Generative art

History In 1988 Clauser identified the aspect of systemic autonomy as a critical element in generative art: It should be evident from the above description of the evolution of generative art that process (or structuring) and change (or transformation) are among its most definitive features, and that these features and the very term 'generative' imply dynamic development and motion.  (the result) is not a creation by the artist but rather the product of the generative process - a self-precipitating structure.  In 1989 Celestino Soddu defined the Generative Design approach to Architecture and Town Design in his book Citta' Aleatorie.  In 1989 Franke referred to "generative mathematics" as "the study of mathematical operations suitable for generating artistic images. " From the mid-1990s Brian Eno popularized the terms generative music and generative systems, making a connection with earlier experimental music by Terry Riley, Steve Reich and Philip Glass.  From the end of the 20th century, communities of generative artists, designers, musicians and theoreticians began to meet, forming cross-disciplinary perspectives.  The first meeting about generative Art was in 1998, at the inaugural International Generative Art conference at Politecnico di Milano University, Italy.  In Australia, the Iterate conference on generative systems in the electronic arts followed in 1999.  On-line discussion has centered around the eu-gene mailing list, which began late 1999, and has hosted much of the debate which has defined the field.  These activities have more recently been joined by the Generator. x conference in Berlin starting in 2005.  In 2012 the new journal GASATHJ, Generative Art Science and Technology Hard Journal was founded by Celestino Soddu and Enrica Colabella jointing several generative artists and scientists in the editorial board.  Some have argued that as a result of this engagement across disciplinary boundaries, the community has converged on a shared meaning of the term.  As Boden and Edmonds put it in 2011: Today, the term "Generative Art" is still current within the relevant artistic community.  Since 1998 a series of conferences have been held in Milan with that title (Generativeart. com), and Brian Eno has been influential in promoting and using generative art methods (Eno, 1996).  Both in music and in visual art, the use of the term has now converged on work that has been produced by the activation of a set of rules and where the artist lets a computer system take over at least some of the decision-making (although, of course, the artist determines the rules). In the call of the Generative Art conferences in Milan (annually starting from 1998), the definition of Generative Art by Celestino Soddu: Generative Art is the idea realized as genetic code of artificial events, as construction of dynamic complex systems able to generate endless variations.  Each Generative Project is a concept-software that works producing unique and non-repeatable events, like music or 3D Objects, as possible and manifold expressions of the generating idea strongly recognizable as a vision belonging to an artist / designer / musician / architect /mathematician.


Generative art

History Discussion on the eu-gene mailing list was framed by the following definition by Adrian Ward from 1999: Generative art is a term given to work which stems from concentrating on the processes involved in producing an artwork, usually (although not strictly) automated by the use of a machine or computer, or by using mathematic or pragmatic instructions to define the rules by which such artworks are executed. A similar definition is provided by Philip Galanter: Generative art refers to any art practice where the artist creates a process, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is then set into motion with some degree of autonomy contributing to or resulting in a completed work of art. Around the 2020s, generative AI models learned to imitate the distinct style of particular authors.  For example, a generative image model such as Stable Diffusion is able to model the stylistic characteristics of an artist like Pablo Picasso (including his particular brush strokes, use of colour, perspective, and so on), and a user can engineer a prompt such as "an astronaut riding a horse, by Picasso" to cause the model to generate a novel image applying the artist's style to an arbitrary subject.  Generative image models have received significant backlash from artists who object to their style being imitated without their permission, arguing that this harms their ability to profit from their own work.


Generative art

Types Johann Kirnberger's Musikalisches W��rfelspiel ("Musical Dice Game") of 1757 is considered an early example of a generative system based on randomness.  Dice were used to select musical sequences from a numbered pool of previously composed phrases.  This system provided a balance of order and disorder.  The structure was based on an element of order on one hand, and disorder on the other.  The fugues of J. S.  Bach could be considered generative, in that there is a strict underlying process that is followed by the composer.  Similarly, serialism follows strict procedures which, in some cases, can be set up to generate entire compositions with limited human intervention.  Composers such as John Cage, Farmers Manual, and Brian Eno have used generative systems in their works.  The artist Ellsworth Kelly created paintings by using chance operations to assign colors in a grid.  He also created works on paper that he then cut into strips or squares and reassembled using chance operations to determine placement.  Artists such as Hans Haacke have explored processes of physical and social systems in artistic context.  Fran��ois Morellet has used both highly ordered and highly disordered systems in his artwork.  Some of his paintings feature regular systems of radial or parallel lines to create Moir�� Patterns.  In other works he has used chance operations to determine the coloration of grids.  Sol LeWitt created generative art in the form of systems expressed in natural language and systems of geometric permutation.  Harold Cohen's AARON system is a longstanding project combining software artificial intelligence with robotic painting devices to create physical artifacts.  Steina and Woody Vasulka are video art pioneers who used analog video feedback to create generative art.  Video feedback is now cited as an example of deterministic chaos, and the early explorations by the Vasulkas anticipated contemporary science by many years.  Software systems exploiting evolutionary computing to create visual form include those created by Scott Draves and Karl Sims.  The digital artist Joseph Nechvatal has exploited models of viral contagion.  Autopoiesis by Ken Rinaldo includes fifteen musical and robotic sculptures that interact with the public and modify their behaviors based on both the presence of the participants and each other.  Jean-Pierre Hebert and Roman Verostko are founding members of the Algorists, a group of artists who create their own algorithms to create art.  A.  Michael Noll, of Bell Telephone Laboratories, Incorporated, programmed computer art using mathematical equations and programmed randomness, starting in 1962.  The French artist Jean-Max Albert, beside environmental sculptures like Iapetus, and O=C=O, developed a project dedicated to the vegetation itself, in terms of biological activity.  The Calmoduline Monument project is based on the property of a protein, calmodulin, to bond selectively to calcium.  Exterior physical constraints (wind, rain, etc. ) modify the electric potential of the cellular membranes of a plant and consequently the flux of calcium.  However, the calcium controls the expression of the calmoduline gene.  The plant can thus, when there is a stimulus, modify its "typical" growth pattern.


Generative art

Types So the basic principle of this monumental sculpture is that to the extent that they could be picked up and transported, these signals could be enlarged, translated into colors and shapes, and show the plant's "decisions" suggesting a level of fundamental biological activity.  Maurizio Bolognini works with generative machines to address conceptual and social concerns.  Mark Napier is a pioneer in data mapping, creating works based on the streams of zeros and ones in Ethernet traffic, as part of the "Carnivore" project.  Martin Wattenberg pushed this theme further, transforming "data sets" as diverse as musical scores (in "Shape of Song", 2001) and Wikipedia edits (History Flow, 2003, with Fernanda Viegas) into dramatic visual compositions.  The Canadian artist San Base developed a "Dynamic Painting" algorithm in 2002.  Using computer algorithms as "brush strokes", Base creates sophisticated imagery that evolves over time to produce a fluid, never-repeating artwork.  Since 1996 there have been ambigram generators that auto generate ambigrams.  Italian composer Pietro Grossi, pioneer of computer music since 1986, he extended his experiments to images, (same procedure used in his musical work) precisely to computer graphics, writing programs with specific auto-decisions, and developing the concept of HomeArt, presented for the first time in the exhibition New Atlantis: the continent of electronic music organized by the Venice Biennale in 1986.  Some contemporary artists who create generative visual artworks are Daniel Shiffman, Zachary Lieberman, Golan Levin, Ben Fry, and Giles Whitaker (artist).  For some artists, graphic user interfaces and computer code have become an independent art form in themselves.  Adrian Ward created Auto-Illustrator as a commentary on software and generative methods applied to art and design. [citation needed] In 1987 Celestino Soddu created the artificial DNA of Italian Medieval towns able to generate endless 3D models of cities identifiable as belonging to the idea.  In 2010, Michael Hansmeyer generated architectural columns in a project called "Subdivided Columns ��� A New Order (2010)".  The piece explored how the simple process of repeated subdivision can create elaborate architectural patterns.  Rather than designing any columns directly, Hansmeyer designed a process that produced columns automatically.  The process could be run again and again with different parameters to create endless permutations.  Endless permutations could be considered a hallmark of generative design.  Writers such as Tristan Tzara, Brion Gysin, and William Burroughs used the cut-up technique to introduce randomization to literature as a generative system.  Jackson Mac Low produced computer-assisted poetry and used algorithms to generate texts; Philip M.  Parker has written software to automatically generate entire books.  Jason Nelson used generative methods with speech-to-text software to create a series of digital poems from movies, television and other audio sources.  In the late 2010s, authors began to experiment with neural networks trained on large language datasets.  David Jhave Johnston's ReRites is an early example of human-edited AI-generated poetry.  Generative systems may be modified while they operate, for example by using interactive programming environments such as SuperCollider, Fluxus and TidalCycles, including patching environments such as Max/MSP, Pure Data and vvvv.


