AI For Business Georgia Tech

AI For Business Georgia Tech — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Manual override

A manual override (MO) or manual analog override (MAO) is a mechanism where control is taken from an automated system and given to the user. For example, a manual override in photography refers to the ability for the human photographer to turn off the automatic aperture sizing, automatic focusing, or any other automated system on the camera. Some manual overrides can be used to veto an automated system's judgment when the system is in error. An example of this is a printer's ink level detection: in one case, a researcher found that when he overrode the system, up to 38% more pages could be printed at good quality by the printer than the automated system would have allowed. Automated systems are becoming increasingly common and integrated into everyday objects such as automobiles and domestic appliances. This development of ubiquitous computing raises general issues of policy and law about the need for manual overrides for matters of great importance such as life-threatening situations and major economic decisions. The loyalty of such autonomous devices then becomes an issue. If they follow rules installed by the manufacturer or required by law and refuse to cede control in some situations then the owners of the devices may feel disempowered, alienated and lacking true ownership. == Major incidents == China Airlines Flight 140 crashed, causing many deaths, due to a misunderstanding about the manual overrides for the autopilot. The Take-Off/Go Around system had been activated to abort a landing. It was programmed to ignore manual controls in this situation but the human pilots tried to continue the landing. The conflicting control signals from the pilots and autopilot then resulted in the aircraft stalling and crashing. The autopilot for this aircraft type was then reprogrammed so that it would never ignore a manual override.
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Wolfgang Ketter

Wolfgang Ketter (born Traben-Trarbach, Germany, 1972) is Chaired Professor of Information Systems for a Sustainable Society at the University of Cologne. and a prominent scientist in the application of artificial intelligence, machine learning and intelligent agents in the design of smart markets, including demand response mechanisms and in particular automated auctions. He is a co-founder of the open energy system platform Power TAC, an automated retail electricity trading platform that simulates the performance of retail markets in an increasingly prosumer- and renewable-energy-influenced electricity landscape. == Career == === Advisory roles === Ketter is an advisor on the energy transition to the German government, in particular, the energy-intensive German state of North Rhine-Westphalia. He is also a fellow of the World Economic Forum and member of the WEF Global Council on Future Mobility and the Global New Mobility Coalition, contributing on the use of AI and machine learning to address issues arising from growth in electrification of energy such as the use of batteries as virtual power plants, the management of electric vehicle charging to prevent grid congestion, or the potential for peer-to-peer electricity trading. Ketter has also been an advisor for over a decade to the Port of Rotterdam on the design of energy cooperatives and energy trading platforms as well as one of the largest auction companies in the world, Royal FloraHolland, where his initial research led to a redesign of auction mechanisms and decision support systems. The cumulative research project team received the Association for Information Systems Impact Award in 2020 === Research === Ketter’s research is multidisciplinary, addressing the overlap of AI and ML in the economics of retail energy and mobility markets. The industry and policy applications of his research interconnect in large-scale projects such as the EU Smart city development project Ruggedised, for which the Erasmus University-based team's publication on the optimization of the City of Rotterdam's electric transit bus network was recognized with the Institute for Operations Research and the Management Sciences Daniel H. Wagner runner-up award. His research focuses on the use of competitive benchmarking and intelligent agents in virtual world simulations of retail energy markets as part of a smart grid. A small-scale version of the Power TAC project led to a publication on demand side management, 'A simulation of household behavior under variable prices' that has several hundred citations in publications representing a variety of scientific disciplines. Two of his publications in the Management Information Systems Quarterly journal and one in Energy Economics form the foundation for the current Power TAC platform. In 2016 and 2019 he was Chair of the Workshop on Information Technologies and Systems. Ketter is Coordinator of the Key Research Initiative Sustainable Smart Energy & Mobility at the University of Cologne, where he is a chaired Professor of Information Systems for a Sustainable Society. At the Rotterdam School of Management, Erasmus University, he is Professor of Next Generation Information Systems as well as Director of the Erasmus Centre for Future Energy Business and Academic Director of Smart Cities and Smart Energy at the Erasmus Centre of Data Analytics. He has been a visiting professor at the Haas School of Business and Berkeley Institute of Data Science, University of California at Berkeley in 2016 to 2017.
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AI Avatar Generators: Free vs Paid (2026)

Comparing the best AI avatar generator? An AI avatar generator is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI avatar generator slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Joseph Keshet

