AI Code Fixer

AI Code Fixer — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Actifsource

Actifsource is a domain-specific modeling workbench. It is realized as plug-in for the software development environment Eclipse. Actifsource supports the creation of multiple domain models which can be linked together. It comes with a UML-like graphical editor to create domain-specific languages and a general graphical editor to edit structures in the created languages. It supports code generation using user-defined generic code templates which are directly linked to the domain models. Code generation is integrated into Eclipse's incremental build process. == Interoperability == Actifsource can use models from other modelling tools by importing and exporting the ecore format which is defined by the Eclipse Modeling Framework. == Licensing policy == There are two versions of actifsource available: The free community edition which can be used freely for non-commercial projects and the enterprise edition which contains additional features. The enterprise edition comes with customer support and maintenance for a limited period of time. This package allows the customers to upgrade to new versions and maintenance releases during their support period.
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Lior Ron (business executive)

Lior Ron (born March 16, 1977) is an Israeli businessman. He is the founder, chairman and former CEO of logistics technology company Uber Freight, co-founder of self-driving truck company Otto, and COO of self-driving technology company Waabi. == Early life and education == Ron grew up in Israel near Haifa. He attended the Technion – Israel Institute of Technology in Haifa, where he earned a bachelor's degree in computer science in 1997. He then joined Israeli Army Intelligence, where he served until 2004. After the Army, he earned a master's degree in computer science at Technion, incorporating artificial intelligence as he developed a biomedical device to assist patients suffering with Parkinson's disease. He then moved to California and earned an MBA from The Stanford Graduate School of Business. His undergraduate work and master's thesis were centered around AI when it was still in its early stages. == Career == === Google === In 2007, Ron joined Google as the Product Lead for Google Maps. He then worked at Motorola Mobility after it was acquired by Google, and in Google's robotics research effort. === Otto === In 2016, Ron left Google to found Otto, a company that makes self-driving kits to retrofit big rig trucks. Quoted in Wired, Ron said he left Google because he “felt an obligation to bring this technology to society sooner rather than later.” Otto launched in May 2016, and was acquired by Uber in late July of the same year. The Uber partnership allowed Ron and Otto the opportunity to develop a freight marketplace for truck drivers. === Uber Freight === On May 18, 2017, Ron and Uber launched Uber Freight, a unit of Uber initially designed as an app connecting long-haul truck drivers with companies in need of cargo shipping, with Ron as CEO. In August 2018, Uber Freight launched a new digital platform focused on shippers, to help them find the right driver for their needs. In 2021, Uber Freight acquired Transplace for $2.25 billion, expanding its services to include managed transportation, logistics software, and consulting. With Ron as CEO, Uber Freight has evolved into a full-scale logistics technology company for shippers and drivers, as Ron introduced more advanced generative AI capabilities to Uber Freight's software and Insights AI logistics platform. In September 2024, the company announced it manages nearly $20 billion in freight, and serves one in three Fortune 500 companies. In May 2025, the company launched the transportation industry's first large-scale AI-powered logistics network, with its large language model embedded directly into its transportation management system. === Waabi === On August 12, 2025, it was reported that Ron had been named chief operating officer of Waabi, a company developing autonomous driving technology using artificial intelligence. He remains as chairman of Uber Freight, with Rebecca Tinucci taking over as CEO. == Controversy == Ron co-founded Otto with Anthony Levandowski, who faces a lawsuit brought in 2017 from Google's parent company Alphabet that alleges Levandowski stole trade secrets while working for Alphabet's self-driving car division before he and Ron co-founded Otto.
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AI Presentation Makers: Free vs Paid (2026)

Curious about the best AI presentation maker? An AI presentation maker is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI presentation maker slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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Is an AI Website Builder Worth It in 2026?

