AI Art Creator Free

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

  • Digistar

    Digistar

    Digistar is the first computer graphics-based planetarium projection and content system. It was designed by Evans & Sutherland and released in 1983. The technology originally focused on accurate and high quality display of stars, including for the first time showing stars from points of view other than Earth's surface, travelling through the stars, and accurately showing celestial bodies from different times in the past and future. Beginning with the Digistar 3 the system now projects full-dome video. == Projector == Unlike modern full-dome systems, which use LCD, DLP, SXRD, or laser projection technology, the Digistar projection system was designed for projecting bright pinpoints of light representing stars. This was accomplished using a calligraphic display, a form of vector graphics, rather than raster graphics. The heart of the Digistar projector is a large cathode-ray tube (CRT). A phosphor plate is mounted atop the tube, and light is then dispersed by a large lens with a 160 degree field of view to cover the planetarium dome. The original lens bore the inscription: "August 1979 mfg. by Lincoln Optical Corp., L.A., CA for Evans and Sutherland Computer Corp., SLC, UT, Digital planetarium CRT projection lens, 43mm, f2.8, 160 degree field of view". The coordinates of the stars and wire-frame models to be displayed by the projector were stored in computer RAM in a display list. The display would read each set of coordinates in turn and drive the CRT's electron beam directly to those coordinates. If the electron beam was enabled while being moved a line would be painted on the phosphor plate. Otherwise, the electron beam would be enabled once at its destination and a star would be painted. Once all coordinates in the display list had been processed, the display would repeat from the top of the display list. Thus, the shorter the display list the more frequently the electron beam would refresh the charge on a given point on the phosphor plate, making the projection of the points brighter. In this way, the stars projected by Digistar were substantially brighter than could be achieved using a raster display, which has to touch every point on the phosphor plate before repeating. Likewise, the calligraphic technology allowed Digistar to have a darker black-level than full-dome projectors, since the portions of the phosphor plate representing dark sky were never hit by the electron beam. As it is only one tube, with no pixelated color filter screen, the Digistar projector is monochromatic. The Digistar projects a bright, phosphorescent green, though many (including both visitors and planetarians) report they cannot distinguish between this green and white. Additionally, unlike a raster display, the calligraphic display is not discretized into pixels, so the displayed stars were a more realistic single spot of light, without the blocky or ropy artifacts that are hard to avoid with raster graphics. Due to the use of vector graphics, as opposed to raster imaging, the Digistar does not have the resolution issues that many full-dome systems have. Thanks to this, and the brightness of the CRT, only one projector is needed to project on the entire dome, whereas most full-dome systems require up to six raster projectors, depending on dome size. The projector in the original Digistar was housed in a square pyramid-shaped sheathing. When powered on, the four sides at the tip of the pyramid would recede into the housing, exposing the lens and appearing as a cut-off pyramid. As Digistar II was being developed, many planetaria were sold Digistar LEA projectors. The LEA, called Digistar 1.5 by many users, was effectively a prototype of the D2 projector, compatible with Digistar and upgradable to Digistar II. There are no significant differences in performance between the LEA and the true D2. == History == Digistar was the brainchild of Stephen McAllister and Brent Watson, both of whom were long-time amateur astronomers and computer graphics engineers. In 1977, E&S had been consulting with Johnson Space Center regarding training simulators for astronauts. McAllister had been writing proof-of-concept software for this consultation and in summer 1977 entered the data for 400 bright stars and wrote the software to display them. Steve and Brent both originally saw the system's purpose as celestial navigation training. Brent, who had until recently worked at Hansen planetarium, asked his planetarium coworkers what they thought of a potential digital planetarium system, and then Steve and Brent both targeted the system toward planetaria. The primary goal of the planetarium system was to use computer graphics to overcome the limitation of traditional star ball technology that only allowed display of star fields from the point of view of Earth's surface. By using computer graphics the stars could be displayed from viewpoints in space, including simulating the appearance of space flight. Likewise, planets and moons within the Solar System could be displayed accurately for any time in history, from any point of view. The system used the location of real stars from the Yale Bright Star Catalogue, as well as random stars. A laboratory prototype of Digistar was used to generate the star fields and tactical displays in the 1982 science fiction film Star Trek II: The Wrath of Khan. Filming was done directly from the Digistar display in the lab. ILM projected the effort would take two weeks, but in fact it took from late November 1981 until mid-February 1982. The last shot recorded was what became the first entirely computer generated feature film sequence. It was the opening scene of the film, a rotating forward translation through a star field that lasted 3.5 minutes. It was recorded in one take, at a rate of one frame every 3.5 seconds, taking four hours for the shoot. The Digistar team members are credited in the film. After prototyping in labs at Evans and Sutherland the team repeatedly used Salt Lake City's Hansen planetarium to beta test the system at the planetarium at night. The Digistar team performed one week of shows at the planetarium as a fund raiser to benefit the planetarium. The company also later gave the planetarium an improved prototype Digistar to replace "Jake", the planetarium's aging Spitz planetarium projector. The first customer installation was to the newly constructed Universe Planetarium at the Science Museum of Virginia in 1983, the largest planetarium dome in the world at the time, for $595,000. By September 1986 there were four installed Digistars. Even at this point the long-term success of the product was very much in doubt, but as of 2019 Digistar has an installed base of over 550 planetaria. === Versions === Digistar (1983) Digistar II (1995) Digistar 3 (2002) Digistar 4 (2010?) Digistar 5 (2012) Digistar 6 (2016) Digistar 7 (2021) == Hardware == Digistar was driven by a VAX-11/780 minicomputer, with custom graphics hardware related to the E&S Picture System 2. Later versions of Digistar 1 used a DEC MicroVAX 2, driving a custom version of a PS/300. The original Digistar and Digistar 2 had a physical control panel that was used for running the star shows. This control panel was approximately 3' x 4' and contained a keyboard, a 6 DOF joystick, and a large array of back-lit buttons. One button that was used for moving the viewpoint forward in space was labeled "Boldly Go". Later iterations of Digistar replaced the physical control panel with a common graphical user interface. Digistar 3 was the first Digistar system to offer full-dome video in 2002, using six projectors. Digistar 4 was able to cover the dome using only two projectors. == System limitations == Though technologically advanced in its day, and the closest system to true full-dome video at the time of its release, the original Digistar and Digistar 2 are limited to only projecting dots and lines—meaning only wireframe models can be projected. To compensate for this, the projector is capable of defocusing specific models, blurring lines and dots together. An example of this is in the Digistar 2's built-in Milky Way model. The model is a circle of parallel lines that, when defocused, appear as the continuous band of the Milky Way across the sky. On more complex models, especially three-dimensional ones, brightness and details may be lost in this process, so it is not useful in all situations. The Digistar and Digistar 2 also suffer focus limitations. Because they use a single lens to cover the entire dome, it is difficult to gain perfect focus across the dome. Coupled with this, stars greater than a certain brightness are "multihit" points, meaning the projector draws two dots at the given position to accommodate the brightness of the star. Errors in the projector can lead the second dot to be slightly out-of-place with the first one. These two issues together, along with other issues that can occur within the projector's focus system, give the stars a blobby look. Some p