Joseph (Yossi) Keshet (Hebrew: יוסף (יוסי) קשת; born: 28 February 1973) is an Israeli professor in the Electrical and Computer Engineering Faculty of the Technion, where he is the director of the Speech, Language, and Deep Learning Lab. His research focuses on human speech processing and machine learning. == Early life and education == Keshet was born in Tel-Aviv. He graduated from the Amal School and began his academic studies at the Department of Electrical Engineering-Systems at Tel-Aviv University in 1991 and received his B.Sc. (Cum Laude) in 1994. Keshet served in the IDF Unit 8200 from 1995 to 2002 as the head of the speech processing research section in the R&D Center. During his service, he received a national award from the Administration for the Development of Weapons and Technological Infrastructure (Maf’at). Keshet was award his M.Sc. from the same department after he completed his Israel Defense Force service in 2002. His Dissertation was titled: Stop consonant spotting in continuous speech and was supervised by Dan Chazan from IBM Research Labs, Haifa. He continued his Ph.D. studies at the Hebrew University of Jerusalem until 2008. Prof. Yoram Singer supervised his thesis on Large Margin Algorithms for Discriminative Continuous Speech. == Career == Keshet was a Research Associate (postdoc) at IDIAP Research Institute, Martigny, Switzerland in 2007, and joined the TTI-Chicago and Department of Computer Science, University of Chicago, Chicago, IL in 2009 as Research Assistant Professor. In 2013, he returned to Israel and joined the Computer Science department at Bar-Ilan University as a senior lecturer and head of the Speech, Language, and Deep Learning Lab. In 2020, Keshet became a Founding Venture Partner at the Disruptive AI Venture Capital. In the same year, he also joined Amazon in Tel-Aviv as an Amazon Scholar. In 2022, Keshet joined the Faculty of Electrical and Computer Engineering at the Technion. == Research == Keshet's research work focuses on both machine learning and computational study of human speech and language. His work on speech and language concentrates on speech processing, speech recognition, acoustic phonetics, and pathological speech. In machine learning, Keshet is focused on deep learning and structured tasks. According to Google Scholar (September 2020), Keshet is one of the 15 most cited researchers in the field of spoken language processing. The algorithms that were developed in the Speech, Language, and Deep Learning Lab can analyze different pathological conditions in the throat and vocal cords based on the subject's voice. Other algorithms showed that the voice can be used to estimate physical and emotional state of the speaker. Another research led by Keshet suggested that it is possible to fool structured AI systems (like Google Voice). == Membership in professional societies == Keshet is the founder and chair of the Machine Learning for Speech and Language Processing Special Interest Group (SIGML) of the International Speech Communication Association (ISCA), from 2011. He is a senior member of the IEEE Signal Processing Society since 2018 and a member of ISCA since 2002. == Publications == Prof. Keshet has authored more than 70 scientific publications and edited one book. === Book === Joseph Keshet and Samy Bengio, Eds., Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods, John Wiley & Sons, March 2009. === Selected articles === Jacob T. Cohen, Alma Cohen, Limor Benyamini, Yossi Adi, Joseph Keshet, Predicting glottal closure insufficiency using fundamental frequency contour analysis, Head & Neck, Journal of the Sciences and Specialities of the Head and Neck, Volume 41, Issue 7, pp. 2324–2331, July 2019. Yehoshua Dissen, Jacob Goldberger, and Joseph Keshet, Formant Estimation and Tracking: A Deep Learning Approach, Journal of the Acoustical Society of America, 145 (2), February 2019. Joseph Keshet, Automatic speech recognition: A primer for speech-language pathology researchers, International Journal of Speech-Language Pathology, Vol. 20 No. 6, pp. 599–609, 2018. Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, Joseph Keshet, Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring, Usenix, 2018. Tzeviya Fuchs, Joseph Keshet, Spoken Term Detection Automatically Adjusted for a Given Threshold, IEEE Journal of Selected Topics in Signal Processing, Dec 2017, Volume 11, Issue 8, pp. 1–8. Moustapha Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet, Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples, Neural Information and Processing Systems (NIPS), 2017. Joseph Keshet, Subhransu Maji, Tamir Hazan, and Tommi Jaakkola, Perturbation Models and PAC-Bayesian Generalization Bounds, in Perturbations, Optimization, and Statistics, Tamir Hazan, George Papandreou, and Daniel Tarlow, Eds., The MIT Press, 2016. Matthew Goldrick, Joseph Keshet, Erin Gustafson, Jordana Heller, and Jeremy Needle, Automatic Analysis of Slips of the Tongue: Insights into the Cognitive Architecture of Speech Production, Cognition, 149, 31–39, 2016. Joseph Keshet, Optimizing the Measure of Performance in Structured Prediction, in Advanced Structured Prediction, Sebastian Nowozin, Peter V. Gehler, Jeremy January, and Christoph H. Lampert, Eds., The MIT Press, 2014. Morgan Sonderegger and Joseph Keshet, Automatic Measurement of Voice Onset Time using Discriminative Structured Prediction, Journal of the Acoustical Society of America, Vol. 132, Issue 6, pp. 3965−3979, 2012. David McAllester, Tamir Hazan and Joseph Keshet, Direct Loss Minimization for Structured Prediction, The 24th Annual Conference on Neural Information Processing Systems (NIPS), 2010. Joseph Keshet, David Grangier and Samy Bengio, Discriminative Keyword Spotting, Speech Communication, Volume 51, Issue 4, pp. 317–329, April 2009. == Personal life == Keshet is married to Lital. They have three children.
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Eager learning

