Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library created to assist in the development of support learning algorithms. It aimed to standardize how environments are specified in [AI](http://101.51.106.216) research, making released research more quickly reproducible [24] [144] while providing users with a basic user interface for connecting with these environments. In 2022, new developments of Gym have actually 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 support knowing (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to fix single jobs. Gym Retro provides the ability to generalize between games with comparable ideas but 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 robotic agents at first do not have knowledge of how to even stroll, however are offered the goals of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the [agents learn](https://mount-olive.com) how to adapt to altering conditions. When an agent is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could create an intelligence "arms race" that could increase a representative's capability to work even outside the context of the competitors. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive [five-on-five](http://e-kou.jp) computer game Dota 2, that find out to play against human players at a high skill level totally through trial-and-error algorithms. Before ending up being a group of 5, the first public [demonstration](https://gitlab.steamos.cloud) [occurred](https://www.selfhackathon.com) at The International 2017, the yearly best championship tournament for [surgiteams.com](https://surgiteams.com/index.php/User:MagaretMccurry6) the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg [Brockman explained](https://git.smartenergi.org) that the bot had discovered by playing against itself for two weeks of actual time, which the knowing software was an action in the direction of producing software application that can handle intricate jobs like a cosmetic surgeon. [152] [153] The system uses a type of support knowing, as the bots find out in 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 objectives. [154] [155] [156]
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<br>By June 2018, the ability of the bots broadened to play together as a complete group of 5, and they were able to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five [defeated](https://innovator24.com) OG, the ruling world champs 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 on that month, where they played in 42,729 total video games in a four-day open online competitors, winning 99.4% of those games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer shows the challenges of [AI](http://git.medtap.cn) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated using deep reinforcement learning (DRL) agents 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 utilizes maker finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It learns totally in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by utilizing domain randomization, a [simulation approach](http://gitlab.digital-work.cn) which exposes the learner to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, [it-viking.ch](http://it-viking.ch/index.php/User:GeorginaBettis) aside from having movement tracking cams, also has RGB cameras to permit the robotic to manipulate an arbitrary things by seeing it. In 2018, OpenAI showed 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 could resolve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating gradually harder environments. ADR varies from manual domain randomization by not requiring a human to specify 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 new [AI](http://pyfup.com:3000) models developed by OpenAI" to let [developers contact](http://www.stardustpray.top30009) it for "any English language [AI](http://39.106.8.246:3003) task". [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 original GPT design ("GPT-1")<br>
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<br>The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world understanding and procedure long-range dependences 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 a not being watched transformer language design and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative versions initially launched to the public. The complete variation of GPT-2 was not right away released due to concern about possible misuse, including applications for writing fake news. [174] Some specialists expressed uncertainty that GPT-2 postured a substantial danger.<br>
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several websites host [interactive](https://edurich.lk) demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue unsupervised language models to be general-purpose students, highlighted by GPT-2 attaining modern 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 avoids certain issues encoding vocabulary with word tokens by utilizing 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 not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were likewise trained). [186]
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<br>[OpenAI stated](http://udyogservices.com) that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and learning in between English and Romanian, and between English and German. [184]
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<br>GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI [cautioned](http://swwwwiki.coresv.net) that such scaling-up of language models could be approaching or encountering the essential capability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous 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 design was not immediately released to the general public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free personal beta that started in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified specifically 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 actually in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://git.kairoscope.net) powering the [code autocompletion](http://63.141.251.154) tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the model can develop working code in over a lots shows languages, most effectively in Python. [192]
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<br>Several concerns with glitches, design flaws and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has been accused of giving off copyrighted code, with no author attribution or license. [197]
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<br>OpenAI revealed that they would terminate 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, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:Maricruz98D) OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar examination with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, evaluate or create approximately 25,000 words of text, and write code in all significant programming languages. [200]
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<br>Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually [decreased](http://47.100.3.2093000) to expose different 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, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask [Language Understanding](https://git.gday.express) (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 version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user 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 expects it to be particularly useful for business, startups and designers looking for to automate services with [AI](https://www.outletrelogios.com.br) 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 designs, which have been created to take more time to think about their actions, leading to greater precision. These models are especially effective in science, coding, and reasoning jobs, and were made available to ChatGPT 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 follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating 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 design is called o3 rather than o2 to prevent confusion with telecommunications providers O2. [215]
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<br>Deep research study<br>
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<br>Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out extensive web surfing, information analysis, and synthesis, [providing detailed](https://bgzashtita.es) reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, it reached an [accuracy](https://moyatcareers.co.ke) of 26.6 percent on HLE (Humanity's Last Exam) criteria. [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 model that is trained to examine the semantic similarity between text and images. It can notably be utilized for image classification. [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 that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and create corresponding images. It can create images of practical objects ("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"). As of 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 outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new rudimentary system for transforming a text description into a 3[-dimensional model](https://careers.synergywirelineequipment.com). [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 much better able to generate images from intricate descriptions without manual timely engineering and render complicated details like hands and text. [221] It was released to the general 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](https://gitlab.companywe.co.kr) triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of created videos is unknown.<br>
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<br>Sora's advancement group named it after the Japanese word for "sky", to symbolize its "unlimited creative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted [videos licensed](https://nmpeoplesrepublick.com) for [89u89.com](https://www.89u89.com/author/sole7081199/) that purpose, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:SybilMartins5) but did not expose the number or the precise sources of the videos. [223]
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<br>OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it might [generate videos](https://catvcommunity.com.tr) approximately one minute long. It likewise shared a technical report highlighting the approaches [utilized](https://git.itk.academy) to train the design, and the design's abilities. [225] It acknowledged a few of its imperfections, [consisting](https://funnyutube.com) of [struggles replicating](http://park8.wakwak.com) complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but noted that they must have been cherry-picked and may not represent Sora's typical output. [225]
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<br>Despite uncertainty from some scholastic leaders following [Sora's public](https://lonestartube.com) demo, significant entertainment-industry figures have revealed substantial interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to create sensible video from text descriptions, citing its prospective to transform storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly strategies for expanding his Atlanta-based motion [picture](https://www.arztstellen.com) 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](http://175.24.174.1733000) is a general-purpose speech recognition model. [228] It is trained on a big dataset of diverse audio and is also a multi-task model that can carry out 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 predict subsequent musical notes in [MIDI music](http://47.76.141.283000) files. It can create songs with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly however then fall under chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the web mental 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](https://saga.iao.ru3043) in 2020, Jukebox is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the tunes "show local musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a significant space" between Jukebox and human-generated music. The Verge specified "It's highly excellent, even if the outcomes seem like mushy versions of tunes that may feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are appealing 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 to discuss toy issues in front of a human judge. The purpose is to research study whether such a technique may assist in auditing [AI](https://pantalassicoembalagens.com.br) decisions and in establishing explainable [AI](https://parejas.teyolia.mx). [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 neuron of eight neural network models which are often studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various 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 an expert system tool constructed on top of GPT-3 that offers a conversational interface that permits users to ask questions in natural language. The system then reacts with a response within seconds.<br>
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