalties received from the book Computers and Thought (edited by Edward Feigenbaum and Julian Feldman), and is currently funded by IJCAI. It is considered to be "the premier award for artificial intelligence researchers under the age of 35". == Laureates == Terry Winograd (1971) Patrick Winston (1973) Chuck Rieger (1975) Douglas Lenat (1977) David Marr (1979) Gerald Sussman (1981) Tom Mitchell (1983) Hector Levesque (1985) Johan de Kleer (1987) Henry Kautz (1989) Rodney Brooks (1991) Martha E. Pollack (1991) Hiroaki Kitano (1993) Sarit Kraus (1995) Stuart Russell (1995) Leslie Kaelbling (1997) Nicholas Jennings (1999) Daphne Koller (2001) Tuomas Sandholm (2003) Peter Stone (2007) Carlos Guestrin (2009) Andrew Ng (2009) Vincent Conitzer (2011) Malte Helmert (2011) Kristen Grauman (2013) Ariel D. Procaccia (2015) Percy Liang (2016) for his contributions to both the approach of semantic parsing for natural language understanding and better methods for learning latent-variable models, sometimes with weak supervision, in machine learning. Devi Parikh (2017) Stefano Ermon (2018) Guy Van den Broeck (2019) for his contributions to statistical and relational artificial intelligence, and the study of tractability in learning and reasoning. Piotr Skowron (2020) for his contributions to computational social choice, and to the theory of committee elections. Fei Fang (2021) for her contributions to integrating machine learning with game theory and the use of these novel techniques to tackle societal challenges such as more effective deployment of security resources, enhancing environmental sustainability, and reducing food insecurity. Bo Li (2022) for her contributions to uncovering the underlying connections among robustness, privacy, and generalization in AI, showing how different models are vulnerable to malicious attacks, and how to eliminate these vulnerabilities using mathematical tools that provide robustness guarantees for learning models and privacy protection. Pin-Yu Chen (2023) for his contributions to consolidating properties of trust, robustness and safety into rigorous algorithmic procedures and computable metrics for improving AI systems. Nisarg Shah (2024) for his contributions to AI and society, in particular foundational work on the theory of algorithmic fairness using principles from social choice theory. Aditya Grover (2025) for his foundational contributions uniting deep generative models, representation learning, and reinforcement learning, and for their applications in advancing scientific reasoning.

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  • Competitions and prizes in artificial intelligence

