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EasyChair

EasyChair is a web-based conference management software system. It has been used since 2002 in the scientific community for tasks such as organising research paper submission and review. In 2012, EasyChair added an open access online publication service for conference proceedings. == Description == EasyChair is a paid web-based conference management software system used, among other tasks, to organize paper submission and review, similar to other event management system software such as OpenConf. EasyChair used to be run by the Department of Computer Science at the University of Manchester but now it is a commercial service, owned by EasyChair Ltd. in Stockport (established 2016). EasyChair used to be free, for standard service, but as of 2022, only minimal services are free. The EasyChair website also provides an open access online publication service for conference proceedings. When launched in 2012, the service was for computer science only, but in 2016 it was expanded to all sciences. == History == The EasyChair software has been in continuous development since 2002. As of 2015, the code base consists of nearly 300,000 lines of code, and it has been used by more than 41,000 conferences. More than two and a half million users in the scientific community reported using it in 2019.
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MIT Computer Science and Artificial Intelligence Laboratory

Computer Science and Artificial Intelligence Laboratory (CSAIL) is a research institute at the Massachusetts Institute of Technology (MIT) formed by the 2003 merger of the Laboratory for Computer Science (LCS) and the Artificial Intelligence Laboratory (AI Lab). Housed within the Ray and Maria Stata Center, CSAIL is the largest on-campus laboratory as measured by research scope and membership. It is part of the Schwarzman College of Computing but is also overseen by the MIT Vice President of Research. == Research activities == CSAIL's research activities are organized around a number of semi-autonomous research groups, each of which is headed by one or more professors or research scientists. These groups are divided up into seven general areas of research: Artificial intelligence Computational biology Graphics and vision Language and learning Theory of computation Robotics Systems (includes computer architecture, databases, distributed systems, networks and networked systems, operating systems, programming methodology, and software engineering, among others) == 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 the 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 the late 1950s. === Project MAC === 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. Corbató, 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, Corbató 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. 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. === AI Lab and LCS === 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. 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. === CSAIL === 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. 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. == Offices == From 1963 to 2004, Project MAC, LCS, the AI Lab, and CSAIL had their offices at 545 Technology Square, taking over more and more floors of the building over the years. In 2004, CSAIL moved to the new Ray and Maria Stata Center, which was built specifically to house it and other departments. == Outreach activities == The IMARA (from Swahili word for "power") group sponsors a variety of outreach programs that bridge the global digital divide. Its aim is to find and implement long-term, sustainable solutions which will increase the availability of educational technology and resources to domestic and international communities. These projects are run under the aegis of CSAIL and staffed by MIT volunteers who give training, install and donate computer setups in greater Boston, Massachusetts, Kenya, Native American Indian tribal reservations in the American Southwest such as the Navajo Nation, the Middle East, and Fiji Islands. The CommuniTech project strives to empower under-served communities through sustainable technology and education and does this through the MIT Used Computer Factory (UCF), providing refurbished computers to under-served families, and through the Families Accessing Computer Technology (FACT) classes, it trains those families to become familiar and comfortable with computer technology. == 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. Corbató, Shafi Goldwasser, Butler W. Lampson, John McCarthy, Silvio Micali, Marvin Minsky, Ronald L. Rivest, Adi Shamir, Barbara Liskov, and Michael Stonebraker 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 Gödel Prize recipients Shafi Goldwasser (two-time recipient), Silvio Micali, Maurice Herlihy, Charles Rackoff, Johan Håstad, 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.
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AlphaGeometry

AlphaGeometry is an artificial intelligence (AI) program that can solve hard problems in Euclidean geometry. The system comprises a data-driven large language model (LLM) and a rule-based symbolic engine (Deductive Database Arithmetic Reasoning). It was developed by DeepMind, a subsidiary of Google. The program solved 25 geometry problems out of 30 from the International Mathematical Olympiad (IMO) under competition time limits—a performance almost as good as the average human gold medallist. For comparison, the previous AI program, called Wu's method, managed to solve only 10 problems. DeepMind published a paper about AlphaGeometry in the peer-reviewed journal Nature on 17 January 2024. AlphaGeometry was featured in MIT Technology Review on the same day. Traditional geometry programs are symbolic engines that rely exclusively on human-coded rules to generate rigorous proofs, which makes them lack flexibility in unusual situations. AlphaGeometry combines such a symbolic engine with a specialized large language model trained on synthetic data of geometrical proofs. When the symbolic engine doesn't manage to find a formal and rigorous proof on its own, it solicits the large language model, which suggests a geometrical construct to move forward. However, it is unclear how applicable this method is to other domains of mathematics or reasoning, because symbolic engines rely on domain-specific rules and because of the need for synthetic data. == AlphaGeometry 2 == AlphaGeometry 2 is an improved version of AlphaGeometry, published on February 5, 2025. They added more features to the representation language to describe more geometry problems that involve movements of objects, and problems containing linear equations of angles, ratios, and distances. They targeted IMO geometry questions from 2000 to 2024. The expanded representation language allowed them to cover 88% of the questions. It uses Gemini finetuned on a synthetically generated dataset of problems and solutions in the representation language. The model is used for making auxiliary constructions like lines and points, to help the tree search. It is also used for autoformalization, i.e. converting a problem in English to a problem in the representation language.
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Spark NLP

