Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library created to help with the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://wiki.kkg.org) research, making published research more quickly reproducible [24] [144] while supplying users with an easy user interface for engaging with these environments. In 2022, new [developments](https://chemitube.com) 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](https://afrocinema.org) in 2018, Gym Retro is a platform for [reinforcement learning](https://raisacanada.com) (RL) research study on [video games](https://www.paknaukris.pro) [147] using RL algorithms and study generalization. Prior RL research study focused mainly on [enhancing](https://copyrightcontest.com) agents to resolve single jobs. Gym Retro provides the capability to generalize in between games with comparable 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 robotic representatives at first do not have understanding of how to even walk, however are provided the goals of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to changing conditions. When an agent is then eliminated 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 balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could create an intelligence "arms race" that might increase an [agent's capability](https://www.cbl.aero) 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 group of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high skill level completely through [experimental](https://lasvegasibs.ae) [algorithms](http://gogs.efunbox.cn). Before becoming a group of 5, the very first public demonstration happened at The International 2017, the yearly premiere champion tournament for the game, where Dendi, a professional Ukrainian 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 learned by playing against itself for two weeks of actual time, which the learning software was an action in the instructions of developing software application that can handle intricate tasks like a cosmetic surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots discover with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the ability of the bots broadened to play together as a full group of 5, and they had the ability to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 overall 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 player reveals the challenges of [AI](http://gite.limi.ink) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated making use of deep support learning (DRL) representatives 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](https://git.gqnotes.com) maker learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It learns completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by [utilizing domain](https://www.ksqa-contest.kr) randomization, a simulation approach which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, also has RGB cameras to allow the robot to manipulate an arbitrary things by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an [octagonal prism](https://xinh.pro.vn). [168]
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<br>In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robot 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 effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating gradually more challenging environments. ADR differs from manual domain randomization by not requiring a human to specify 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](http://49.232.207.113:3000) designs established by OpenAI" to let developers contact it for "any English language [AI](https://gogs.2dz.fi) job". [170] [171]
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<br>Text generation<br>
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<br>The company has actually promoted generative pretrained transformers (GPT). [172]
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<br>OpenAI's initial GPT model ("GPT-1")<br>
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<br>The [original paper](https://videoflixr.com) on generative pre-training of a transformer-based language design was [composed](http://8.222.216.1843000) by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of could obtain world understanding and process 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 a not being watched transformer language design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions at first released to the general public. The full version of GPT-2 was not right away launched due to issue about possible misuse, consisting of applications for writing phony news. [174] Some professionals expressed uncertainty 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, alerted of "the innovation to absolutely 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 design. [177] Several sites host [interactive demonstrations](https://posthaos.ru) of different instances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue without supervision language designs to be general-purpose learners, shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more trained on any task-specific input-output examples).<br>
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<br>The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by [encoding](https://gitlab.edebe.com.br) 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 without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were also trained). [186]
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<br>OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and might generalize the [function](http://47.106.228.1133000) of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer [knowing](https://octomo.co.uk) between English and Romanian, and in between English and German. [184]
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<br>GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or coming across the fundamental ability 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 full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the general public for issues of possible abuse, although OpenAI prepared to enable [gain access](https://www.mgtow.tv) to through a paid cloud API after a two-month totally free private beta that began in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has furthermore been [trained](http://dancelover.tv) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://bbs.yhmoli.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the design can produce working code in over a lots programs languages, most successfully in Python. [192]
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<br>Several issues with problems, design defects and security vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has actually been accused of discharging copyrighted code, with no author attribution or license. [197]
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<br>OpenAI revealed that they would discontinue assistance for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<br>On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the updated 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, evaluate or generate approximately 25,000 words of text, and [compose code](http://154.9.255.1983000) in all major shows languages. [200]
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<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different technical details and statistics about GPT-4, such as the accurate size of the model. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, [mediawiki.hcah.in](https://mediawiki.hcah.in/index.php?title=User:TyroneMcCabe) OpenAI announced and launched GPT-4o, which can process and [generate](https://www.homebasework.net) text, [surgiteams.com](https://surgiteams.com/index.php/User:JunkoZ85423) images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI 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 useful for business, startups and developers looking for to automate services with [AI](https://systemcheck-wiki.de) 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 think of their reactions, causing greater accuracy. These models are especially reliable in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Employee. [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, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TabithaWithers0) OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and [security researchers](http://39.100.93.1872585) had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecoms services provider 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 abilities of OpenAI's o3 model to perform comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, 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 in between text and images. It can significantly be utilized 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 design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce pictures of practical objects ("a stained-glass window with a picture of a blue strawberry") in addition to things that do not exist in truth ("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 version of the design with more practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new simple system for transforming 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 DALL-E 3, a more effective model better able to produce images from complex descriptions without manual prompt 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 model that can generate videos based on brief detailed prompts [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 created videos is unidentified.<br>
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<br>Sora's development team named it after the Japanese word for "sky", to symbolize its "limitless imaginative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos licensed for that function, but did not expose the number or the precise 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, specifying that it might produce videos as much as one minute long. It likewise shared a technical report highlighting the techniques utilized to train the design, and the [design's capabilities](http://yijichain.com). [225] It acknowledged some of its imperfections, consisting of struggles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", however noted that they should have been cherry-picked and might not represent Sora's normal output. [225]
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<br>Despite uncertainty from some [scholastic leaders](https://comunidadebrasilbr.com) following Sora's public demonstration, noteworthy entertainment-industry figures have shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to produce [reasonable video](http://charmjoeun.com) from text descriptions, mentioning its prospective to transform storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly strategies for expanding 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 acknowledgment model. [228] It is trained on a large dataset of varied audio and is also a multi-task model that can carry out 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](https://www.aspira24.com) is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to start fairly however then fall into chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the [internet psychological](http://129.151.171.1223000) 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 snippet of lyrics and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11880731) outputs song [samples](https://datemyfamily.tv). OpenAI specified the tunes "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a significant gap" between Jukebox and human-generated music. The Verge specified "It's technically remarkable, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting tunes are memorable 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 released the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The function is to research study whether such a method might assist in [auditing](https://wakeuptaylor.boardhost.com) [AI](https://work-ofie.com) choices and in establishing explainable [AI](http://gitlab.rainh.top). [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 nerve cell of eight neural network designs which are often studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different versions of Inception, and various variations 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 tool developed on top of GPT-3 that offers a conversational user interface that permits users to ask concerns in natural language. The system then reacts with an answer within seconds.<br>
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