Comparing the best AI website builder? An AI website builder 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 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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Cognitive philology

Cognitive philology is the science that studies written and oral texts as the product of human mental processes. Studies in cognitive philology compare documentary evidence emerging from textual investigations with results of experimental research, especially in the fields of cognitive and ecological psychology, neurosciences and artificial intelligence. "The point is not the text, but the mind that made it". Cognitive Philology aims to foster communication between literary, textual, philological disciplines on the one hand and researches across the whole range of the cognitive, evolutionary, ecological and human sciences on the other. Cognitive philology: investigates transmission of oral and written text, and categorization processes which lead to classification of knowledge, mostly relying on the information theory; studies how narratives emerge in so called natural conversation and selective process which lead to the rise of literary standards for storytelling, mostly relying on embodied semantics; explores the evolutive and evolutionary role played by rhythm and metre in human ontogenetic and phylogenetic development and the pertinence of the semantic association during processing of cognitive maps; Provides the scientific ground for multimedia critical editions of literary texts. Among the founding thinkers and noteworthy scholars devoted to such investigations are: Alan Richardson: Studies Theory of Mind in early-modern and contemporary literature. Anatole Pierre Fuksas Benoît de Cornulier David Herman: Professor of English at North Carolina State University and an adjunct professor of linguistics at Duke University. He is the author of "Universal Grammar and Narrative Form" and the editor of "Narratologies: New Perspectives on Narrative Analysis". Domenico Fiormonte François Recanati Gilles Fauconnier, a professor in Cognitive science at the University of California, San Diego. He was one of the founders of cognitive linguistics in the 1970s through his work on pragmatic scales and mental spaces. His research explores the areas of conceptual integration and compressions of conceptual mappings in terms of the emergent structure in language. Julián Santano Moreno Luca Nobile Manfred Jahn in Germany Mark Turner Paolo Canettieri
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Optical braille recognition

Optical braille recognition is technology to capture and process images of braille characters into natural language characters. It is used to convert braille documents for people who cannot read them into text, and for preservation and reproduction of the documents. == History == In 1984, a group of researchers at the Delft University of Technology designed a braille reading tablet, in which a reading head with photosensitive cells was moved along set of rulers to capture braille text line-by-line. In 1988, a group of French researchers at the Lille University of Science and Technology developed an algorithm, called Lectobraille, which converted braille documents into plain text. The system photographed the braille text with a low-resolution CCD camera, and used spatial filtering techniques, median filtering, erosion, and dilation to extract the braille. The braille characters were then converted to natural language using adaptive recognition. The Lectobraille technique had an error rate of 1%, and took an average processing time of seven seconds per line. In 1993, a group of researchers from the Katholieke Universiteit Leuven developed a system to recognize braille that had been scanned with a commercially available scanner. The system, however, was unable to handle deformities in the braille grid, so well-formed braille documents were required. In 1999, a group at the Hong Kong Polytechnic University implemented an optical braille recognition technique using edge detection to translate braille into English or Chinese text. In 2001, Murray and Dais created a handheld recognition system, that scanned small sections of a document at once. Because of the small area scanned at once, grid deformation was less of an issue, and a simpler, more efficient algorithm was employed. In 2003, Morgavi and Morando designed a system to recognize braille characters using artificial neural networks. This system was noted for its ability to handle image degradation more successfully than other approaches. == Challenges == Many of the challenges to successfully processing braille text arise from the nature of braille documents. Braille is generally printed on solid-color paper, with no ink to produce contrast between the raised characters and the background paper. However, imperfections in the page can appear in a scan or image of the page. Many documents are printed inter-point, meaning they are double-sided. As such, the depressions of the braille of one side appear interlaid with the protruding braille of the other side. == Techniques == Some optical braille recognition techniques attempt to use oblique lighting and a camera to reveal the shadows of the depressions and protrusions of the braille. Others make use of commercially available document scanners.
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Top 10 AI Voice Assistants Compared (2026)

Comparing the best AI voice assistant? An AI voice assistant 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 voice assistant slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Léon Bottou