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  • GOFAI

    GOFAI

    In the philosophy of artificial intelligence, GOFAI (good old-fashioned artificial intelligence) is classical symbolic AI, as opposed to other approaches, such as neural networks, situated robotics, narrow symbolic AI or neuro-symbolic AI. The term was coined by philosopher John Haugeland in his 1985 book Artificial Intelligence: The Very Idea. Haugeland coined the term to address two questions: Can GOFAI produce human-level artificial intelligence in a machine? Is GOFAI the primary method that brains use to display intelligence? AI founder Herbert A. Simon speculated in 1963 that the answers to both these questions was "yes". His evidence was the performance of programs he had co-written, such as Logic Theorist and the General Problem Solver, and his psychological research on human problem solving. AI research in the 1950s and 60s had an enormous influence on intellectual history: it inspired the cognitive revolution, led to the founding of the academic field of cognitive science, and was the essential example in the philosophical theories of computationalism, functionalism and cognitivism in ethics and the psychological theories of cognitivism and cognitive psychology. The specific aspect of AI research that led to this revolution was what Haugeland called "GOFAI". In AI development and technology, GOFAI is used to refer to programs that are built with deliberate, explicit instructions for a single task. This is in contrast to approaches that use machine learning. Examples of GOFAI applications include AlphaGo and Apple's initial Siri design. == Western rationalism == Haugeland places GOFAI within the rationalist tradition in western philosophy, which holds that abstract reason is the "highest" faculty, that it is what separates man from the animals, and that it is the most essential part of our intelligence. This assumption is present in Plato and Aristotle, in Shakespeare, Hobbes, Hume and Locke, it was central to the Enlightenment, to the logical positivists of the 1930s, and to the computationalists and cognitivists of the 1960s. As Shakespeare wrote: What a piece of work is a man, How noble in reason, how infinite in faculty ... In apprehension how like a god, The beauty of the world, The paragon of animals. Symbolic AI in the 1960s was able to successfully simulate the process of high-level reasoning, including logical deduction, algebra, geometry, spatial reasoning and means-ends analysis, all of them in precise English sentences, just like the ones humans used when they reasoned. Many observers, including philosophers, psychologists and the AI researchers themselves became convinced that they had captured the essential features of intelligence. This was not just hubris or speculation -- this was entailed by rationalism. If it was not true, then it brings into question a large part of the entire Western philosophical tradition. Continental philosophy, which included Nietzsche, Husserl, Heidegger and others, rejected rationalism and argued that our high-level reasoning was limited and prone to error, and that most of our abilities come from our intuitions, culture, and instinctive feel for the situation. Philosophers who were familiar with this tradition were the first to criticize GOFAI and the assertion that it was sufficient for intelligence, such as Hubert Dreyfus and Haugeland. == Haugeland's GOFAI == Critics and supporters of Haugeland's position, from philosophy, psychology, or AI research have found it difficult to define "GOFAI" precisely, and thus the literature contains a variety of interpretations. Drew McDermott, for example, finds Haugeland's description of GOFAI "incoherent" and argues that GOFAI is a "myth". Haugeland coined the term GOFAI in order to examine the philosophical implications of “the claims essential to all GOFAI theories”, which he listed as: 1. our ability to deal with things intelligently is due to our capacity to think about them reasonably (including sub-conscious thinking); and 2. our capacity to think about things reasonably amounts to a faculty for internal “automatic” symbol manipulation This is very similar to the sufficient side of the physical symbol systems hypothesis proposed by Herbert A. Simon and Allen Newell in 1963: "A physical symbol system has the necessary and sufficient means for general intelligent action." It is also similar to Hubert Dreyfus' "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules. " Haugeland's description of GOFAI refers to symbol manipulation governed by a set of instructions for manipulating the symbols. The "symbols" he refers to are discrete physical things that are assigned a definite semantics -- like and . They do not refer to signals, or unidentified numbers, or matrixes of unidentified numbers, or the zeros and ones of digital machinery. Thus, Haugeland's GOFAI does not include "good old fashioned" techniques such as cybernetics, perceptrons, dynamic programming or control theory or modern techniques such as neural networks or support vector machines. These questions ask if GOFAI is sufficient for general intelligence -- they ask if there is nothing else required to create fully intelligent machines. Thus GOFAI, for Haugeland, does not include systems that combine symbolic AI with other techniques, such as neuro-symbolic AI, and also does not include narrow symbolic AI systems that are designed only to solve a specific problem and are not expected to exhibit general intelligence. == Replies == === Replies from AI scientists === Russell and Norvig wrote, in reference to Dreyfus and Haugeland:The technology they criticized came to be called Good Old-Fashioned AI (GOFAI). GOFAI corresponds to the simplest logical agent design ... and we saw ... that it is indeed difficult to capture every contingency of appropriate behavior in a set of necessary and sufficient logical rules; we called that the qualification problem. Later symbolic AI work after the 1980's incorporated more robust approaches to open-ended domains such as probabilistic reasoning, non-monotonic reasoning, and machine learning. Currently, most AI researchers believe deep learning, and more likely, a synthesis of neural and symbolic approaches (neuro-symbolic AI), will be required for general intelligence.

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  • Mike Vernal

    Mike Vernal

    Mike Vernal (born September 7, 1980) is an American business executive who is a venture capitalist at Conviction. He was previously an investor at Sequoia Capital in Silicon Valley and was one of the top executives at Facebook between 2008 and 2016. Prior to joining Sequoia Capital, he was Vice President of Search, Local, and Developer products at Facebook. == Career == Vernal joined Facebook in 2008. From 2009 to 2013, Vernal managed the Facebook Platform team and is credited with managing the Facebook Platform transition from desktop to mobile. During his time at Facebook, he served as vice president and was considered among the “top executives” who ran the company. In 2016, after eight years at Facebook, Vernal announced his plans to leave the company. In May 2016, he joined Sequoia Capital, a venture-capital firm specializing in technology startups. He is an early investor in Rippling, Clay, Notion and Statsig. In July 2023, The Information reported that Vernal was departing Sequoia. At Conviction, he has led investments in Listen Labs, OpenEvidence and Thinking Machines Lab.