Generative art

Types This is a standard approach to programming by artists, but may also be used to create live music and/or video by manipulating generative systems on stage, a performance practice that has become known as live coding.  As with many examples of software art, because live coding emphasizes human authorship rather than autonomy, it may be considered in opposition to generative art.  In 2020, Erick "Snowfro" Calderon launched the Art Blocks platform for combining the ideas of generative art and the blockchain, with resulting artworks created as NFTs on the Ethereum blockchain.  One of the key innovations with the generative art created in this way is that all the source code and algorithm for creating the art has to be finalized and put on the blockchain permanently, without any ability to alter it further.  Only when the artwork is sold ("minted"), the artwork is generated; the result is random yet should reflect the overall aesthetic defined by the artist.  Calderon argues that this process forces the artist to be very thoughtful of the algorithm behind the art:Until today, a [generative] artist would create an algorithm, press the spacebar 100 times, pick five of the best ones and print them in high quality.  Then they would frame them, and put them in a gallery.  Maybe.  Because Art Blocks forces the artist to accept every single output of the algorithm as their signed piece, the artist has to go back and tweak the algorithm until it's perfect.  They can't just cherry pick the good outputs.  That elevates the level of algorithmic execution because the artist is creating something that they know they're proud of before they even know what's going to come out on the other side.


Synthetic media

History Synthetic media as a process of automated art dates back to the automata of ancient Greek civilization, where inventors such as Daedalus and Hero of Alexandria designed machines capable of writing text, generating sounds, and playing music.  The tradition of automaton-based entertainment flourished throughout history, with mechanical beings' seemingly magical ability to mimic human creativity often drawing crowds throughout Europe, China, India, and so on.  Other automated novelties such as Johann Philipp Kirnberger's "Musikalisches W��rfelspiel" (Musical Dice Game) 1757 also amused audiences.  Despite the technical capabilities of these machines, however, none were capable of generating original content and were entirely dependent upon their mechanical designs.  The field of AI research was born at a workshop at Dartmouth College in 1956, begetting the rise of digital computing used as a medium of art as well as the rise of generative art.  Initial experiments in AI-generated art included the Illiac Suite, a 1957 composition for string quartet which is generally agreed to be the first score composed by an electronic computer.  Lejaren Hiller, in collaboration with Leonard Issacson, programmed the ILLIAC I computer at the University of Illinois at Urbana���Champaign (where both composers were professors) to generate compositional material for his String Quartet No.  4.  In 1960, Russian researcher R. Kh. Zaripov published worldwide first paper on algorithmic music composing using the "Ural-1" computer.  In 1965, inventor Ray Kurzweil premiered a piano piece created by a computer that was capable of pattern recognition in various compositions.  The computer was then able to analyze and use these patterns to create novel melodies.  The computer was debuted on Steve Allen's I've Got a Secret program, and stumped the hosts until film star Harry Morgan guessed Ray's secret.  Before 1989, artificial neural networks have been used to model certain aspects of creativity.  Peter Todd (1989) first trained a neural network to reproduce musical melodies from a training set of musical pieces.  Then he used a change algorithm to modify the network's input parameters.  The network was able to randomly generate new music in a highly uncontrolled manner.  In 2014, Ian Goodfellow and his colleagues developed a new class of machine learning systems: generative adversarial networks (GAN).  Two neural networks contest with each other in a game (in the sense of game theory, often but not always in the form of a zero-sum game).  Given a training set, this technique learns to generate new data with the same statistics as the training set.  For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics.  Though originally proposed as a form of generative model for unsupervised learning, GANs have also proven useful for semi-supervised learning, fully supervised learning, and reinforcement learning.  In a 2016 seminar, Yann LeCun described GANs as "the coolest idea in machine learning in the last twenty years".


Synthetic media

History In 2017, Google unveiled transformers, a new type of neural network architecture specialized for language modeling that enabled for rapid advancements in natural language processing.  Transformers proved capable of high levels of generalization, allowing networks such as GPT-3 and Jukebox from OpenAI to synthesize text and music respectively at a level approaching humanlike ability.  There have been some attempts to use GPT-3 and GPT-2 for screenplay writing, resulting in both dramatic (the Italian short film Frammenti di Anime Meccaniche, written by GPT-2) and comedic narratives (the short film Solicitors by Youtube Creator Calamity AI written by GPT-3).


Synthetic media

Branches_of_synthetic_media Deepfakes (a portmanteau of "deep learning" and "fake") are the most prominent form of synthetic media.  They are media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks.  They often combine and superimpose existing media onto source media using machine learning techniques known as autoencoders and generative adversarial networks (GANs).  Deepfakes have garnered widespread attention for their uses in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud.  This has elicited responses from both industry and government to detect and limit their use.  The term deepfakes originated around the end of 2017 from a Reddit user named "deepfakes".  He, as well as others in the Reddit community r/deepfakes, shared deepfakes they created; many videos involved celebrities' faces swapped onto the bodies of actresses in pornographic videos, while non-pornographic content included many videos with actor Nicolas Cage's face swapped into various movies.  In December 2017, Samantha Cole published an article about r/deepfakes in Vice that drew the first mainstream attention to deepfakes being shared in online communities.  Six weeks later, Cole wrote in a follow-up article about the large increase in AI-assisted fake pornography.  In February 2018, r/deepfakes was banned by Reddit for sharing involuntary pornography.  Other websites have also banned the use of deepfakes for involuntary pornography, including the social media platform Twitter and the pornography site Pornhub.  However, some websites have not yet banned Deepfake content, including 4chan and 8chan.  Non-pornographic deepfake content continues to grow in popularity with videos from YouTube creators such as Ctrl Shift Face and Shamook.  A mobile application, Impressions, was launched for iOS in March 2020.  The app provides a platform for users to deepfake celebrity faces into videos in a matter of minutes.  Image synthesis is the artificial production of visual media, especially through algorithmic means.  In the emerging world of synthetic media, the work of digital-image creation���once the domain of highly skilled programmers and Hollywood special-effects artists���could be automated by expert systems capable of producing realism on a vast scale.  One subfield of this includes human image synthesis, which is the use of neural networks to make believable and even photorealistic renditions of human-likenesses, moving or still.  It has effectively existed since the early 2000s.  Many films using computer generated imagery have featured synthetic images of human-like characters digitally composited onto the real or other simulated film material.  Towards the end of the 2010s deep learning artificial intelligence has been applied to synthesize images and video that look like humans, without need for human assistance, once the training phase has been completed, whereas the old school 7D-route required massive amounts of human work.  The website This Person Does Not Exist showcases fully automated human image synthesis by endlessly generating images that look like facial portraits of human faces.  Beyond deepfakes and image synthesis, audio is another area where AI is used to create synthetic media.