Léon-Yves Bottou (French pronunciation: [leɔ̃ bɔtu]; born 1965) is a researcher best known for his work in machine learning and data compression. His work presents stochastic gradient descent as a fundamental learning algorithm. He is also one of the main creators of the DjVu image compression technology (together with Yann LeCun and Patrick Haffner), and the maintainer of DjVuLibre, the open source implementation of DjVu. He is the original developer of the Lush programming language. == Life == Léon Bottou was born in France in 1965. He obtained the Diplôme d'Ingénieur from École Polytechnique in 1987, a Magistère de Mathématiques Fondamentales et Appliquées et d’Informatique from École Normale Supérieure in 1988, a Diplôme d'Études Approndies in Computer Science in 1988, in 1988, and a PhD from Université Paris-Sud in 1991. In 1988, in collaboration with Yann LeCun, he published SN, a software package for simulating artificial neural networks. His master's thesis concerned using Time Delay Neural Networks for speech recognition. He then joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, where he collaborated with Vladimir Vapnik on local learning algorithms. in 1992, he returned to France and founded Neuristique S.A., a company that produced machine learning tools and one of the first data mining software packages, including Lush, an object-oriented programming language based on C and Lisp designed for training and using large-scale neural networks. In 1995, he returned to Bell Laboratories, where he developed a number of new machine learning methods, such as Graph Transformer Networks (similar to conditional random field), and applied them to handwriting recognition and OCR. The bank check recognition system that he helped develop was widely deployed by NCR and other companies, reading over 10% of all the checks in the US in the late 1990s and early 2000s. In 1996, he joined AT&T Labs and worked primarily on the DjVu image compression technology, that is used by some websites, notably the Internet Archive, to distribute scanned documents. Between 2002 and 2010, he was a research scientist at NEC Laboratories in Princeton, New Jersey, where he focused on the theory and practice of machine learning with large-scale datasets, on-line learning, and stochastic optimization methods. He developed the open source software LaSVM for fast large-scale support vector machine, and stochastic gradient descent software for training linear SVM and Conditional Random Fields. In 2010 he joined the Microsoft adCenter in Redmond, Washington, and in 2012 became a Principal Researcher at Microsoft Research in New York City. In March 2015 he joined Facebook Artificial Intelligence Research, also in New York City, as a research lead. His work in gradient descent argued that both stochastic gradient descent and batch gradient descent reach similar levels of loss with the same number of training samples, but SGD is faster when running on large datasets. He also argued that second-order gradient descent methods, such as quasi-Newton methods, can be beneficial compared to plain SGD. See (Bottou et al 2018) for a review. He was program chair of the 2013 Conference on Neural Information Processing Systems and the 2009 International Conference on Machine Learning. In 2007, he was received one of the first Blavatnik Awards for Young Scientists from the Blavatnik Family Foundation and the New York Academy of Sciences.
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Cloud-to-cloud integration

Cloud-to-Cloud Integration ( C2I ) allows users to connect disparate cloud computing platforms. While Paas (Platform as a service) and Saas (Software as a service) continue to gain momentum, different vendors have different implementations for cloud computing, e.g. Database, REST, SOAP API. Another name for Cloud-to-Cloud Integration is Cloud-Surfing. See also Cloud-based integration
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Michael Kearns (computer scientist)