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  • HiLog

    HiLog

    HiLog is a programming logic with higher-order syntax, which allows arbitrary terms to appear in predicate and function positions. However, the model theory of HiLog is first-order. Although syntactically HiLog strictly extends first order logic, HiLog can be embedded into this logic. HiLog was first described in 1989. It was later extended in the direction of many-sorted logic. The XSB system parses HiLog syntax, but the integration of HiLog into XSB is only partial. In particular, HiLog is not integrated with the XSB module system. A full implementation of HiLog is available in the Flora-2 system. It has been shown that HiLog can be embedded into first-order logic through a fairly simple transformation. For instance, p(X)(Y,Z(V)(W)) gets embedded as the following first-order term: apply(p(X),Y,apply(apply(Z,V),W)). The Framework for Logic-Based Dialects (RIF-FLD) of the Rule Interchange Format (RIF) is largely based on the ideas underlying HiLog and F-logic. == Examples == In all the examples below, capitalized symbols denote variables and the comma denotes logical conjunction, as in most logic programming languages. The first and the second examples show that variables can appear in predicate positions. Predicates can even be complex terms, such as closure(P) or maplist(F) below. The third example shows that variables can also appear in place of atomic formulas, while the fourth example illustrates the use of variables in place of function symbols. The first example defines a generic transitive closure operator, which can be applied to an arbitrary binary predicate. The second example is similar. It defines a LISP-like mapping operator, which applies to an arbitrary binary predicate. The third example shows that the Prolog meta-predicate call/1 can be expressed in HiLog in a natural way and without the use of extra-logical features. The last example defines a predicate that traverses arbitrary binary trees represented as first-order terms.

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  • Plum Voice

    Plum Voice

    The Plum Group, Inc. (DBA Plum Voice) is a company. Plum is headquartered in New York City with offices in Boston and Denver. == History == Plum Voice, founded in 2000 as The Plum Group, Inc., was incorporated to create technologies for personalized audio communication. By 2001, Plum had commercialized the open-standard Plum VoiceXML IVR platform which facilitated the creation of dynamic telecom applications. 2001 - Commercial launch of Plum VoiceXML IVR platform for customer-premises deployment 2002 - Launch of Plum Voice Hosting Centers for 24x7x365 managed IVR hosting 2004 - Plum Voice application suite receives a "Product of the Year" award from Customer Interactions magazine 2008 - Plum Survey builder launched, a do-it-yourself IVR survey tool. 2010 - Plum launched QuickFuse, a web-based rapid development platform used to create voice applications. 2013 - Plum launched VoiceTrends, an analytics and reporting toolkit designed specifically for voice applications. Plum achieves PCI-DSS Level 1. 2015 - Plum launched Plum Insight, a multi-channel (voice, web, mobile) survey platform. Plum achieves HIPAA compliance. 2016 - Plum launched a new version of QuickFuse called Fuse+. 2020 - Plum sunsets QuickFuse, rebrands Fuse+ as Plum Fuse.

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  • Leading the Future

    Leading the Future

    Leading the Future is an American super PAC network focused on lobbying for policies friendly to the artificial intelligence industry. It was launched in 2025 with over $100 million from industry stakeholders including Andreessen Horowitz, OpenAI President Greg Brockman and Palantir co-founder Joe Lonsdale. The launch was preceded by talks between Collin McCune, head of government affairs at Andreessen Horowitz, and Chris Lehane, chief global affairs officer at OpenAI. Among the members of the network are the American Mission PAC, which supported Chris Gober, and the Think Big PAC, which targeted Alex Bores. Leading the Future is affiliated with the nonprofit Build American AI, which Axios describes as a dark money advocacy "offshoot" operating alongside the super PAC. NBC News states that the network’s efforts are modeled after the pro-cryptocurrency group Fairshake. Leading the Future is led by Zac Moffatt and Josh Vlasto, the latter of whom previously served as an advisor to Fairshake. In response to the creation of Leading the Future, former members of Congress Brad Carson and Chris Stewart co-founded the super PAC network Public First, aiming to counter the group’s influence. In April 2026, an investigation by Model Republic linked Leading the Future to The Wire By Acutus, an automated news website that allegedly used AI agents posing as human journalists to solicit interviews. The site's content was found to closely mirror the PAC's deregulatory policy goals while targeting researchers and advocates skeptical of rapid AI development. In May 2026, Wired revealed that Build American AI used a "dark money" campaign to pay TikTok and Instagram influencers $5,000 per video to promote scripted narratives framing Chinese AI as a "national security threat." According to internal documents and staff at the marketing agency managing the project, the campaign's explicit goal was to "subtly shift public debate" toward the deregulation of AI industries while intentionally avoiding technical discussions regarding AI quality or safety. During the 2026 primary season Leading the Future went on to endorse several candidates in both Democratic and Republican races with several of them going on to win.

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  • Lukas Biewald

    Lukas Biewald

    Lukas Biewald (born 1981) is an American entrepreneur and a prominent figure in artificial intelligence. He is recognized for his contributions to machine learning and as the CEO and co-founder of Weights & Biases, a company that builds developer tools for AI, that sold to CoreWeave in 2025 for $1.7B. He previously founded and was CEO of Figure Eight, a human-in-the-loop machine learning platform. He has co-authored 26 AI research papers from 2004 through 2018. == Early life and education == Biewald was born in Boston, Massachusetts in 1981. He attended Cambridge Rindge and Latin School and later earned both a Bachelor's and Master's degree in Computer science from Stanford University. == Early Career and Founding Figure Eight == After graduation, Biewald joined Yahoo! as an engineer, working on machine translations to improve search results, and eventually led the Search Relevance Team for Yahoo! Japan. He later joined Powerset, a natural language search technology company, as their Senior Scientist, which was acquired by Microsoft in 2008 for an estimated $100M. In 2007, Biewald co-founded Figure Eight (formerly CrowdFlower), a data labeling and crowdsourcing company that created datasets for training machine learning models. Figure Eight was acquired by Appen in 2019 for $300 million. == Weights and Biases == In 2017, Biewald co-founded Weights & Biases with Chris Van Pelt and Shawn Lewis. The company provides tools for tracking machine learning experiments, model management, and collaborative AI and LLM app development. The platform has been adopted by organizations such as OpenAI, Salesforce, and Microsoft. In March 2025 Coreweave acquired Weights and Biases at $1.7 billion, with the transaction closing on May 5, 2025. == Gradient Dissent == Biewald hosts the bi-weekly podcast Gradient Dissent. Guest have included: Anthony Goldbloom – Co-founder & CEO of Kaggle. “How to Win Kaggle Competitions” (podcast, Sep. 9, 2020). Shared tips on data-science competitions from the founder of the largest ML community. Richard Socher – Founder & CEO of You.com; former Chief Scientist at Salesforce. “The Challenges of Making ML Work in the Real World” (podcast, September 28, 2020). A leading NLP researcher, he spoke on multimodal search engines powered by large language models. Jensen Huang – Founder & CEO of NVIDIA. “NVIDIA’s CEO on the Next Generation of AI and MLOps” (podcast, March 3, 2022). Huang’s GPUs power modern ML research and production. Emad Mostaque – Co-founder & CEO of Stability AI. “Stable Diffusion, Stability AI, and What’s Next” (podcast, Nov. 15, 2022). Leads the company behind Stable Diffusion, which helped spark the generative-AI imaging boom. Drago Anguelov – Head of Research at Waymo. “Robustness, Safety, and Scalability at Waymo” (podcast, July 14, 2022). Covered Waymo’s self-driving AI advances and deployment challenges. Jeremy Howard – Co-founder of fast.ai. “The Simple but Profound Insight Behind Diffusion” (podcast, Jan. 5, 2023). Known for democratizing deep-learning education; discussed diffusion models and accessible AI tooling. Aidan Gomez – Co-founder & CEO of Cohere. “Scaling LLMs and Accelerating Adoption” (podcast, April 20, 2023). Co-author of “Attention Is All You Need,” he shared how Cohere delivers large-scale NLP models as a service. Chelsea Finn – Stanford Assistant Professor (AI & Robotics). “Shaping the World of Robotics with Chelsea Finn” (podcast, February 15, 2024). A pioneer in meta-learning and robotics, she detailed robots learning complex tasks like cooking. Andrew Feldman – Co-founder & CEO of Cerebras Systems. "Launching the Fastest AI Inference Solution" (podcast, August 27, 2024). Described wafer-scale AI chips achieving new training performance records. Thomas Dohmke – CEO of GitHub. “GitHub CEO on Copilot and the Future of Software Development” (podcast, June 10, 2025). Discussed building Copilot and the future of AI-assisted coding. Martin Shkreli – Founder of Godel Terminal. “From Pharma to AGI Hype, and Developing AI in Finance: Martin Shkreli’s Journey” (podcast, May 20, 2025). Shkreli reflects on his pharma controversies, prison experience, and his new AI-driven trading platform. Jarek Kutylowski – Founder & CEO of DeepL. “How DeepL Built a Translation Powerhouse with AI” (podcast, July 8, 2025). Shared how DeepL’s neural-MT rivals Google Translate through model and infrastructure innovation. == Awards and recognition == In 2010, Lukas Biewald won the Netexplorateur Award for creating the GiveWork iPhone app, which allows users to perform small tasks that assist refugees and people in developing countries. In 2010, Inc Magazine included Biewald and Van Pelt on its list of the Top 30 Entrepreneurs Under 30. == Publications == Ensuring quality in crowdsourced search relevance evaluation: The effects of training question distribution by John Le, Andy Edmonds, Vaughn Hester, Lukas Biewald. SIGIR 2010 Workshop on Crowdsourcing for Search Evaluation, July 2010. Superficial Data Analysis: Exploring Millions of Social Stereotypes by Lukas Biewald, Brendan O’Connor. O’Reilly July 2009 Biewald has co-authored 26 AI research papers from 2004 through 2018.