In artificial intelligence, eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to lazy learning, where generalization beyond the training data is delayed until a query is made to the system. The main advantage gained in employing an eager learning method, such as an artificial neural network, is that the target function will be approximated globally during training, thus requiring much less space than using a lazy learning system. Eager learning systems also deal much better with noise in the training data. Eager learning is an example of offline learning, in which post-training queries to the system have no effect on the system itself, and thus the same query to the system will always produce the same result. The main disadvantage with eager learning is that it is generally unable to provide good local approximations in the target function.
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Nabil Ali

Nabil Ali Mohammed Abd AL Azeez (Arabic:نبيل علي) (3 January 1938 – 27 January 2016) was an Egyptian scientist, writer, and intellectual who worked in the field of natural language processing and computational linguistics. Ali is considered a pioneer of Arabic language computing, making significant innovations in early computational linguistics. == Education and career == Ali earned a bachelor's degree in Aeronautical Engineering in 1960, and a master's degree in 1967. In 1971, he earned a PhD in Aeronautics. From 1961 to 1972 Ali worked as an engineering officer in the Egyptian Air Force, specializing in maintenance and training. In 1972, he shifted focus to computing, and from 1972 to 1977 he worked as a computer manager at Egyptair. While in this position, Ali introduced the first automated reservation system for airlines in the Arab world. He later held various computing positions in Egypt, Kuwait, Europe, Canada and the US. Ali started working for Sakhr Software, an Arabic language technology company, in 1983. From 1985 to 1999, he was vice president of Sakhr's council for Research and Development. As a director of the Multilingual Advanced Systems Foundation and project manager at the Egyptian National Company for Scientific and Technical Information, Ali did extensive research on information culture and artificial intelligence relating to the Arabic language. Over the course of his career, Ali developed more than 20 educational programs relating to computational linguistics. He developed the first Arabic lexical database and the first knowledge base for Arabic poetry, as well as many other pieces of Arabic language software. == Awards == 1994: General Book Authority Award for Best Book (in the field of future studies). 2003: General Book Authority Award for Best Culture Book (in the field of "Challenges of the Information Age"). 2007: General Book Authority "Innovation in Information Technology" Award. 2012: King Faisal International Award, with Professor Ali Helmy Mousa, in the field of computer processing of the Arabic Language. == Works == Arabic Language and Computer (Research study), Dar Localization, 1988. Al Arab and the Information Age, Knowledge World Series No. 184, April 1994. Arab Culture and the Information Age: A Vision for the Future of Arab Culture Discourse, World of Knowledge Series, No. 265 January 2001. The Digital Gap: an Arab Vision for a Knowledge Society (in partnership with Dr. Nadia Hegazy), World of Knowledge Series, No. 318 August 2005. The Arab Mind and the Knowledge Society: Manifestations of the Crisis and Suggestions for Solutions, Part 1, The World of Knowledge Series, No. 369, November 2009. The Arab Mind and the Knowledge Society: Manifestations of the Crisis and Suggestions for Solutions, Part 2, The World of Knowledge Series, No. 370, December 2009. == Tribute == On 3 January 2020, Google Doodle celebrated Nabil Ali Mohamed's 82nd Birthday.
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How to Choose an AI Website Builder

Shopping for the best AI website builder? An AI website builder is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI website builder slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
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Judea Pearl