    Competitions and prizes in artificial intelligence

    There are a number of competitions and prizes to promote research in artificial intelligence. == General machine intelligence == The David E. Rumelhart Prize is an annual award for making a "significant contemporary contribution to the theoretical foundations of human cognition". The prize is $100,000. The Human-Competitive Award is an annual challenge started in 2004 to reward results "competitive with the work of creative and inventive humans". The prize is $10,000. Entries are required to use evolutionary computing. The Intel AI Global Impact Festival is an international annual competition held by Intel Corporation for school, and college students with prizes upwards of $15,000. It is about artificial intelligence technology. There are two age brackets in this competition, 13-18 Age Group, and 18 and Above Age Group. The IJCAI Award for Research Excellence is a biannual award given at the International Joint Conference on Artificial Intelligence (IJCAI) to researchers in artificial intelligence as a recognition of excellence of their career. The 2011 Federal Virtual World Challenge, advertised by The White House and sponsored by the U.S. Army Research Laboratory's Simulation and Training Technology Center, held a competition offering a total of US$52,000 in cash prize awards for general artificial intelligence applications, including "adaptive learning systems, intelligent conversational bots, adaptive behavior (objects or processes)" and more. The Machine Intelligence Prize is awarded annually by the British Computer Society for progress towards machine intelligence. The Kaggle – "the world's largest community of data scientists compete to solve most valuable problems". == Conversational behaviour == The Loebner prize is an annual competition to determine the best Turing test competitors. The winner is the computer system that, in the judges' opinions, demonstrates the "most human" conversational behaviour, they have an additional prize for a system that in their opinion passes a Turing test. This second prize has not yet been awarded. == Automatic control == === Pilotless aircraft === The International Aerial Robotics Competition is a long-running event begun in 1991 to advance the state of the art in fully autonomous air vehicles. This competition is restricted to university teams (although industry and governmental sponsorship of teams is allowed). Key to this event is the creation of flying robots which must complete complex missions without any human intervention. Successful entries are able to interpret their environment and make real-time decisions based only on a high-level mission directive (e.g., "find a particular target inside a building having certain characteristics which is among a group of buildings 3 kilometers from the aerial robot launch point"). In 2000, a $30,000 prize was awarded during the 3rd Mission (search and rescue), and in 2008, $80,000 in prize money was awarded at the conclusion of the 4th Mission (urban reconnaissance). === Driverless cars === The DARPA Grand Challenge is a series of competitions to promote driverless car technology, aimed at a congressional mandate stating that by 2015 one-third of the operational ground combat vehicles of the US Armed Forces should be unmanned. While the first race had no winner, the second awarded a $2 million prize for the autonomous navigation of a hundred-mile trail, using GPS, computers and a sophisticated array of sensors. In November 2007, DARPA introduced the DARPA Urban Challenge, a sixty-mile urban area race requiring vehicles to navigate through traffic. In November 2010 the US Armed Forces extended the competition with the $1.6 million prize Multi Autonomous Ground-robotic International Challenge to consider cooperation between multiple vehicles in a simulated-combat situation. Roborace will be a global motorsport championship with autonomously driving, electric vehicles. The series will be run as a support series during the Formula E championship for electric vehicles. This will be the first global championship for driverless cars. == Data-mining and prediction == The Netflix Prize was a competition for the best collaborative filtering algorithm that predicts user ratings for films, based on previous ratings. The competition was held by Netflix, an online DVD-rental service. The prize was $1,000,000. The Pittsburgh Brain Activity Interpretation Competition will reward analysis