Synthetic media

Branches_of_synthetic_media Synthesized audio will be capable of generating any conceivable sound that can be achieved through audio waveform manipulation, which might conceivably be used to generate stock audio of sound effects or simulate audio of currently imaginary things.  Artificial intelligence art is any visual artwork created through the use of artificial intelligence (AI) programs.  Many mechanisms for creating AI art have been developed, including procedural "rule-based" generation of images using mathematical patterns, algorithms which simulate brush strokes and other painted effects, and deep learning algorithms, such as generative adversarial networks (GANs) and transformers.  One of the first significant AI art systems is AARON, developed by Harold Cohen beginning in the late 1960s at the University of California at San Diego.  AARON is the most notable example of AI art in the era of GOFAI programming because of its use of a symbolic rule-based approach to generate technical images.  Cohen developed AARON with the goal of being able to code the act of drawing.  In its primitive form, AARON created simple black and white drawings.  Cohen would later finish the drawings by painting them.  Throughout the years, he also began to develop a way for AARON to also paint.  Cohen designed AARON to paint using special brushes and dyes that were chosen by the program itself without mediation from Cohen.  Generative adversarial networks (GANs) were designed in 2014.  This system uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful.  DeepDream, released by Google in 2015, uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia, thus creating deliberately over-processed images.  After DeepDream's release, several companies released apps that transform photos into art-like images with the style of well-known sets of paintings.  The website Artbreeder, launched in 2018, uses the models StyleGAN and BigGAN to allow users to generate and modify images such as faces, landscapes, and paintings.  Several programs use text-to-image models to generate a variety of images based on various text prompts.  They include EleutherAI's VQGAN+CLIP which was released in 2021, OpenAI's DALL-E which released a series of images in January 2021, Google Brain's Imagen and Parti which was announced in May 2022, Microsoft's NUWA-Infinity, and Stable Diffusion which was released in August 2022.  Stability. ai has a Stable Diffusion web interface called DreamStudio.  Stable Diffusion is source-available software, enabling further development such as plugins for Krita, Photoshop, Blender, and GIMP, as well as the Automatic1111 web-based open source user interface.  Stable Diffusion's main pre-trained model is shared on the Hugging Face Hub.  The capacity to generate music through autonomous, non-programmable means has long been sought after since the days of Antiquity, and with developments in artificial intelligence, two particular domains have arisen: The robotic creation of music, whether through machines playing instruments or sorting of virtual instrument notes (such as through MIDI files) Directly generating waveforms that perfectly recreate instrumentation and human voice without the need for instruments, MIDI, or organizing premade notes.


Synthetic media

Branches_of_synthetic_media Speech synthesis has been identified as a popular branch of synthetic media and is defined as the artificial production of human speech.  A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products.  A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.  Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database.  Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity.  For specific usage domains, the storage of entire words or sentences allows for high-quality output.  Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.  Virtual assistants such as Siri and Alexa have the ability to turn text into audio and synthesize speech.  In 2016, Google DeepMind unveiled WaveNet, a deep generative model of raw audio waveforms that could learn to understand which waveforms best resembled human speech as well as musical instrumentation.  Some projects offer real-time generations of synthetic speech using deep learning, such as 15. ai, a web application text-to-speech tool developed by an MIT research scientist.  Natural-language generation (NLG, sometimes synonymous with text synthesis) is a software process that transforms structured data into natural language.  It can be used to produce long form content for organizations to automate custom reports, as well as produce custom content for a web or mobile application.  It can also be used to generate short blurbs of text in interactive conversations (a chatbot) which might even be read out by a text-to-speech system.  Interest in natural-language generation increased in 2019 after OpenAI unveiled GPT2, an AI system that generates text matching its input in subject and tone.  GPT2 is a transformer, a deep machine learning model introduced in 2017 used primarily in the field of natural language processing (NLP).  AI-generated media can be used to develop a hybrid graphics system that could be used in video games, movies, and virtual reality, as well as text-based games such as AI Dungeon 2, which uses either GPT-2 or GPT-3 to allow for near-infinite possibilities that are otherwise impossible to create through traditional game development methods.  Computer hardware company Nvidia has also worked on developed AI-generated video game demos, such as a model that can generate an interactive game based on non-interactive videos.  Through procedural generation, synthetic media techniques may eventually be used to "help designers and developers create art assets, design levels, and even build entire games from the ground up.


Synthetic media

See_also 15.ai Algorithmic art Artificial imagination Artificial intelligence art Automated journalism Computational creativity Computer music Cybernetic art DALL-E Deepfakes Generative adversarial network Generative art Generative artificial intelligence GPT-3 Human image synthesis Transformer (machine learning model) WaveNet


AI winter

Early_episodes NLP research has its roots in the early 1930s and begins its existence with the work on machine translation (MT).  However, significant advancements and applications began to emerge after the publication of Warren Weaver's influential memorandum in 1949.  The memorandum generated great excitement within the research community.  In the following years, notable events unfolded: IBM embarked on the development of the first machine, MIT appointed its first full-time professor in machine translation, and several conferences dedicated to MT took place.  The culmination came with the public demonstration of the IBM-Georgetown machine, which garnered widespread attention in respected newspapers in 1954.  Just like all AI booms that have been followed by desperate AI winters, the media tended to exaggerate the significance of these developments.  Headlines about the IBM-Georgetown experiment proclaimed phrases like "The bilingual machine," "Robot brain translates Russian into King's English," and "Polyglot brainchild]. " However, the actual demonstration involved the translation of a curated set of only 49 Russian sentences into English, with the machine's vocabulary limited to just 250 words.  To put things into perspective, a 2006 study made by Paul Nation found that humans need a vocabulary of around 8,000 to 9,000-word families to comprehend written texts with 98% accuracy.  During the Cold War, the US government was particularly interested in the automatic, instant translation of Russian documents and scientific reports.  The government aggressively supported efforts at machine translation starting in 1954.  Another factor that propelled the field of mechanical translation was the interest shown by the Central Intelligence Agency (CIA).  During that period, the CIA firmly believed in the importance of developing machine translation capabilities and supported such initiatives.  They also recognized that this program had implications that extended beyond the interests of the CIA and the intelligence community.  At the outset, the researchers were optimistic.  Noam Chomsky's new work in grammar was streamlining the translation process and there were "many predictions of imminent 'breakthroughs'". However, researchers had underestimated the profound difficulty of word-sense disambiguation.  In order to translate a sentence, a machine needed to have some idea what the sentence was about, otherwise it made mistakes.  An apocryphal example is "the spirit is willing but the flesh is weak. " Translated back and forth with Russian, it became "the vodka is good but the meat is rotten. " Later researchers would call this the commonsense knowledge problem.  By 1964, the National Research Council had become concerned about the lack of progress and formed the Automatic Language Processing Advisory Committee (ALPAC) to look into the problem.  They concluded, in a famous 1966 report, that machine translation was more expensive, less accurate and slower than human translation.  After spending some 20 million dollars, the NRC ended all support.  Careers were destroyed and research ended.  Machine translation shared the same path with natural language processing from the rule-based approaches through the statistical approaches up to the neural network approaches, which have in 2023 culminated in large language models.


AI winter

Early_episodes Simple networks or circuits of connected units, including Walter Pitts and Warren McCulloch's neural network for logic and Marvin Minsky's SNARC system, have failed to deliver the promised results and were abandoned in the late 1950s.  Following the success of programs such as the Logic Theorist and the General Problem Solver, algorithms for manipulating symbols seemed more promising at the time as means to achieve logical reasoning viewed at the time as the essence of intelligence, either natural or artificial.  Interest in perceptrons, invented by Frank Rosenblatt, was kept alive only by the sheer force of his personality.  He optimistically predicted that the perceptron "may eventually be able to learn, make decisions, and translate languages".  Mainstream research into perceptrons ended partially because the 1969 book Perceptrons by Marvin Minsky and Seymour Papert emphasized the limits of what perceptrons could do.  Major funding for projects neural network approaches was difficult to find in the 1970s and early 1980s.  Important theoretical work continued despite the lack of funding.  The "winter" of neural network approach came to an end in the middle 1980s, when the work of John Hopfield, David Rumelhart and others revived large scale interest.  Rosenblatt did not live to see this, however, as he died in a boating accident shortly after Perceptrons was published.


AI winter

Current_AI_.22Summer.22_2005-present AI reached the highest levels of interest and funding in its history in the 2020s, by every possible measure, including: publications, patent applications, total investment ($50 billion in 2022), and job openings (800,000 U.S. job openings in 2022). The successes of the current "AI spring" or "AI boom" are advances in language translation (in particular, Google Translate), image recognition (spurred by the ImageNet training database) as commercialized by Google Image Search, and in game-playing systems such as AlphaZero (chess champion) and AlphaGo (go champion), and Watson (Jeopardy champion). A turning point was in 2012 when AlexNet (a deep learning network) won the ImageNet Large Scale Visual Recognition Challenge with half as many errors as the second place winner. The recent release of OpenAI's AI chatbot ChatGPT which now has over 100 million users, has reinvigorated the discussion about artificial intelligence and its effects on the world. Google CEO Sundar Pichai has stated that AI will be the most important technology that humans create.