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  • OpenAI Five

    OpenAI Five

    OpenAI Five is a computer program by OpenAI that plays the five-on-five video game Dota 2. Its first public appearance occurred in 2017, where it was demonstrated in a live one-on-one game against the professional player Dendi, who lost to it. The following year, the system had advanced to the point of performing as a full team of five, and began playing against and showing the capability to defeat professional teams. By choosing a game as complex as Dota 2 to study machine learning, OpenAI thought they could more accurately capture the unpredictability and continuity seen in the real world, thus constructing more general problem-solving systems. The algorithms and code used by OpenAI Five were eventually borrowed by another neural network in development by the company, one which controlled a physical robotic hand. OpenAI Five has been compared to other similar cases of artificial intelligence (AI) playing against and defeating humans, such as AlphaStar in the video game StarCraft II, AlphaGo in the board game Go, Deep Blue in chess, and Watson on the television game show Jeopardy!. == History == Development on the algorithms used for the bots began in November 2016. OpenAI decided to use Dota 2, a competitive five-on-five video game, as a base due to it being popular on the live streaming platform Twitch, having native support for Linux, and had an application programming interface (API) available. Before becoming a team of five, the first public demonstration occurred at The International 2017 in August, the annual premiere championship tournament for the game, where Dendi, a Ukrainian professional player, lost against an OpenAI bot in a live one-on-one matchup. After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of real time, and that the learning software was a step in the direction of creating software that can handle complex tasks "like being a surgeon". OpenAI used a methodology called reinforcement learning, as the bots learn over time by playing against itself hundreds of times a day for months, in which they are rewarded for actions such as killing an enemy and destroying towers. By June 2018, the ability of the bots expanded to play together as a full team of five and were able to defeat teams of amateur and semi-professional players. At The International 2018, OpenAI Five played in two games against professional teams, one against the Brazilian-based paiN Gaming and the other against an all-star team of former Chinese players. Although the bots lost both matches, OpenAI still considered it a successful venture, stating that playing against some of the best players in Dota 2 allowed them to analyze and adjust their algorithms for future games. The bots' final public demonstration occurred in April 2019, where they won a best-of-three series against The International 2018 champions OG at a live event in San Francisco. A four-day online event to play against the bots, open to the public, occurred the same month. There, the bots played in 42,729 public games, winning 99.4% of those games. == Architecture == Each OpenAI Five bot is a neural network containing a single layer with a 4096-unit LSTM that observes the current game state extracted from the Dota developer's API. The neural network conducts actions via numerous possible action heads (no human data involved), and every head has meaning. For instance, the number of ticks to delay an action, what action to select – the X or Y coordinate of this action in a grid around the unit. In addition, action heads are computed independently. The AI system observes the world as a list of 20,000 numbers and takes an action by conducting a list of eight enumeration values. Also, it selects different actions and targets to understand how to encode every action and observe the world. OpenAI Five has been developed as a general-purpose reinforcement learning training system on the "Rapid" infrastructure. Rapid consists of two layers: it spins up thousands of machines and helps them 'talk' to each other and a second layer runs software. By 2018, OpenAI Five had played around 180 years worth of games in reinforcement learning running on 256 GPUs and 128,000 CPU cores, using Proximal Policy Optimization, a policy gradient method. == Comparisons with other game AI systems == Prior to OpenAI Five, other AI versus human experiments and systems have been successfully used before, such as Jeopardy! with Watson, chess with Deep Blue, and Go with AlphaGo. In comparison with other games that have used AI systems to play against human players, Dota 2 differs as explained below: Long run view: The bots run at 30 frames per second for an average match time of 45 minutes, which results in 80,000 ticks per game. OpenAI Five observes every fourth frame, generating 20,000 moves. By comparison, chess usually ends before 40 moves, while Go ends before 150 moves. Partially observed state of the game: Players and their allies can only see the map directly around them. The rest of it is covered in a fog of war which hides enemies units and their movements. Thus, playing Dota 2 requires making inferences based on this incomplete data, as well as predicting what their opponent could be doing at the same time. By comparison, Chess and Go are "full-information games", as they do not hide elements from the opposing player. Continuous action space: Each playable character in a Dota 2 game, known as a hero, can take dozens of actions that target either another unit or a position. The OpenAI Five developers allow the space into 170,000 possible actions per hero. Without counting the perpetual aspects of the game, there are an average of ~1,000 valid actions each tick. By comparison, the average number of actions in chess is 35 and 250 in Go. Continuous observation space: Dota 2 is played on a large map with ten heroes, five on each team, along with dozens of buildings and non-player character (NPC) units. The OpenAI system observes the state of a game through developers' bot API, as 20,000 numbers that constitute all information a human is allowed to get access to. A chess board is represented as about 70 lists, whereas a Go board has about 400 enumerations. == Reception == OpenAI Five have received acknowledgement from the AI, tech, and video game community at large. Microsoft founder Bill Gates called it a "big deal", as their victories "required teamwork and collaboration". Chess champion Garry Kasparov, who lost against the Deep Blue AI in 1997, stated that despite their losing performance at The International 2018, the bots would eventually "get there, and sooner than expected". In a conversation with MIT Technology Review, AI experts also considered OpenAI Five system as a significant achievement, as they noted that Dota 2 was an "extremely complicated game", so even beating non-professional players was impressive. PC Gamer wrote that their wins against professional players was a significant event in machine learning. In contrast, Motherboard wrote that the victory was "basically cheating" due to the simplified hero pools on both sides, as well as the fact that bots were given direct access to the API, as opposed to using computer vision to interpret pixels on the screen. The Verge wrote that the bots were evidence that the company's approach to reinforcement learning and its general philosophy about AI was "yielding milestones". In 2019, DeepMind unveiled a similar bot for StarCraft II, AlphaStar. Like OpenAI Five, AlphaStar used reinforcement learning and self-play. The Verge reported that "the goal with this type of AI research is not just to crush humans in various games just to prove it can be done. Instead, it's to prove that — with enough time, effort, and resources — sophisticated AI software can best humans at virtually any competitive cognitive challenge, be it a board game or a modern video game." They added that the DeepMind and OpenAI victories were also a testament to the power of certain uses of reinforcement learning. It was OpenAI's hope that the technology could have applications outside of the digital realm. In 2018, they were able to reuse the same reinforcement learning algorithms and training code from OpenAI Five for Dactyl, a human-like robot hand with a neural network built to manipulate physical objects. In 2019, Dactyl solved the Rubik's Cube.