of fMRI data "to predict what individuals perceive and how they act and feel in a novel Virtual Reality world involving searching for and collecting objects, interpreting changing instructions, and avoiding a threatening dog." The prize in 2007 was $22,000. The Face Recognition Grand Challenge (May 2004 to March 2006) aimed to promote and advance face recognition technology. The American Meteorological Society's artificial intelligence competition involves learning a classifier to characterise precipitation based on meteorological analyses of environmental conditions and polarimetric radar data. == Cooperation and coordination == === Robot football === The RoboCup and Federation of International Robot-soccer Association (FIRA) are annual international robot soccer competitions. The International RoboCup Federation challenge is by 2050 "a team of fully autonomous humanoid robot soccer players shall win the soccer game, comply with the official rule of the FIFA, against the winner of the most recent World Cup." == Logic, reasoning and knowledge representation == The Herbrand Award is a prize given by Conference on Automated Deduction (CADE) Inc. to honour persons or groups for important contributions to the field of automated deduction. The prize is $1000. The CADE ATP System Competition (CASC) is a yearly competition of fully automated theorem provers for classical first order logic associated with the Conference on Automated Deduction (CADE) and International Joint Conference on Automated Reasoning (IJCAR). The competition was part of the Alan Turing Centenary Conference in 2012, with total prizes of 9000 GBP given by Google. The SUMO prize is an annual prize for the best open source ontology extension of the Suggested Upper Merged Ontology (SUMO), a formal theory of terms and logical definitions describing the world. The prize is $3000. The Hutter Prize for lossless compression of human knowledge is a cash prize which rewards compression improvements on a specific 100 MB English text file. The prize awards 500 euros for each one percent improvement, up to €50,000. The organizers believe that text compression and AI are equivalent problems and 3 prizes have been given, at around € 2k. The Cyc TPTP Challenge is a competition to develop reasoning methods for the Cyc comprehensive ontology and database of everyday common sense knowledge. The prize is 100 euros for "each winner of two related challenges". The Eternity II challenge was a constraint satisfaction problem very similar to the Tetravex game. The objective is to lay 256 tiles on a 16x16 grid while satisfying a number of constraints. The problem is known to be NP-complete. The prize was US$2,000,000. The competition ended in December 2010. == Games == The World Computer Chess Championship has been held since 1970. The International Computer Games Association continues to hold an annual Computer Olympiad which includes this event plus computer competitions for many other games. The Ing Prize was a substantial money prize attached to the World Computer Go Congress, starting from 1985 and expiring in 2000. It was a graduated set of handicap challenges against young professional players with increasing prizes as the handicap was lowered. At the time it expired in 2000, the unclaimed prize was 400,000 NT dollars for winning a 9-stone handicap match. The AAAI General Game Playing Competition is a competition to develop programs that are effective at general game playing. Given a definition of a game, the program must play it effectively without human intervention. Since the game is not known in advance the competitors cannot especially adapt their programs to a particular scenario. The prize in 2006 and 2007 was $10,000. The General Video Game AI Competition (GVGAI) poses the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. Concretely, it tackles the problem of devising an algorithm that is able to play any game it is given, even if the game is not known a priori. Additionally, the contests poses the challenge of creating level and rule generators for any game is given. This area of study can be seen as an approximation of General Artificial Intelligence, with very little room for game dependent heuristics. The competition runs yearly in different tracks: single player planning, two-player planning, single player learning, level and rule generation, and each track prizes ranging from 200 to 500 US dollars for winners and runner-ups. The 2007 Ultimate Computer Ches