Spark NLP is an open-source text processing library for advanced natural language processing for the Python, Java and Scala programming languages. The library is built on top of Apache Spark and its Spark ML library. Its purpose is to provide an API for natural language processing pipelines that implement recent academic research results as production-grade, scalable, and trainable software. The library offers pre-trained neural network models, pipelines, and embeddings, as well as support for training custom models. == Features == The design of the library makes use of the concept of a pipeline which is an ordered set of text annotators. Out of the box annotators include, tokenizer, normalizer, stemming, lemmatizer, regular expression, TextMatcher, chunker, DateMatcher, SentenceDetector, DeepSentenceDetector, POS tagger, ViveknSentimentDetector, sentiment analysis, named entity recognition, conditional random field annotator, deep learning annotator, spell checking and correction, dependency parser, typed dependency parser, document classification, and language detection. The Models Hub is a platform for sharing open-source as well as licensed pre-trained models and pipelines. It includes pre-trained pipelines with tokenization, lemmatization, part-of-speech tagging, and named entity recognition that exist for more than thirteen languages; word embeddings including GloVe, ELMo, BERT, ALBERT, XLNet, Small BERT, and ELECTRA; sentence embeddings including Universal Sentence Embeddings (USE) and Language Agnostic BERT Sentence Embeddings (LaBSE). It also includes resources and pre-trained models for more than two hundred languages. Spark NLP base code includes support for East Asian languages such as tokenizers for Chinese, Japanese, Korean; for right-to-left languages such as Urdu, Farsi, Arabic, Hebrew and pre-trained multilingual word and sentence embeddings such as LaUSE and a translation annotator. == Usage in healthcare == Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction. The library offers access to several clinical and biomedical transformers: JSL-BERT-Clinical, BioBERT, ClinicalBERT, GloVe-Med, GloVe-ICD-O. It also includes over 50 pre-trained healthcare models, that can recognize the entities such as clinical, drugs, risk factors, anatomy, demographics, and sensitive data. == Spark OCR == Spark OCR is another commercial extension of Spark NLP for optical character recognition (OCR) from images, scanned PDF documents, and DICOM files. It is a software library built on top of Apache Spark. It provides several image pre-processing features for improving text recognition results such as adaptive thresholding and denoising, skew detection & correction, adaptive scaling, layout analysis and region detection, image cropping, removing background objects. Due to the tight coupling between Spark OCR and Spark NLP, users can combine NLP and OCR pipelines for tasks such as extracting text from images, extracting data from tables, recognizing and highlighting named entities in PDF documents or masking sensitive text in order to de-identify images. Several output formats are supported by Spark OCR such as PDF, images, or DICOM files with annotated or masked entities, digital text for downstream processing in Spark NLP or other libraries, structured data formats (JSON and CSV), as files or Spark data frames. Users can also distribute the OCR jobs across multiple nodes in a Spark cluster. == License and availability == Spark NLP is licensed under the Apache 2.0 license. The source code is publicly available on GitHub as well as documentation and a tutorial. Prebuilt versions of Spark NLP are available in PyPi and Anaconda Repository for Python development, in Maven Central for Java & Scala development, and in Spark Packages for Spark development. == Award == In March 2019, Spark NLP received Open Source Award for its contributions in natural language processing in Python, Java, and Scala.
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Prequel (mobile application)

Prequel, Inc. is an American technology company and mobile app developer known for developing the Prequel mobile application, which enables editing photos and videos with filters and effects generated using artificial intelligence. Prequel was founded in 2018 by Serge Aliseenko and Timur Khabirov, who currently serves as the company's CEO. It is headquartered in New York City. As of August 2022, it had been downloaded more than 100 million times. == History == In 2016, entrepreneur Timur Khabirov and investor Serge Aliseenko registered a US corporation named AIAR Labs Inc, which was developing AR solutions as an outsourced contractor. Of several proprietary products, Prequel was selected for beta-testing as a product focused on editing photos and videos. In 2018, Prequel was released on the Apple App Store. The launch cost $3 million USD, financed with the founders’ personal funds. The first release included approximately 10 filters for photos and the same amount of effects that augmented images with rose petals, rain and snow, VHS and film reel simulations, glitch, grain, sun puddles, and lomography. By June 2020, the app had also been released for Android. In 2021, Prequel founders Timur Khabirov and Serge Aliseenko launched a venture studio for startups working with artificial, computer vision, and AR-based visual art. In December 2022, Prequel reached the number 14 slot on the global rankings for Apple App Store’s Top Charts and the number 5 slot on the App Store’s U.S. charts. In March 2023, Prequel launched a new app called Artique, which is an AI-powered image editing app for businesses. Artique provides advertising and marketing graphic design using ready-made templates that users can customize, while giving suggestions and visual cues through artificial intelligence. Prequel was also one of the companies participating in discussions about artificial intelligence at SXSW 2023. == Features == Prequel describes its app as an "Aesthetic Pic Editor. The app uses artificial intelligence to create and edit content. Prequel can be used to touch up faces on images and videos and can also tie various decorative elements to certain points on the human body and face. Prequel filters include the "Cartoon" filter, which converts selfies into cartoon-style pictures. Other filters include Kidcore, Dust, Grain, Fisheye, Retro Style, Miami, Disco, and VHS-style filters, as well as the ability to create Renaissance-style pictures. Prequel also gives users the ability to apply color correction tools and to make moving images with 3D effects out of 2D images. Prequel allows users to take photos and videos directly through the app and apply filters and effects in real time. The app also comes with manual editing options for photos, such as adjusting the brightness and/or exposure and cropping photos, as well as an option to automatically apply adjustments. The Prequel app uses the Core ML, MNN, and TFLight frameworks to work with its neural networks. Some AI solutions are launched server-side, and some on the user's mobile device. A resulting photo or video edited with the app is called "a prequel." The app daily generates over 2 million such prequels, which are published by users in Instagram, TikTok, and other social media. As of 2022, the app has more than 800 filters and effects, along with video templates and support for GIFs and stickers. Prequel is free-to-use, but has a premium version that gives users access to more effects, filters, and beauty tools. Since its launch in 2018, Prequel has been downloaded more than 100 million times.
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AlphaGo