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  • Dental AI

    Dental AI

    Dental artificial intelligence (Dental AI) refers to the application of artificial intelligence (AI) and machine-learning methods to oral healthcare data. These systems can be used to find patterns or make predictions that can aid in diagnosis, treatment, patient communication, or practice management. == History and development == Research into AI for dentistry dates to the 1990s and 2000s, alongside early CAD/CAM and image-analysis work in dental radiology. Recent developments in deep learning, especially those involving computer vision, such as convolutional neural networks, trained on large image datasets, led to a rapid improvement in performance, as well as a move from prototype technology to productization suitable for use in dental chairs. Dental schools and continuing education programs started incorporating AI content in the 2020s. == Definition and core technologies == The dental AI software accomplishes this task by using various dental images and patient data. Dental images and data used by the dental AI software include bitewing and periapical X-rays, complete mouth X-rays, detailed 3D images, intraoral images, and the patient’s medical history. The dental AI software utilizes several core technologies in accomplishing its task of assisting the dentist. First, the dental AI software utilizes machine learning and deep learning using programs that can learn from examples. Such programs are referred to as convolutional neural network (CNN) and can detect cavities and identify bone changes related to gum disease. The dental AI software utilizes computer vision, which enables the AI software to identify and quantify important features in images and data, whether they are 2D images or 3D images. Natural language processing (NLP) is used for the AI software to understand written text and can automatically generate dental notes and communicate with the patient. Furthermore, the dental AI software utilizes predictive analytics to identify patients that are more prone to dental complications and can suggest the best intervals for checkups or future dental procedures. == Applications in dentistry == Reported clinical and operational applications include diagnostic assistance for caries and periodontal disease, treatment planning assistance, patient education overlays, quality assurance, curriculum assistance for dental education, and claims documentation. Systematic reviews continue to find image-based applications such as caries detection with some variability in study design and a need for prospective validation. == Academic research and clinical validation == Several peer-reviewed studies have measured the effectiveness of AI for applications such as interproximal caries detection and periodontal bone level assessment, showing improvements over unaided readings with a focus on bias within the dataset. The Dental AI Council found variability among clinicians for diagnosis and treatment planning, suggesting the use of a standard tool as an assist. == Industry adoption == Multiple vendors offer FDA-cleared chairside AI for dental imaging: Pearl — Received U.S. FDA 510(k) clearance for its real-time radiologic aid (“Second Opinion”) in 2022 (2D), with subsequent clearances including pediatric and CBCT (“Second Opinion 3D”). TIME gave “Second Opinion” a special mention on its Best Inventions of 2022 list. Overjet — FDA-cleared for bone-level quantification and detection/outline of caries and calculus (e.g., K210187), with additional clearances expanding capabilities. VideaHealth — Received an FDA 510(k) covering 30+ detections across common dental findings (K232384), including indications for patients ages 3 and up; trade coverage has described elements of this as the first pediatric dental-AI clearance. == Regulations == In the U.S., AI-enabled dental imaging software is generally reviewed via the FDA’s 510(k) pathway. The FDA maintains a public AI-Enabled Medical Devices List, which includes numerous medical-imaging AI tools (including dental). Specific dental clearances include Overjet (K210187), VideaHealth (K232384), and Pearl entries such as “Second Opinion 3D” (K243989).

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  • Quantum Artificial Intelligence Lab

    Quantum Artificial Intelligence Lab

    The Quantum Artificial Intelligence Lab (also called the Quantum AI Lab or QuAIL) is a joint initiative of NASA, Universities Space Research Association, and Google (specifically, Google Research) whose goal is to pioneer research on how quantum computing might help with machine learning and other difficult computer science problems. The lab is hosted at NASA's Ames Research Center. == History == The Quantum AI Lab was announced by Google Research in a blog post on May 16, 2013. At the time of launch, the Lab was using the most advanced commercially available quantum computer, D-Wave Two from D-Wave Systems. On October 10, 2013, Google released a short film describing the current state of the Quantum AI Lab. On October 18, 2013, Google announced that it had incorporated quantum physics into Minecraft. In January 2014, Google reported results comparing the performance of the D-Wave Two in the lab with that of classical computers. The results were ambiguous and provoked heated discussion on the Internet. On 2 September 2014, it was announced that the Google Quantum AI Lab, in partnership with UC Santa Barbara, would be launching an initiative to create quantum information processors based on superconducting electronics. On the 23rd of October 2019, the Quantum AI Lab announced in a paper that it had achieved quantum supremacy with their Sycamore processor. The claim of quantum supremacy achievement has since been debated, with a far more accurate simulation on a classical computer being possible in 2.5 days as a conservative estimate. == Present == On December 9, 2024, Google introduced the Willow processor, describing it as a "state-of-the-art quantum chip". Google claims that this new chip takes just five minutes to solve a problem that takes traditional supercomputers ten septillion years. However, experts say Willow is, for now, a largely experimental device.