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  • Datasource

    Datasource

    A datasource or DataSource is a name given to the connection set up to a database from a server. The name is commonly used when creating a query to the database. The data source name (DSN) need not be the same as the filename for the database. For example, a database file named friends.mdb could be set up with a DSN of school. Then DSN school would be used to refer to the database when performing a query. == Sun's version of DataSource [1] == A factory for connections to the physical data source that this DataSource object represents. An alternative to the DriverManager facility, a DataSource object is the preferred means of getting a connection. An object that implements the DataSource interface will typically be registered with a naming service based on the Java Naming and Directory Interface (JNDI) API. The DataSource interface is implemented by a driver vendor. There are three types of implementations: Basic implementation — produces a standard Connection object Connection pooling implementation — produces a Connection object that will automatically participate in connection pooling. This implementation works with a middle-tier connection pooling manager. Distributed transaction implementation — produces a Connection object that may be used for distributed transactions and almost always participates in connection pooling. This implementation works with a middle-tier transaction manager and almost always with a connection pooling manager. A DataSource object has properties that can be modified when necessary. For example, if the data source is moved to a different server, the property for the server can be changed. The benefit is that because the data source's properties can be changed, any code accessing that data source does not need to be changed. A driver that is accessed via a DataSource object does not register itself with the DriverManager. Rather, a DataSource object is retrieved through a lookup operation and then used to create a Connection object. With a basic implementation, the connection obtained through a DataSource object is identical to a connection obtained through the DriverManager facility. == Sun's DataSource Overview [2] == A DataSource object is the representation of a data source in the Java programming language. In basic terms, a data source is a facility for storing data. It can be as sophisticated as a complex database for a large corporation or as simple as a file with rows and columns. A data source can reside on a remote server, or it can be on a local desktop machine. Applications access a data source using a connection, and a DataSource object can be thought of as a factory for connections to the particular data source that the DataSource instance represents. The DataSource interface provides two methods for establishing a connection with a data source. Using a DataSource object is the preferred alternative to using the DriverManager for establishing a connection to a data source. They are similar to the extent that the DriverManager class and DataSource interface both have methods for creating a connection, methods for getting and setting a timeout limit for making a connection, and methods for getting and setting a stream for logging. Their differences are more significant than their similarities, however. Unlike the DriverManager, a DataSource object has properties that identify and describe the data source it represents. Also, a DataSource object works with a Java Naming and Directory Interface (JNDI) naming service and can be created, deployed, and managed separately from the applications that use it. A driver vendor will provide a class that is a basic implementation of the DataSource interface as part of its Java Database Connectivity (JDBC) 2.0 or 3.0 driver product. What a system administrator does to register a DataSource object with a JNDI naming service and what an application does to get a connection to a data source using a DataSource object registered with a JNDI naming service are described later in this chapter. Being registered with a JNDI naming service gives a DataSource object two major advantages over the DriverManager. First, an application does not need to hardcode driver information, as it does with the DriverManager. A programmer can choose a logical name for the data source and register the logical name with a JNDI naming service. The application uses the logical name, and the JNDI naming service will supply the DataSource object associated with the logical name. The DataSource object can then be used to create a connection to the data source it represents. The second major advantage is that the DataSource facility allows developers to implement a DataSource class to take advantage of features like connection pooling and distributed transactions. Connection pooling can increase performance dramatically by reusing connections rather than creating a new physical connection each time a connection is requested. The ability to use distributed transactions enables an application to do the heavy duty database work of large enterprises. Although an application may use either the DriverManager or a DataSource object to get a connection, using a DataSource object offers significant advantages and is the recommended way to establish a connection. Since 1.4 Since Java EE 6 a JNDI-bound DataSource can alternatively be configured in a declarative way directly from within the application. This alternative is particularly useful for self-sufficient applications or for transparently using an embedded database. == Yahoo's version of DataSource [3] == A DataSource is an abstract representation of a live set of data that presents a common predictable API for other objects to interact with. The nature of your data, its quantity, its complexity, and the logic for returning query results all play a role in determining your type of DataSource. For small amounts of simple textual data, a JavaScript array is a good choice. If your data has a small footprint but requires a simple computational or transformational filter before being displayed, a JavaScript function may be the right approach. For very large datasets—for example, a robust relational database—or to access a third-party webservice you'll certainly need to leverage the power of a Script Node or XHR DataSource.