AlphaGo is a computer program that plays the board game Go. It was developed by the London-based DeepMind Technologies, an acquired subsidiary of Google. Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master. After retiring from competitive play, AlphaGo Master was succeeded by an even more powerful version known as AlphaGo Zero, which was completely self-taught without learning from human games. AlphaGo Zero was then generalized into a program known as AlphaZero, which played additional games, including chess and shogi. AlphaZero has in turn been succeeded by a program known as MuZero which learns without being taught the rules. AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously acquired by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play. A neural network is trained to identify the best moves and the winning percentages of these moves. This neural network improves the strength of the tree search, resulting in stronger move selection in the next iteration. In October 2015, in a match against Fan Hui, the original AlphaGo became the first computer Go program to beat a human professional Go player without handicap on a full-sized 19×19 board. In March 2016, it beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicap. Although it lost to Lee Sedol in the fourth game, Lee resigned in the final game, giving a final score of 4 games to 1 in favour of AlphaGo. In recognition of the victory, AlphaGo was awarded an honorary 9-dan by the Korea Baduk Association. The lead up and the challenge match with Lee Sedol were documented in a documentary film also titled AlphaGo, directed by Greg Kohs. The win by AlphaGo was chosen by Science as one of the Breakthrough of the Year runners-up on 22 December 2016. At the 2017 Future of Go Summit, the Master version of AlphaGo beat Ke Jie, the number one ranked player in the world at the time, in a three-game match, after which AlphaGo was awarded professional 9-dan by the Chinese Weiqi Association. After the match between AlphaGo and Ke Jie, DeepMind retired AlphaGo, while continuing AI research in other areas. The self-taught AlphaGo Zero achieved a 100–0 victory against the early competitive version of AlphaGo, and its successor AlphaZero was perceived as the world's top player in Go by the end of the 2010s. == History == Go is considered much more difficult for computers to win than other games such as chess, because its strategic and aesthetic nature makes it hard to directly construct an evaluation function, and its much larger branching factor makes it prohibitively difficult to use traditional AI methods such as alpha–beta pruning, tree traversal and heuristic search. Almost two decades after IBM's computer Deep Blue beat world chess champion Garry Kasparov in the 1997 match, the strongest Go programs using artificial intelligence techniques only reached about amateur 5-dan level, and still could not beat a professional Go player without a handicap. In 2012, the software program Zen, running on a four PC cluster, beat Masaki Takemiya (9p) twice at five- and four-stone handicaps. In 2013, Crazy Stone beat Yoshio Ishida (9p) at a four-stone handicap. According to DeepMind's David Silver, the AlphaGo research project was formed around 2014 to test how well a neural network using deep learning can compete at Go. AlphaGo represents a significant improvement over previous Go programs. In 500 games against other available Go programs, including Crazy Stone and Zen, AlphaGo running on a single computer won all but one. In a similar matchup, AlphaGo running on multiple computers won all 500 games played against other Go programs, and 77% of games played against AlphaGo running on a single computer. The distributed version in October 2015 was using 1,202 CPUs and 176 GPUs. === Match against Fan Hui === In October 2015, the distributed version of AlphaGo defeated the European Go champion Fan Hui, a 2-dan (out of 9 dan possible) professional, five to zero. This was the first time a computer Go program had beaten a professional human player on a full-sized board without handicap. The announcement of the news was delayed until 27 January 2016 to coincide with the publication of a paper in the journal Nature describing the algorithms used. === Match against Lee Sedol === AlphaGo played South Korean professional Go player Lee Sedol, ranked 9-dan, one of the best players at Go, with five games taking place at the Four Seasons Hotel in Seoul, South Korea on 9, 10, 12, 13, and 15 March 2016, which were video-streamed live. Out of five games, AlphaGo won four games and Lee won the fourth game which made him recorded as the only human player who beat AlphaGo in all of its 74 official games. AlphaGo ran on Google's cloud computing with its servers located in the United States. The match used Chinese rules with a 7.5-point komi, and each side had two hours of thinking time plus three 60-second byoyomi periods. The version of AlphaGo playing against Lee used a similar amount of computing power as was used in the Fan Hui match. The Economist reported that it used 1,920 CPUs and 280 GPUs. At the time of play, Lee Sedol had the second-highest number of Go international championship victories in the world after South Korean player Lee Chang-ho who kept the world championship title for 16 years. Since there is no single official method of ranking in international Go, the rankings may vary among the sources. While he was ranked top sometimes, some sources ranked Lee Sedol as the fourth-best player in the world at the time. AlphaGo was not specifically trained to face Lee nor was designed to compete with any specific human players. The first three games were won by AlphaGo following resignations by Lee. However, Lee beat AlphaGo in the fourth game, winning by resignation at move 180. AlphaGo then continued to achieve a fourth win, winning the fifth game by resignation. The prize was US$1 million. Since AlphaGo won four out of five and thus the series, the prize will be donated to charities, including UNICEF. Lee Sedol received $150,000 for participating in all five games and an additional $20,000 for his win in Game 4. In June 2016, at a presentation held at a university in the Netherlands, Aja Huang, one of the Deep Mind team, revealed that they had patched the logical weakness that occurred during the 4th game of the match between AlphaGo and Lee, and that after move 78 (which was dubbed the "divine move" by many professionals), it would play as intended and maintain Black's advantage. Before move 78, AlphaGo was leading throughout the game, but Lee's move caused the program's computing powers to be diverted and confused. Huang explained that AlphaGo's policy network of finding the most accurate move order and continuation did not precisely guide AlphaGo to make the correct continuation after move 78, since its value network did not determine Lee's 78th move as being the most likely, and therefore when the move was made AlphaGo could not make the right adjustment to the logical continuation. === Sixty online games === On 29 December 2016, a new account on the Tygem server named "Magister" (shown as 'Magist' at the server's Chinese version) from South Korea began to play games with professional players. It changed its account name to "Master" on 30 December, then moved to the FoxGo server on 1 January 2017. On 4 January, DeepMind confirmed that the "Magister" and the "Master" were both played by an updated version of AlphaGo, called AlphaGo Master. As of 5 January 2017, AlphaGo Master's online record was 60 wins and 0 losses, including three victories over Go's top-ranked player, Ke Jie, who had been quietly briefed in advance that Master was a version of AlphaGo. After losing to Master, Gu Li offered a bounty of 100,000 yuan (US$14,400) to the first human player who could defeat Master. Master played at the pace of 10 games per day. Many quickly suspected it to be an AI player due to little or no resting between games. Its adversaries included many world champions such as Ke Jie, Park Jeong-hwan, Yuta Iyama, Tuo Jiaxi, Mi Yuting, Shi Yue, Chen Yaoye, Li Qincheng, Gu Li, Chang Hao, Tang Weixing, Fan Tingyu, Zhou Ruiyang, Jiang Weijie, Chou Chun-hsun, Kim Ji-seok, Kang Dong-yun, Park Yeong-hun, and Won Seong-jin; national champions or world championship runners-up such as Lian Xiao, Tan Xiao, Meng Tailing, Dang Yifei, Huang Yunsong, Yang Dingxin, Gu Zihao, Shin Jinseo, Cho Han-seung, and An Sungjoon. All 60 games except one were fast-paced games with three 20 or 30 seconds byo-yomi. Master offered to extend the byo-yomi to one minute when playing with Nie Weiping in consideration of his age. After winning its 59th game Master revealed itse
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Alec Radford

Alec Radford is an American artificial intelligence researcher. == Biography == Radford grew up in Texas. He graduated from Cistercian Preparatory School in 2011, where he became an Eagle Scout, and dropped out of Olin College in August 2014, where he and fellow students Slater Victoroff, Diana Yuan, and Madison May had formed the startup Indico in their dorm room. In 2015, the quartet were joined by Luke Metz and the firm and the Facebook AI research lab in New York used generative adversarial networks to create realistic low pixel images. A demonstration of Indico's technology was used without proper attribution in an April 2016 demonstration by Nvidia chief executive Jensen Huang. Radford joined OpenAI around 2016, where he worked on natural-language processing. The following year, Radford trained a neural network on Amazon reviews. The model was fairly basic, with layers which allowed for human understanding. Upon exploring it, he saw that it had a special neuron linked to the sentiment of the reviews, which it had created on its own. This was a drastic improvement from previous neural networks that had analysed sentiment, because they had to be told to do so and specially trained on data that was explicitly labeled according to sentiment. This development made OpenAI chief scientist Ilya Sutskever consider that a future model, using more diverse language data, could map far more structures of meaning, eventually becoming a "learned core module" for superintelligence. In 2018, Radford was the lead author on OpenAI's seminal research paper on generative pre-trained transformers, which form the foundation of ChatGPT. At OpenAI, he worked on early GPT models, Whisper, a speech recognition model, and the image generator DALL-E. He left OpenAI in December 2024 to pursue independent research. Around March 2025, Radford joined Thinking Machines Lab as an advisor. He joined along with Bob McGrew who was previously the chief research officer of OpenAI. In April 2026, Radford, Nick Levine, and David Duvenaud released Talkie, an AI model trained on books, newspapers, scientific journals, patents, and case law published before December 31, 1930. When asked about the state of the world in 2026, it stated that one billion people would live in Europe, that London and New York would be connected by steamships that transit between the two in ten days, and "winter will be passed in Paris, and the summer in London."
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Harvey (software)