Michael Justin Kearns is an American computer scientist, professor and National Center Chair at the University of Pennsylvania, the founding director of Penn's Singh Program in Networked & Social Systems Engineering (NETS), the founding director of Warren Center for Network and Data Sciences, and also holds secondary appointments in Penn's Wharton School and department of Economics. He is a leading researcher in computational learning theory and algorithmic game theory, and interested in machine learning, artificial intelligence, computational finance, algorithmic trading, computational social science and social networks. He previously led the Advisory and Research function in Morgan Stanley's Artificial Intelligence Center of Excellence team, and is currently an Amazon Scholar within Amazon Web Services. == Biography == Kearns was born into an academic family, where his father David R Kearns is Professor Emeritus at University of California, San Diego in chemistry, who won Guggenheim Fellowship in 1969, and his uncle Thomas R. Kearns is Professor Emeritus at Amherst College in Philosophy and Law, Jurisprudence, and Social Thought. His paternal grandfather Clyde W. Kearns was a pioneer in insecticide toxicology and was a professor at University of Illinois at Urbana–Champaign in Entomology, and his maternal grandfather Chen Shou-Yi (1899–1978) was a professor at Pomona College in history and literature, who was born in Canton (Guangzhou, China) into a family noted for their scholarship and educational leadership. Kearns received his B.S. degree at the University of California at Berkeley in math and computer science in 1985, and Ph.D. in computer science from Harvard University in 1989, under the supervision of Turing Award winner Leslie Valiant. His doctoral dissertation was The Computational Complexity of Machine Learning, later published by MIT press as part of the ACM Doctoral Dissertation Award Series in 1990. Before joining AT&T Bell Labs in 1991, he continued with postdoctoral positions at the Laboratory for Computer Science at MIT hosted by Ronald Rivest, and at the International Computer Science Institute (ICSI) in UC Berkeley hosted by Richard M. Karp, both of whom are Turing Award winners. Kearns is currently a full professor and National Center Chair at the University of Pennsylvania, where his appointment is split across the Department of Computer and Information Science, and Statistics and Operations and Information Management in the Wharton School. Prior to joining the Penn faculty in 2002, he spent a decade (1991–2001) in AT&T Labs and Bell Labs, including as head of the AI department with colleagues including Michael L. Littman, David A. McAllester, and Richard S. Sutton; Secure Systems Research department; and Machine Learning department with members such as Michael Collins and the leader Fernando Pereira. Other AT&T Labs colleagues in Algorithms and Theoretical Computer Science included Yoav Freund, Ronald Graham, Mehryar Mohri, Robert Schapire, and Peter Shor, as well as Sebastian Seung, Yann LeCun, Corinna Cortes, and Vladimir Vapnik (the V in VC dimension). Kearns was named Fellow of the Association for Computing Machinery (2014) for contributions to machine learning, and a fellow of the American Academy of Arts and Sciences (2012). His former graduate students and postdoctoral visitors include Ryan W. Porter, John Langford, and Jennifer Wortman Vaughan. Kearns' work has been reported by media, such as MIT Technology Review (2014) Can a Website Help You Decide to Have a Kid?, Bloomberg News (2014) Schneiderman (and Einstein) Pressure High-Speed Trading and NPR audio (2012) Online Education Grows Up, And For Now, It's Free. == Academic life == === Computational learning theory === Kearns and Umesh Vazirani published An introduction to computational learning theory, which has been a standard text on computational learning theory since it was published in 1994. === Weak learnability and the origin of Boosting algorithms === The question "is weakly learnability equivalent to strong learnability?" posed by Kearns and Valiant (Unpublished manuscript 1988, ACM Symposium on Theory of Computing 1989) is the origin of boosting machine learning algorithms, which got a positive answer by Robert Schapire (1990, proof by construction, not practical) and Yoav Freund (1993, by voting, not practical) and then they developed the practical AdaBoost (European Conference on Computational Learning Theory 1995, Journal of Computer and System Sciences 1997), an adaptive boosting algorithm that won the prestigious Gödel Prize (2003). == Honors and awards == 2021. Member of the U. S. National Academy of Sciences. 2014. ACM Fellow. For contributions to machine learning, artificial intelligence, and algorithmic game theory and computational social science. 2012. American Academy of Arts and Sciences Fellow. == Selected works == 2019. The Ethical Algorithm: The Science of Socially Aware Algorithm Design. (with Aaron Roth). Oxford University Press. 1994. An introduction to computational learning theory. (with Umesh Vazirani). MIT press. Widely used as a text book in computational learning theory courses. 1990. The computational complexity of machine learning. MIT press. Based on his 1989 doctoral dissertation; ACM Doctoral Dissertation Award Series in 1990 Archived 2014-11-03 at the Wayback Machine 1989. Cryptographic limitations on learning Boolean formulae and finite automata. (with Leslie Valiant) Proceedings of the twenty-first annual ACM symposium on Theory of computing (STOC'89). The open question: is weakly learnability equivalent to strong learnability?; The origin of boosting algorithms; Important publication in machine learning.
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Nicholas Carlini