Judea Pearl (Hebrew: יהודה פרל; born September 4, 1936) is an Israeli-American electrical engineer, computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks (see the article on belief propagation). He is also credited for developing a theory of causal and counterfactual inference based on structural models (see article on causality). In 2011, the Association for Computing Machinery (ACM) awarded Pearl with the Turing Award, the highest distinction in computer science, "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning". He is the author of several books, including the technical Causality: Models, Reasoning and Inference, and The Book of Why, a book on causality aimed at the general public. Judea Pearl is the father of journalist Daniel Pearl, who was kidnapped and murdered by terrorists in Pakistan connected with Al-Qaeda and the International Islamic Front in 2002. == Biography == Judea Pearl was born in Tel Aviv, British Mandate for Palestine, in 1936 to Eliezer and Tova Pearl, who were Polish Jewish immigrants, grew up in Bnei Brak. His grandfather Chaim Pearl was one of Bnei Brak's founders. He is a descendant of Menachem Mendel of Kotzk on his mother's side. After serving in the Israel Defense Forces and joining a kibbutz, Pearl decided to study engineering in 1956. He received a B.S. in electrical engineering from the Technion 1960. That same year, he emigrated to the United States and pursued graduate studies. He received an M.S. in electrical engineering from the Newark College of Engineering (now New Jersey Institute of Technology) in 1961, and went on to receive an M.S. in physics from Rutgers University and a PhD in electrical engineering from the Polytechnic Institute of Brooklyn (now the New York University Tandon School of Engineering) in 1965. He worked at RCA Research Laboratories (now SRI International) in Princeton, New Jersey on superconductive parametric amplifiers and storage devices and at Electronic Memories, Inc., on advanced memory systems. When semiconductors "wiped out" Pearl's work, as he later expressed it, he joined UCLA's School of Engineering in 1970 and started work on probabilistic artificial intelligence. He is one of the founding editors of the Journal of Causal Inference. Pearl is currently a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. He and his wife, Ruth, had three children. In addition, as of 2011, he is a member of the International Advisory Board of NGO Monitor. Former Israeli Chief Rabbi, Rabbi Yisrael Meir Lau, partnered with Judea Pearl in the documentary With My Whole Broken Heart. == Murder of Daniel Pearl == In 2002, his son, Daniel Pearl, a journalist working for the Wall Street Journal was kidnapped and murdered in Pakistan, leading Judea and the other members of the family and friends to create the Daniel Pearl Foundation. On the seventh anniversary of Daniel's death, Judea wrote an article in the Wall Street Journal titled Daniel Pearl and the Normalization of Evil: When will our luminaries stop making excuses for terror?. Emeritus Chief Rabbi Jonathan Sacks quoted Judea Pearl's beliefs in a lesson on Judaism: "I asked Judea Pearl, father of the murdered journalist Daniel Pearl, why he was working for reconciliation between Jews and Muslims...he replied with heartbreaking lucidity, 'Hate killed my son. Therefore I am determined to fight hate.'" == Views == On his religious views, Pearl states that he is a "practicing disbeliever." He is very connected to Jewish traditions such as holidays and kiddush on Friday night. Pearl sits on the NGO Monitor international advisory board, a right-wing organization based in Jerusalem that reports on non-governmental organization activity from a pro-Israel perspective. == Research == Pearl is credited for "laying the foundations of modern artificial intelligence, so computer systems can process uncertainty and relate causes to effects." He is one of the pioneers of Bayesian networks and the probabilistic approach to artificial intelligence, and one of the first to mathematize causal modeling in the empirical sciences. His work is also intended as a high-level cognitive model. He is interested in the philosophy of science, knowledge representation, nonstandard logics, and learning. Pearl is described as "one of the giants in the field of artificial intelligence" by UCLA computer science professor Richard E. Korf. His work on causality has "revolutionized the understanding of causality in statistics, psychology, medicine and the social sciences" according to the Association for Computing Machinery. === Notable contributions === A summary of Pearl's scientific contributions is available in a chronological account authored by Stuart J. Russell (2012). An annotated bibliography of Pearl's contributions was compiled by the ACM in 2012. A video describing Pearl's major contributions to AI is available here. Pearl's opinion pieces, touching on Jewish identity, the war on terrorism, and the Middle East conflict can be accessed here. === Books === Heuristics, Addison-Wesley, 1984 Probabilistic Reasoning in Intelligent Systems, Morgan-Kaufmann, 1988 Pearl, Judea (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press. I Am Jewish: Personal Reflections Inspired by the Last Words of Daniel Pearl, Jewish Lights, 2004. (Winner of a 2004 National Jewish Book Award) Causal Inference in Statistics: A Primer, (with Madelyn Glymour and Nicholas Jewell), Wiley, 2016. ISBN 978-1-119-18684-7 A previous survey: Causal inference in statistics: An overview, Statistics Surveys, 3:96–146, 2009. Pearl, Judea; Dana Mackenzie (2018). "The Book of Why: The New Science of Cause and Effect". Science. 361 (6405): 855. Bibcode:2018Sci...361..855.. doi:10.1126/science.aau9731. === Awards ===
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Dental AI