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  • TensorFlow

    TensorFlow

    TensorFlow is a software library for machine learning and artificial intelligence. It can be used across a range of tasks, but is used mainly for training and inference of neural networks. It is one of the most popular deep learning frameworks, alongside others such as PyTorch. It is free and open-source software released under the Apache License 2.0. It was developed by the Google Brain team for Google's internal use in research and production. The initial version was released under the Apache License 2.0 in 2015. Google released an updated version, TensorFlow 2.0, in September 2019. TensorFlow can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java, facilitating its use in a range of applications in many sectors. == History == === DistBelief === Starting in 2011, Google Brain built DistBelief as a proprietary machine learning system based on deep learning neural networks. Its use grew rapidly across diverse Alphabet companies in both research and commercial applications. Google assigned multiple computer scientists, including Jeff Dean, to simplify and refactor the codebase of DistBelief into a faster, more robust application-grade library, which became TensorFlow. In 2009, the team, led by Geoffrey Hinton, had implemented generalized backpropagation and other improvements, which allowed generation of neural networks with substantially higher accuracy, for instance a 25% reduction in errors in speech recognition. === TensorFlow === TensorFlow is Google Brain's second-generation system. Version 1.0.0 was released on February 11, 2017. While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. Its flexible architecture allows for easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. TensorFlow computations are expressed as stateful dataflow graphs. The name TensorFlow derives from the operations that such neural networks perform on multidimensional data arrays, which are referred to as tensors. During the Google I/O Conference in June 2016, Jeff Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google. In March 2018, Google announced TensorFlow.js version 1.0 for machine learning in JavaScript. In Jan 2019, Google announced TensorFlow 2.0. It became officially available in September 2019. In May 2019, Google announced TensorFlow Graphics for deep learning in computer graphics. === Tensor processing unit (TPU) === In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow. A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. Google announced they had been running TPUs inside their data centers for more than a year, and had found them to deliver an order of magnitude better-optimized performance per watt for machine learning. In May 2017, Google announced the second-generation, as well as the availability of the TPUs in Google Compute Engine. The second-generation TPUs deliver up to 180 teraflops of performance, and when organized into clusters of 64 TPUs, provide up to 11.5 petaflops. In May 2018, Google announced the third-generation TPUs delivering up to 420 teraflops of performance and 128 GB high bandwidth memory (HBM). Cloud TPU v3 Pods offer 100+ petaflops of performance and 32 TB HBM. In February 2018, Google announced that they were making TPUs available in beta on the Google Cloud Platform. === Edge TPU === In July 2018, the Edge TPU was announced. Edge TPU is Google's purpose-built ASIC chip designed to run TensorFlow Lite machine learning (ML) models on small client computing devices such as smartphones known as edge computing. === TensorFlow Lite === In May 2017, Google announced TensorFlow Lite as a software stack to support machine learning models for mobile and embedded devices, and in November 2017, provided the developer preview. In January 2019, the TensorFlow team released a developer preview of the mobile GPU inference engine with OpenGL ES 3.1 Compute Shaders on Android devices and Metal Compute Shaders on iOS devices. In May 2019, Google announced that their TensorFlow Lite Micro (also known as TensorFlow Lite for Microcontrollers) and ARM's uTensor would be merging. It was renamed as LiteRT in 2024. === TensorFlow 2.0 === As TensorFlow's market share among research papers was declining to the advantage of PyTorch, the TensorFlow Team announced a release of a new major version of the library in September 2019. TensorFlow 2.0 introduced many changes, the most significant being TensorFlow eager, which changed the automatic differentiation scheme from the static computational graph to the "Define-by-Run" scheme originally made popular by Chainer and later PyTorch. Other major changes included removal of old libraries, cross-compatibility between trained models on different versions of TensorFlow, and significant improvements to the performance on GPU. == Features == === AutoDifferentiation === AutoDifferentiation is the process of automatically calculating the gradient vector of a model with respect to each of its parameters. With this feature, TensorFlow can automatically compute the gradients for the parameters in a model, which is useful to algorithms such as backpropagation which require gradients to optimize performance. To do so, the framework must keep track of the order of operations done to the input Tensors in a model, and then compute the gradients with respect to the appropriate parameters. === Eager execution === TensorFlow includes an "eager execution" mode, which means that operations are evaluated immediately as opposed to being added to a computational graph which is executed later. Code executed eagerly can be examined step-by step-through a debugger, since data is augmented at each line of code rather than later in a computational graph. This execution paradigm is considered to be easier to debug because of its step by step transparency. === Distribute === In both eager and graph executions, TensorFlow provides an API for distributing computation across multiple devices with various distribution strategies. This distributed computing can often speed up the execution of training and evaluating of TensorFlow models and is a common practice in the field of AI. === Losses === To train and assess models, TensorFlow provides a set of loss functions (also known as cost functions). Some popular examples include mean squared error (MSE) and binary cross entropy (BCE). === Metrics === In order to assess the performance of machine learning models, TensorFlow gives API access to commonly used metrics. Examples include various accuracy metrics (binary, categorical, sparse categorical) along with other metrics such as Precision, Recall, and Intersection-over-Union (IoU). === TF.nn === TensorFlow.nn is a module for executing primitive neural network operations on models. Some of these operations include variations of convolutions (1/2/3D, Atrous, depthwise), activation functions (Softmax, RELU, GELU, Sigmoid, etc.) and their variations, and other operations (max-pooling, bias-add, etc.). === Optimizers === TensorFlow offers a set of optimizers for training neural networks, including ADAM, ADAGRAD, and Stochastic Gradient Descent (SGD). When training a model, different optimizers offer different modes of parameter tuning, often affecting a model's convergence and performance. == Usage and extensions == === TensorFlow === TensorFlow serves as a core platform and library for machine learning. TensorFlow's APIs use Keras to allow users to make their own machine-learning models. In addition to building and training their model, TensorFlow can also help load the data to train the model, and deploy it using TensorFlow Serving. TensorFlow provides a stable Python Application Program Interface (API), as well as APIs without backwards compatibility guarantee for JavaScript, C++, and Java. Third-party language binding packages are also available for C#, Haskell, Julia, MATLAB, Object Pascal, R, Scala, Rust, OCaml, and Crystal. Bindings that are now archived and unsupported include Go and Swift. === TensorFlow.js === TensorFlow also has a library for machine learning in JavaScript. Using the provided JavaScript APIs, TensorFlow.js allows users to use either Tensorflow.js models or converted models from TensorFlow or TFLite, retrain the given models, and run on the web. === LiteRT === LiteRT, formerly known as Te