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  • Supreme Commander (video game)

    Supreme Commander (video game)

    Supreme Commander (sometimes SupCom) is a 2007 real-time strategy video game designed by Chris Taylor and developed by his company, Gas Powered Games. The game is considered to be a spiritual successor, not a direct sequel, to Taylor's 1997 game Total Annihilation. First announced in the August 2005 edition of PC Gamer magazine, the game was released in Europe on February 16, 2007, and in North America on February 20. The standalone expansion Supreme Commander: Forged Alliance was released on November 6 of the same year. The sequel, Supreme Commander 2, was released in 2010. Nowadays, the original Supreme Commander is played through the community client called Forged Alliance Forever; the game has been further developed and balanced, and offers a wide variety of community mods. The gameplay of Supreme Commander focuses on using a giant bipedal mech called an Armored Command Unit (ACU), the so-called "Supreme Commander", to build a base, upgrading units to reach higher technology tiers, and conquering opponents. The player can command one of three factions: the Aeon Illuminate, the Cybran Nation, or the United Earth Federation (UEF). The expansion game added the Seraphim faction. Supreme Commander was highly anticipated in pre-release previews, and was well received by critics, with a Metacritic average of 86 out of 100. == Gameplay == Supreme Commander, like its spiritual predecessors, Total Annihilation and Spring, begins with the player solely possessing a single, irreplaceable construction unit called the "Armored Command Unit," or ACU, the titular Supreme Commander. Normally the loss of this unit results in the loss of the game (Skirmish missions can be set for a variety of victory conditions). These mech suits are designed to be transported through quantum gateways across the galaxy and contain all the materials and blueprints necessary to create an army from a planet's native resources in hours. All standard units except Commanders and summoned Support Commanders (sACU) are self-sufficient robots. All units and structures belong to one of four technology tiers, or "Tech" levels, each tier being stronger and/or more efficient than the previous. Certain lower-tier structures can be upgraded into higher ones without having to rebuild them. The first tier is available at the start of the game and consists of small, relatively weak units and structures. The second tier expands the player's abilities greatly, especially in terms of stationary weapons and shielding, and introduces upgraded versions of tier one units. The third tier level has very powerful assault units designed to overcome the fortifications of the most entrenched player. The fourth tier is a limited range of "experimental" technology. These are usually massive units which take a lot of time and energy to produce, but provide a significant tactical advantage. Supreme Commander features a varied skirmish AI. The typical Easy' and Normal modes are present, but the Hard difficulty level has four possible variants. Horde AI will swarm the player with hordes of lower level units, Tech AI will upgrade its units as fast as possible and assault the player with advanced units, the Balanced AI attempts to find a balance between the two, and the Supreme AI decides which of the three hard strategies is best for the map. The single player campaign consists of eighteen missions, six for each faction. The player is an inexperienced Commander who plays a key role in their faction's campaign to bring the "Infinite War" to an end. Despite the low number of campaign missions, each mission can potentially last hours. At the start of a mission, objectives are assigned for the player to complete. Once the player accomplishes them, the map is expanded, sometimes doubling or tripling in size, and new objectives are assigned. As the mission is commonly divided into three segments, the player will often have to overcome several enemy positions to achieve victory. === Resource management === Because humans have developed replication technology, making advanced use of rapid prototyping and nanotechnology, only two types of resources are required to wage war: Energy and Mass. Energy is obtained by constructing power generators on any solid surface (except fuel generators, which can only be built on fuel deposits), while Mass is obtained either by placing mass extractors on limited mass deposit spots (the most efficient method, although it requires map control) or by building mass fabricators to convert energy into mass. Constructor units can gather energy by "reclaiming" it from organic debris such as trees and mass from rocks and wrecked units. Each player has a certain amount of resource storage, which can be expanded by the construction of storage structures. This gives the player reserves in times of shortage or allows them to stockpile resources. If the resource generation exceeds the player's capacity, the material is wasted. On the contrary, if the storages are depleted and the demand of one of the resources exceeds the production, then all the productions speed is reduced. In addition, if an energy deficit occurs, shields will stop working. An adjacency system allows certain structures to benefit from being built directly adjacent to others. Energy-consuming structures will use less energy when built adjacent to power generators and power generators will produce more energy when built adjacent to power storage structures. The same applies to their mass-producing equivalents. Likewise, factories will consume less energy and mass when built adjacent to power generators and mass fabricators/extractors, respectively. However, by placing structures in close proximity, they become more vulnerable to collateral damage if an adjacent structure is destroyed. Furthermore, most resource generation structures can cause chain reactions when destroyed (especially Tier III structures, which produce large amounts of resources but often have large detonations that can wipe out a nearby army). === Warfare === Supreme Commander uses a "strategic zoom" system that allows the player to seamlessly zoom from a detailed close up view of an individual unit all the way out to a view of the entire map, at which point it resembles a fullscreen version of the minimap denoting individual units with icons. The camera also has a free movement mode and can be slaved to track a selected unit and there is a split screen mode which also supports multiple monitors. This system allows Supreme Commander to use vast maps up to 80 km x 80 km, with players potentially controlling a thousand units each. Units in Supreme Commander are built to scale as they would be in the real world. For example, battleships dwarf submarines. Late into the game, the larger "experimental" units, such as the Cybran Monkeylord, an enormous spider-shaped assault unit, can actually crush smaller enemy units by stepping on them. Because of the wide range of planets colonized by humanity in the setting, the theatres of war range from desert to arctic, and all battlespaces are employed. Technologies emerging in modern warfare are frequently employed in Supreme Commander. For example, stealth technology and both tactical and strategic missile and missile defense systems can be used. Supreme Commander introduced several innovations designed to reduce the amount of micromanagement inherent in many RTS games. Engineers units have the command "assist", that will help follow other engineers and help them finish their orders or improve production rate of factories. In addition, engineers with the order "patrol" will repair units, buildings and recycle wrecks in their along their patrol route. Holding the shift key causes any orders given to a unit (or group of units) to be queued. In this manner a unit may be ordered to attack several targets in succession, or to make best speed to a given point on the map and then attack towards a specified location engaging any hostiles it encounters along the way. After orders have been issued, holding the shift key causes all issued orders to be displayed on the map where they can be subsequently modified to accommodate a change of plan. Further, when a unit is ordered to attack a target, the player can issue an order to perform a coordinated attack to another unit. This order coordinates the arrival time of the units at the target automatically by adjusting the speed of the units involved. As in other RTS games, air transports can be used to convey units to specified destinations, in Supreme Commander though by shift queuing orders a transport containing several units can be ordered to drop specific units at subsequent waypoints. An air transport can also be ordered to create a ferry route, an airbridge wherein any land units ordered to the start of the ferry route will be conveyed by the air transport to the specified destination. The output from a production factory can be routed to a ferry route causing all units co