Dental artificial intelligence (Dental AI) refers to the application of artificial intelligence (AI) and machine-learning methods to oral healthcare data. These systems can be used to find patterns or make predictions that can aid in diagnosis, treatment, patient communication, or practice management. == History and development == Research into AI for dentistry dates to the 1990s and 2000s, alongside early CAD/CAM and image-analysis work in dental radiology. Recent developments in deep learning, especially those involving computer vision, such as convolutional neural networks, trained on large image datasets, led to a rapid improvement in performance, as well as a move from prototype technology to productization suitable for use in dental chairs. Dental schools and continuing education programs started incorporating AI content in the 2020s. == Definition and core technologies == The dental AI software accomplishes this task by using various dental images and patient data. Dental images and data used by the dental AI software include bitewing and periapical X-rays, complete mouth X-rays, detailed 3D images, intraoral images, and the patient’s medical history. The dental AI software utilizes several core technologies in accomplishing its task of assisting the dentist. First, the dental AI software utilizes machine learning and deep learning using programs that can learn from examples. Such programs are referred to as convolutional neural network (CNN) and can detect cavities and identify bone changes related to gum disease. The dental AI software utilizes computer vision, which enables the AI software to identify and quantify important features in images and data, whether they are 2D images or 3D images. Natural language processing (NLP) is used for the AI software to understand written text and can automatically generate dental notes and communicate with the patient. Furthermore, the dental AI software utilizes predictive analytics to identify patients that are more prone to dental complications and can suggest the best intervals for checkups or future dental procedures. == Applications in dentistry == Reported clinical and operational applications include diagnostic assistance for caries and periodontal disease, treatment planning assistance, patient education overlays, quality assurance, curriculum assistance for dental education, and claims documentation. Systematic reviews continue to find image-based applications such as caries detection with some variability in study design and a need for prospective validation. == Academic research and clinical validation == Several peer-reviewed studies have measured the effectiveness of AI for applications such as interproximal caries detection and periodontal bone level assessment, showing improvements over unaided readings with a focus on bias within the dataset. The Dental AI Council found variability among clinicians for diagnosis and treatment planning, suggesting the use of a standard tool as an assist. == Industry adoption == Multiple vendors offer FDA-cleared chairside AI for dental imaging: Pearl — Received U.S. FDA 510(k) clearance for its real-time radiologic aid (“Second Opinion”) in 2022 (2D), with subsequent clearances including pediatric and CBCT (“Second Opinion 3D”). TIME gave “Second Opinion” a special mention on its Best Inventions of 2022 list. Overjet — FDA-cleared for bone-level quantification and detection/outline of caries and calculus (e.g., K210187), with additional clearances expanding capabilities. VideaHealth — Received an FDA 510(k) covering 30+ detections across common dental findings (K232384), including indications for patients ages 3 and up; trade coverage has described elements of this as the first pediatric dental-AI clearance. == Regulations == In the U.S., AI-enabled dental imaging software is generally reviewed via the FDA’s 510(k) pathway. The FDA maintains a public AI-Enabled Medical Devices List, which includes numerous medical-imaging AI tools (including dental). Specific dental clearances include Overjet (K210187), VideaHealth (K232384), and Pearl entries such as “Second Opinion 3D” (K243989).
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Top 10 AI Presentation Makers Compared (2026)

Trying to pick the best AI presentation maker? An AI presentation maker is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI presentation maker slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
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Alexander Gammerman