Nicholas Carlini is an American researcher affiliated with Anthropic and previously with Google DeepMind who has published research in the fields of computer security and machine learning. He is known for his work on adversarial machine learning, particularly his work on the Carlini & Wagner attack in 2016. This attack was particularly useful in defeating defensive distillation, a method used to increase model robustness, and has since been effective against other defenses against adversarial input. In 2018, Carlini demonstrated an attack on Mozilla's DeepSpeech model, showing that hidden commands could be embedded in speech inputs, which the model would execute even if they were inaudible to humans. He also led a team at UC Berkeley that successfully broke seven out of nine defenses against adversarial attacks presented at the 2018 International Conference on Learning Representations. In addition to his work on adversarial attacks, Carlini has made significant contributions to understanding the privacy risks of machine learning models. In 2020, he revealed that large language models, like GPT-2, could memorize and output personally identifiable information. His research demonstrated that this issue worsened with larger models, and he later showed similar vulnerabilities in generative image models, such as Stable Diffusion. == Life and career == Nicholas Carlini obtained his Bachelor of Arts in Computer Science and Mathematics from the University of California, Berkeley, in 2013. He then continued his studies at the same university, where he pursued a PhD under the supervision of David Wagner, completing it in 2018. Carlini became known for his work on adversarial machine learning. In 2016, he worked alongside Wagner to develop the Carlini & Wagner attack, a method of generating adversarial examples against machine learning models. The attack was proved to be useful against defensive distillation, a popular mechanism where a student model is trained based on the features of a parent model to increase the robustness and generalizability of student models. The attack gained popularity when it was shown that the methodology was also effective against most other defenses, rendering them ineffective. In 2018, Carlini demonstrated an attack against Mozilla Foundation's DeepSpeech model where he showed that by hiding malicious commands inside normal speech input the speech model would respond to the hidden commands even when the commands were not discernible by humans. In the same year, Carlini and his team at UC Berkeley showed that out of the 11 papers presenting defenses to adversarial attacks accepted in that year's ICLR conference, seven of the defenses could be broken. Since 2021, he and his team have been working on large language models, creating a questionnaire where humans typically scored 35% whereas AI models scored in the 40%, with GPT-3 getting 38% which could be improved to 40% through few shot prompting. The best performer in the test was UnifiedQA, a model developed by Google specifically for answer questions and answer sets. Carlini has also developed methods to cause large language models like ChatGPT to answer harmful questions like how to construct bombs. He is also known for his work studying the privacy of machine learning models. In 2020, he showed for the first time that large language models would memorize some of the text data that they were trained on. For example, he found that GPT-2 could output personally identifiable information. He then led an analysis of larger models and studied how memorization increased with model size. Then, in 2022 he showed the same vulnerability in generative image models, and specifically diffusion models, by showing that Stable Diffusion could output images of people's faces that it was trained on. Following on this, Carlini then showed that ChatGPT would also sometimes output exact copies of webpages it was trained on, including personally identifiable information. Some of these studies have since been referenced by the courts in debating the copyright status of AI models. == Other work == Carlini received the Best of Show award at the 2020 IOCCC for implementing a tic-tac-toe game entirely with calls to printf, expanding on work from a research paper of his from 2015. The judges commented on his submission "This year's Best of Show (carlini) is such a novel way of obfuscation that it would be worth of a special mention in the (future) Best of IOCCC list!". [sic] == Awards == Best Student Paper Award, IEEE S&P 2017 ("Towards Evaluating the Robustness of Neural Networks") Best Paper Award, ICML 2018 ("Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples") Distinguished Paper Award, USENIX 2021 ("Poisoning the Unlabeled Dataset of Semi-Supervised Learning") Distinguished Paper Award, USENIX 2023 ("Tight Auditing of Differentially Private Machine Learning") Best Paper Award, ICML 2024 ("Stealing Part of a Production Language Model") Best Paper Award, ICML 2024 ("Considerations for Differentially Private Learning with Large-Scale Public Pretraining")
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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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WhatsApp

