Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of support learning algorithms. It aimed to standardize how environments are defined in [AI](http://222.85.191.97:5000) research, making released research more quickly reproducible [24] [144] while supplying users with a simple interface for connecting with these environments. In 2022, new developments of Gym have been transferred 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 study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to [resolve single](http://repo.bpo.technology) jobs. Gym Retro gives the [capability](http://122.51.17.902000) to [generalize](http://8.141.83.2233000) in between games with similar principles 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 initially do not have knowledge of how to even walk, however are offered the objectives of finding out to move and to push the opposing representative 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 put in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might produce an intelligence "arms race" that might increase a representative's capability to operate 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 utilized in the competitive five-on-five video game Dota 2, that learn to play against human players at a high [ability](https://gogs.dev.dazesoft.cn) level entirely through trial-and-error algorithms. Before becoming a group of 5, the first public demonstration occurred at The International 2017, the yearly best championship tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by [playing](http://www.homeserver.org.cn3000) against itself for two weeks of actual time, which the knowing software application was an action in the instructions of developing software that can manage complicated jobs like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots discover over time by playing against themselves numerous times a day for months, and are rewarded for actions such as [eliminating](http://47.107.80.2363000) an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the [capability](https://dispatchexpertscudo.org.uk) of the bots broadened to play together as a complete team of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The [International](https://www.kayserieticaretmerkezi.com) 2018, OpenAI Five played in two exhibition matches against expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the video game at the time, 2:0 in a [live exhibition](https://git.magicvoidpointers.com) match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those video games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot player reveals the obstacles of [AI](https://ransomware.design) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep support [learning](http://modiyil.com) (DRL) representatives 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 uses [maker finding](https://git.eisenwiener.com) out to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It finds out entirely in simulation utilizing the very same RL algorithms and [training code](https://turizm.md) as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation approach 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](https://skillnaukri.com) video cameras, 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 manipulate a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of generating gradually harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://cvmira.com) designs established by OpenAI" to let designers call on it for "any English language [AI](https://134.209.236.143) job". [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 model ("GPT-1")<br>
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<br>The initial 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 demonstrated how a generative model of language could obtain world knowledge and procedure long-range dependencies by pre-training on a varied corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative variations at first released to the general public. The complete variation of GPT-2 was not instantly released due to issue about prospective misuse, consisting of applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 presented a significant threat.<br>
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<br>In action 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 totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue not being watched language [designs](https://exajob.com) to be general-purpose students, shown by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both individual characters and multiple-character tokens. [181]
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<br>GPT-3<br>
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were likewise trained). [186]
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<br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper offered [examples](http://162.14.117.2343000) of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
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<br>GPT-3 [drastically improved](https://coolroomchannel.com) benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the essential ability constraints of [predictive language](http://47.107.29.613000) designs. [187] [Pre-training](https://skillsinternational.co.in) GPT-3 needed several 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 immediately launched to the public for issues of possible abuse, although OpenAI prepared to enable 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](https://realhindu.in). [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, Codex is a [descendant](http://gogsb.soaringnova.com) of GPT-3 that has actually in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://www.4bride.org) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the model can produce working code in over a dozen programs languages, the majority of successfully in Python. [192]
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<br>Several concerns with glitches, style flaws and [security vulnerabilities](https://gitea.easio-com.com) were pointed out. [195] [196]
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<br>GitHub Copilot has actually been implicated of emitting copyrighted code, with no author attribution or license. [197]
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<br>OpenAI revealed that they would terminate support for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<br>On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar exam with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise read, analyze or create as much as 25,000 words of text, and compose code in all major programming languages. [200]
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<br>Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to expose various 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 generate text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, and vision standards, [setting](https://bootlab.bg-optics.ru) new records in audio speech recognition 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 released GPT-4o mini, a smaller version 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 expects it to be especially useful for enterprises, start-ups and designers looking for to automate services with [AI](http://121.36.27.6:3000) 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 been designed to take more time to think about their reactions, leading to higher accuracy. These designs are especially efficient in science, coding, and reasoning 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 . OpenAI also revealed o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecommunications services service provider O2. [215]
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<br>Deep research<br>
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<br>Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out comprehensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy 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 design that is trained to evaluate the semantic resemblance between text and images. It can notably be utilized for image classification. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>[Revealed](https://jobedges.com) 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 analyze natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can develop images of practical items ("a stained-glass window with a picture of a blue strawberry") along with items that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.<br>
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<br>DALL-E 2<br>
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<br>In April 2022, OpenAI announced DALL-E 2, an upgraded variation of the design with more practical results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new basic system for converting a text description into a 3-dimensional design. [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 create images from complicated descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched 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 model that can produce videos based on short detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.<br>
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<br>Sora's advancement team called it after the Japanese word for "sky", to represent its "endless innovative potential". [223] Sora's innovation is an adaptation of the innovation behind the [DALL ·](http://119.3.29.1773000) E 3 text-to-image model. [225] OpenAI trained the system using [publicly-available videos](https://git.tesinteractive.com) in addition to copyrighted videos licensed for that purpose, but did not reveal the number or the exact sources of the videos. [223]
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<br>OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might produce videos approximately one minute long. It also shared a technical report highlighting the approaches used to train the design, and the design's abilities. [225] It acknowledged a few of its shortcomings, [including battles](http://chotaikhoan.me) replicating intricate physics. [226] Will Douglas Heaven of the MIT [Technology](https://welcometohaiti.com) Review called the demonstration videos "excellent", however kept in mind that they must have been cherry-picked and might not represent Sora's normal output. [225]
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<br>Despite [uncertainty](https://hireteachers.net) from some academic leaders following Sora's public demonstration, notable [entertainment-industry figures](https://gogs.adamivarsson.com) have shown considerable interest in the technology's capacity. In an interview, actor/filmmaker [Tyler Perry](http://park8.wakwak.com) revealed his astonishment at the technology's ability to generate realistic video from text descriptions, citing its potential to [revolutionize storytelling](http://39.98.116.22230006) and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to stop briefly prepare 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](https://candays.com) in 2022, Whisper is a general-purpose speech [acknowledgment](https://aloshigoto.jp) design. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can carry out multilingual speech recognition as well as speech translation and language identification. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>[Released](https://jollyday.club) in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 designs. According to The Verge, a tune created by MuseNet tends to start fairly however then fall under chaos the longer it plays. [230] [231] In popular culture, [initial applications](http://47.242.77.180) of this tool were used as early as 2020 for the internet psychological 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 create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and [outputs tune](https://globviet.com) samples. OpenAI stated the songs "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge specified "It's technically excellent, even if the outcomes sound like mushy variations of tunes that might feel familiar", while [Business Insider](http://football.aobtravel.se) specified "surprisingly, some 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 launched the Debate Game, which teaches devices to dispute toy problems in front of a human judge. The function is to research whether such a [technique](https://circassianweb.com) might assist in auditing [AI](https://fumbitv.com) choices and in developing explainable [AI](https://git.learnzone.com.cn). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network models which are frequently studied in interpretability. [240] Microscope was produced to analyze the [functions](https://noblessevip.com) that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various 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 provides a conversational user interface that permits users to ask concerns in natural language. The system then reacts with a response within seconds.<br>
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