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<br>Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](https://4kwavemedia.com) research study, making published research study more quickly reproducible [24] [144] while offering users with a simple user interface for engaging with these environments. In 2022, [brand-new developments](https://oeclub.org) 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 [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ShayneTerrill) reinforcement knowing (RL) research on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to solve single tasks. Gym Retro provides the capability to generalize in between video games with similar principles but different 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 lack knowledge of how to even stroll, but are offered the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the [agents discover](https://jobs.ondispatch.com) how to adapt to changing conditions. When a representative is then eliminated from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could produce an intelligence "arms race" that could increase a representative's capability to [function](http://omkie.com3000) 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 used in the competitive five-on-five computer game Dota 2, that discover to play against human players at a high [skill level](http://betim.rackons.com) completely through experimental algorithms. Before becoming a group of 5, the first public demonstration took place at The International 2017, the yearly premiere championship tournament for the video game, where Dendi, an [expert Ukrainian](https://friendfairs.com) player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of real time, and that the [knowing software](https://telecomgurus.in) was an action in the instructions of creating software application that can manage complicated jobs like a surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots find out with 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 goals. [154] [155] [156] |
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<br>By June 2018, the capability of the bots expanded 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 exhibit matches against professional gamers, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later 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 mechanisms in Dota 2's bot player shows the challenges of [AI](https://localjobpost.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep support knowing (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 utilizes machine discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It finds out totally in [simulation utilizing](http://116.205.229.1963000) the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation issue by using domain randomization, a simulation technique which exposes the [student](https://git.peaksscrm.com) to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB video cameras to allow the robotic to control an arbitrary object by seeing it. In 2018, OpenAI showed 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 fix a Rubik's Cube. The [robotic](https://meeting2up.it) had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by [improving](http://31.184.254.1768078) the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually more hard 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, a multi-purpose API which it said was "for accessing new [AI](http://116.63.157.3:8418) models established by OpenAI" to let developers get in touch with it for "any English language [AI](http://47.108.239.202:3001) task". [170] [171] |
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<br>Text generation<br> |
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<br>The company 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 original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language might obtain world knowledge and procedure long-range reliances by pre-training on a varied corpus with long stretches of 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 model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions at first released to the public. The complete variation of GPT-2 was not right away launched due to concern about potential misuse, including applications for writing phony news. [174] Some experts expressed [uncertainty](http://git.pushecommerce.com) that GPT-2 posed 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 [identify](https://www.ignitionadvertising.com) "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to completely 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 total variation of the GPT-2 language model. [177] Several sites host interactive presentations of different 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 models to be general-purpose learners, illustrated by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further 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 a minimum of 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete version 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 models with as few as 125 million [criteria](https://jobs.colwagen.co) were likewise trained). [186] |
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<br>OpenAI mentioned that GPT-3 was [successful](http://121.40.209.823000) at certain "meta-learning" jobs and might generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184] |
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<br>GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month free private beta that began in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was licensed solely 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](https://www.nepaliworker.com) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://heovktgame.club) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in [personal](http://47.118.41.583000) beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, most successfully in Python. [192] |
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<br>Several issues with problems, style defects and security vulnerabilities were pointed out. [195] [196] |
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<br>GitHub Copilot has been implicated of giving off copyrighted code, with no author attribution or license. [197] |
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<br>OpenAI revealed that they would terminate assistance for [Codex API](https://brightworks.com.sg) on March 23, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) 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 announced that the updated technology passed a simulated law school bar exam 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 check out, evaluate or [generate](https://gitea.uchung.com) approximately 25,000 words of text, and write code in all major programming languages. [200] |
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<br>Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also [capable](http://park7.wakwak.com) of taking images as input on [ChatGPT](http://51.222.156.2503000). [202] OpenAI has declined to reveal different technical details and stats 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 [produce](http://183.221.101.893000) text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the [Massive Multitask](https://gitea.chofer.ddns.net) Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207] |
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<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized variation of GPT-4o changing 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 anticipates it to be especially helpful for enterprises, startups and developers looking for to automate services with [AI](http://gnu5.hisystem.com.ar) 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 designs, which have been developed to take more time to believe about their responses, leading to greater accuracy. These models are particularly effective 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 follower of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications companies 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 abilities of OpenAI's o3 design to carry out [extensive web](https://stationeers-wiki.com) surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [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 evaluate the semantic resemblance between text and images. It can notably be utilized for image [category](http://63.32.145.226). [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 uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can create pictures of realistic things ("a stained-glass window with an image of a blue strawberry") in addition to objects 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 updated version of the design with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new simple 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 effective design much better able to generate images from complicated descriptions without manual prompt engineering and render complex details like hands and text. [221] It was launched to the 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 create videos based on short detailed prompts [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.<br> |
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<br>Sora's advancement group called it after the Japanese word for "sky", to represent its "limitless innovative capacity". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos [licensed](https://upskillhq.com) for that purpose, but did not expose the number or the [precise sources](https://sondezar.com) 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 could produce videos approximately one minute long. It likewise shared a technical report highlighting the methods used to train the design, and the design's abilities. [225] It acknowledged some of its drawbacks, consisting of struggles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the [presentation videos](https://jobz1.live) "remarkable", however kept in mind that they should have been cherry-picked and might not represent Sora's common output. [225] |
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have actually shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry [revealed](https://iamzoyah.com) his astonishment at the innovation's ability to create reasonable video from text descriptions, citing its prospective to change storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause strategies for expanding his Atlanta-based film studio. [227] |
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<br>Speech-to-text<br> |
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<br>Whisper<br> |
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<br>Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can perform multilingual speech recognition as well as 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 forecast subsequent [musical](https://gitlab.edebe.com.br) notes in MIDI music files. It can generate songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly but then fall under chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet 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 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 stated the tunes "show local musical coherence [and] follow traditional chord patterns" but acknowledged that the [tunes lack](http://repo.bpo.technology) "familiar bigger musical structures such as choruses that duplicate" and that "there is a considerable space" between Jukebox and human-generated music. The Verge mentioned "It's highly excellent, even if the outcomes seem like mushy variations of songs that might feel familiar", while [Business](http://profilsjob.com) Insider stated "surprisingly, a few of the resulting tunes are appealing and sound legitimate". [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 introduced the Debate Game, which teaches devices to debate toy problems in front of a human judge. The purpose is to research whether such a technique may assist in auditing [AI](https://moontube.goodcoderz.com) choices and in establishing explainable [AI](http://182.92.202.113:3000). [237] [238] |
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<br>Microscope<br> |
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<br>Released in 2020, Microscope [239] is a [collection](https://social.ppmandi.com) of visualizations of every [substantial layer](https://gitlab.rlp.net) and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different versions 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 constructed on top of GPT-3 that offers a conversational interface that allows users to ask questions in natural language. The system then responds with a response within seconds.<br> |
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