WhatsApp Messenger, commonly known simply as WhatsApp, is an American social media, instant messaging (IM), and Voice over IP (VoIP) service accessible via desktop and mobile app. Owned by Meta Platforms, the service allows users to send text messages, voice messages, and video messages, make voice and video calls, and share images, documents, user locations, and other content. The service requires a cellular mobile telephone number to register. WhatsApp was launched in May 2009. In January 2018, WhatsApp released a standalone business app called WhatsApp Business which can communicate with the standard WhatsApp client. As of May 2025, the service had 3 billion monthly active users, making it the most used messenger app. The name of the app is meant to sound like "what's up". The service was created by WhatsApp Inc. of Mountain View, California, which was acquired by Facebook in February 2014 for approximately US$19.3 billion. It became the world's most popular messaging application in 2015, with 900 million users, and had more than 2 billion active users worldwide in February 2020. WhatsApp Business had approximately 200 million monthly users in 2023. By 2016, it had become the primary means of Internet communication in regions including the Americas, the Indian subcontinent, and large parts of Europe and Africa. == History == === 2009–2014 === WhatsApp was founded by Brian Acton and Jan Koum, former employees of Yahoo. Koum incorporated WhatsApp Inc. in California on February 24, 2009. A month earlier, Koum had purchased an iPhone, and he and Acton decided to create an app for the App Store. The idea started off as an app that would display statuses in a phone's Contacts menu, showing if a person was at work or on a call. Their discussions often took place at the home of Koum's Russian friend Alex Fishman in West San Jose. They realized that to take the idea further, they would need an iPhone developer. Fishman visited RentACoder.com, found Russian developer Igor Solomennikov, and introduced him to Koum. Koum named the app WhatsApp to sound like "what's up" and it was published on the Apple App Store and BlackBerry App World in May and June 2009 respectively. However, when early versions of WhatsApp kept crashing, Koum considered giving up and looking for a new job. Acton encouraged him to wait for a "few more months". In June 2009, when the app had been downloaded by only a handful of Fishman's Russian-speaking friends, Apple launched push technology, allowing users to be pinged even when not using the app. Koum updated WhatsApp so that everyone in the user's network would be notified when a user's status changed. This new facility, to Koum's surprise, was used by users to ping "each other with jokey custom statuses like, 'I woke up late' or 'I'm on my way.'" Fishman said, "At some point it sort of became instant messaging". WhatsApp 2.0, released for iPhone in August 2009, featured a purpose-designed messaging component; the number of active users suddenly increased to 250,000. Although Acton was working on another startup idea, he decided to join the company. In October 2009, Acton persuaded five former friends at Yahoo! to invest $250,000 in seed funding, and Acton became a co-founder and was given a stake. He officially joined WhatsApp on November 1. Koum then hired a friend in Los Angeles, Chris Peiffer, to develop a BlackBerry version, which arrived two months later. Subsequently, WhatsApp for Symbian OS was added in May 2010, and for Android OS in August 2010. In 2010 Google made multiple acquisition offers for WhatsApp, which were all declined. To cover the cost of sending verification texts to users, WhatsApp was changed from a free service to a paid one. In December 2009, the ability to send photos was added to the iOS version. By early 2011, WhatsApp was one of the top 20 apps in the U.S. Apple App Store. In April 2011, Sequoia Capital invested about $8 million for more than 15% of the company, after months of negotiation by Sequoia partner Jim Goetz. By February 2013, WhatsApp had about 200 million active users and 50 staff members. Sequoia invested another $50 million at a $1.5 billion valuation. Some time in 2013 WhatsApp acquired Santa Clara–based startup SkyMobius, the developers of Vtok, a video and voice calling app. As of December 2013, the service had 400 million monthly active users. That year, the company had $148 million in expenses and a net loss of $138 million. === 2014–2015 === On February 19, 2014, one year after the venture capital financing round at a $1.5 billion valuation, Facebook, Inc. (now Meta Platforms) agreed to acquire the company for US$19 billion, its largest acquisition to date. At the time, it was the largest acquisition of a venture-capital-backed company in history. Sequoia Capital received an approximate 5,000% return on its initial investment. Facebook paid $4 billion in cash, $12 billion in Facebook shares, and an additional $3 billion in restricted stock units granted to WhatsApp's founders Koum and Acton. Employee stock was scheduled to vest over four years subsequent to closing. Days after the announcement, WhatsApp users experienced a loss of