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  • Fuzzy concept

    Fuzzy concept

    A fuzzy concept is an idea of which the boundaries of application can vary considerably according to context or conditions, instead of being fixed once and for all. That means the idea is somewhat vague or imprecise. Yet it is not unclear or meaningless. It has a definite meaning, which can often be made more exact with further elaboration and specification — including a closer definition of the context in which the concept is used. The inverse of a "fuzzy concept" is a "crisp concept" (i.e. a precise concept). Fuzzy concepts are often used to navigate imprecision in the real world, when precise information is not available and an approximate indication is sufficient to be helpful. Although the linguist George Philip Lakoff already defined the semantics of a fuzzy concept in 1973 (inspired by an unpublished 1971 paper by Eleanor Rosch,) the term "fuzzy concept" rarely received a standalone entry in dictionaries, handbooks and encyclopedias. Sometimes it was defined in encyclopedia articles on fuzzy logic, or it was simply equated with a mathematical “fuzzy set”. A fuzzy concept can be "fuzzy" for many different reasons in different contexts. This makes it harder to provide a precise definition that covers all cases. Paradoxically, the definition of fuzzy concepts may itself be somewhat "fuzzy". Lotfi A. Zadeh, known as "the father of fuzzy logic", claimed that "vagueness connotes insufficient specificity, whereas fuzziness connotes unsharpness of class boundaries". Not all scholars agree. With increasing academic literature on the subject, the term "fuzzy concept" is now more widely recognized as a philosophical, linguistic or scientific category, and the study of the characteristics of fuzzy concepts and fuzzy language is known as fuzzy semantics. “Fuzzy logic” has become a generic term for many different kinds of many-valued logics, and is applied in many different areas of research, computer programming and industrial design. For engineers, "Fuzziness is imprecision or vagueness of definition." For computer scientists, a fuzzy concept is an idea which is "to an extent applicable" in a situation. It means that the concept can have gradations of significance or unsharp (variable) boundaries of application — a "fuzzy statement" is a statement which is true "to some extent", and that extent can often be represented by a scaled value (a score). For mathematicians, a "fuzzy concept" is usually a fuzzy set or a combination of such sets (see fuzzy mathematics and fuzzy set theory). In cognitive linguistics, the things that belong to a "fuzzy category" exhibit gradations of family resemblance, and the borders of the category are not clearly defined. Through most of the 20th century, the idea of reasoning with fuzzy concepts faced considerable resistance from Western academic elites. They did not want to endorse the use of imprecise concepts in research or argumentation, and they often regarded fuzzy logic with suspicion, derision or even hostility. That may partly explain why the idea of a "fuzzy concept" did not get a separate entry in encyclopedias, handbooks and dictionaries. Yet although people might not be aware of it, the use of fuzzy concepts has risen gigantically in all walks of life from the 1970s onward. That is mainly due to advances in electronic engineering, fuzzy mathematics and digital computer programming. The new technology allows very complex inferences about "variations on a theme" to be anticipated and fixed in a program. The Perseverance Mars rover, a driverless NASA vehicle used to explore the Jezero crater on the planet Mars, features fuzzy logic programming that steers it through rough terrain. Similarly, to the North, the Chinese Mars rover Zhurong used fuzzy logic algorithms to calculate its travel route in Utopia Planitia from sensor data. New neuro-fuzzy computational methods make it possible for machines to identify, measure, adjust and respond to fine gradations of significance with great precision. It means that practically useful concepts can be coded, sharply defined, and applied to all kinds of tasks, even if ordinarily these concepts are never exactly defined. Nowadays engineers, statisticians and programmers often represent fuzzy concepts mathematically, using fuzzy logic, fuzzy values, fuzzy variables and fuzzy sets (see also fuzzy set theory). Fuzzy logic is not "woolly thinking", but a "precise logic of imprecision" which reasons with graded concepts and gradations