Alexander Gammerman (born 2 November 1944) is a British computer scientist, and professor at Royal Holloway University of London. He is the co-inventor of conformal prediction. He is the founding director of the Centre for Machine Learning at Royal Holloway, University of London, and a Fellow of the Royal Statistical Society. == Career == Gammerman's academic career has been pursued in the Soviet Union and the United Kingdom. He started working as a Research Fellow in the Agrophysical Research Institute, St. Petersburg. In 1983, he emigrated to the United Kingdom and was appointed as a lecturer in the Computer Science Department at Heriot-Watt University, Edinburgh. Together with Roger Thatcher, Gammerman published several articles on Bayesian inference. In 1993, he was appointed to the established chair in Computer Science at University of London tenable at Royal Holloway and Bedford New College, where he served as the Head of Computer Science department from 1995 to 2005. In 1998, the Centre for Reliable Machine Learning was established, and Gammerman became the first director of the centre. Gammerman has written 7 books. == Honours and awards == In 1996, Gammerman received the P.W. Allen Award from the Forensic Science Society. In 2006, he became an Honorary Professor, at University College London. In 2009, he became a Distinguished Professor at Complutense University of Madrid, Spain. In 2019, he received a research grant funded by the energy company Centrica about predicting the time to the next failure of equipment. In 2020, he received the Amazon Research Award for the project titled Conformal Martingales for Change-Point Detection == Selected books == Measures of Complexity (2016), Springer, ISBN 3319357786. Algorithmic Learning in a Random World (2005), Springer, ISBN 0387001522. Causal Models and Intelligent Data Management (1999), Springer, ISBN 978-3-642-58648-4. Probabilistic Reasoning and Bayesian Belief Networks (1998), Nelson Thornes Ltd, ISBN 1872474268. Computational Learning and Probabilistic Reasoning (1996), Wiley, ISBN 0471962791.
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General Internet Corpus of Russian

General Internet Corpus of Russian (GICR) is a corpus of Russian internet texts that has been accessible on request through an online query interface since 2013. The corpus includes rich text materials from the blogosphere, social networks, major news sources and literary magazines. == Goals of the project == The project has the status of an educational and scientific one, and many tasks of computational linguistics are solved by independent researchers and research groups with the materials obtained by GICR. While other corpus projects of Russian are focused on fiction and edited texts, General Internet Corpus provides linguists timely opportunity to learn the language as it is, with all the slang and regional peculiarities. Corpus gives the opportunity to carry out research in Linguistic research of a wide range: dialectological research, study of word distribution, study of the language of the social networks, study of the influence of gender, age and other factors on the language, frequency of words, fixed expressions and different constructions, stylistic features of texts of different segments of the Internet, etc. Social media analysis Corpus-based machine learning for evaluating automatic tagging At various times, student papers and independent researches were carried out on the project material by students, graduates and employees of MSU, MIPT, Russian State Humanitarian University, Novosibirsk State University, Higher School of Economics, Russian Academy of Sciences, SFU, CSU, SGMP, IAAS of MSU. Scientific project leaders: Belikov V. - RSUH, Moscow, Russia Selegey V. - RSUH, ABBYY, Moscow, Russia Sharoff S. - RSUH, Moscow, Russia; University of Leeds, UK The organizations involved in support of GICR: Russian State University of Humanities ABBYY Company Moscow Institute of Physics and Technology Skolkovo Institute of Science and Technology == Size and content of the corpus == Corpus size for the summer 2016 is 19.8 billion tokens, of which 49% are from VKontakte, 40% are from LiveJournal, another 4% - from Mail.ru Blogs and News, and 2% - from Russian Magazine Hall. The sources collected in news segment are: RIA Novosti, Regnum, Lenta.ru, Rosbalt. Texts are provided with metamarkup (by date of creation of the text, sex, place and year of birth of the author, Internet genre, etc.); all texts are provided with automatic morphological tagging and lemmatization. Most of the texts collected are of 2013–2014 years of creation, although in some segments, such as in Russian Magazine Hall, there are some texts collected since 1994. GICR is one of the few mega-corpora projects nowadays, which means its available size is reaching several billion of words. == Access == Currently the interface of GICR is in beta stage, so access to the search in the corpora is provided and is free, but is available for researchers on request.
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Oracle Cloud