MIT Artificial Intelligence Laboratory

History Computing Research at MIT began with Vannevar Bush's research into a differential analyzer and Claude Shannon's electronic Boolean algebra in the 1930s, the wartime MIT Radiation Laboratory, the post-war Project Whirlwind and Research Laboratory of Electronics (RLE), and MIT Lincoln Laboratory's SAGE in the early 1950s.  At MIT, research in the field of artificial intelligence began in late 1950s.  On July 1, 1963, Project MAC (the Project on Mathematics and Computation, later backronymed to Multiple Access Computer, Machine Aided Cognitions, or Man and Computer) was launched with a $2 million grant from the Defense Advanced Research Projects Agency (DARPA).  Project MAC's original director was Robert Fano of MIT's Research Laboratory of Electronics (RLE).  Fano decided to call MAC a "project" rather than a "laboratory" for reasons of internal MIT politics ��� if MAC had been called a laboratory, then it would have been more difficult to raid other MIT departments for research staff.  The program manager responsible for the DARPA grant was J.  C.  R.  Licklider, who had previously been at MIT conducting research in RLE, and would later succeed Fano as director of Project MAC.  Project MAC would become famous for groundbreaking research in operating systems, artificial intelligence, and the theory of computation.  Its contemporaries included Project Genie at Berkeley, the Stanford Artificial Intelligence Laboratory, and (somewhat later) University of Southern California's (USC's) Information Sciences Institute.  An "AI Group" including Marvin Minsky (the director), John McCarthy (inventor of Lisp), and a talented community of computer programmers were incorporated into Project MAC.  They were interested principally in the problems of vision, mechanical motion and manipulation, and language, which they view as the keys to more intelligent machines.  In the 1960s and 1970s the AI Group developed a time-sharing operating system called Incompatible Timesharing System (ITS) which ran on PDP-6 and later PDP-10 computers.  The early Project MAC community included Fano, Minsky, Licklider, Fernando J.  Corbat��, and a community of computer programmers and enthusiasts among others who drew their inspiration from former colleague John McCarthy.  These founders envisioned the creation of a computer utility whose computational power would be as reliable as an electric utility.  To this end, Corbat�� brought the first computer time-sharing system, Compatible Time-Sharing System (CTSS), with him from the MIT Computation Center, using the DARPA funding to purchase an IBM 7094 for research use.  One of the early focuses of Project MAC would be the development of a successor to CTSS, Multics, which was to be the first high availability computer system, developed as a part of an industry consortium including General Electric and Bell Laboratories.  In 1966, Scientific American featured Project MAC in the September thematic issue devoted to computer science, that was later published in book form.  At the time, the system was described as having approximately 100 TTY terminals, mostly on campus but with a few in private homes.  Only 30 users could be logged in at the same time.


MIT Artificial Intelligence Laboratory

History The project enlisted students in various classes to use the terminals simultaneously in problem solving, simulations, and multi-terminal communications as tests for the multi-access computing software being developed.  In the late 1960s, Minsky's artificial intelligence group was seeking more space, and was unable to get satisfaction from project director Licklider.  Minsky found that although Project MAC as a single entity could not get the additional space he wanted, he could split off to form his own laboratory and then be entitled to more office space.  As a result, the MIT AI Lab was formed in 1970, and many of Minsky's AI colleagues left Project MAC to join him in the new laboratory, while most of the remaining members went on to form the Laboratory for Computer Science.  Talented programmers such as Richard Stallman, who used TECO to develop EMACS, flourished in the AI Lab during this time.  Those researchers who did not join the smaller AI Lab formed the Laboratory for Computer Science and continued their research into operating systems, programming languages, distributed systems, and the theory of computation.  Two professors, Hal Abelson and Gerald Jay Sussman, chose to remain neutral ��� their group was referred to variously as Switzerland and Project MAC for the next 30 years. [citation needed] Among much else, the AI Lab led to the invention of Lisp machines and their attempted commercialization by two companies in the 1980s: Symbolics and Lisp Machines Inc.  This divided the AI Lab into "camps" which resulted in a hiring away of many of the talented programmers.  The incident inspired Richard Stallman's later work on the GNU Project.  "Nobody had envisioned that the AI lab's hacker group would be wiped out, but it was. " . . .  "That is the basis for the free software movement ��� the experience I had, the life that I've lived at the MIT AI lab ��� to be working on human knowledge, and not be standing in the way of anybody's further using and further disseminating human knowledge".  On the fortieth anniversary of Project MAC's establishment, July 1, 2003, LCS was merged with the AI Lab to form the MIT Computer Science and Artificial Intelligence Laboratory, or CSAIL.  This merger created the largest laboratory (over 600 personnel) on the MIT campus and was regarded as a reuniting of the diversified elements of Project MAC. [according to whom? ] In 2018, CSAIL launched a five-year collaboration program with IFlytek, a company sanctioned the following year for allegedly using its technology for surveillance and human rights abuses in Xinjiang.  In October 2019, MIT announced that it would review its partnerships with sanctioned firms such as iFlyTek and SenseTime.  In April 2020, the agreement with iFlyTek was terminated.  CSAIL moved from the School of Engineering to the newly formed Schwarzman College of Computing by February 2020.


MIT Artificial Intelligence Laboratory

Notable_researchers (Including members and alumni of CSAIL's predecessor laboratories) MacArthur Fellows Tim Berners-Lee, Erik Demaine, Dina Katabi, Daniela L. Rus, Regina Barzilay, Peter Shor, Richard Stallman, and Joshua Tenenbaum Turing Award recipients Leonard M. Adleman, Fernando J. Corbat��, Shafi Goldwasser, Butler W. Lampson, John McCarthy, Silvio Micali, Marvin Minsky, Ronald L. Rivest, Adi Shamir, Barbara Liskov, Michael Stonebraker, and Tim Berners-Lee IJCAI Computers and Thought Award recipients Terry Winograd, Patrick Winston, David Marr, Gerald Jay Sussman, Rodney Brooks Rolf Nevanlinna Prize recipients Madhu Sudan, Peter Shor, Constantinos Daskalakis G��del Prize recipients Shafi Goldwasser (two-time recipient), Silvio Micali, Maurice Herlihy, Charles Rackoff, Johan H��stad, Peter Shor, and Madhu Sudan Grace Murray Hopper Award recipients Robert Metcalfe, Shafi Goldwasser, Guy L. Steele, Jr., Richard Stallman, and W. Daniel Hillis Textbook authors Harold Abelson and Gerald Jay Sussman, Richard Stallman, Thomas H. Cormen, Charles E. Leiserson, Patrick Winston, Ronald L. Rivest, Barbara Liskov, John Guttag, Jerome H. Saltzer, Frans Kaashoek, Clifford Stein, and Nancy Lynch David D. Clark, former chief protocol architect for the Internet; co-author with Jerome H. Saltzer (also a CSAIL member) and David P. Reed of the influential paper "End-to-End Arguments in Systems Design" Eric Grimson, expert on computer vision and its applications to medicine, appointed Chancellor of MIT March 2011 Bob Frankston, co-developer of VisiCalc, the first computer spreadsheet Seymour Papert, inventor of the Logo programming language Joseph Weizenbaum, creator of the ELIZA computer-simulated therapistRobert Metcalfe, who later invented Ethernet at Xerox PARC and later founded 3Com Mark Raibert, who created the robot company Boston Dynamics Drew Houston, co-founder of Dropbox Colin Angle and Helen Greiner who, with previous CSAIL director Rodney Brooks, founded iRobot Jeremy Wertheimer, who developed ITA Software used by travel websites like Kayak and Orbitz Max Krohn, co-founder of OkCupid