of truth. Fuzzy concepts and fuzzy logic often play a significant role in artificial intelligence programming, for example because they can model human cognitive processes more easily than other methods. == Origins == Vagueness and fuzziness have probably always been a part of human experience. In the West, ancient texts show that philosophers and scientists were already thinking critically about this in classical antiquity. Most often, they regarded vagueness as a problem: as an obstacle to clear thinking, as a source of confusion, or as an evasive tactic. It got in the way of providing clear orientation, guidance, direction and leadership. Therefore, vagueness became associated with a hermeneutic of suspicion — it was considered as something to avoid, as something undesirable. By contrast, in the ancient Chinese tradition of Daoist thought of Laozi and Zhuang Zhou, "vagueness is not regarded with suspicion, but is simply an acknowledged characteristic of the world around us" — a subject for meditation and a source of insight. === Sorites paradox === The ancient Sorites paradox raised the logical problem, of how we could exactly define the threshold at which a change in quantitative gradation turns into a qualitative or categorical difference. With some physical processes, this threshold seems relatively easy to identify. For example, water turns into steam at 100 °C or 212 °F. Of course, the boiling point depends partly on atmospheric pressure, which decreases at higher altitudes; it is also affected by the level of humidity — in that sense, the boiling point is "somewhat fuzzy", because it can vary under different conditions. Nevertheless, for every altitude, level of air pressure and degree of humidity, we can predict accurately what the boiling point will be, if we know the relevant conditions. With many other processes and gradations, however, the point of change is much more difficult to locate, and remains somewhat vague. Thus, the boundaries between qualitatively different things may be unsharp: we know that there are boundaries, but we cannot define them exactly. For example, to identify "the oldest city in the world", we have to define what counts as a city, and at what point a growing human settlement becomes a city. === The continuum fallacy and Loki's wager === According to the modern idea of the continuum fallacy, the fact that a statement is to an extent vague, does not automatically mean that it has no validity. The question then arises, of how (by what method or approach) we could ascertain and define the validity that the fuzzy statement does have. The Nordic myth of Loki's wager suggested that concepts that lack precise meanings or lack precise boundaries of application cannot be operated with, because they evade any clear definition. However, the 20th-century idea of "fuzzy concepts" proposes that "somewhat vague terms" can be operated with, because we can explicate and define the variability of their application — by assigning numbers to gradations of applicability. This idea sounds simple enough, but it had large implications. === Precursors and pioneers === In Western civilization, the intellectual recognition of fuzzy concepts has been traced back to a diversity of famous and less well-known thinkers, including (among many others) Eubulides, Epicurus, Plato, Cicero, William Ockham and John Buridan, Georg Wilhelm Friedrich Hegel, Karl Marx and Friedrich Engels, Friedrich Nietzsche, William James, Hugh MacColl, Charles S. Peirce, Hans Reichenbach, Carl Gustav Hempel, Max Black, Arto Salomaa, Ludwig Wittgenstein, Jan Łukasiewicz, Emil Leon Post, Alfred Tarski, Georg Cantor, Nicolai A. Vasiliev, Kurt Gödel, Stanisław Jaśkowski, Willard Van Orman Quine, George J. Klir, Petr Hájek, Joseph Goguen, Ronald R. Yager, Enrique Héctor Ruspini, Jan Pavelka, Didier Dubois, Bernadette Bouchon-Meunier, and Donald Knuth. Across at least two and a half millennia, all of them had something to say about graded concepts with unsharp boundaries. This suggests at least that the awareness of the existence of concepts with "fuzzy" characteristics, in one form or another, has a very long history in human thought. Quite a few 20th century logicians, mathematicians and philosophers also tried to analyze the characteristics of fuzzy concepts as a recognized species, sometimes with the aid of some kind of many-valued logic or substructural logic. An early attempt in the post-WW2 era to create a mathematical theory of sets with gradations of

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