Oracle Cloud is a cloud computing service offered by Oracle Corporation providing servers, storage, network, applications and services through a global network of Oracle Corporation managed data centers. The company allows these services to be provisioned on demand over the Internet. Oracle Cloud provides infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and data as a service (DaaS). These services are used to build, deploy, integrate, and extend applications in the cloud. This platform supports numerous open standards (SQL, HTML5, REST, etc.), open-source applications (Kubernetes, Spark, Hadoop, Kafka, MySQL, Terraform, etc.), and a variety of programming languages, databases, tools, and frameworks including Oracle-specific, open source, and third-party software and systems. == Services == === Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) === Oracle's cloud infrastructure was made generally available (GA) on October 20, 2016 under the name "Oracle Bare Metal Cloud Services". Oracle Bare Metal Cloud Services was rebranded as Oracle Cloud Infrastructure in 2018 and dubbed Oracle's "Generation 2 Cloud" at Oracle OpenWorld 2018. Oracle Cloud Infrastructure offerings include the following services: Compute: The company provides Virtual Machine Instances to provide different shapes (VM sizes) catering to different types of workloads and performance characteristics. They also provide on-demand Bare metal servers and Bare metal GPU servers, without a hypervisor. In 2016, Oracle Cloud Infrastructure launched with bare metal instances with Intel processors. These first bare metal instances offered were powered by Intel servers. In 2018, Oracle Cloud added bare metal instances powered by AMD processors, followed by Ampere Cloud-native processors in 2021. In 2021, Oracle also released its first VM-based compute instances based on Arm processors. Storage: The platform provides block volumes, file storage, object storage, and archive storage for database, analytics, content, and other applications across common protocols and APIs. Networking: This cloud platform provides network with fully configurable IP addresses, subnets, routing, and firewalls to support new or existing private networks with end-to-end security. Governance: For auditing, identity and access management, the platform has data integrity checks, traceability, and access management features. Database Management / Data Management: Oracle offers a data management platform for database workloads as well as hyper-scale big data and streaming workloads including OLTP, data warehousing, Spark, machine learning, text search, image analytics, data catalog, and deep learning. The platform allows Oracle, MySQL, and NoSQL databases to be deployed on demand as managed cloud services. Oracle Databases uniquely offer the Oracle Autonomous Database (optimized for data warehouse, transaction processing, or JSON), the Exadata shape, as well as Real Application Clusters (RAC). Load Balancing: The cloud platform offers load balancing capability to automatically route traffic across fault domains and availability domains for high availability and fault-tolerance for hosted applications. Edge Services: These services can monitor the path between users and resources and adapt to changes and outages. They include Domain Name System (DNS) services from Oracle's acquisition of Dyn. FastConnect: The cloud platform provides private connectivity across on-premises and cloud networks through providers like Equinix, AT&T, and Colt. Application Development: For application development, the company's cloud offers an open, standards-based application development platform to build, deploy, and manage API-first, mobile-first cloud applications. This platform supports container-native, cloud-native, and low code development. This platform also provides a DevOps platform for CI/CD, diagnostics for Java applications, and integration with SaaS and on-prem applications. Services include Java, mobile, digital assistants (evolution from chatbots), messaging, application container cloud, developer cloud, visual builder, API catalog, AI platform, DataScience.com (Oracle acquired) and blockchain. Integration: This is a platform offering with adapters to integrate on-premise and cloud applications. Capabilities include data integration and replication, API management, integration analytics, along with data migration and integration. They offer services such as data integration platform cloud, data integrator cloud service, GoldenGate cloud service, integration cloud, process cloud service, API platform cloud service, apiary cloud service, and SOA cloud service. Business Analytics: The company provides this business analytics platform which can analyze and generate insights from data across various applications, data warehouses, and data lakes. The services offered include analytics cloud, business intelligence, big data discovery, big data preparation, data visualization, and essbase. Security: The Oracle Cloud Platform provides identity and security applications for providing secure access and monitoring of hybrid cloud environment and addressing IT governance and compliance requirements. This platform delivers an identity SOC (Security Operations Center) through a combined offering of SIEM, UEBA, CASB, and IDaaS. The services offered include Identity Cloud Service and CASB Cloud Service. Management: The platform provides an integrated monitoring, management, and analytics platform. This platform also uses machine learning and big data on the operational data set. The platform is used to improve IT stability, prevent application outages, improve DevOps, and harden security. Services offered include Application Performance Monitoring, Infrastructure Monitoring, Log Analytics, Orchestration, IT Analytics, Configuration and Compliance, Security Monitoring, and Analytics. Content and Experience: This is a platform for content, website, and workflow management. This service is used to provide content collaboration and web presence. This tool comes integrated with Oracle on-premise and SaaS services. The services offered are Content and Experience Cloud, WebCenter Portal Cloud, and DIVA Cloud. In 2016, Oracle acquired Dyn, an internet infrastructure company. On May 16, 2018 Oracle announced that it had acquired DataScience.com, a privately held cloud workspace platform for data science projects and workloads. In April 2020, Oracle became the cloud infrastructure provider for Zoom, an online and video meeting platform. The same month, Nissan announced its migration to Oracle Cloud for its high-performance computing (HPC) workloads used for simulating the structural impacts of a car design. Xerox announced a partnership with Oracle Cloud in 2021, where Xerox will use Oracle's cloud-computing capabilities within its business incubator. === Software as a Service (SaaS) === Oracle provides SaaS applications also known as Oracle Cloud Applications. These applications are offered across a variety of products, industrial sectors with various deployment options to adhere to compliance standards. The below list mentions Oracle Cloud Applications provided by Oracle Corporation. Customer Experience (CX) Human Capital Management (HCM) Enterprise Resource Planning (ERP) Supply Chain Management (SCM) Enterprise Performance Management (EPM) Internet of Things Applications (IoT) SaaS Analytics Data Industry Solutions (Communications, Financial Services, Consumer Goods, High Tech and Manufacturing, Higher Education, Hospitality, Utilities) Deployment (adhering to standards for sectors such as Financial Services, Retail Services, Public Sector, Defense) Block-Chain Cloud Service (in partnership with SAP, IBM and Microsoft) Blockchain Applications On July 28, 2016 Oracle bought NetSuite, the very first cloud company, for $9.3 billion. === Data as a Service (DaaS) === This platform is known as the Oracle Data Cloud. This platform aggregates and analyzes consumer data powered by Oracle ID Graph across channels and devices to create cross-channel consumer understanding. == Deployment models == Oracle Cloud is available in 44 regions as of July 2023, including North America, South America, UK, European Union, Middle East, Africa, India, Australia, Korea, and Japan. Oracle Cloud is available as a public cloud (Oracle-managed regions); to selected government agencies as an Oracle-managed government cloud in the United States (with FedRAMP High and DISA SRG IL5 compliance) and United Kingdom; and as a "private cloud" or "hybrid cloud" as an Oracle-managed database-only service or full-service dedicated region - what Oracle calls "Cloud at Customer". == Architecture == Oracle's public and government cloud is offered through a global network of Oracle-managed data centers, connected by an Oracle-managed backbone network. Oracle's Exadata Cloud at Customer leverages this network for contr
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European Association for Machine Translation