MIT Computer Science and Artificial Intelligence Laboratory


Beijing Academy of Artificial Intelligence

Products_and_applications As of 2023[update], BAAI's research focuses on large pre-trained models (LLMs) and open-source AI infrastructure. WuDao (Chinese: ������; pinyin: w��d��o) is a large multimodal pre-trained language model. WuDao 2.0, was announced on 31 May 2022 and has been compared to GPT-3 at the time. But, in comparison, GPT-3 has 175 billion parameters, while WuDao has 1.75 trillion parameters; making it the largest pre-trained model in the world at the time. WuDao was trained on 4.9 terabytes of images and texts (which included 1.2 terabytes of Chinese text and 1.2 terabytes of English text). The chairman of BAAI said that WuDao was an attempt to "create the biggest, most powerful AI model possible"; although direct comparisons between models based on parameter count (i.e. between Wu Dao and GPT-3) do not directly correlate to quality. WuDao has demonstrated ability to perform natural language processing and image recognition, in addition to generation of text and images. The model can not only write essays, poems and couplets in traditional Chinese, it can both generate text based on static images and generate nearly photorealistic images based on natural language descriptions. It has also showed ability to power virtual chat agents and predict the 3D structures of proteins like AlphaFold. FlagAI is an open-source extensible toolkit for large-scale model training and inference. Its goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality. Moreover, its open repository includes not only all source-code, but several pre-trained large models. FlagAI is an approved incubation project at the sandbox level of the Linux Foundation. Jiuding is an AI-computing platform which focuses on supporting AI innovation. As of September 2022 it provides 1000P computation capacity with 400Gbit/s high-speed interconnection per server, and support AI chipsets of different architectures. BAAI's platform also includes code compilers for the different AI architectures. MetaWorm is a computational model of the Caenorhabditis elegans (C. elegans) nematode simulating the worm's nervous system along with a "digital body" simulation in real-time. MetaWorm 1.0 exhibits behaviours that parallel C. elegans in the real world.


Statistics

Statistics Statistics (from German: Statistik, orig.  "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.  In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied.  Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal".  Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.  When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples.  Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole.  An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.  In contrast, an observational study does not involve experimental manipulation.  Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e. g. , observational errors, sampling variation).  Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other.  Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.  A standard statistical procedure involves the collection of data leading to a test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model.  A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets.  Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test.  Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual relationship between populations is missed giving a "false negative").  Multiple problems have come to be associated with this framework, ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.  Statistical measurement processes are also prone to error in regards to the data that they generate.  Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e. g.


Statistics

Statistics , blunder, such as when an analyst reports incorrect units) can also occur.  The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.


Statistics

Statistical_data When full census data cannot be collected, statisticians collect sample data by developing specific experiment designs and survey samples.  Statistics itself also provides tools for prediction and forecasting through statistical models.  To use a sample as a guide to an entire population, it is important that it truly represents the overall population.  Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole.  A major problem lies in determining the extent that the sample chosen is actually representative.  Statistics offers methods to estimate and correct for any bias within the sample and data collection procedures.  There are also methods of experimental design that can lessen these issues at the outset of a study, strengthening its capability to discern truths about the population.  Sampling theory is part of the mathematical discipline of probability theory.  Probability is used in mathematical statistics to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures.  The use of any statistical method is valid when the system or population under consideration satisfies the assumptions of the method.  The difference in point of view between classic probability theory and sampling theory is, roughly, that probability theory starts from the given parameters of a total population to deduce probabilities that pertain to samples.  Statistical inference, however, moves in the opposite direction���inductively inferring from samples to the parameters of a larger or total population.  A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables.  There are two major types of causal statistical studies: experimental studies and observational studies.  In both types of studies, the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed.  The difference between the two types lies in how the study is actually conducted.  Each can be very effective.  An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements with different levels using the same procedure to determine if the manipulation has modified the values of the measurements.  In contrast, an observational study does not involve experimental manipulation.  Instead, data are gathered and correlations between predictors and response are investigated.  While the tools of data analysis work best on data from randomized studies, they are also applied to other kinds of data���like natural experiments and observational studies���for which a statistician would use a modified, more structured estimation method (e. g. , Difference in differences estimation and instrumental variables, among many others) that produce consistent estimators.  The basic steps of a statistical experiment are: Planning the research, including finding the number of replicates of the study, using the following information: preliminary estimates regarding the size of treatment effects, alternative hypotheses, and the estimated experimental variability.  Consideration of the selection of experimental subjects and the ethics of research is necessary.


Statistics

Statistical_data Statisticians recommend that experiments compare (at least) one new treatment with a standard treatment or control, to allow an unbiased estimate of the difference in treatment effects.  Design of experiments, using blocking to reduce the influence of confounding variables, and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error.  At this stage, the experimenters and statisticians write the experimental protocol that will guide the performance of the experiment and which specifies the primary analysis of the experimental data.  Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol.  Further examining the data set in secondary analyses, to suggest new hypotheses for future study.  Documenting and presenting the results of the study. Experiments on human behavior have special concerns.  The famous Hawthorne study examined changes to the working environment at the Hawthorne plant of the Western Electric Company.  The researchers were interested in determining whether increased illumination would increase the productivity of the assembly line workers.  The researchers first measured the productivity in the plant, then modified the illumination in an area of the plant and checked if the changes in illumination affected productivity.  It turned out that productivity indeed improved (under the experimental conditions).  However, the study is heavily criticized today for errors in experimental procedures, specifically for the lack of a control group and blindness.  The Hawthorne effect refers to finding that an outcome (in this case, worker productivity) changed due to observation itself.  Those in the Hawthorne study became more productive not because the lighting was changed but because they were being observed.  An example of an observational study is one that explores the association between smoking and lung cancer.  This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis.  In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a cohort study, and then look for the number of cases of lung cancer in each group.  A case-control study is another type of observational study in which people with and without the outcome of interest (e. g.  lung cancer) are invited to participate and their exposure histories are collected.  Various attempts have been made to produce a taxonomy of levels of measurement.  The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales.  Nominal measurements do not have meaningful rank order among values, and permit any one-to-one (injective) transformation.  Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation.  Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit), and permit any linear transformation.  Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.


Statistics

Statistical_data Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature.  Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating-point arithmetic.  But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented.  Other categorizations have been proposed.  For example, Mosteller and Tukey (1977) distinguished grades, ranks, counted fractions, counts, amounts, and balances.  Nelder (1990) described continuous counts, continuous ratios, count ratios, and categorical modes of data.  (See also: Chrisman (1998), van den Berg (1991). ) The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions.  "The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations.  Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer.


Adversarial machine learning

Attack_modalities Attacks against (supervised) machine learning algorithms have been categorized along three primary axes: influence on the classifier, the security violation and their specificity.  Classifier influence: An attack can influence the classifier by disrupting the classification phase.  This may be preceded by an exploration phase to identify vulnerabilities.  The attacker's capabilities might be restricted by the presence of data manipulation constraints.  Security violation: An attack can supply malicious data that gets classified as legitimate.  Malicious data supplied during training can cause legitimate data to be rejected after training.  Specificity: A targeted attack attempts to allow a specific intrusion/disruption.  Alternatively, an indiscriminate attack creates general mayhem. This taxonomy has been extended into a more comprehensive threat model that allows explicit assumptions about the adversary's goal, knowledge of the attacked system, capability of manipulating the input data/system components, and on attack strategy.  This taxonomy has further been extended to include dimensions for defense strategies against adversarial attacks.  Below are some of the most commonly encountered attack scenarios.  Poisoning consists of contaminating the training dataset with data designed to increase errors in the output.  Given that learning algorithms are shaped by their training datasets, poisoning can effectively reprogram algorithms with potentially malicious intent.  Concerns have been raised especially for user-generated training data, e. g.  for content recommendation or natural language models.  The ubiquity of fake accounts offers many opportunities for poisoning.  Facebook reportedly removes around 7 billion fake accounts per year.  Poisoning has been reported as the leading concern for industrial applications.  On social medias, disinformation campaigns attempt to bias recommendation and moderation algorithms, to push certain content over others.  A particular case of data poisoning is the backdoor attack, which aims to teach a specific behavior for inputs with a given trigger, e. g.  a small defect on images, sounds, videos or texts.  For instance, intrusion detection systems are often trained using collected data.  An attacker may poison this data by injecting malicious samples during operation that subsequently disrupt retraining.  As machine learning is scaled, it often relies on multiple computing machines.  In federated learning, for instance, edge devices collaborate with a central server, typically by sending gradients or model parameters.  However, some of these devices may deviate from their expected behavior, e. g.  to harm the central server's model or to bias algorithms towards certain behaviors (e. g. , amplifying the recommendation of disinformation content).  On the other hand, if the training is performed on a single machine, then the model is very vulnerable to a failure of the machine, or an attack on the machine; the machine is a single point of failure.  In fact, the machine owner may themselves insert provably undetectable backdoors.  The current leading solutions to make (distributed) learning algorithms provably resilient to a minority of malicious (a. k. a.  Byzantine) participants are based on robust gradient aggregation rules.  The robust aggregation rules do not always work especially when the data across participants has a non-iid distribution.


