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<br>Announced in 2016, [gratisafhalen.be](https://gratisafhalen.be/author/napoleonfad/) Gym is an open-source Python library developed to assist in the advancement of support learning algorithms. It aimed to standardize how environments are specified in [AI](https://elmerbits.com) research study, making published research more quickly reproducible [24] [144] while offering users with a basic user interface for connecting with these environments. In 2022, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:FerminBrannon00) brand-new developments of Gym have actually been relocated 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 video games [147] utilizing RL [algorithms](https://git.tbaer.de) and research study generalization. Prior RL research focused mainly on optimizing representatives to resolve single tasks. Gym Retro gives the capability to generalize in between video 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 robot agents at first do not have understanding of how to even walk, however are offered the objectives of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents discover how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and [positioned](https://pattonlabs.com) in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor [Mordatch](https://sadegitweb.pegasus.com.mx) argued that competition in between representatives could develop an intelligence "arms race" that might increase an agent's ability 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 five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high ability level entirely through experimental algorithms. Before ending up being a team of 5, the first public presentation took place at The International 2017, the yearly best champion tournament for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for two weeks of genuine time, and that the learning software application was an action in the instructions of developing software that can manage complex jobs like a surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots learn in time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156] |
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<br>By June 2018, the capability of the bots expanded to play together as a complete team of 5, and they were able to [beat teams](http://betterlifenija.org.ng) of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public look came later that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those [video games](https://git.alien.pm). [165] |
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<br>OpenAI 5's systems in Dota 2's bot gamer shows the obstacles of [AI](http://120.46.37.243:3000) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown using deep support 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 learning to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It discovers entirely in [simulation](http://51.222.156.2503000) using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by utilizing domain randomization, a simulation method which exposes the learner to a variety of [experiences](http://luodev.cn) instead of [attempting](https://social.web2rise.com) to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB cameras to enable the robotic to manipulate an arbitrary object by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by [improving](https://iraqitube.com) the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing progressively more difficult environments. ADR differs from manual domain randomization by not requiring 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 new [AI](https://www.bisshogram.com) designs developed by OpenAI" to let developers get in touch with it for "any English language [AI](https://puming.net) job". [170] [171] |
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<br>Text generation<br> |
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<br>The business has actually promoted generative pretrained transformers (GPT). [172] |
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<br>OpenAI's original GPT design ("GPT-1")<br> |
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<br>The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world knowledge and procedure long-range reliances by pre-training on a diverse 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](http://8.136.197.2303000) language model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative variations initially released to the general public. The complete version of GPT-2 was not right away released due to concern about prospective abuse, including applications for [composing](http://git.sanshuiqing.cn) phony news. [174] Some experts expressed [uncertainty](https://8.129.209.127) that GPT-2 posed a significant risk.<br> |
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake news". [175] Other researchers, such as Jeremy Howard, warned 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 impossible to filter". [176] In November 2019, OpenAI released the total 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 unsupervised language designs to be general-purpose learners, illustrated by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 [zero-shot tasks](https://git.iidx.ca) (i.e. the design 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both specific 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 model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full [variation](https://szmfettq2idi.com) of GPT-3 contained 175 billion specifications, [184] two orders of [magnitude bigger](https://guyanajob.com) than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186] |
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<br>OpenAI 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 offered examples of translation and cross-linguistic transfer learning in 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 warned that such scaling-up of language designs could be approaching or experiencing the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month free private beta that started in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was certified specifically to [Microsoft](http://git.sanshuiqing.cn). [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 in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://catvcommunity.com.tr) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the design can produce working code in over a dozen programs languages, the majority of effectively in Python. [192] |
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<br>Several problems with problems, design defects and security vulnerabilities were cited. [195] [196] |
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<br>GitHub Copilot has actually been accused 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 revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar exam with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, examine or generate approximately 25,000 words of text, and compose code in all major shows languages. [200] |
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<br>Observers reported that the version of ChatGPT using GPT-4 was an [improvement](https://huconnect.org) on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise [capable](https://lokilocker.com) of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and statistics about GPT-4, such as the precise size of the design. [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 create text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision criteria, setting 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 variation 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 expects it to be especially beneficial for enterprises, start-ups and developers seeking to automate services with [AI](https://vmi456467.contaboserver.net) representatives. [208] |
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<br>o1<br> |
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<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been designed to take more time to consider their actions, leading to greater precision. These designs are particularly efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [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 revealed o3-mini, a lighter and much faster version of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and [security researchers](https://avpro.cc) had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications providers O2. [215] |
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<br>Deep research<br> |
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<br>Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [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://kiaoragastronomiasocial.com) Pre-training) is a design that is trained to evaluate the semantic resemblance 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 produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to analyze natural [language](https://cheapshared.com) inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can produce pictures of practical things ("a stained-glass window with a picture of a blue strawberry") as well as things that do not exist in truth ("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 variation of the model with more practical results. [219] In December 2022, OpenAI released on GitHub [software](https://iesoundtrack.tv) for Point-E, a brand-new rudimentary 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 better able to produce images from complicated descriptions without manual timely engineering and render complicated 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 design that can [generate](http://home.rogersun.cn3000) videos based on short detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.<br> |
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<br>Sora's development team called it after the Japanese word for "sky", to symbolize its "limitless creative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed 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, [wiki.whenparked.com](https://wiki.whenparked.com/User:LetaX2026348693) 2024, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1074855) stating that it could create videos as much as one minute long. It likewise shared a technical report highlighting the approaches used to train the model, and the design's abilities. [225] It acknowledged some of its imperfections, including battles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but kept in mind 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 scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have revealed substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to produce sensible video from text descriptions, citing its potential to revolutionize storytelling and content production. He said that his excitement about [Sora's possibilities](http://93.104.210.1003000) was so strong that he had actually chosen to pause prepare for expanding his studio. [227] |
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<br>Speech-to-text<br> |
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<br>Whisper<br> |
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<br>[Released](https://body-positivity.org) in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of varied audio and is likewise a multi-task design that can perform multilingual speech acknowledgment 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 start fairly however then fall into mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized 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 in 2020, Jukebox is an open-sourced algorithm to [generate](https://idemnaposao.rs) music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the songs "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a substantial gap" in between Jukebox and human-generated music. The Verge stated "It's technologically outstanding, even if the outcomes sound like mushy versions of tunes that may feel familiar", while Business Insider mentioned "remarkably, a few of the resulting songs are appealing 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 the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research whether such a technique might assist in auditing [AI](https://just-entry.com) decisions and in establishing explainable [AI](https://samman-co.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 considerable layer and neuron of eight [neural network](http://27.154.233.18610080) models which are often studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks quickly. The designs consisted of 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 a [synthetic intelligence](https://www.askmeclassifieds.com) tool built on top of GPT-3 that supplies a conversational user interface that allows users to ask questions in natural language. The system then responds with a response within seconds.<br> |
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