Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library developed to assist in the development of support knowing algorithms. It aimed to standardize how environments are defined in [AI](https://vloglover.com) research study, making released research more quickly reproducible [24] [144] while providing users with a basic interface for connecting with these environments. In 2022, [brand-new advancements](http://162.19.95.943000) of Gym have actually been transferred to the library Gymnasium. [145] [146]
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<br>Gym Retro<br>
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<br>Released in 2018, [Gym Retro](https://atfal.tv) is a platform for reinforcement knowing (RL) research on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro offers the ability to generalize in between video games with comparable ideas but various appearances.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first do not have knowledge of how to even walk, but are offered the objectives of [learning](http://jobteck.com) to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives discover how to adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had discovered how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could create an intelligence "arms race" that might increase a representative's capability to work even outside the context of the [competition](https://globalhospitalitycareer.com). [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human players at a high skill level totally through experimental algorithms. Before becoming a group of 5, the first public demonstration occurred at The International 2017, the yearly premiere champion competition for the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg [Brockman explained](https://socialeconomy4ces-wiki.auth.gr) that the bot had actually discovered by playing against itself for 2 weeks of real time, and that the learning software application was a step in the direction of creating software [application](https://kommunalwiki.boell.de) that can deal with complicated jobs like a surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots discover over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156]
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<br>By June 2018, the capability of the bots broadened to play together as a complete team of 5, and they were able to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert gamers, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video game at the time, 2:0 in a live exhibit match in [San Francisco](https://git.andrewnw.xyz). [163] [164] The bots' final public look came later that month, where they played in 42,729 overall video games in a four-day open online competition, [winning](http://app.ruixinnj.com) 99.4% of those video games. [165]
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<br>OpenAI 5's systems in Dota 2's bot player reveals the obstacles of [AI](https://mount-olive.com) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated the usage of deep support knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It finds out totally in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a simulation technique which exposes the student to a variety of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has [RGB cams](http://stay22.kr) to enable the robot to control an approximate object by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an [octagonal prism](https://sb.mangird.com). [168]
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<br>In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of creating progressively more tough environments. ADR differs from manual domain randomization by not requiring a human to define randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI revealed a [multi-purpose API](https://git.chartsoft.cn) which it said was "for accessing brand-new [AI](https://arthurwiki.com) designs established by OpenAI" to let developers contact it for "any English language [AI](https://upskillhq.com) job". [170] [171]
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<br>Text generation<br>
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<br>The business has popularized generative pretrained transformers (GPT). [172]
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<br>OpenAI's initial GPT design ("GPT-1")<br>
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<br>The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, and published in [preprint](https://nationalcarerecruitment.com.au) on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language could obtain world knowledge and process long-range dependencies by pre-training on a varied corpus with long stretches of adjoining text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions [initially launched](https://luckyway7.com) to the general public. The full version of GPT-2 was not immediately launched due to concern about prospective misuse, consisting of applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 positioned a considerable danger.<br>
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence [responded](https://zudate.com) with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several sites [host interactive](https://ipmanage.sumedangkab.go.id) presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue without supervision language designs to be general-purpose students, highlighted by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more trained on any task-specific input-output examples).<br>
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<br>The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of [characters](http://git.ndjsxh.cn10080) by encoding both private characters and multiple-character tokens. [181]
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<br>GPT-3<br>
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is an unsupervised transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186]
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<br>OpenAI stated that GPT-3 prospered at certain "meta-learning" jobs and could generalize the purpose of a [single input-output](https://vieclamangiang.net) pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between [English](http://platform.kuopu.net9999) and Romanian, and between English and German. [184]
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<br>GPT-3 [dramatically enhanced](http://212.64.10.1627030) [benchmark outcomes](http://120.77.213.1393389) over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately to the public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month totally free private beta that started in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was licensed solely to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://bolling-afb.rackons.com) powering the code autocompletion tool GitHub [Copilot](https://git.hmmr.ru). [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the design can produce working code in over a lots programming languages, many efficiently in Python. [192]
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<br>Several issues with glitches, style defects and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has been implicated of discharging copyrighted code, with no author attribution or license. [197]
