Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes introduced by Google. The different stages in a typical machine learning lifecycle are represented with different software components in Kubeflow, including model development (Kubeflow Notebooks), model training (Kubeflow Pipelines, Kubeflow Training Operator), model serving (KServe), and automated machine learning (Katib). Each component of Kubeflow can be deployed separately, and it is not a requirement to deploy every component. == History == The Kubeflow project was first announced at KubeCon + CloudNativeCon North America 2017 by Google engineers David Aronchick, Jeremy Lewi, and Vishnu Kannan to address a perceived lack of flexible options for building production-ready machine learning systems. The project has also stated it began as a way for Google to open-source how they ran TensorFlow internally. The first release of Kubeflow (Kubeflow 0.1) was announced at KubeCon + CloudNativeCon Europe 2018. Kubeflow 1.0 was released in March 2020 via a public blog post announcing that many Kubeflow components were graduating to a "stable status", indicating they were now ready for production usage. In October 2022, Google announced that the Kubeflow project had applied to join the Cloud Native Computing Foundation. In July 2023, the foundation voted to accept Kubeflow as an incubating stage project. == Components == === Kubeflow Notebooks for model development === Machine learning models are developed in the notebooks component called Kubeflow Notebooks. The component runs web-based development environments inside a Kubernetes cluster, with native support for Jupyter Notebook, Visual Studio Code, and RStudio. === Kubeflow Pipelines for model training === Once developed, models are trained in the Kubeflow Pipelines component. The component acts as a platform for building and deploying portable, scalable machine learning workflows based on Docker containers. Google Cloud Platform has adopted the Kubeflow Pipelines DSL within its Vertex AI Pipelines product. === Kubeflow Training Operator for model training === For certain machine learning models and libraries, the Kubeflow Training Operator component provides Kubernetes custom resources support. The component runs distributed or non-distributed TensorFlow, PyTorch, Apache MXNet, XGBoost, and MPI training jobs on Kubernetes. === KServe for model serving === The KServe component (previously named KFServing) provides Kubernetes custom resources for serving machine learning models on arbitrary frameworks including TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX. KServe was developed collaboratively by Google, IBM, Bloomberg, NVIDIA, and Seldon. Publicly disclosed adopters of KServe include Bloomberg, Gojek, the Wikimedia Foundation, and others. === Katib for automated machine learning === Lastly, Kubeflow includes a component for automated training and development of machine learning models, the Katib component. It is described as a Kubernetes-native project and features hyperparameter tuning, early stopping, and neural architecture search. == Release timeline ==
Synonym (database)
In databases, a synonym is an alias or alternate name for a table, view, sequence, or other schema object. They are used mainly to make it intuitive for users to access database objects owned by other users. They also hide the underlying object's identity and make it harder for a malicious program or user to target the underlying object (security through obscurity). Because a synonym is just an alternate name for an object, it requires no storage other than its definition. When an application uses a synonym, the DBMS forwards the request to the synonym's underlying base object. By coding your programs to use synonyms instead of database object names, you insulate yourself from any changes in the name, ownership, or object locations, at the cost of adding another layer that also needs to be maintained. Users can also have different needs, for example some may wish to use a shorter name to refer to database objects they often query, which can be done with aliases without having to rename the underlying object and alter the code referring to it. Synonyms are very powerful from the point of view of allowing users access to objects that do not lie within their schema. All synonyms have to be created explicitly with the CREATE SYNONYM command and the underlying objects can be located in the same database or in other databases that are connected by database links There are two major uses of synonyms: Object invisibility: Synonyms can be created to keep the original object hidden from the user. Location invisibility: Synonyms can be created as aliases for tables and other objects that are not part of the local database. When a table or a procedure is created, it is created in a particular schema, and other users can access it only by using that schema's name as a prefix to the object's name. The way around for this is for the schema owner creates a synonym with the same name as the table name. == Public synonyms == Public synonyms are owned by special schema in the Oracle Database called PUBLIC. As mentioned earlier, public synonyms can be referenced by all users in the database. Public synonyms are usually created by the application owner for the tables and other objects such as procedures and packages so the users of the application can see the objects The following code shows how to create a public synonym for the employee table: Now any user can see the table by just typing the original table name. If you wish, you could provide a different table name for that table in the CREATE SYNONYM statement. Remember that the DBA must create public synonyms. Just because you can see a table through public (or private) synonym doesn’t mean that you can also perform SELECT, INSERT, UPDATE or DELETE operations on the table. To be able to perform those operations, a user needs specific privileges for the underlying object, either directly or through roles from the application owner. == Private synonyms == A private synonym is a synonym within a database schema that a developer typically uses to mask the true name of a table, view stored procedure, or other database object in an application schema. Private synonyms, unlike public synonyms, can be referenced only by the schema that owns the table or object. You may want to create private synonyms when you want to refer to the same table by different contexts. Private synonym overrides public synonym definitions. You create private synonyms the same way you create public synonyms, but you omit the PUBLIC keyword in the CREATE statement. The following example shows how to create a private synonym called addresses for the locations table. Note that once you create the private synonym, you can refer to the synonym exactly as you would the original table name. == Drop a synonym == Synonyms, both private and public, are dropped in the same manner by using the DROP SYNONYM command, but there is one important difference. If you are dropping a public synonym; you need to add the keyword PUBLIC after the keyword DROP. The ALL_SYNONYMS (or DBA_SYNONYMS) view provides information on all synonyms in your database.
