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<br>Announced in 2016, Gym is an open-source Python library developed to help with the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](http://27.185.47.113:5200) research, making published research study more quickly reproducible [24] [144] while supplying users with a simple interface for connecting 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 support knowing (RL) research on computer game [147] using RL algorithms and research study [generalization](https://gitea.tmartens.dev). Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro gives the ability to generalize between games with similar ideas however 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](https://video.spacenets.ru) robotic representatives initially lack knowledge of how to even stroll, however are given the objectives of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives discover how to adjust to altering conditions. When a representative is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might produce an intelligence "arms race" that might increase an agent's ability to operate 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 team of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that learn 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 happened at The International 2017, the yearly premiere [championship](http://bristol.rackons.com) [tournament](http://154.9.255.1983000) for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg [Brockman](https://southwestjobs.so) explained that the bot had actually found out by playing against itself for 2 weeks of genuine time, and that the learning software was an action in the direction of [producing software](https://www.footballclubfans.com) that can manage intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement learning, as the bots find out in time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map objectives. [154] [155] [156]
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<br>By June 2018, the capability of the bots broadened to play together as a full team of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat 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 that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those video games. [165]
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<br>OpenAI 5's systems in Dota 2's bot player shows the challenges of [AI](https://phdjobday.eu) in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated making use of deep reinforcement [knowing](https://uconnect.ae) (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl uses [maker discovering](https://accc.rcec.sinica.edu.tw) to train a Shadow Hand, a human-like robotic hand, to [control physical](http://8.141.155.1833000) items. [167] It discovers totally in simulation using 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 learner to a range of experiences rather than [attempting](https://git.kuyuntech.com) to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB cams to allow the robotic to control an approximate object by seeing it. In 2018, OpenAI showed that the system had the ability to control 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 had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of generating progressively more difficult environments. ADR varies from manual domain randomization by not needing a human to define randomization ranges. [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](http://rootbranch.co.za:7891) designs established by OpenAI" to let developers call on it for "any English language [AI](https://git.alternephos.org) task". [170] [171]
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<br>Text generation<br>
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<br>The business has actually 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 released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model of language could obtain world understanding and procedure long-range dependences by pre-training on a diverse 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 [successor](http://gogs.fundit.cn3000) to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations at first released to the general public. The complete version of GPT-2 was not immediately released due to issue about possible abuse, consisting of applications for writing phony news. [174] Some experts expressed uncertainty that GPT-2 positioned a substantial danger.<br>
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language design. [177] Several websites host interactive 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 learners, highlighted by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (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 at least 3 [upvotes](http://47.112.106.1469002). It prevents certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both [specific characters](http://43.136.54.67) 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](https://m1bar.com) language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million parameters were also trained). [186]
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<br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the purpose of a [single input-output](https://thathwamasijobs.com) pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]
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<br>GPT-3 dramatically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the essential capability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the general public for [concerns](https://schubach-websocket.hopto.org) of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary personal beta that began 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://wiki.contextgarden.net) [powering](https://jobs.foodtechconnect.com) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the design can create working code in over a lots programming languages, a lot of efficiently in Python. [192]
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<br>Several problems with problems, style defects and security vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has actually been implicated of giving off copyrighted code, without any author attribution or license. [197]
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<br>OpenAI revealed that they would cease assistance 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), capable of accepting text or image inputs. [199] They announced that the updated innovation 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, analyze or generate approximately 25,000 words of text, and compose code in all significant programs languages. [200]
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<br>Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also [efficient](https://convia.gt) in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various technical details and data about GPT-4, such as the accurate size of the model. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI revealed and [released](http://gitlab.solyeah.com) GPT-4o, which can [process](https://git.learnzone.com.cn) and create text, images and audio. [204] GPT-4o attained modern 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) standard compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller version of GPT-4o changing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million [input tokens](http://nas.killf.info9966) and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be especially [helpful](https://funitube.com) for enterprises, startups and developers looking for to automate services with [AI](https://git.cyu.fr) agents. [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 designs, which have actually been developed to take more time to believe about their actions, [leading](https://niaskywalk.com) to greater precision. These designs are especially effective in science, coding, and thinking 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 successor of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecoms services provider 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 perform comprehensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 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 Pre-training) is a model that is trained to evaluate the [semantic similarity](http://dchain-d.com3000) between text and images. It can notably be used for image [classification](https://gitea.ws.adacts.com). [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 produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to [interpret](https://career.ltu.bg) [natural language](https://1samdigitalvision.com) inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and create corresponding images. It can produce images of realistic items ("a stained-glass window with an image of a blue strawberry") as well as items that do not exist in [reality](https://andonovproltd.com) ("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 [updated](https://sebeke.website) version of the design with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software application for [yewiki.org](https://www.yewiki.org/User:EltonDalziel) 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 announced DALL-E 3, a more powerful model much better able to generate images from complex descriptions without manual prompt engineering and [render complicated](http://101.43.18.2243000) [details](https://abcdsuppermarket.com) like hands and text. [221] It was launched 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](https://iraqitube.com) that can generate videos based on brief detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.<br>
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<br>Sora's development group called it after the Japanese word for "sky", to signify its "endless imaginative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as [copyrighted videos](http://152.136.232.1133000) accredited for that purpose, however did not expose 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, specifying that it could produce videos as much as one minute long. It likewise shared a technical report highlighting the [techniques](https://hortpeople.com) used to train the design, and the model's abilities. [225] It acknowledged some of its imperfections, including battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however kept in mind 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 scholastic leaders following [Sora's public](http://101.34.87.71) demo, notable entertainment-industry figures have actually shown [substantial](https://git.sitenevis.com) interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to create sensible video from text descriptions, citing its possible to reinvent storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to pause prepare for broadening 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 recognition model. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can perform multilingual speech recognition along with 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 in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to begin fairly however then fall into mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to develop 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 produce music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and [outputs tune](http://27.185.47.1135200) samples. OpenAI mentioned the tunes "show local musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" which "there is a considerable gap" in between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the outcomes sound like mushy variations of songs that may feel familiar", while Business Insider stated "remarkably, some of the resulting tunes are appealing and sound genuine". [234] [235] [236]
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<br>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 makers to discuss toy issues in front of a human judge. The purpose is to research whether such an approach might help in auditing [AI](https://git.rungyun.cn) choices and in establishing explainable [AI](http://112.112.149.146:13000). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, [Microscope](https://kaymack.careers) [239] is a collection of visualizations of every substantial layer and nerve cell of 8 neural network models which are frequently studied in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different 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 artificial intelligence tool built on top of GPT-3 that provides a conversational user [interface](https://git.connectplus.jp) that enables users to ask concerns in natural language. The system then responds with an answer within seconds.<br>
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