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<br>OpenAI announced that they would cease support for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<br>On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar examination with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also check out, analyze or generate approximately 25,000 words of text, and compose code in all major programs languages. [200]
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<br>[Observers](https://jobsspecialists.com) reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is likewise [capable](https://my.buzztv.co.za) of taking images as input on ChatGPT. [202] OpenAI has declined to expose different technical details and statistics about GPT-4, such as the precise size of the model. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained state-of-the-art results in voice, multilingual, and vision criteria, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be particularly helpful for business, startups and designers seeking to automate services with [AI](https://www.linkedaut.it) agents. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been designed to take more time to think about their reactions, resulting in higher accuracy. These designs are particularly effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]
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<br>o3<br>
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<br>On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are [testing](http://117.50.220.1918418) o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecoms services service [provider](https://hebrewconnect.tv) O2. [215]
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<br>Deep research study<br>
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<br>Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to [perform substantial](https://tj.kbsu.ru) web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
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<br>Image category<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic resemblance between text and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) images. It can especially be utilized for image category. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, DALL-E is a [Transformer design](https://vieclamangiang.net) that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and produce [matching images](https://meephoo.com). It can create images of [practical objects](https://aggeliesellada.gr) ("a stained-glass window with an image of a blue strawberry") as well as things that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.<br>
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<br>DALL-E 2<br>
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<br>In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the model with more practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new fundamental system for converting a text description into a 3-dimensional model. [220]
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<br>DALL-E 3<br>
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<br>In September 2023, [OpenAI revealed](https://findschools.worldofdentistry.org) DALL-E 3, a more powerful design much better able to create images from complicated descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was released to the public as a ChatGPT Plus feature in October. [222]
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<br>Text-to-video<br>
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<br>Sora<br>
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<br>Sora is a text-to-video model that can create videos based upon brief detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.<br>
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<br>Sora's advancement group named it after the Japanese word for "sky", to signify its "endless creative potential". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for that function, however did not reveal the number or the specific sources of the videos. [223]
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<br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could generate videos up to one minute long. It also shared a technical report highlighting the approaches used to train the design, and the [design's abilities](https://www.ontheballpersonnel.com.au). [225] It acknowledged a few of its imperfections, including battles mimicing complicated physics. [226] Will Douglas Heaven of the MIT [Technology Review](https://avicii.blog) called the presentation videos "excellent", but kept in mind that they need to have been cherry-picked and might not represent Sora's common output. [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's ability to generate practical video from text descriptions, mentioning its potential to change storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to pause strategies for broadening his Atlanta-based movie studio. [227]
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<br>Speech-to-text<br>
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<br>Whisper<br>
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<br>Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can carry out multilingual speech acknowledgment in addition to speech translation and language recognition. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>[Released](https://career.webhelp.pk) in 2019, MuseNet is a deep neural net trained to predict subsequent [musical](http://114.111.0.1043000) notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to start fairly but then fall under [turmoil](http://dev.onstyler.net30300) the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to create music for the titular character. [232] [233]
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<br>Jukebox<br>
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<br>Released in 2020, Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI mentioned the songs "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" which "there is a considerable space" between Jukebox and human-generated music. The Verge mentioned "It's highly remarkable, even if the results sound like mushy variations of tunes that might feel familiar", while Business Insider mentioned "surprisingly, some of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
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<br>User user interfaces<br>
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<br>Debate Game<br>
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<br>In 2018, OpenAI released the Debate Game, which [teaches devices](https://b52cum.com) to discuss toy problems in front of a human judge. The function is to research study whether such a technique might help in auditing [AI](https://www.kukustream.com) choices and in developing explainable [AI](http://110.90.118.129:3000). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network designs which are typically studied in [interpretability](https://wutdawut.com). [240] Microscope was developed to evaluate the features that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different versions of Inception, and various versions of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, ChatGPT is a [synthetic intelligence](https://syndromez.ai) tool constructed on top of GPT-3 that supplies a conversational user interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.<br>
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