Harvey is a generative artificial intelligence (AI) product developed by the Counsel AI Corporation for the legal industry. The product has been described as a provider of customised large language models (LLMs) for law firms and in-house legal teams. It is named after the lead character of the legal drama Suits, Harvey Specter. == History == Harvey was founded in the summer of 2022 by Winston Weinberg, who was a securities and antitrust litigator at O'Melveny & Myers, and Gabriel Pereyra, who was a research scientist at Google DeepMind and Meta. Pereyra and Weinberg were roommates in Los Angeles. Pereyra was brainstorming startup ideas with his research colleagues. He showed Weinberg OpenAI's GPT-3 text-generating system, and Weinberg realized that it could be used to improve legal workflows. They developed an early chain-of-thought prompt based on GPT-3, focused on California tenant law. They ran the model on 100 legal questions from a public forum and hired three attorneys to evaluate the answers and determine whether they could be sent to clients unchanged. Out of those 100 questions, 86 were approved. After that, Pereyra and Weinberg contacted Sam Altman and Jason Kwon, General Counsel at OpenAI, about their results. Shortly after, on July 4, 2022, they met with OpenAI's C-suite, and OpenAI became their seed investor. OpenAI also gave Pereyra and Weinberg early access to GPT-4. Gordon Moodie, a corporate partner at Wachtell, Lipton, Rosen & Katz, also joined Harvey in July 2023 as the company's chief product officer. In March 2024, Harvey had 82 employees and stated that it intended to double that figure by the end of 2024. The company has reportedly hired a large number of lawyers, including from White & Case, Latham & Watkins, Skadden, Gunderson Dettmer, Katten Muchin Rosenman, and Paul Weiss. Harvey CEO Weinberg explained that many members of the company's sales team were formerly attorneys at 'Big Law', i.e. large US law firms, and that the sales team's experience was useful in convincing attorneys to trial the company's software. The integration of former 'Big Law' attorneys into product and sales teams has been attributed as a major factor in Harvey's success. In February 2026, Harvey announced its first brand partnership with actor Gabriel Macht, who portrayed the character Harvey Specter in Suits, to launch the company's Instagram page. In May 2026, it was announced the company is sponsoring the Golden State Valkyries and the New York Liberty. == Funding == In November 2022, it was reported that Harvey raised US$5 million in funding led by the OpenAI Startup Fund, together with other investors such as Jeff Dean, the head of Google AI, Elad Gil, the founder of Mixer Labs, Sarah Guo, the founder of Conviction, and other angel investors. Harvey raised another $23 million in April 2023 in a funding round led by Sequoia Capital. Harvey announced in December 2023 that it had raised $80 million in a Series B funding round led by Elad Gil and Kleiner Perkins which valued the company at $715 million. Other investors in the round included Sequoia Capital and the OpenAI Startup Fund. In July 2024, Harvey announced that it had raised $100 million in a Series C funding round that valued the company at $1.5 billion. The round was led by venture capital firm GV, and other participants included OpenAI, Kleiner Perkins, Sequoia Capital, Elad Gil, and SV Angel. In February 2025, Harvey announced it had raised $300 million in a Series D funding round that valued the company at $3 billion. Just months later, in June 2025, Harvey closed a $300 million Series E co-led by Kleiner Perkins and Coatue, again with participation from Conviction, Elad Gil, OpenAI, and Sequoia, boosting its valuation to about $5 billion and supporting international growth and expanded legal product offerings. In December 2025, Harvey secured a $160 million Series F round led by Andreessen Horowitz, with continued participation from investors including EQT, WndrCo, Sequoia, Kleiner Perkins, Conviction, and Elad Gil, valuing the legal AI company at roughly $8 billion. In March 2026, Harvey raised $200 million at a valuation of $11 billion, in a round co-led by GIC and Sequoia Capital. == Features == In May 2024, Harvey launched its products on Microsoft Azure and stated that it would offer a Harvey on Azure version of its product going forward. It was also reported that Harvey would begin offering general commercial access to some of its products, such as its case law models, as well as product bundles that included its AI assistant, specialised models, and its Vault feature for running prompts on large document collections. == Applications == Various law firms around the world are customers of Harvey. US law firm Paul Weiss began testing Harvey within the firm in January 2023, and became a client of the company later that year. Gina Lynch, the firm's chief knowledge and innovation officer, explained that the firm was not using hard metrics, such as time saved, to assess productivity gains because the time and effort needed to carefully review the output made efficiency gains difficult to measure. In February 2023, the UK law firm, Allen & Overy (now A&O Shearman), announced that it had been trialing Harvey since November 2022 within its Markets Innovation Group. This was reported to be the first known use of a generative AI product within the UK magic circle law firms. According to Allen & Overy, during the trial, 3,500 lawyers had used Harvey for around 40,000 queries in the course of their day to day work. The firm's press release stated that "Whilst the output needs careful review by an A&O lawyer, Harvey can help generate insights, recommendations and predictions based on large volumes of data". David Wakeling, head of the Markets Innovation Group, also cautioned that "You must validate everything coming out of the system. You have to check everything". The Irish law firm, A&L Goodbody, announced in February 2024 that it would be working with Harvey to enhance its services in relation to document analysis, due diligence, litigation, and regulatory compliance. In June 2024, UK law firm Ashurst announced that it would partner with Harvey and roll out its services to its branches worldwide. In September 2024, PwC announced that it would be adopting Harvey to empower its lawyers in Singapore. Singapore law firm WongPartnership also announced that month that it had become the first Southeast Asian law firm to test Harvey's generative AI solutions.
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Flat-field correction