Adversarial machine learning

Attack_modalities Nevertheless, in the context of heterogeneous honest participants, such as users with different consumption habits for recommendation algorithms or writing styles for language models, there are provable impossibility theorems on what any robust learning algorithm can guarantee.  Evasion attacks consist of exploiting the imperfection of a trained model.  For instance, spammers and hackers often attempt to evade detection by obfuscating the content of spam emails and malware.  Samples are modified to evade detection; that is, to be classified as legitimate.  This does not involve influence over the training data.  A clear example of evasion is image-based spam in which the spam content is embedded within an attached image to evade textual analysis by anti-spam filters.  Another example of evasion is given by spoofing attacks against biometric verification systems.  Evasion attacks can be generally split into two different categories: black box attacks and white box attacks.  Model extraction involves an adversary probing a black box machine learning system in order to extract the data it was trained on.  This can cause issues when either the training data or the model itself is sensitive and confidential.  For example, model extraction could be used to extract a proprietary stock trading model which the adversary could then use for their own financial benefit.  In the extreme case, model extraction can lead to model stealing, which corresponds to extracting a sufficient amount of data from the model to enable the complete reconstruction of the model.  On the other hand, membership inference is a targeted model extraction attack, which infers the owner of a data point, often by leveraging the overfitting resulting from poor machine learning practices.  Concerningly, this is sometimes achievable even without knowledge or access to a target model's parameters, raising security concerns for models trained on sensitive data, including but not limited to medical records and/or personally identifiable information.  With the emergence of transfer learning and public accessibility of many state of the art machine learning models, tech companies are increasingly drawn to create models based on public ones, giving attackers freely accessible information to the structure and type of model being used.


Neural style transfer

Extensions NST has also been extended to videos. Subsequent work improved the speed of NST for images.[clarification needed] In a paper by Fei-Fei Li et al. adopted a different regularized loss metric and accelerated method for training to produce results in real-time (three orders of magnitude faster than Gatys). Their idea was to use not the pixel-based loss defined above but rather a 'perceptual loss' measuring the differences between higher-level layers within the CNN. They used a symmetric encoder-decoder CNN. Training uses a similar loss function to the basic NST method but also regularizes the output for smoothness using a total variation (TV) loss. Once trained, the network may be used to transform an image into the style used during training, using a single feed-forward pass of the network. However the network is restricted to the single style in which it has been trained. In a work by Chen Dongdong et al. they explored the fusion of optical flow information into feedforward networks in order to improve the temporal coherence of the output. Most recently, feature transform based NST methods have been explored for fast stylization that are not coupled to single specific style and enable user-controllable blending of styles, for example the whitening and coloring transform (WCT).


Autoencoder

Applications The two main applications of autoencoders are dimensionality reduction and information retrieval, but modern variations have been applied to other tasks.  Dimensionality reduction was one of the first deep learning applications.  For Hinton's 2006 study, he pretrained a multi-layer autoencoder with a stack of RBMs and then used their weights to initialize a deep autoencoder with gradually smaller hidden layers until hitting a bottleneck of 30 neurons.  The resulting 30 dimensions of the code yielded a smaller reconstruction error compared to the first 30 components of a principal component analysis (PCA), and learned a representation that was qualitatively easier to interpret, clearly separating data clusters.  Representing dimensions can improve performance on tasks such as classification.  Indeed, the hallmark of dimensionality reduction is to place semantically related examples near each other.  If linear activations are used, or only a single sigmoid hidden layer, then the optimal solution to an autoencoder is strongly related to principal component analysis (PCA).  The weights of an autoencoder with a single hidden layer of size p {\displaystyle p} (where p {\displaystyle p} is less than the size of the input) span the same vector subspace as the one spanned by the first p {\displaystyle p} principal components, and the output of the autoencoder is an orthogonal projection onto this subspace.  The autoencoder weights are not equal to the principal components, and are generally not orthogonal, yet the principal components may be recovered from them using the singular value decomposition.  However, the potential of autoencoders resides in their non-linearity, allowing the model to learn more powerful generalizations compared to PCA, and to reconstruct the input with significantly lower information loss.  Information retrieval benefits particularly from dimensionality reduction in that search can become more efficient in certain kinds of low dimensional spaces.  Autoencoders were indeed applied to semantic hashing, proposed by Salakhutdinov and Hinton in 2007.  By training the algorithm to produce a low-dimensional binary code, all database entries could be stored in a hash table mapping binary code vectors to entries.  This table would then support information retrieval by returning all entries with the same binary code as the query, or slightly less similar entries by flipping some bits from the query encoding.  The encoder-decoder architecture, often used in natural language processing and neural networks, can be scientifically applied in the field of SEO (Search Engine Optimization) in various ways: Text Processing: By using an autoencoder, it's possible to compress the text of web pages into a more compact vector representation.  This can help reduce page loading times and improve indexing by search engines.  Noise Reduction: Autoencoders can be used to remove noise from the textual data of web pages.  This can lead to a better understanding of the content by search engines, thereby enhancing ranking in search engine result pages.  Meta Tag and Snippet Generation: Autoencoders can be trained to automatically generate meta tags, snippets, and descriptions for web pages using the page content.


Autoencoder

Applications This can optimize the presentation in search results, increasing the Click-Through Rate (CTR).  Content Clustering: Using an autoencoder, web pages with similar content can be automatically grouped together.  This can help organize the website logically and improve navigation, potentially positively affecting user experience and search engine rankings.  Generation of Related Content: An autoencoder can be employed to generate content related to what is already present on the site.  This can enhance the website's attractiveness to search engines and provide users with additional relevant information.  Keyword Detection: Autoencoders can be trained to identify keywords and important concepts within the content of web pages.  This can assist in optimizing keyword usage for better indexing.  Semantic Search: By using autoencoder techniques, semantic representation models of content can be created.  These models can be used to enhance search engines' understanding of the themes covered in web pages. In essence, the encoder-decoder architecture or autoencoders can be leveraged in SEO to optimize web page content, improve their indexing, and enhance their appeal to both search engines and users.  Another application for autoencoders is anomaly detection.  By learning to replicate the most salient features in the training data under some of the constraints described previously, the model is encouraged to learn to precisely reproduce the most frequently observed characteristics.  When facing anomalies, the model should worsen its reconstruction performance.  In most cases, only data with normal instances are used to train the autoencoder; in others, the frequency of anomalies is small compared to the observation set so that its contribution to the learned representation could be ignored.  After training, the autoencoder will accurately reconstruct "normal" data, while failing to do so with unfamiliar anomalous data.  Reconstruction error (the error between the original data and its low dimensional reconstruction) is used as an anomaly score to detect anomalies.  Recent literature has however shown that certain autoencoding models can, counterintuitively, be very good at reconstructing anomalous examples and consequently not able to reliably perform anomaly detection.  The characteristics of autoencoders are useful in image processing.  One example can be found in lossy image compression, where autoencoders outperformed other approaches and proved competitive against JPEG 2000.  Another useful application of autoencoders in image preprocessing is image denoising.  Autoencoders found use in more demanding contexts such as medical imaging where they have been used for image denoising as well as super-resolution.  In image-assisted diagnosis, experiments have applied autoencoders for breast cancer detection and for modelling the relation between the cognitive decline of Alzheimer's disease and the latent features of an autoencoder trained with MRI.  In 2019 molecules generated with variational autoencoders were validated experimentally in mice.  Recently, a stacked autoencoder framework produced promising results in predicting popularity of social media posts, which is helpful for online advertising strategies.  Autoencoders have been applied to machine translation, which is usually referred to as neural machine translation (NMT).  Unlike traditional autoencoders, the output does not match the input - it is in another language.


Autoencoder

Applications In NMT, texts are treated as sequences to be encoded into the learning procedure, while on the decoder side sequences in the target language(s) are generated.  Language-specific autoencoders incorporate further linguistic features into the learning procedure, such as Chinese decomposition features.  Machine translation is rarely still done with autoencoders, due to the availability of more effective transformer networks.