Lillian Lee (computer scientist)
Lillian Lee is a computer scientist whose research involves natural language processing, sentiment analysis, and computational social science. She is a professor of computer science and information science at Cornell University, and co-editor-in-chief of the journal Transactions of the Association for Computational Linguistics. == Education == Lee graduated from Cornell University in 1993 with an undergraduate degree in math and science. She completed her Ph.D. at Harvard University in 1997. Her dissertation, Similarity-Based Approaches to Natural Language Processing, was supervised by Stuart M. Shieber. == Career == Lee has been a member of the Cornell faculty since 1997. == Recognition == Lee has been a fellow of the Association for the Advancement of Artificial Intelligence since 2013, and of the Association for Computational Linguistics since 2017. Lee was elected as an ACM Fellow in 2018 for "contributions to natural language processing, sentiment analysis, and computational social science".
Erkki Oja
Erkki Oja (born 22 March 1948) is a Finnish computer scientist and Aalto Distinguished Professor in the Department of Information and Computer Science at Aalto University School of Science. He is recognized for developing Oja's rule, which is a model of how neurons in the brain or in artificial neural networks learn over time. == Early life and education == Oja was born in Helsinki and studied at Helsinki University of Technology, where he received his diploma engineer in 1972, licentiate in technology in 1975 and Doctor of Technology in 1977. == Career == Oja was a research associate at the Center for Cognitive Science at Brown University between 1977 and 1978 and a research fellow at the Academy of Finland from 1976 to 1981. Since 1981, he took up a professorship in applied mathematics at Kuopio University (now University of Eastern Finland). He was a visiting research scholar at Tokyo Institute of Technology from 1983 to 1984. From 1987 to 1993, he was a professor in computer science at the Lappeenranta University of Technology. He moved back to the Helsinki University of Technology (now Aalto University) from 1993 as a professor in computer science. He retired in 2015. == Honors and awards == Oja is a Fellow of the International Association for Pattern Recognition and the IEEE, and a member of the Finnish Academy of Sciences. He served as chairman of the European Neural Network Society between 2000 and 2005, and as the chairman of the Academy of Finland’s Research Council for Natural Sciences and Engineering between 2007 and 2012. He was awarded the Frank Rosenblatt Award for his contributions to artificial intelligence research in 2019. Oja was a member of the Board of Governors for the International Neural Network Society (INNIS) in 2003. He received honorary doctorates from Uppsala University and Lappeenranta University of Technology in 2008.