Flat-field correction (FFC) is a digital imaging technique to mitigate pixel-to-pixel differences in the photodetector sensitivity and distortions in the optical path. It is a standard calibration procedure in everything from personal digital cameras to large telescopes. == Overview == Flat fielding refers to the process of compensating for different gains and dark currents in a detector. Once a detector has been appropriately flat-fielded, a uniform signal will create a uniform output (hence flat-field). This then means any further signal is due to the phenomenon being detected and not a systematic error. A flat-field image is acquired by imaging a uniformly-illuminated screen, thus producing an image of uniform color and brightness across the frame. For handheld cameras, the screen could be a piece of paper at arm's length, but a telescope will frequently image a clear patch of sky at twilight, when the illumination is uniform and there are few, if any, stars visible. Once the images are acquired, processing can begin. A flat-field consists of two numbers for each pixel, the pixel's gain and its dark current (or dark frame). The pixel's gain is how the amount of signal given by the detector varies as a function of the amount of light (or equivalent). The gain is almost always a linear variable, as such the gain is given simply as the ratio of the input and output signals. The dark-current is the amount of signal given out by the detector when there is no incident light (hence dark frame). In many detectors this can also be a function of time, for example in astronomical telescopes it is common to take a dark-frame of the same time as the planned light exposure. The gain and dark-frame for optical systems can also be established by using a series of neutral density filters to give input/output signal information and applying a least squares fit to obtain the values for the dark current and gain. C = ( R − D ) × m ( F − D ) = ( R − D ) × G {\displaystyle C={\frac {(R-D)\times m}{(F-D)}}=(R-D)\times G} where: C = corrected image R = raw image F = flat field image D = dark frame image m = image-averaged value of (F−D) G = Gain = m ( F − D ) {\displaystyle m \over (F-D)} In this equation, capital letters are 2D matrices, and lowercase letters are scalars. All matrix operations are performed element-by-element. In order for an astrophotographer to capture a light frame, they must place a light source over the imaging instrument's objective lens such that the light source emanates evenly through the users optics. The photographer must then adjust the exposure of their imaging device (charge-coupled device (CCD) or digital single-lens reflex camera (DSLR) ) so that when the histogram of the image is viewed, a peak reaching about 40–70% of the dynamic range (maximum range of pixel values) of the imaging device is seen. The photographer typically takes 15–20 light frames and performs median stacking. Once the desired light frames are acquired, the objective lens is covered so that no light is allowed in, then 15–20 dark frames are taken, each of equal exposure time as a light frame. These are called Dark-Flat frames. == In X-ray imaging == In X-ray imaging, the acquired projection images generally suffer from fixed-pattern noise, which is one of the limiting factors of image quality. It may stem from beam inhomogeneity, gain variations of the detector response due to inhomogeneities in the photon conversion yield, losses in charge transport, charge trapping, or variations in the performance of the readout. Also, the scintillator screen may accumulate dust and/or scratches on its surface, resulting in systematic patterns in every acquired X-ray projection image. In X-ray computed tomography (CT), fixed-pattern noise is known to significantly degrade the achievable spatial resolution and generally leads to ring or band artifacts in the reconstructed images. Fixed pattern noise can be easily removed using flat field correction. In conventional flat field correction, projection images without sample are acquired with and without the X-ray beam turned on, which are referred to as flat fields (F) and dark fields (D). Based on the acquired flat and dark fields, the measured projection images (P) with sample are then normalized to new images (N) according to: N = ( P − D ) ( F − D ) {\displaystyle N={\frac {(P-D)}{(F-D)}}} == Dynamic flat field correction == While conventional flat field correction is an elegant and easy procedure that largely reduces fixed-pattern noise, it heavily relies on the stationarity of the X-ray beam, scintillator response and CCD sensitivity. In practice, however, this assumption is only approximately met. Indeed, detector elements are characterized by intensity dependent, nonlinear response functions and the incident beam often shows time dependent non-uniformities, which render conventional FFC inadequate. In synchrotron X-ray tomography, many factors may cause flat field variations: instability of the bending magnets of the synchrotron, temperature variations due to the water cooling in mirrors and the monochromator, or vibrations of the scintillator and other beamline components. The latter is responsible for the biggest variations in the flat fields. To deal with such variations, a dynamic flat field correction procedure can be employed that estimates a flat field for each individual projection. Through principal component analysis of a set of flat fields, which are acquired prior and/or posterior to the actual scan, eigen flat fields can be computed. A linear combination of the most important eigen flat fields can then be used to individually normalize each X-ray projection: N j = P j − D ¯ F ¯ + ∑ k w j k u k − D ¯ {\displaystyle N_{j}={\frac {P_{j}-{\bar {D}}}{{\bar {F}}+\sum _{k}w_{jk}u_{k}-{\bar {D}}}}} where N j {\displaystyle N_{j}} = intensity normalized X-ray projection P j {\displaystyle P_{j}} = raw X-ray projection F ¯ {\displaystyle {\bar {F}}} = mean flat field image (average of flat fields) u k {\displaystyle u_{k}} = k-th eigen flat field w j k {\displaystyle w_{jk}} = weight of the eigen flat field u k {\displaystyle u_{k}} D ¯ {\displaystyle {\bar {D}}} = mean dark field (average of dark fields)
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Tim Houlne

Tim Houlne is an American business executive, entrepreneur, and author known for his work in outsourcing and homeshoring, remote working, and artificial intelligence (AI) in customer service. He is the founder and CEO of Humach, a company that uses human agents and AI in customer experience solutions. Previously, he was co-founder and CEO of Working Solutions, a virtual contact center company in the United States. == Early life and education == Houlne graduated from Missouri Western State University (MWSU) in 1986 with a bachelor's degree in business administration and from the University of Texas in Dallas with an MBA. In 2024, MWSU and North Central Missouri College renamed the Convergent Technology Alliance Center to the Houlne Center for Convergent Technology. The 20,000 square-foot learning laboratory provides training and applied education experiences in industries such as AI, cybersecurity, manufacturing and construction, and service technologies. == Career == In 1998, Houlne co-founded Working Solutions, a Plano, Texas-based U.S. outsourcing company that provides customer service using remote, home-based agents. As CEO, he oversaw the development of a virtual workforce model that routes service calls to either domestic or offshore agents, according to client needs and service requirements. In 2015, Houlne founded Humach, a customer experience outsourcing provider that uses human service agents with AI-based digital agents. The company derives its name from the combination of services provided by humans and machines. Its clients include Amazon, Carfax and McDonald's. The company acquired InfiniteAI in 2020, and Markets EQ in 2025. In 2013, Houlne was named a finalist for the Ernst & Young Entrepreneur of the Year Award (Southwest Region).He is the co-author of several books focused on the evolution of work, the gig economy, and the influence of AI in customer-facing roles. == Works == The New World of Work: From the Cube to the Cloud (2013) ISBN 0982562276 OCLC 813933360 The New World of Work, Second Edition: The Cube, the Cloud and What's Next (2023) ISBN 9781642258318 OCLC 1389815847 The Intelligent Workforce: How Humans & Machines Will Co-Create a Better Future (2024) ISBN 9798887501604 OCLC 1439598569
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Learning vector quantization