Linear discriminant analysis

Applications In addition to the examples given below, LDA is applied in positioning and product management.  In bankruptcy prediction based on accounting ratios and other financial variables, linear discriminant analysis was the first statistical method applied to systematically explain which firms entered bankruptcy vs.  survived.  Despite limitations including known nonconformance of accounting ratios to the normal distribution assumptions of LDA, Edward Altman's 1968 model is still a leading model in practical applications.  In computerised face recognition, each face is represented by a large number of pixel values.  Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification.  Each of the new dimensions is a linear combination of pixel values, which form a template.  The linear combinations obtained using Fisher's linear discriminant are called Fisher faces, while those obtained using the related principal component analysis are called eigenfaces.  In marketing, discriminant analysis was once often used to determine the factors which distinguish different types of customers and/or products on the basis of surveys or other forms of collected data.  Logistic regression or other methods are now more commonly used.  The use of discriminant analysis in marketing can be described by the following steps: Formulate the problem and gather data���Identify the salient attributes consumers use to evaluate products in this category���Use quantitative marketing research techniques (such as surveys) to collect data from a sample of potential customers concerning their ratings of all the product attributes.  The data collection stage is usually done by marketing research professionals.  Survey questions ask the respondent to rate a product from one to five (or 1 to 7, or 1 to 10) on a range of attributes chosen by the researcher.  Anywhere from five to twenty attributes are chosen.  They could include things like: ease of use, weight, accuracy, durability, colourfulness, price, or size.  The attributes chosen will vary depending on the product being studied.  The same question is asked about all the products in the study.  The data for multiple products is codified and input into a statistical program such as R, SPSS or SAS.  (This step is the same as in Factor analysis).  Estimate the Discriminant Function Coefficients and determine the statistical significance and validity���Choose the appropriate discriminant analysis method.  The direct method involves estimating the discriminant function so that all the predictors are assessed simultaneously.  The stepwise method enters the predictors sequentially.  The two-group method should be used when the dependent variable has two categories or states.  The multiple discriminant method is used when the dependent variable has three or more categorical states.  Use Wilks's Lambda to test for significance in SPSS or F stat in SAS.  The most common method used to test validity is to split the sample into an estimation or analysis sample, and a validation or holdout sample.  The estimation sample is used in constructing the discriminant function.  The validation sample is used to construct a classification matrix which contains the number of correctly classified and incorrectly classified cases.


Linear discriminant analysis

Applications The percentage of correctly classified cases is called the hit ratio.  Plot the results on a two dimensional map, define the dimensions, and interpret the results.  The statistical program (or a related module) will map the results.  The map will plot each product (usually in two-dimensional space).  The distance of products to each other indicate either how different they are.  The dimensions must be labelled by the researcher.  This requires subjective judgement and is often very challenging.  See perceptual mapping. The main application of discriminant analysis in medicine is the assessment of severity state of a patient and prognosis of disease outcome.  For example, during retrospective analysis, patients are divided into groups according to severity of disease ��� mild, moderate and severe form.  Then results of clinical and laboratory analyses are studied in order to reveal variables which are statistically different in studied groups.  Using these variables, discriminant functions are built which help to objectively classify disease in a future patient into mild, moderate or severe form.  In biology, similar principles are used in order to classify and define groups of different biological objects, for example, to define phage types of Salmonella enteritidis based on Fourier transform infrared spectra, to detect animal source of Escherichia coli studying its virulence factors etc.  This method can be used to separate the alteration zones[clarification needed].  For example, when different data from various zones are available, discriminant analysis can find the pattern within the data and classify it effectively.


Nearest neighbor search

Applications The nearest neighbour search problem arises in numerous fields of application, including: Pattern recognition ��� in particular for optical character recognition Statistical classification ��� see k-nearest neighbor algorithm Computer vision ��� for point cloud registration Computational geometry ��� see Closest pair of points problem Cryptanalysis ��� for lattice problem Databases ��� e.g. content-based image retrieval Coding theory ��� see maximum likelihood decoding Semantic Search Data compression ��� see MPEG-2 standard Robotic sensing Recommendation systems, e.g. see Collaborative filtering Internet marketing ��� see contextual advertising and behavioral targeting DNA sequencing Spell checking ��� suggesting correct spelling Plagiarism detection Similarity scores for predicting career paths of professional athletes. Cluster analysis ��� assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense, usually based on Euclidean distance Chemical similarity Sampling-based motion planning


One-class classification

One-class classification In machine learning, one-class classification (OCC), also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary. This is different from and more difficult than the traditional classification problem, which tries to distinguish between two or more classes with the training set containing objects from all the classes. Examples include the monitoring of helicopter gearboxes, motor failure prediction, or the operational status of a nuclear plant as 'normal': In this scenario, there are few, if any, examples of catastrophic system states; only the statistics of normal operation are known. While many of the above approaches focus on the case of removing a small number of outliers or anomalies, one can also learn the other extreme, where the single class covers a small coherent subset of the data, using an information bottleneck approach.


Mixture model

Examples Financial returns often behave differently in normal situations and during crisis times.  A mixture model for return data seems reasonable.  Sometimes the model used is a jump-diffusion model, or as a mixture of two normal distributions.  See Financial economics �� Challenges and criticism and Financial risk management �� Banking for further context.  Assume that we observe the prices of N different houses.  Different types of houses in different neighborhoods will have vastly different prices, but the price of a particular type of house in a particular neighborhood (e. g. , three-bedroom house in moderately upscale neighborhood) will tend to cluster fairly closely around the mean.  One possible model of such prices would be to assume that the prices are accurately described by a mixture model with K different components, each distributed as a normal distribution with unknown mean and variance, with each component specifying a particular combination of house type/neighborhood.  Fitting this model to observed prices, e. g. , using the expectation-maximization algorithm, would tend to cluster the prices according to house type/neighborhood and reveal the spread of prices in each type/neighborhood.  (Note that for values such as prices or incomes that are guaranteed to be positive and which tend to grow exponentially, a log-normal distribution might actually be a better model than a normal distribution. ) Assume that a document is composed of N different words from a total vocabulary of size V, where each word corresponds to one of K possible topics.  The distribution of such words could be modelled as a mixture of K different V-dimensional categorical distributions.  A model of this sort is commonly termed a topic model.  Note that expectation maximization applied to such a model will typically fail to produce realistic results, due (among other things) to the excessive number of parameters.  Some sorts of additional assumptions are typically necessary to get good results.  Typically two sorts of additional components are added to the model: A prior distribution is placed over the parameters describing the topic distributions, using a Dirichlet distribution with a concentration parameter that is set significantly below 1, so as to encourage sparse distributions (where only a small number of words have significantly non-zero probabilities).  Some sort of additional constraint is placed over the topic identities of words, to take advantage of natural clustering. For example, a Markov chain could be placed on the topic identities (i. e. , the latent variables specifying the mixture component of each observation), corresponding to the fact that nearby words belong to similar topics.  (This results in a hidden Markov model, specifically one where a prior distribution is placed over state transitions that favors transitions that stay in the same state. ) Another possibility is the latent Dirichlet allocation model, which divides up the words into D different documents and assumes that in each document only a small number of topics occur with any frequency. The following example is based on an example in Christopher M.  Bishop, Pattern Recognition and Machine Learning.


