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<br>Announced in 2016, Gym is an open-source Python [library designed](http://162.55.45.543000) to help with the development of support knowing algorithms. It aimed to standardize how [environments](https://parejas.teyolia.mx) are specified in [AI](http://152.136.187.229) research study, making released research more quickly reproducible [24] [144] while supplying users with a simple user interface for interacting with these environments. In 2022, brand-new advancements of Gym have been moved to the library Gymnasium. [145] [146]
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<br>Gym Retro<br>
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<br>Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research on computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing representatives to solve single tasks. Gym Retro gives the ability to generalize between video games with comparable concepts however various looks.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack knowledge of how to even stroll, however are given the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adapt to [changing conditions](http://111.61.77.359999). When an agent is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between [representatives](https://gitlab.isc.org) could create an intelligence "arms race" that might [increase](https://thunder-consulting.net) an agent's ability to work even outside the context of the competition. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five [video game](https://teengigs.fun) Dota 2, that learn to play against human players at a high skill level completely through experimental algorithms. Before ending up being a team of 5, the very first public demonstration occurred at The International 2017, the annual premiere championship tournament 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 that the bot had actually learned by playing against itself for 2 weeks of real time, which the knowing software application was a step in the direction of creating software application that can handle complicated tasks like a surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots find out over time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the ability of the bots expanded to play together as a complete team of 5, and they were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional gamers, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those video games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer shows the difficulties of [AI](https://lifefriendsurance.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated making use of deep reinforcement learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It learns completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has RGB cams to permit the [robotic](http://112.48.22.1963000) to control an arbitrary object by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually harder 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 which it said was "for accessing brand-new [AI](https://skillsvault.co.za) models established by OpenAI" to let designers call on it for "any English language [AI](https://hiremegulf.com) task". [170] [171]
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<br>Text generation<br>
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<br>The company has actually popularized generative pretrained transformers (GPT). [172]
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<br>[OpenAI's](https://24frameshub.com) original GPT design ("GPT-1")<br>
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<br>The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative model of language might obtain world knowledge and procedure long-range reliances by pre-training on a varied corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions initially released to the general public. The full version of GPT-2 was not immediately launched due to issue about prospective misuse, including applications for writing fake news. [174] Some experts revealed uncertainty that GPT-2 positioned a significant risk.<br>
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence [responded](http://park7.wakwak.com) with a tool to identify "neural fake news". [175] Other scientists, [yewiki.org](https://www.yewiki.org/User:CorazonHarman) such as Jeremy Howard, warned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language model. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue not being watched language models to be general-purpose students, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 [zero-shot tasks](https://splink24.com) (i.e. the model was not additional trained on any task-specific input-output examples).<br>
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<br>The corpus it was trained on, called WebText, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:LinHeilman) contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters 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 a without supervision transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were also trained). [186]
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<br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and might generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
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<br>GPT-3 significantly improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or [encountering](https://www.nas-store.com) the basic ability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly [launched](https://meetpit.com) to the general public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month free private beta that began in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified exclusively to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Gertie4309) Codex is a descendant of GPT-3 that has actually furthermore been [trained](http://www.chemimart.kr) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://becalm.life) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the design can develop working code in over a lots programs languages, many successfully in Python. [192]
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<br>Several issues with glitches, design defects and security vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has actually been accused of emitting copyrighted code, without any author attribution or license. [197]
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<br>OpenAI announced that they would [terminate support](https://internship.af) 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the upgraded innovation passed a simulated law school bar examination with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, evaluate or generate up to 25,000 words of text, and compose code in all major programming languages. [200]
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<br>Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also efficient in taking images as input on [ChatGPT](http://47.94.142.23510230). [202] OpenAI has decreased to reveal various technical details and data 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, [gratisafhalen.be](https://gratisafhalen.be/author/jai0489735/) OpenAI announced and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI released GPT-4o mini, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=998410) a smaller version of GPT-4o changing 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 useful for enterprises, start-ups and designers seeking to automate services with [AI](https://www.stmlnportal.com) representatives. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been created to take more time to consider their responses, resulting in greater precision. These designs are particularly efficient in science, coding, and thinking jobs, and were made available to [ChatGPT](http://64.227.136.170) Plus and Staff member. [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 revealed o3, the [successor](http://47.106.205.1408089) of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a [lighter](https://git.chocolatinie.fr) and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, [security](https://www.menacopt.com) and security researchers had the opportunity to obtain early access to these models. [214] The design is called o3 rather than o2 to prevent confusion with telecoms services company O2. [215]
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<br>Deep research<br>
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<br>Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the [abilities](https://fromkorea.kr) of OpenAI's o3 model to carry out extensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
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<br>Image classification<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP ([Contrastive Language-Image](https://git.fhlz.top) Pre-training) is a model that is trained to examine the semantic resemblance in between text and images. It can especially be used 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 model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can create [pictures](http://47.112.106.1469002) of practical items ("a stained-glass window with an image of a blue strawberry") as well as items 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 announced DALL-E 2, an upgraded version of the design with more practical results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new simple system for transforming 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 announced DALL-E 3, a more [effective model](http://111.61.77.359999) better able to produce images from complicated descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released to the public as a ChatGPT Plus function 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 generate videos based on short detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is [unknown](http://hybrid-forum.ru).<br>
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<br>Sora's development team named it after the Japanese word for "sky", to symbolize its "unlimited creative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos accredited for that purpose, however did not reveal the number or the precise sources of the videos. [223]
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<br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might create videos up to one minute long. It likewise shared a technical report highlighting the approaches used to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, including struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but noted that they need to have been cherry-picked and might not represent Sora's normal output. [225]
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<br>Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry [figures](https://www.cdlcruzdasalmas.com.br) have shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to generate practical video from text descriptions, citing its possible to reinvent storytelling and [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/dewaynerodri) material development. He said that his enjoyment about Sora's possibilities was so strong that he had decided to pause prepare for expanding his Atlanta-based motion picture 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 model that can perform multilingual speech acknowledgment as well as speech translation and language identification. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly however then fall under turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to produce music for the titular character. [232] [233]
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<br>Jukebox<br>
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<br>[Released](https://gitea.aambinnes.com) in 2020, Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song samples. OpenAI stated the songs "show regional musical coherence [and] follow conventional chord patterns" however [acknowledged](https://dev.ncot.uk) that the tunes lack "familiar bigger musical structures such as choruses that repeat" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge specified "It's technically remarkable, even if the outcomes sound like mushy versions of songs that may feel familiar", while Business Insider stated "remarkably, some of the resulting tunes are catchy and sound genuine". [234] [235] [236]
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<br>Interface<br>
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<br>Debate Game<br>
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<br>In 2018, [OpenAI launched](https://stepaheadsupport.co.uk) the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The function is to research study whether such an approach may assist in auditing [AI](http://121.4.70.4:3000) and in establishing explainable [AI](https://platform.giftedsoulsent.com). [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 8 neural network models which are [typically studied](https://www.opad.biz) in interpretability. [240] Microscope was created to evaluate the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various variations 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 an expert system tool constructed on top of GPT-3 that offers a conversational interface that enables users to ask questions in natural language. The system then reacts with a response within seconds.<br>
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