In computer science, learning vector quantization (LVQ) is a prototype-based supervised classification algorithm. LVQ is the supervised counterpart of vector quantization systems. LVQ can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all Hebbian learning-based approach. It is a precursor to self-organizing maps (SOM) and related to neural gas and the k-nearest neighbor algorithm (k-NN). LVQ was invented by Teuvo Kohonen. == Definition == An LVQ system is represented by prototypes W = ( w ( i ) , . . . , w ( n ) ) {\displaystyle W=(w(i),...,w(n))} which are defined in the feature space of observed data. In winner-take-all training algorithms one determines, for each data point, the prototype which is closest to the input according to a given distance measure. The position of this so-called winner prototype is then adapted, i.e. the winner is moved closer if it correctly classifies the data point or moved away if it classifies the data point incorrectly. An advantage of LVQ is that it creates prototypes that are easy to interpret for experts in the respective application domain. LVQ systems can be applied to multi-class classification problems in a natural way. A key issue in LVQ is the choice of an appropriate measure of distance or similarity for training and classification. Recently, techniques have been developed which adapt a parameterized distance measure in the course of training the system, see e.g. (Schneider, Biehl, and Hammer, 2009) and references therein. LVQ can be a valuable aid in classifying text documents. == Algorithm == The algorithms are presented as in. Set up: Let the data be denoted by x i ∈ R D {\displaystyle x_{i}\in \mathbb {R} ^{D}} , and their corresponding labels by y i ∈ { 1 , 2 , … , C } {\displaystyle y_{i}\in \{1,2,\dots ,C\}} . The complete dataset is { ( x i , y i ) } i = 1 N {\displaystyle \{(x_{i},y_{i})\}_{i=1}^{N}} . The set of code vectors is w j ∈ R D {\displaystyle w_{j}\in \mathbb {R} ^{D}} . The learning rate at iteration step t {\displaystyle t} is denoted by α t {\displaystyle \alpha _{t}} . The hyperparameters w {\displaystyle w} and ϵ {\displaystyle \epsilon } are used by LVQ2 and LVQ3. The original paper suggests ϵ ∈ [ 0.1 , 0.5 ] {\displaystyle \epsilon \in [0.1,0.5]} and w ∈ [ 0.2 , 0.3 ] {\displaystyle w\in [0.2,0.3]} . === LVQ1 === Initialize several code vectors per label. Iterate until convergence criteria is reached. Sample a datum x i {\displaystyle x_{i}} , and find out the code vector w j {\displaystyle w_{j}} , such that x i {\displaystyle x_{i}} falls within the Voronoi cell of w j {\displaystyle w_{j}} . If its label y i {\displaystyle y_{i}} is the same as that of w j {\displaystyle w_{j}} , then w j ← w j + α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}+\alpha _{t}(x_{i}-w_{j})} , otherwise, w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} . === LVQ2 === LVQ2 is the same as LVQ3, but with this sentence removed: "If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} and w k ← w k + α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\alpha _{t}(x_{i}-w_{k})} .". If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then nothing happens. === LVQ3 === Initialize several code vectors per label. Iterate until convergence criteria is reached. Sample a datum x i {\displaystyle x_{i}} , and find out two code vectors w j , w k {\displaystyle w_{j},w_{k}} closest to it. Let d j := ‖ x i − w j ‖ , d k := ‖ x i − w k ‖ {\displaystyle d_{j}:=\|x_{i}-w_{j}\|,d_{k}:=\|x_{i}-w_{k}\|} . If min ( d j d k , d k d j ) > s {\displaystyle \min \left({\frac {d_{j}}{d_{k}}},{\frac {d_{k}}{d_{j}}}\right)>s} , where s = 1 − w 1 + w {\displaystyle s={\frac {1-w}{1+w}}} , then If w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have the same class, and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have different classes, then w j ← w j + α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}+\alpha _{t}(x_{i}-w_{j})} and w k ← w k − α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}-\alpha _{t}(x_{i}-w_{k})} . If w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, and w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have different classes, then w j ← w j − α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\alpha _{t}(x_{i}-w_{j})} and w k ← w k + α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\alpha _{t}(x_{i}-w_{k})} . If w j {\displaystyle w_{j}} and w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have the same class, then w j ← w j − ϵ α t ( x i − w j ) {\displaystyle w_{j}\leftarrow w_{j}-\epsilon \alpha _{t}(x_{i}-w_{j})} and w k ← w k + ϵ α t ( x i − w k ) {\displaystyle w_{k}\leftarrow w_{k}+\epsilon \alpha _{t}(x_{i}-w_{k})} . If w k {\displaystyle w_{k}} and x i {\displaystyle x_{i}} have different classes, and w j {\displaystyle w_{j}} and x i {\displaystyle x_{i}} have different classes, then the original paper simply does not explain what happens in this case, but presumably nothing happens in this case. Otherwise, skip. Note that condition min ( d j d k , d k d j ) > s {\displaystyle \min \left({\frac {d_{j}}{d_{k}}},{\frac {d_{k}}{d_{j}}}\right)>s} , where s = 1 − w 1 + w {\displaystyle s={\frac {1-w}{1+w}}} , precisely means that the point x i {\displaystyle x_{i}} falls between two Apollonian spheres.
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Moral Machine