service, leading to anger across social media. The acquisition was influenced by the data provided by Onavo, Facebook's research app for monitoring competitors and trending usage of social activities on mobile phones, as well as startups that were performing "unusually well". The acquisition caused many users to try, or move to, other message services. Telegram claimed that it acquired 8 million new users, and Line, 2 million. At a keynote presentation at the Mobile World Congress in Barcelona in February 2014, Facebook CEO Mark Zuckerberg said that Facebook's acquisition of WhatsApp was closely related to the Internet.org vision. A TechCrunch article said about Zuckerberg's vision:The idea, he said, is to develop a group of basic internet services that would be free of charge to use – "a 911 for the internet". These could be a social networking service like Facebook, a messaging service, maybe search and other things like weather. Providing a bundle of these free of charge to users will work like a gateway drug of sorts – users who may be able to afford data services and phones these days just don't see the point of why they would pay for those data services. This would give them some context for why they are important, and that will lead them to pay for more services like this – or so the hope goes. Three days after announcing the Facebook purchase, Koum said they were working to introduce voice calls. He also said that new mobile phones would be sold in Germany with the WhatsApp brand, and that their ultimate goal was to be on all smartphones. In August 2014, WhatsApp was the most popular messaging app in the world, with more than 600 million users. By early January 2015, WhatsApp had 700 million monthly users and over 30 billion messages every day. In April 2015, Forbes predicted that between 2012 and 2018, the telecommunications industry would lose $386 billion because of "over-the-top" services like WhatsApp and Skype. That month, WhatsApp had over 800 million users. By September 2015, it had grown to 900 million; and by February 2016, one billion. On November 30, 2015, the Android WhatsApp client made links to Telegram unclickable and not copyable. Multiple sources confirmed that it was intentional, not a bug, and that it had been implemented when the Android source code that recognized Telegram URLs had been identified. (The word "telegram" appeared in WhatsApp's code.) Some considered it an anti-competitive measure; WhatsApp offered no explanation. === 2016–2019 === On January 18, 2016, WhatsApp's co-founder Jan Koum announced that it would no longer charge users a $1 annual subscription fee, in an effort to remove a barrier faced by users without payment cards. He also said that the app would not display any third-party ads, and that it would have new features such as the ability to communicate with businesses. On May 18, 2017, the European Commission announced that it was fining Facebook €110 million for "providing misleading information about WhatsApp takeover" in 2014. The Commission said that in 2014 when Facebook acquired the messaging app, it "falsely claimed it was technically impossible to automatically combine user information from Facebook and WhatsApp." However, in the summer of 2016, WhatsApp had begun sharing user information with its parent company, allowing information such as phone numbers to be used for targeted Facebook advertisements. Facebook acknowledged the breach, but said the errors in their 2014 filings were "not intentional". In September 2017, WhatsApp's co-founder Brian Acton left the company to start a nonprofit group, later revealed as the Signal Foundation, which developed the WhatsApp competitor Signal. He explained his reasons for leaving in an interview with Forbes a year later. WhatsApp also
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Comparison of machine translation applications

Machine translation is an algorithm which attempts to translate text or speech from one natural language to another. == General information == Basic general information for popular machine translation applications. == Languages features comparison == The following table compares the number of languages which the following machine translation programs can translate between. (Moses and Moses for Mere Mortals allow you to train translation models for any language pair, though collections of translated texts (parallel corpus) need to be provided by the user. The Moses site provides links to training corpora.) This is not an all-encompassing list. Some applications have many more language pairs than those listed below. This is a general comparison of key languages only. A full and accurate list of language pairs supported by each product should be found on each of the product's websites. === Multi-pair translations === === Paired translations ===
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How to Choose an AI Text-to-image Tool

Curious about the best AI text-to-image tool? An AI text-to-image tool is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI text-to-image tool 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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