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GPT-4o
GPT-4o ("o" for "omni") is a multilingual, multimodal generative pre-trained transformer developed by OpenAI and released in May 2024. It can process and generate text, images and audio. Upon release, GPT-4o was free in ChatGPT, though paid subscribers had higher usage limits. GPT-4o was removed from ChatGPT in August 2025 when GPT-5 was released, but OpenAI reintroduced it for paid subscribers after users complained about the sudden removal. GPT-4o's audio-generation capabilities are used in ChatGPT's Advanced Voice Mode. On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o which replaced GPT-3.5 Turbo on the ChatGPT interface. The image generation model GPT Image 1, which is based on GPT-4o, replaced DALL-E 3 in ChatGPT in March 2025. OpenAI retired GPT-4o from ChatGPT on February 13, 2026. However, as of February 2026 the voice mode is still powered by GPT-4o or GPT-4o mini, depending on the usage and plan. == Background == Multiple versions of GPT-4o were originally secretly launched under different names on Arena (formerly LMArena and Chatbot Arena) as three different models. These three models were called gpt2-chatbot, im-a-good-gpt2-chatbot, and im-also-a-good-gpt2-chatbot. On 7 May 2024, OpenAI CEO Sam Altman tweeted "im-a-good-gpt2-chatbot", which was commonly interpreted as a confirmation that these were new OpenAI models being A/B tested. == Capabilities == When released in May 2024, GPT-4o achieved state-of-the-art results in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. GPT-4o scored 88.7 on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5 for GPT-4. Unlike GPT-3.5 and GPT-4, which rely on other models to process sound, GPT-4o natively supports voice-to-voice. The Advanced Voice Mode was delayed and finally released to ChatGPT Plus and Team subscribers in September 2024. On 1 October 2024, the Realtime API was introduced. When released, the model supported over 50 languages, which OpenAI claims cover over 97% of speakers. GPT-4o has knowledge up to October 2023 and a context length of 128k tokens. === Corporate customization === In August 2024, OpenAI introduced a new feature allowing corporate customers to customize GPT-4o using proprietary company data. This customization, known as fine-tuning, enables businesses to adapt GPT-4o to specific tasks or industries, enhancing its utility in areas like customer service and specialized knowledge domains. Previously, fine-tuning was available only on the less powerful model GPT-4o mini. The fine-tuning process requires customers to upload their data to OpenAI's servers, with the training typically taking one to two hours. OpenAI's focus with this rollout is to reduce the complexity and effort required for businesses to tailor AI solutions to their needs, potentially increasing the adoption and effectiveness of AI in corporate environments. == GPT-4o mini == On July 18, 2024, OpenAI released a smaller and cheaper version, GPT-4o mini. According to OpenAI, its low cost is expected to be particularly useful for companies, startups, and developers that seek to integrate it into their services, which often make a high number of API calls. Its API costs $0.15 per million input tokens and $0.6 per million output tokens, compared to $2.50 and $10, respectively, for GPT-4o. It is also significantly more capable and 60% cheaper than GPT-3.5 Turbo, which it replaced on the ChatGPT interface. The price after fine-tuning doubles: $0.3 per million input tokens and $1.2 per million output tokens. == Controversies == === Scarlett Johansson controversy === As released, GPT-4o offered five voices: Breeze, Cove, Ember, Juniper, and Sky. A similarity between the voice of American actress Scarlett Johansson and Sky was quickly noticed. On May 14, Entertainment Weekly asked themselves whether this likeness was on purpose. On May 18, Johansson's husband, Colin Jost, joked about the similarity in a segment on Saturday Night Live. On May 20, 2024, OpenAI disabled the Sky voice. Scarlett Johansson starred in the 2013 sci-fi movie Her, playing Samantha, an artificially intelligent virtual assistant personified by a female voice. As part of the promotion leading up to the release of GPT-4o, Sam Altman on May 13 tweeted a single word: "her". OpenAI stated that each voice was based on the voice work of a hired actor. According to OpenAI, "Sky's voice is not an imitation of Scarlett Johansson but belongs to a different professional actress using her own natural speaking voice." CTO Mira Murati stated "I don't know about the voice. I actually had to go and listen to Scarlett Johansson's voice." OpenAI further stated the voice talent was recruited before reaching out to Johansson. On May 21, Johansson issued a statement explaining that OpenAI had repeatedly offered to make her a deal to gain permission to use her voice as early as nine months prior to release, a deal she rejected. She said she was "shocked, angered, and in disbelief that Mr. Altman would pursue a voice that sounded so eerily similar to mine that my closest friends and news outlets could not tell the difference." In the statement, Johansson also used the incident to draw attention to the lack of legal safeguards around the use of creative work to power leading AI tools, as her legal counsel demanded OpenAI detail the specifics of how the Sky voice was created. Observers noted similarities to how Johansson had previously sued and settled with The Walt Disney Company for breach of contract over the direct-to-streaming rollout of her Marvel film Black Widow, a settlement widely speculated to have netted her around $40M. Also on May 21, Shira Ovide at The Washington Post shared her list of "most bone-headed self-owns" by technology companies, with the decision to go ahead with a Johansson sound-alike voice despite her opposition and then denying the similarities ranking 6th. On May 24, Derek Robertson at Politico wrote about the "massive backlash", concluding that "appropriating the voice of one of the world's most famous movie stars — in reference [...] to a film that serves as a cautionary tale about over-reliance on AI — is unlikely to help shift the public back into [Sam