Moral Machine is an online platform, developed by Iyad Rahwan's Scalable Cooperation group at the Massachusetts Institute of Technology, that generates moral dilemmas and collects information on the decisions that people make between two destructive outcomes. The platform is the idea of Iyad Rahwan and social psychologists Azim Shariff and Jean-François Bonnefon, who conceived of the idea ahead of the publication of their article about the ethics of self-driving cars. The key contributors to building the platform were MIT Media Lab graduate students Edmond Awad and Sohan Dsouza. The presented scenarios are often variations of the trolley problem, and the information collected would be used for further research regarding the decisions that machine intelligence must make in the future. For example, as artificial intelligence plays an increasingly significant role in autonomous driving technology, research projects like Moral Machine help to find solutions for challenging life-and-death decisions that will face self-driving vehicles. Moral Machine was active from January 2016 to July 2020. The Moral Machine continues to be available on their website for people to experience. == The experiment == The Moral Machine was an ambitious project; it was the first attempt at using such an experimental design to test a large number of humans in over 200 countries worldwide. The study was approved by the Institute Review Board (IRB) at Massachusetts Institute of Technology (MIT). The setup of the experiment asks the viewer to make a decision on a single scenario in which a self-driving car is about to hit pedestrians. The user can decide to have the car either swerve to avoid hitting the pedestrians or keep going straight to preserve the lives it is transporting. Participants can complete as many scenarios as they want to, however the scenarios themselves are generated in groups of thirteen. Within this thirteen, a single scenario is entirely random while the other twelve are generated from a space in a database of 26 million different possibilities. They are chosen with two dilemmas focused on each of six dimensions of moral preferences: character gender, character age, character physical fitness, character social status, character species, and character number. The experiment setup remains the same throughout multiple scenarios but each scenario tests a different set of factors. Most notably, the characters involved in the scenario are different in each one. Characters may include ones such as: Stroller, girl, boy, pregnant, Male Doctor, Female Doctor, Female Athlete, Executive Female, Male Athlete, Executive Male, Large Woman, Large Man, homeless, old man, old woman, dog, criminal, and a cat. Through these different characters researchers were able to understand how a wide variety of people will judge scenarios based on those involved. == Analysis == The Moral Machine collected 40 million moral decisions from 4 million participants in 233 countries, analysis of which revealed trends within individual countries and humanity as a whole. It tested for nine factors: preference for sparing humans versus pets, passengers versus pedestrians, men versus women, young versus elderly, fit versus overweight, higher versus lower social status, jaywalkers versus law abiders, larger versus smaller groups, and inaction (i.e. staying on course) versus swerving. Globally, participants favored human lives over lives of animals like dogs and cats. They preferred to spare more lives if possible, and younger lives as opposed to older. Babies were most often spared with cats being the least spared. In terms of gender variations, people tended to spare men over women for doctors and the elderly. All countries generally shared the preference to spare pedestrians over passengers and law-abiders over criminals. Participants from less wealthy countries showed a higher tendency of sparing pedestrians who crossed illegally compared to those from more wealthy and developed countries. This is most likely due to their experience living in a society where individuals are more likely to deviate from rules due to less stringent enforcement of laws. Countries of higher economic inequality overwhelmingly prefer to save wealthier individuals over poorer ones. === Cultural differences === Researchers subdivided 130 countries with similar results into three ‘cultural clusters’. North America and European countries with significant Christian populations had a higher preference for inaction on the part of the driver and thus had less of a preference for sparing pedestrians as compared to other clusters. East Asian and Islamic countries, together constituting the second cluster, did not have as much preference to spare younger humans compared to the other two clusters and had a higher preference for sparing law-abiding humans. Latin America and Francophone countries had a higher preference for sparing women, the young, the fit, and those of higher status, but a lower preference for sparing humans over pets or other animals. Individualistic cultures tended to spare larger groups, and collectivist cultures had a stronger preference for sparing the lives of older people. For instance, China ranked far below the world average for preference to spare the younger over elderly, while the average respondent from the US exhibited a much higher tendency to save younger lives and larger groups. == Applications of the data == The findings from the moral machine can help decision makers when designing self-driving automotive systems. Designers must make sure that these vehicles are able to solve problems on the road that aligns with the moral values of humans around it. This is a challenge because of the complex nature of humans who may all make different decisions based on their personal values. However, by collecting a large amount of decisions from humans all over the world, researchers can begin to understand patterns in the context of a particular culture, community, and people. == Other features == The Moral Machine was deployed in June 2016. In October 2016, a feature was added that offered users the option to fill a survey about their demographics, political views, and religious beliefs. Between November 2016 and March 2017, the website was progressively translated into nine languages in addition to English (Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Russian, and Spanish). Overall, the Moral Machine offers four different modes, with the focus being on the data-gathering feature of the website, called the Judge mode. This means that the Moral Machine, in addition to providing their own scenarios for users to judge, also invites users to create their own scenarios to be submitted and approved so that other people may also judge those scenarios. Data is also open sourced for anyone to explore via an interactive map that is featured on the Moral Machine website. == In the literature == Studies and research on the Moral Machine have taken a wide variety of approaches. However, theological examinations of the topic are still scarce where two bodies of work that examine such perspective currently exist in this regard: One is Buddhist while the other is Christian.
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Objective vision

Objective Vision (Object Oriented Visionary) is a project mainly aimed at real-time computer vision and simulation vision of living creatures. it has three sections containing an open-source library of programming functions for using inside the projects, Virtual laboratory for scholars to check the application of functions directly and by command-line code for external and instant access, and the research section consists of paperwork and libraries to expand the scientific prove of works. == Background == The process has been used in the OVC libraries is as same as what's happening when living see a picture, and it's designed to give the researchers to experience the brain's visual cortex most close simulation for picture perception. The OVC was designed to work as a simulated visual cortex that has a critical job in processing and classify the objects to make it easier to work with pictures and graphical perception and processing. The human brain is much more aware of how it solves complex problems such as playing chess or solving algebra equations, which is why computer programmers have had so much success building machines that emulate this type of activity. but when the whole process is still a riddle that how the entities visionary system works. The project was simulated the visionary system by how it starts to convert the signals to image(actually the edges and colors) and then recognizing the shapes to find a relation between brain's information and image. The Objective Visionary system actually is concentrating on the separable sections, this separation gives the application visionary system the excellence processing result, because with this method the system do not waste much time on processing non significant sections and signals. this operation in the Objective Vision project called objective processing and because the O.V. mission is focused on human visionary simulation, so the developer refers with Objective Vision. == History == Objective-Vision is a Human (Natural) Visionary simulation Project developed by Michael Bidollahkhany. Following an explosion of interest during the 21st century were characterized by the maturing of the field and the significant growth of active applications; simulation of visionary systems, visionary based autonomous vehicle guidance, medical imaging (2D and 3D) and automatic surveillance are the most rapidly developing areas. This progress can be seen in an increasing number of software and hardware products on the market, as well as in a number of digital image processing software and APIs and also machine vision courses offered at universities worldwide. Therefore, the OVC project has been released as a research software project in 2016. One of important parts of this project was O.V.C. (Objective Vision Class library), that was designed to able companies and scientists to use the brain's most likely functionalities as visionary libraries to simplify and accelerate the image processing algorithms developments. The project started under MIT copyright license, but since 2018 the project continued as classified based on sponsors opinion. == The Algorithm == As developers claimed the algorithm used in the class library and developer's kit of project has been developed based on natural visionary system, and the functionalities containing image processing, optimization and labeling etc. are mostly upgraded and near techniques. Suppose that we've a picture of a jungle, or somewhere else, with this library developer will be able to manipulate not only the pixel of images for data extraction, but automatically based on which algorithm is used and image quality, he can manipulate directly a list of objects, same pixels and every data project needs to have, said the developer in his lecture answering how the algorithm works. === Viewpoint === For long times digital image processing and storing, was actually by processing just pixels; this Project tries to present a new kind of image processing and even storing, "objective vision" or "object-oriented visionary" is called. This project officially launched in May 2016, with the aim of making more adaptation between Computer Vision (Include Visionary, Digital image processing, discernment and even Perception) and Human Visual System; about development of the project: "...so we decided to research on Human Vision System, besides we worked on Artificial Retinal image processing and new visionary optimization unit(Presented at Istanbul Technical University Conference(Turkey 2015-2016)) and grew our research to Visionary CORTEX of Brain", Michael Bidollahkhany said. == Applications == The OVC application areas include: 2D and 3D feature toolkits Egomotion estimation Human–computer interaction (HCI) Mobile robotics Motion understanding Object identification Segmentation and recognition Stereopsis stereo vision: depth perception from two cameras Structure from motion (SFM) Motion tracking == Programming language == In first initial release of Objective Visionary Project the algorithm has been written in C++ and C#, and the virtual laboratory has been developed in C# and Delphi. Based on developers last lecture since the second release the complete algorithm has been re-written in C# based on .Net Core 1.0 to make it easier to work on different operating systems.
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Sense Networks