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  • TD-Gammon

    TD-Gammon

    TD-Gammon is a computer backgammon program developed in the 1990s by Gerald Tesauro at IBM's Thomas J. Watson Research Center. Its name comes from the fact that it is an artificial neural net trained by a form of temporal-difference learning, specifically TD-Lambda. It explored strategies that humans had not pursued and led to advances in the theory of correct backgammon play. In 1993, TD-Gammon (version 2.1) was trained with 1.5 million games of self-play, and achieved a level of play just slightly below that of the top human backgammon players of the time. In 1998, during a 100-game series, it was defeated by the world champion by a mere margin of 8 points. Its unconventional assessment of some opening strategies had been accepted and adopted by expert players. TD-gammon is commonly cited as an early success of reinforcement learning and neural networks, and was cited in, for example, papers for deep Q-learning and AlphaGo. == Algorithm for play and learning == During play, TD-Gammon examines on each turn all possible legal moves and all their possible responses (lookahead search), feeds each resulting board position into its evaluation function, and chooses the move that leads to the board position that got the highest score. In this respect, TD-Gammon is no different than almost any other computer board-game program. TD-Gammon's innovation was in how it learned its evaluation function. TD-Gammon's learning algorithm consists of updating the weights in its neural net after each turn to reduce the difference between its evaluation of previous turns' board positions and its evaluation of the present turn's board position—hence "temporal-difference learning". The score of any board position is a set of four numbers reflecting the program's estimate of the likelihood of each possible game result: White wins normally, Black wins normally, White wins a gammon, Black wins a gammon. For the final board position of the game, the algorithm compares with the actual result of the game rather than its own evaluation of the board position. The core of TD-gammon is a neural network with 3 layers. The input layer has two types of neurons. One type codes for the board position. They are non-negative integers ranging from 0 to 15, indicating the number of White or Black checkers at each board location. There are 99 input neurons for each, totaling 198 neurons. Another type codes for hand-crafted features previously used in Neurogammon. These features encoded standard concepts used by human experts, such as "advanced anchor," "blockade strength," "home board strength" and the probability of a "blot" (single checker) being hit. The hidden layer contains hidden neurons. Later versions had more of these. The output layer contains 4 neurons, representing the network's estimate of the probability ("equity") that the current board would lead to. The 4 neurons code for: White normal win, White gammon win, Black normal win, Black gammon win. Backgammon win is so rare that Tesauro opted to not represent it. After each turn, the learning algorithm updates each weight in the neural net according to the following rule: w t + 1 − w t = α ( Y t + 1 − Y t ) ∑ k = 1 t λ t − k ∇ w Y k {\displaystyle w_{t+1}-w_{t}=\alpha (Y_{t+1}-Y_{t})\sum _{k=1}^{t}\lambda ^{t-k}\nabla _{w}Y_{k}} where: It was found that picking small λ {\displaystyle \lambda } offered performance roughly equally good, and large λ {\displaystyle \lambda } degraded performance. Because of this, after 1992, TD-Gammon was trained with λ = 0 {\displaystyle \lambda =0} , degenerating into standard TD-learning. This saved compute by a factor of 2. == Development history == Version 1.0 used simple 1-ply search: every next move is scored by the neural net, and the highest-scoring move is selected. Versions 2.0 and 2.1 used 2-ply search: Make a 1-ply analysis to remove unlikely moves ("forward pruning"). Make a 2-play minimax analysis for only the likely moves. Pick the best move, probability-weighted by each of the opponent's 21 possible dice rolls (weighting non-doubles twice as much as doubles). Versions 3.0 and 3.1 used 3-ply search, using 21 2 = 441 {\displaystyle 21^{2}=441} possible dice rolls instead of 21. The last version, 3.1, was trained specifically for an exhibition match against Malcolm Davis at the 1998 AAAI Hall of Champions. It lost at -8 points, mainly due to one blunder, where TD-Gammon opted to double and got gammoned at -32 points. == Experiments and stages of training == Unlike previous neural-net backgammon programs such as Neurogammon (also written by Tesauro), where an expert trained the program by supplying the "correct" evaluation of each position, TD-Gammon was at first programmed "knowledge-free". In early experimentation, using only a raw board encoding with no human-designed features, TD-Gammon reached a level of play comparable to Neurogammon: that of an intermediate-level human backgammon player. Even though TD-Gammon discovered insightful features on its own, Tesauro wondered if its play could be improved by using hand-designed features like Neurogammon's. Indeed, the self-training TD-Gammon with expert-designed features soon surpassed all previous computer backgammon programs. It stopped improving after about 1,500,000 games (self-play) using a three-layered neural network, with 198 input units encoding expert-designed features, 80 hidden units, and one output unit representing predicted probability of winning. == Advances in backgammon theory == TD-Gammon's exclusive training through self-play (rather than imitation learning) enabled it to explore strategies that humans previously had not considered or had ruled out erroneously. Its success with unorthodox strategies had a significant impact on the backgammon community. Late 1991, Bill Robertie, Paul Magriel, and Malcolm Davis, were invited to play against TD-Gammon (version 1.0). A total of 51 games were played, with TD-Gammon losing at -0.25 ppg. Robertie found TD-Gammon to be at the level of a competent advanced player, and better than any previous backgammon program. Robertie subsequently wrote about the use of TD-Gammon for backgammon study. For example, on the opening play, the conventional wisdom was that given a roll of 2-1, 4-1, or 5-1, White should move a single checker from point 6 to point 5. Known as "slotting", this technique trades the risk of a hit for the opportunity to develop an aggressive position. TD-Gammon found that the more conservative play of splitting 24-23 was superior. Tournament players began experimenting with TD-Gammon's move, and found success. Within a few years, slotting had disappeared from tournament play, replaced by splitting, though in 2006 it made a reappearance for 2-1. Backgammon expert Kit Woolsey found that TD-Gammon's positional judgement, especially its weighing of risk against safety, was superior to his own or any human's. TD-Gammon's excellent positional play was undercut by occasional poor endgame play. The endgame requires a more analytical approach, sometimes with extensive lookahead. TD-Gammon's limitation to two-ply lookahead put a ceiling on what it could achieve in this part of the game. TD-Gammon's strengths and weaknesses were the opposite of symbolic artificial intelligence programs and most computer software in general: it was good at matters that require an intuitive "feel" but bad at systematic analysis. It is also poor at doubling strategies. This is likely due to the fact that the neural network is trained without the doubling cube, with the doubling added by feeding the neural network's cubeless equity estimates into theoretically-based heuristic formulae. This was particularly the case in the 1998 exhibition match, where it played 100 games against Malcolm Davis. A single doubling blunder lost the match. TD-gammon was never commercialized or released to the public in some other form, but it inspired commercial backgammon programs based on neural networks, such as JellyFish (1994) and Snowie (1998).

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  • Comparison of raster graphics editors

    Comparison of raster graphics editors

    Raster graphics editors can be compared by many variables, including availability. == List == == General information == Basic general information about the editor: creator, company, license, etc. == Operating system support == The operating systems on which the editors can run natively, that is, without emulation, virtual machines or compatibility layers. In other words, the software must be specifically coded for the operation system; for example, Adobe Photoshop for Windows running on Linux with Wine does not fit. == Features == == Color spaces == == File support ==

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  • Preferential entailment

    Preferential entailment

    Preferential entailment is a non-monotonic logic based on selecting only models that are considered the most plausible. The plausibility of models is expressed by an ordering among models called a preference relation, hence the name preference entailment. Formally, given a propositional formula F {\displaystyle F} and an ordering over propositional models ≤ {\displaystyle \leq } , preferential entailment selects only the models of F {\displaystyle F} that are minimal according to ≤ {\displaystyle \leq } . This selection leads to a non-monotonic inference relation: F ⊨ pref G {\displaystyle F\models _{\text{pref}}G} holds if and only if all minimal models of F {\displaystyle F} according to ≤ {\displaystyle \leq } are also models of G {\displaystyle G} . Circumscription can be seen as the particular case of preferential entailment when the ordering is based on containment of the sets of variables assigned to true (in the propositional case) or containment of the extensions of predicates (in the first-order logic case).

