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<br>Announced in 2016, Gym is an open-source Python library developed to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](https://git.laser.di.unimi.it) research, making published research more quickly reproducible [24] [144] while providing users with an easy interface for engaging with these environments. In 2022, brand-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 learning (RL) research on computer game [147] using [RL algorithms](https://www.hrdemployment.com) and research study generalization. Prior RL research [focused](http://118.195.226.1249000) mainly on enhancing representatives to solve single jobs. Gym Retro offers the capability to generalize between 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 at first do not have understanding of how to even walk, but are [offered](http://114.34.163.1743333) the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this [adversarial knowing](https://www.eruptz.com) process, the agents learn how to adjust to altering conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might create an intelligence "arms race" that might increase an agent's capability 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 group of 5 OpenAI-curated bots [utilized](https://myclassictv.com) in the competitive five-on-five computer game Dota 2, that find out to play against human gamers at a high ability level totally through experimental algorithms. Before becoming a team of 5, the first [public demonstration](http://www.hnyqy.net3000) happened at The International 2017, the yearly best championship competition for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a [live one-on-one](https://sosyalanne.com) matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for 2 weeks of genuine time, which the learning software was a step in the direction of developing software that can handle intricate tasks like a surgeon. [152] [153] The system utilizes a form of support knowing, as the bots find out over time by playing against themselves [numerous](https://shankhent.com) 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 ability of the bots expanded to play together as a full group of 5, and they were able to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 [exhibition matches](http://git.moneo.lv) against expert gamers, but ended up losing both games. [160] [161] [162] In April 2019, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:DeniceBales2) OpenAI Five defeated OG, the reigning 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 games in a four-day open online competition, winning 99.4% of those games. [165]
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<br>OpenAI 5's systems in Dota 2's bot gamer reveals the challenges of [AI](http://dnd.achoo.jp) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown using deep reinforcement knowing (DRL) agents 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 utilizes maker finding out to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It finds out totally in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB cams to permit the robotic to control an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI demonstrated that Dactyl could solve a [Rubik's Cube](https://trabajosmexico.online). The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a [simulation approach](http://106.52.215.1523000) of producing progressively 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 new [AI](https://sportworkplace.com) models developed by OpenAI" to let developers call on it for "any English language [AI](https://pk.thehrlink.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 initial GPT model ("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 colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and procedure 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 an unsupervised transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions initially released to the public. The complete version of GPT-2 was not right away released due to concern about prospective misuse, including applications for composing fake news. [174] Some specialists revealed uncertainty that GPT-2 presented a substantial hazard.<br>
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to [discover](https://git.sortug.com) "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language design. [177] Several sites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue not being watched language designs to be general-purpose learners, shown by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot jobs (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, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 mentioned that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186]
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<br>OpenAI specified 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](https://asg-pluss.com) in between English and Romanian, and between English and German. [184]
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<br>GPT-3 significantly improved benchmark [outcomes](http://115.238.48.2109015) over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the essential capability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, 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 launched to the general public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month complimentary 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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://120.237.152.218:8888) 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 dozen shows languages, many effectively in Python. [192]
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<br>Several concerns with glitches, design flaws and security vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has been implicated of giving off copyrighted code, without any 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, 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 innovation passed a simulated law school bar exam with a score around the leading 10% of [test takers](https://social.ppmandi.com). (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, analyze or generate as much as 25,000 words of text, and write code in all significant programs languages. [200]
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<br>Observers reported that the version of [ChatGPT](https://bertlierecruitment.co.za) using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and statistics about GPT-4, such as the exact 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 generate text, [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:CassandraLechuga) images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision standards, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria 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](https://git.sitenevis.com) 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 helpful for enterprises, start-ups and designers seeking to automate services with [AI](https://siman.co.il) representatives. [208]
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<br>o1<br>
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<br>On September 12, [surgiteams.com](https://surgiteams.com/index.php/User:JanessaX94) 2024, OpenAI released the o1-preview and o1-mini models, which have actually been created to take more time to think about their actions, leading to higher precision. These models are particularly reliable in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
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<br>o3<br>
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<br>On December 20, 2024, OpenAI revealed o3, the successor of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and faster version of OpenAI o3. As of December 21, 2024, this model is not available for [public usage](https://www.miptrucking.net). According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecoms companies O2. [215]
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<br>Deep research<br>
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<br>Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform comprehensive web browsing, 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 of 26.6 percent on HLE (Humanity's Last Exam) [standard](https://support.mlone.ai). [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 design that is trained to analyze the semantic resemblance in between text and images. It can significantly be utilized for [wiki.whenparked.com](https://wiki.whenparked.com/User:ECYDessie34967) 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 model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can produce images of practical things ("a stained-glass window with an image of a blue strawberry") along with 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 revealed DALL-E 2, an upgraded variation of the design with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software application 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://apk.tw) DALL-E 3, a more powerful design better able to create images from [complicated descriptions](https://git.nothamor.com3000) without manual timely engineering and render intricate details like hands and text. [221] It was launched to the general 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 generate videos based upon brief detailed prompts [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 [optimum length](http://gitlab.rainh.top) of created videos is unknown.<br>
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<br>Sora's advancement team named it after the Japanese word for "sky", to symbolize its "endless innovative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that purpose, however did not expose the number or the specific 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 up to one minute long. It likewise shared a [technical report](http://git.picaiba.com) highlighting the techniques utilized to train the design, and the model's abilities. [225] It acknowledged some of its drawbacks, including battles simulating [complicated physics](https://hcp.com.gt). [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but noted that they must have been cherry-picked and may not represent Sora's common output. [225]
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<br>Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to generate reasonable video from text descriptions, citing its prospective to change storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause plans 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 design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task design that can carry out multilingual speech recognition as well as [speech translation](https://findschools.worldofdentistry.org) 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 forecast subsequent musical 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 begin fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet psychological 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 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 bit of lyrics and outputs song samples. OpenAI mentioned the songs "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the outcomes seem like mushy versions of tunes that may feel familiar", while Business Insider stated "remarkably, some of the resulting songs are catchy and sound legitimate". [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 introduced](https://source.ecoversities.org) the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The function is to research study whether such an approach might assist in auditing [AI](https://gitea.mpc-web.jp) [decisions](https://elsingoteo.com) and in developing explainable [AI](https://cagit.cacode.net). [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 substantial layer and neuron of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, different variations of Inception, and different 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 developed on top of GPT-3 that offers a [conversational interface](https://sublimejobs.co.za) that permits users to ask questions in natural language. The system then reacts with a response within seconds.<br>
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