Sense Networks is a New York City based company with a focus on applications that analyze big data from mobile phones, carrier networks, and taxicabs, particularly by using machine learning technology to make sense of large amounts of location (latitude/longitude) data. In 2009, Sense was named one of "The 25 Most Intriguing Startups in the World" by Bloomberg Businessweek and was called "The Next Google" on the cover of Newsweek. In 2014, Sense Networks was acquired by YP, "the local search and advertising company owned by Cerberus Capital Management and AT&T." It was subsequently sold off to Verve in 2017 == History == Sense Networks was founded by Greg Skibiski in February 2006 (2003?) near his home in Northampton, Massachusetts. After establishing an office in NoHo, New York City near Silicon Alley, Skibiski recruited Alex Pentland, Director of Human Dynamics Research and former Academic Head of the MIT Media Lab, Tony Jebara, Associate Professor and Head of the Machine Learning Laboratory at Columbia University, and Christine Lemke, who would later become co-founders. Sense Networks investors include Intel Capital, Javelin Venture Partners, and Kenan Altunis. Founder Greg Skibiski was pushed out by lead investor Intel Capital in November 2009 following the company's B round of financing. During the same week, the company won the Emerging Communications Conference "Company to Watch" Award. The company has three published patent applications for analyzing sensor data streams: System and Method of Performing Location Analytics (US 20090307263), Comparing Spatial-Temporal Trails in Location Analytics (US 20100079336), and Anomaly Detection in Sensor Analytics (US 20100082301). The company was acquired by the Yellow Pages in 2014. This is a marketing conglomerate under AT&T and Cerberus Capital Management. == Products and services == The Citysense consumer application that shows hotspots of human activity in real-time from mobile phone location and taxicab GPS data was named by ReadWriteWeb (in The New York Times) as "Top 10 Internet of Things Products of 2009". The Cabsense consumer application that shows the best place to catch a New York City taxicab based on GPS data from the vehicle was launched in March 2010. The Macrosense platform is for mobile application providers and mobile phone carriers to analyze billions of customer location data points for predictive analytics in advertising and churn management applications. == Privacy and data ownership == The company allows users to opt-out of their service through their website, and users may monitor their profile through their application. The company does not collect identifiable data (such as phone numbers or names); it collects data received from cellphone to construct anonymous profiles of consumers. This anonymous data/profiles may then be sold to third parties. The company's privacy and data ownership policies are based on The New Deal on Data, as advocated by Alex "Sandy" Pentland, head of the Human Dynamics group at MIT.
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GNOWSYS

GNOWSYS (Gnowledge Networking and Organizing system) is a specification for a generic distributed network based memory/knowledge management. It is developed as an application for developing and maintaining semantic web content. It is written in Python. It is implemented as a Django app. The GNOWSYS project was launched by Nagarjuna G. in 2001, while he was working at Homi Bhabha Centre for Science Education (HBCSE). The memory of GNOWSYS is designed as a node-oriented space. A node is described by other nodes to which it has links. The nodes are organized and processed according to a complex data structure called the neighborhood. == Applications == The application can be used for web-based knowledge representation and content management projects, for developing structured knowledge bases, as a collaborative authoring tool, suitable for making electronic glossaries, dictionaries and encyclopedias, for managing large web sites or links, developing an online catalogue for a library of any thing including books, to make ontologies, classifying and networking any objects, etc. This tool is also intended to be used for an on-line tutoring system with dependency management between various concepts or software packages. For example, the dependency relations between Debian packages have been represented by the gnowledge portal Archived 2018-05-14 at the Wayback Machine. == Component Classes == The kernel is designed to provide support to persistently store highly granular nodes of knowledge representation like terms, predicates and very complex propositional systems like arguments, rules, axiomatic systems, loosely held paragraphs, and more complex structured and consistent compositions. All the component classes in GNOWSYS are classified according to complexity into three groups, where the first two groups are used to express all possible well formed formulae permissible in a first order logic. === Terms === ‘Object’, ‘Object Type’ for declarative knowledge, ‘Event’, ‘Event Type’, for temporal objects, and ‘Meta Types’ for expressing upper ontology. The objects in this group are essentially any thing about which the knowledge engineer intends to express and store in the knowledge base, i.e., they are the objects of discourse. The instances of these component classes can be stored with or without expressing ‘instance of’ or ‘sub-class of’ relations among them. === Predicates === This group consists of ‘Relation’, and ‘Relation Type’ for expressing declarative knowledge, and ‘Function’ and ‘Function Type’ for expressing procedural knowledge. This group is to express qualitative and quantitative relations among the various instances stored in the knowledge base. While instantiating the predicates can be characterized by their logical properties of relations, quantifiers and cardinality as monadic predicates of these predicate objects. === Structures === ‘System’, ‘Encapsulated Class’, ‘Program’, and ‘Process’, are other base classes for complex structures, which can be combined iteratively to produce more complex systems. The component class ‘System’ is to store in the knowledge base a set of propositions composed into ontologies, axiomatic systems, complex systems like say a human body, an artifact like a vehicle etc., with or without consistency check. An ‘Encapsulated Class’ is to com- pose declarative and behavioural objects in a flexible way to build classes. A ‘Program’ is not only to store the logic of any complete program or a component class, composed from the already available behavioural instances in the knowledge base with built-in connectives (conditions, and loops), but also execute them as web services. A ‘Process’ is to structure temporal objects with sequence, concurrency, synchronous or asynchronous specifications. Every node in the database keeps the neighbourhood information, such as its super-class, sub-class, instance-of, and other relations, in which the object has a role, in the form of predicates. This feature makes computation of drawing graphs and inferences, on the one hand, and dependency and navigation paths on the other hand very easy. All the data and metadata is indexed in a central catalogue making query and locating resources efficient.
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