Mixture model

Examples Imagine that we are given an N��N black-and-white image that is known to be a scan of a hand-written digit between 0 and 9, but we don't know which digit is written.  We can create a mixture model with K = 10 {\displaystyle K=10} different components, where each component is a vector of size N 2 {\displaystyle N^{2}} of Bernoulli distributions (one per pixel).  Such a model can be trained with the expectation-maximization algorithm on an unlabeled set of hand-written digits, and will effectively cluster the images according to the digit being written.  The same model could then be used to recognize the digit of another image simply by holding the parameters constant, computing the probability of the new image for each possible digit (a trivial calculation), and returning the digit that generated the highest probability.  Mixture models apply in the problem of directing multiple projectiles at a target (as in air, land, or sea defense applications), where the physical and/or statistical characteristics of the projectiles differ within the multiple projectiles.  An example might be shots from multiple munitions types or shots from multiple locations directed at one target.  The combination of projectile types may be characterized as a Gaussian mixture model.  Further, a well-known measure of accuracy for a group of projectiles is the circular error probable (CEP), which is the number R such that, on average, half of the group of projectiles falls within the circle of radius R about the target point.  The mixture model can be used to determine (or estimate) the value R.  The mixture model properly captures the different types of projectiles.  The financial example above is one direct application of the mixture model, a situation in which we assume an underlying mechanism so that each observation belongs to one of some number of different sources or categories.  This underlying mechanism may or may not, however, be observable.  In this form of mixture, each of the sources is described by a component probability density function, and its mixture weight is the probability that an observation comes from this component.  In an indirect application of the mixture model we do not assume such a mechanism.  The mixture model is simply used for its mathematical flexibilities.  For example, a mixture of two normal distributions with different means may result in a density with two modes, which is not modeled by standard parametric distributions.  Another example is given by the possibility of mixture distributions to model fatter tails than the basic Gaussian ones, so as to be a candidate for modeling more extreme events.  When combined with dynamical consistency, this approach has been applied to financial derivatives valuation in presence of the volatility smile in the context of local volatility models.  This defines our application.  The mixture model-based clustering is also predominantly used in identifying the state of the machine in predictive maintenance.  Density plots are used to analyze the density of high dimensional features.


Mixture model

Examples If multi-model densities are observed, then it is assumed that a finite set of densities are formed by a finite set of normal mixtures.  A multivariate Gaussian mixture model is used to cluster the feature data into k number of groups where k represents each state of the machine.  The machine state can be a normal state, power off state, or faulty state.  Each formed cluster can be diagnosed using techniques such as spectral analysis.  In the recent years, this has also been widely used in other areas such as early fault detection.  In image processing and computer vision, traditional image segmentation models often assign to one pixel only one exclusive pattern.  In fuzzy or soft segmentation, any pattern can have certain "ownership" over any single pixel.  If the patterns are Gaussian, fuzzy segmentation naturally results in Gaussian mixtures.  Combined with other analytic or geometric tools (e. g. , phase transitions over diffusive boundaries), such spatially regularized mixture models could lead to more realistic and computationally efficient segmentation methods.  Probabilistic mixture models such as Gaussian mixture models (GMM) are used to resolve point set registration problems in image processing and computer vision fields.  For pair-wise point set registration, one point set is regarded as the centroids of mixture models, and the other point set is regarded as data points (observations).  State-of-the-art methods are e. g.  coherent point drift (CPD) and Student's t-distribution mixture models (TMM).  The result of recent research demonstrate the superiority of hybrid mixture models (e. g.  combining Student's t-Distritubtion and Watson distribution/Bingham distribution to model spatial positions and axes orientations separately) compare to CPD and TMM, in terms of inherent robustness, accuracy and discriminative capacity.


Data mining

Data mining Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.  Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.  Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.  Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.  The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.  It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e. g. , machine learning) and business intelligence.  The book Data Mining: Practical Machine Learning Tools and Techniques with Java (which covers mostly machine learning material) was originally to be named Practical Machine Learning, and the term data mining was only added for marketing reasons.  Often the more general terms (large scale) data analysis and analytics���or, when referring to actual methods, artificial intelligence and machine learning���are more appropriate.  The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).  This usually involves using database techniques such as spatial indices.  These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics.  For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system.  Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, although they do belong to the overall KDD process as additional steps.  The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e. g. , analyzing the effectiveness of a marketing campaign, regardless of the amount of data.  In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.  The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered.


Data mining

Data mining These methods can, however, be used in creating new hypotheses to test against the larger data populations.


Data mining

Background The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns. in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.


Data mining

Privacy_concerns_and_ethics While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to user behavior (ethical and otherwise).  The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics.  In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.  Data mining requires data preparation which uncovers information or patterns which compromise confidentiality and privacy obligations.  A common way for this to occur is through data aggregation.  Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent).  This is not data mining per se, but a result of the preparation of data before���and for the purposes of���the analysis.  The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.  It is recommended[according to whom? ] to be aware of the following before data are collected: The purpose of the data collection and any (known) data mining projects.  How the data will be used.  Who will be able to mine the data and use the data and their derivatives.  The status of security surrounding access to the data.  How collected data can be updated. Data may also be modified so as to become anonymous, so that individuals may not readily be identified.  However, even "anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.  The inadvertent revelation of personally identifiable information leading to the provider violates Fair Information Practices.  This indiscretion can cause financial, emotional, or bodily harm to the indicated individual.  In one instance of privacy violation, the patrons of Walgreens filed a lawsuit against the company in 2011 for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies.  Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers.  However, the U. S. ���E. U.  Safe Harbor Principles, developed between 1998 and 2000, currently effectively expose European users to privacy exploitation by U. S.  companies.  As a consequence of Edward Snowden's global surveillance disclosure, there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency, and attempts to reach an agreement with the United States have failed.


Data mining

Privacy_concerns_and_ethics In the United Kingdom in particular there have been cases of corporations using data mining as a way to target certain groups of customers forcing them to pay unfairly high prices.  These groups tend to be people of lower socio-economic status who are not savvy to the ways they can be exploited in digital market places.  In the United States, privacy concerns have been addressed by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA).  The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses.  According to an article in Biotech Business Week, "'[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC.  More importantly, the rule's goal of protection through informed consent is approach a level of incomprehensibility to average individuals. " This underscores the necessity for data anonymity in data aggregation and mining practices.  U. S.  information privacy legislation such as HIPAA and the Family Educational Rights and Privacy Act (FERPA) applies only to the specific areas that each such law addresses.  The use of data mining by the majority of businesses in the U. S.  is not controlled by any legislation.


Nonlinear dimensionality reduction

Other_algorithms Relational perspective map is a multidimensional scaling algorithm.  The algorithm finds a configuration of data points on a manifold by simulating a multi-particle dynamic system on a closed manifold, where data points are mapped to particles and distances (or dissimilarity) between data points represent a repulsive force.  As the manifold gradually grows in size the multi-particle system cools down gradually and converges to a configuration that reflects the distance information of the data points.  Relational perspective map was inspired by a physical model in which positively charged particles move freely on the surface of a ball.  Guided by the Coulomb force between particles, the minimal energy configuration of the particles will reflect the strength of repulsive forces between the particles.  The Relational perspective map was introduced in.  The algorithm firstly used the flat torus as the image manifold, then it has been extended (in the software VisuMap to use other types of closed manifolds, like the sphere, projective space, and Klein bottle, as image manifolds.  Contagion maps use multiple contagions on a network to map the nodes as a point cloud.  In the case of the Global cascades model the speed of the spread can be adjusted with the threshold parameter t ��� [ 0 , 1 ] {\displaystyle t\in [0,1]} .  For t = 0 {\displaystyle t=0} the contagion map is equivalent to the Isomap algorithm.  Curvilinear component analysis (CCA) looks for the configuration of points in the output space that preserves original distances as much as possible while focusing on small distances in the output space (conversely to Sammon's mapping which focus on small distances in original space).  It should be noticed that CCA, as an iterative learning algorithm, actually starts with focus on large distances (like the Sammon algorithm), then gradually change focus to small distances.  The small distance information will overwrite the large distance information, if compromises between the two have to be made.  The stress function of CCA is related to a sum of right Bregman divergences.  CDA trains a self-organizing neural network to fit the manifold and seeks to preserve geodesic distances in its embedding.  It is based on Curvilinear Component Analysis (which extended Sammon's mapping), but uses geodesic distances instead.  Diffeomorphic Dimensionality Reduction or Diffeomap learns a smooth diffeomorphic mapping which transports the data onto a lower-dimensional linear subspace.  The methods solves for a smooth time indexed vector field such that flows along the field which start at the data points will end at a lower-dimensional linear subspace, thereby attempting to preserve pairwise differences under both the forward and inverse mapping.  Manifold alignment takes advantage of the assumption that disparate data sets produced by similar generating processes will share a similar underlying manifold representation.  By learning projections from each original space to the shared manifold, correspondences are recovered and knowledge from one domain can be transferred to another.  Most manifold alignment techniques consider only two data sets, but the concept extends to arbitrarily many initial data sets.