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  • Tag (metadata)

    Tag (metadata)

    In information systems, a tag is a keyword or term assigned to a piece of information (such as an Internet bookmark, multimedia, database record, or computer file). This kind of metadata helps describe an item and allows it to be found again by browsing or searching. Tags are generally chosen informally and personally by the item's creator or by its viewer, depending on the system, although they may also be chosen from a controlled vocabulary. Tagging was popularized by websites associated with Web 2.0 and is an important feature of many Web 2.0 services. It is now also part of other database systems, desktop applications, and operating systems. == Overview == People use tags to aid classification, mark ownership, note boundaries, and indicate online identity. Tags may take the form of words, images, or other identifying marks. An analogous example of tags in the physical world is museum object tagging. People were using textual keywords to classify information and objects long before computers. Computer based search algorithms made the use of such keywords a rapid way of exploring records. Tagging gained popularity due to the growth of social bookmarking, image sharing, and social networking websites. These sites allow users to create and manage labels (or "tags") that categorize content using simple keywords. Websites that include tags often display collections of tags as tag clouds, as do some desktop applications. On websites that aggregate the tags of all users, an individual user's tags can be useful both to them and to the larger community of the website's users. Tagging systems have sometimes been classified into two kinds: top-down and bottom-up. Top-down taxonomies are created by an authorized group of designers (sometimes in the form of a controlled vocabulary), whereas bottom-up taxonomies (called folksonomies) are created by all users. This definition of "top down" and "bottom up" should not be confused with the distinction between a single hierarchical tree structure (in which there is one correct way to classify each item) versus multiple non-hierarchical sets (in which there are multiple ways to classify an item); the structure of both top-down and bottom-up taxonomies may be either hierarchical, non-hierarchical, or a combination of both. Some researchers and applications have experimented with combining hierarchical and non-hierarchical tagging to aid in information retrieval. Others are combining top-down and bottom-up tagging, including in some large library catalogs (OPACs) such as WorldCat. When tags or other taxonomies have further properties (or semantics) such as relationships and attributes, they constitute an ontology. In folder system a file cannot exist in two or more folders so tag system has been thought more convenient. But transitioning to tag system requires awareness of difference between properties of two systems. In folder system the information of classification is put outside of the file and we can change folder at once. In tag system the information of classification is put inside the file so changing its tag means changing the file and it needs to be saved again and takes time. Metadata tags as described in this article should not be confused with the use of the word "tag" in some software to refer to an automatically generated cross-reference; examples of the latter are tags tables in Emacs and smart tags in Microsoft Office. == History == The use of keywords as part of an identification and classification system long predates computers. Paper data storage devices, notably edge-notched cards, that permitted classification and sorting by multiple criteria were already in use prior to the twentieth century, and faceted classification has been used by libraries since the 1930s. In the late 1970s and early 1980s, Emacs, the text editor for Unix systems, offered a companion software program called Tags that could automatically build a table of cross-references called a tags table that Emacs could use to jump between a function call and that function's definition. This use of the word "tag" did not refer to metadata tags, but was an early use of the word "tag" in software to refer to a word index. Online databases and early websites deployed keyword tags as a way for publishers to help users find content. In the early days of the World Wide Web, the keywords meta element was used by web designers to tell web search engines what the web page was about, but these keywords were only visible in a web page's source code and were not modifiable by users. In 1997, the collaborative portal "A Description of the Equator and Some ØtherLands" produced by documenta X, Germany, used the folksonomic term Tag for its co-authors and guest authors on its Upload page. In "The Equator" the term Tag for user-input was described as an abstract literal or keyword to aid the user. However, users defined singular Tags, and did not share Tags at that point. In 2003, the social bookmarking website Delicious provided a way for its users to add "tags" to their bookmarks (as a way to help find them later); Delicious also provided browseable aggregated views of the bookmarks of all users featuring a particular tag. Within a couple of years, the photo sharing website Flickr allowed its users to add their own text tags to each of their pictures, constructing flexible and easy metadata that made the pictures highly searchable. The success of Flickr and the influence of Delicious popularized the concept, and other social software websites—such as YouTube, Technorati, and Last.fm—also implemented tagging. In 2005, the Atom web syndication standard provided a "category" element for inserting subject categories into web feeds, and in 2007 Tim Bray proposed a "tag" URN. == Examples == === Within a blog === Many systems (and other web content management systems) allow authors to add free-form tags to a post, along with (or instead of) placing the post into a predetermined category. For example, a post may display that it has been tagged with baseball and tickets. Each of those tags is usually a web link leading to an index page listing all of the posts associated with that tag. The blog may have a sidebar listing all the tags in use on that blog, with each tag leading to an index page. To reclassify a post, an author edits its list of tags. All connections between posts are automatically tracked and updated by the blog software; there is no need to relocate the page within a complex hierarchy of categories. === Within application software === Some desktop applications and web applications feature their own tagging systems, such as email tagging in Gmail and Mozilla Thunderbird, bookmark tagging in Firefox, audio tagging in iTunes or Winamp, and photo tagging in various applications. Some of these applications display collections of tags as tag clouds. === Assigned to computer files === There are various systems for applying tags to the files in a computer's file system. In Apple's Mac System 7, released in 1991, users could assign one of seven editable colored labels (with editable names such as "Essential", "Hot", and "In Progress") to each file and folder. In later iterations of the Mac operating system ever since OS X 10.9 was released in 2013, users could assign multiple arbitrary tags as extended file attributes to any file or folder, and before that time the open-source OpenMeta standard provided similar tagging functionality for Mac OS X. Several semantic file systems that implement tags are available for the Linux kernel, including Tagsistant. Microsoft Windows allows users to set tags only on Microsoft Office documents and some kinds of picture files. Cross-platform file tagging standards include Extensible Metadata Platform (XMP), an ISO standard for embedding metadata into popular image, video and document file formats, such as JPEG and PDF, without breaking their readability by applications that do not support XMP. XMP largely supersedes the earlier IPTC Information Interchange Model. Exif is a standard that specifies the image and audio file formats used by digital cameras, including some metadata tags. TagSpaces is an open-source cross-platform application for tagging files; it inserts tags into the filename. === For an event === An official tag is a keyword adopted by events and conferences for participants to use in their web publications, such as blog entries, photos of the event, and presentation slides. Search engines can then index them to make relevant materials related to the event searchable in a uniform way. In this case, the tag is part of a controlled vocabulary. === In research === A researcher may work with a large collection of items (e.g. press quotes, a bibliography, images) in digital form. If he/she wishes to associate each with a small number of themes (e.g. to chapters of a book, or to sub-themes of the overall subject), then a group of tags for these themes can be attached to each of the items in

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