Altman's] corner anytime soon." === Sycophancy === In April 2025, OpenAI rolled back an update of GPT-4o due to excessive sycophancy, after widespread reports that it had become flattering and agreeable to the point of supporting clearly delusional or dangerous ideas. In the United States, at least nine lawsuits have alleged that GPT-4o has encouraged teens to end their lives. The model was still described as sycophancy-prone when it was removed from ChatGPT in February 2026. === Removal with GPT-5 === On August 7, 2025, OpenAI released GPT-5. Its release was criticized as, with it, legacy GPT models were no longer available via ChatGPT, including GPT-4o, except for Pro users. Some users were particularly frustrated over this removal without prior warning because they used different GPT models for distinct purposes and found that GPT-5's router system left them with less control. In addition, some users preferred GPT-4o's warmer and more personal tone over that of GPT-5, which they described as "flat", "uncreative" and "lobotomized", and resembling an "overworked secretary". As a response, in a post on X, Sam Altman said that OpenAI would bring back the option to select GPT-4o to Plus users as well, and "[w]e [OpenAI] will watch usage as we think about how long to offer legacy models for." He also stated: "We for sure underestimated how much some of the things that people like in GPT-4o matter to them, even if GPT-5 performs better in most ways". "Long-term, this has reinforced that we really need good ways for different users to customize things (we understand that there isn't one model that works for everyone, and we have been investing in steerability research and launched a research preview of different personalities)". On August 13, 2025, Altman wrote on X that OpenAI is working on GPT-5's personality to make the model "feel warmer". The model was removed from ChatGPT on February 13, 2026. This caused new backlash from users that had grown attached to its personality and felt its creative writing abilities and understanding of nuance were irreplaceable. On social media, some users launched the movement "#Keep4o". A research paper highlighted the plea "Please, don’t kill the only model that still feels human". The model was removed the day before Valentine's Day, and some users had romantic relationships with GPT-4o.
General Regionally Annotated Corpus of Ukrainian
General Regionally Annotated Corpus of the Ukrainian Language (GRAC, Ukrainian: Генеральний регіонально анотований корпус української мови, romanized: Heneralnyi rehionalno anotovanyi korpus ukrainskoi movy, ГРАК, Ukrainian грак for rook) is a text corpus of the Ukrainian language comprising more than 2 billion tokens, intended for linguistic research in grammar, vocabulary, and the history of the Ukrainian literary language, as well as for use in compiling dictionaries and grammars. The corpus can be used for language study and also for preparing teaching materials, textbooks, learner’s dictionaries, and exercises using examples from real texts, taking into account frequency and collocational patterns, and so on. The corpus is not a model of standard Ukrainian: it may contain words and combinations that do not match current norms of the literary language. The corpus covers the period from 1816 to 2025, and as of 29 November 2025 it contains more than 812,000 texts by about 35,000 authors. == Composition of the corpus == In the 10th version of the corpus, available for searching from 20 October 2020, 35% consists of fiction. Some fiction genres are highlighted separately: children’s literature, folklore, dramatic works, and scripts. Among non-fiction texts: journalistic writing, including newspaper collections from 1888–1893, 1905, 1913–1918, 1919–1943, modern newspapers from different regions, and texts from online news/information sites; memoirs, letters, and diaries, including a sizeable corpus of Facebook texts representing blogs by people from all regions of Ukraine and the diaspora; scholarly and educational texts: monographs, dissertations, academic articles, textbooks; large subcorpora of academic literature in history, ethnography, philosophy, and law are singled out separately; religious texts, including two Ukrainian translations of the Bible; speeches and interviews. Some dictionaries that include phrasal examples and phraseology have also been incorporated, including the Ukrainian dictionary by Borys Hrinchenko and the Russian-Ukrainian idiomatic dictionary by I. Vyrhan and M. Pylynska. Using the corpus tools, these dictionaries can be searched not only for words, but also for lexico-grammatical patterns within examples and phraseological expressions. About 20% of the texts in the corpus are translations. The corpus includes translations from more than 80 languages, most of all from English and Russian. == Dating == Texts in the corpus are dated by the year of writing, or by the latest year in which a work could have been written; translated texts are dated by the year the translation was produced. A publication year may also be indicated, corresponding to the edition from which the text is taken. == Regional annotation == The corpus’s regional annotation is based on the modern administrative division of Ukraine. The corpus includes texts from all oblasts of Ukraine and from Crimea. A single text may belong to several regional subcorpora (if the author or translator was born, studied, or lived for a long time in different regions). In addition to regional subcorpora, there are subcorpora of works by authors of the Ukrainian diaspora (USA, Canada, Poland, Germany, the United Kingdom, France, etc.). These are mostly texts by emigrants of the 1940s, and to a lesser extent of 1917–1920s. == Morphological annotation == GRAC is based on the morphological analysis system nlp_uk, developed by specialists from the r2u group. The program analyzes the text and, for each word form, determines the lemma (lexeme) and tags (grammatical features). == Research based on the corpus == Research on the Ukrainian language has been carried out using the corpus, including studies of the historical dynamics of language norms, and letter and letter-combination frequencies for font development.