The European Association for Machine Translation is the European branch of the International Association for Machine Translation Archived 2010-06-24 at the Wayback Machine. It is a non-profit organisation and organises conferences and workshops on the subject of machine translation. It was registered in 1991 in Switzerland and is the only organisation of its type in Europe.
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Abeba Birhane

Abeba Birhane is an Ethiopian-born cognitive scientist who works at the intersection of complex adaptive systems, machine learning, algorithmic bias, and critical race studies. Birhane's work with Vinay Prabhu uncovered that large-scale image datasets commonly used to develop AI systems, including ImageNet and 80 Million Tiny Images, carried racist and misogynistic labels and offensive images. She has been recognized by VentureBeat as a top innovator in computer vision and named as one of the 100 most influential persons in AI 2023 by TIME magazine. == Early life and education == Birhane was born in Ethiopia. She received her Bachelors of Science in Psychology and a Bachelors of Arts in Philosophy from The Open University. In 2015, she completed her Master of Science in Cognitive Science and, in 2021, her Ph.D. at the Complex Software Lab in the School of Computer Science at University College Dublin. == Career and research == Birhane studied the impacts of emerging AI technologies and how they shape individuals and local communities. She found that AI algorithms tend to disproportionately impact vulnerable groups such as older workers, trans people, immigrants, and children. Her research on relational ethics won the best paper award at NeurIPS’s Black in AI workshop in 2019. She has also studied and written about algorithmic colonization driven by corporate agendas. Her work in decolonizing computational sciences addressed the inherited oppressions in current systems especially towards women of color. In 2020, Birhane and Vinay Prabhu, principal machine learning scientist at UnifyID, published a paper examining the problematic data collection, labelling, classification, and consequences of large image datasets. These datasets, including ImageNet and MIT's 80 Million Tiny Images, have been used to develop thousands of AI algorithms and systems. Birhane and Prabhu found that they contained many racist and misogynistic labels and slurs as well as offensive images. This resulted in MIT voluntarily and formally taking down the 80 Million Tiny Images dataset. More recently, Birhane has worked with Rediet Abebe, George Obaido, and Sekou Remy on researching the barriers to data sharing in Africa. They found that power imbalances are significant in the data sharing process, even when the data comes from Africa. Their research was published at the ACM Conference on Fairness, Accountability, and Transparency. In 2024, Birhane established the AI Accountability Lab research group at Trinity College Dublin. == Selected awards == 2019 NeurIPS Black in AI Workshop Best Paper Award 2020 Venture Beat AI Innovations Award in the category Computer Vision Innovation (received with Vinay Prabhu) 2021 100 Brilliant Women in AI Ethics Hall of Fame Honoree 2022 Lero Director’s Prize for PhD/PostDoctoral Contribution. 2023 100 Most Influential People in AI by TIME magazine
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