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Google Neural Machine Translation (GNMT) is a neural machine translation (NMT) system developed by Google and introduced in November 2016 that uses an artificial neural network to increase fluency and accuracy in Google Translate. [1] [2] [3] [4] The neural network consists of two main blocks, an encoder and a decoder, both of LSTM architecture with 8 1024-wide layers each and a simple 1-layer 1024-wide feedforward attention mechanism connecting them. [4] [5] The total number of parameters has been variously described as over 160 million, [6] approximately 210 million, [7] 278 million [8] or 380 million. [9]
GNMT improves on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. [2] GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. [2] With the large end-to-end framework, the system learns over time to create better, more natural translations. [1] GNMT attempts to translate whole sentences at a time, rather than just piece by piece. [1] The GNMT network can undertake interlingual machine translation by encoding the semantics of the sentence, rather than by memorizing phrase-to-phrase translations. [2] [10]
The Google Brain project was established in 2011 in the "secretive Google X research lab" [11] by Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University Computer Science professor Andrew Ng. [12] [13] [14] Ng's work has led to some of the biggest breakthroughs at Google and Stanford. [11]
In November 2016, Google Neural Machine Translation system (GNMT) was introduced. Since then, Google Translate began using neural machine translation (NMT) in preference to its previous statistical methods (SMT) [1] [15] [16] [17] which had been used since October 2007, with its proprietary, in-house SMT technology. [18] [19]
Training GNMT was a big effort at the time and took, by a 2021 OpenAI estimate, on the order of 100 PFLOP/s*day (up to 1022 FLOPs) of compute which was 1.5 orders of magnitude larger than Seq2seq model of 2014 [20] (but about 2x smaller than GPT-J-6B in 2021 [21] ).
Google Translate's NMT system uses a large artificial neural network capable of deep learning. [1] [2] [3] By using millions of examples, GNMT improves the quality of translation, [2] using broader context to deduce the most relevant translation. The result is then rearranged and adapted to approach grammatically based human language. [1] GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. [2] GNMT did not create its own universal interlingua but rather aimed at finding the commonality between many languages using insights from psychology and linguistics. [22] The new translation engine was first enabled for eight languages: to and from English and French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish in November 2016. [23] In March 2017, three additional languages were enabled: Russian, Hindi and Vietnamese along with Thai for which support was added later. [24] [25] Support for Hebrew and Arabic was also added with help from the Google Translate Community in the same month. [26] In mid April 2017 Google Netherlands announced support for Dutch and other European languages related to English. [27] Further support was added for nine Indian languages: Hindi, Bengali, Marathi, Gujarati, Punjabi, Tamil, Telugu, Malayalam and Kannada at the end of April 2017. [28]
This section needs to be updated.(April 2023) |
The GNMT system is said to represent an improvement over the former Google Translate in that it will be able to handle "zero-shot translation", that is it directly translates one language into another (for example, Japanese to Korean). [2] Google Translate previously first translated the source language into English and then translated the English into the target language rather than translating directly from one language to another. [10]
A July 2019 study in Annals of Internal Medicine found that "Google Translate is a viable, accurate tool for translating non–English-language trials". Only one disagreement between reviewers reading machine-translated trials was due to a translation error. Since many medical studies are excluded from systematic reviews because the reviewers do not understand the language, GNMT has the potential to reduce bias and improve accuracy in such reviews. [29]
As of December 2021, all of the languages of Google Translate support GNMT, with Latin being the most recent addition.
Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or other animals. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.
Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
Jürgen Schmidhuber is a German computer scientist noted for his work in the field of artificial intelligence, specifically artificial neural networks. He is a scientific director of the Dalle Molle Institute for Artificial Intelligence Research in Switzerland. He is also director of the Artificial Intelligence Initiative and professor of the Computer Science program in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) division at the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia.
Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. As of 2022, Google Translate supports 133 languages at various levels; it claimed over 500 million total users as of April 2016, with more than 100 billion words translated daily, after the company stated in May 2013 that it served over 200 million people daily.
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Machine translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another.
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Andrew Yan-Tak Ng is a British-American computer scientist and technology entrepreneur focusing on machine learning and artificial intelligence (AI). Ng was a cofounder and head of Google Brain and was the former Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people.
Otávio Good is a Brazilian and American computer programmer and inventor. He is the original author of Word Lens, the first augmented reality translation application that replaces printed text into the desired language in video without connection to the Internet.
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Jeffrey Adgate "Jeff" Dean is an American computer scientist and software engineer. Since 2018, he has been the lead of Google AI. He was appointed Alphabet's chief scientist in 2023 after a reorganization of Alphabet's AI focused groups.
Google Brain was a deep learning artificial intelligence research team under the umbrella of Google AI, a research division at Google dedicated to artificial intelligence. Formed in 2011, Google Brain combined open-ended machine learning research with information systems and large-scale computing resources. The team has created tools such as TensorFlow, which allow for neural networks to be used by the public, with multiple internal AI research projects. The team aims to create research opportunities in machine learning and natural language processing. The team was merged into former Google sister company DeepMind to form Google DeepMind in April 2023.
DeepMind Technologies Limited, doing business as Google DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Google. Founded in the UK in 2010, it was acquired by Google in 2014, The company is based in London, with research centres in Canada, France, Germany and the United States.
Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
An AI accelerator, deep learning processor, or neural processing unit is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and machine vision. Typical applications include algorithms for robotics, Internet of Things, and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2024, a typical AI integrated circuit chip contains tens of billions of MOSFET transistors.
Ian J. Goodfellow is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning. He was previously employed as a research scientist at Google Brain and director of machine learning at Apple and has made several important contributions to the field of deep learning including the invention of the generative adversarial network (GAN). Goodfellow co-wrote, as the first author, the textbook Deep Learning (2016) and wrote the chapter on deep learning in the authoritative textbook of the field of artificial intelligence, Artificial Intelligence: A Modern Approach.
Meta AI is an artificial intelligence laboratory that belongs to Meta Platforms Inc. Meta AI intends to develop various forms of artificial intelligence, improving augmented and artificial reality technologies. Meta AI is an academic research laboratory focused on generating knowledge for the AI community. This is in contrast to Facebook's Applied Machine Learning (AML) team, which focuses on practical applications of its products.
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