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 development of support knowing algorithms. It aimed to standardize how environments are specified in [AI](http://isarch.co.kr) research, making [released](https://remnantstreet.com) research study more easily reproducible [24] [144] while users with a basic interface for connecting with these environments. In 2022, brand-new developments 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 reinforcement learning (RL) research on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to fix single jobs. Gym Retro offers the capability to generalize in between games with comparable concepts however 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 robot agents initially do not have knowledge of how to even stroll, but are given the goals of learning to move and to push the [opposing agent](https://abstaffs.com) out of the ring. [148] Through this adversarial knowing procedure, the agents discover how to adjust to changing conditions. When a representative is then eliminated from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, recommending it had actually found out how to [stabilize](http://47.95.216.250) in a generalized way. [148] [149] OpenAI's Igor [Mordatch](http://demo.qkseo.in) argued that competition between representatives might create an intelligence "arms race" that might increase a representative's capability to function 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 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human players at a high skill level [totally](https://zamhi.net) through trial-and-error algorithms. Before becoming a group of 5, the first public presentation happened at The International 2017, the annual premiere championship tournament for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of genuine time, which the learning software was a step in the direction of developing software [application](https://wamc1950.com) that can deal with complex jobs like a surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots learn over time by [playing](http://git.hiweixiu.com3000) against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156]
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<br>By June 2018, the capability 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 exhibit matches against professional players, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs 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 on that month, where they played in 42,729 overall games in a four-day open online competitors, winning 99.4% of those video games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer shows the difficulties of [AI](https://lius.familyds.org:3000) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown the use of deep support knowing (DRL) [representatives](http://git.bzgames.cn) 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 machine discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It discovers entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation method which exposes the [learner](https://www.informedica.llc) to a variety of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB electronic cameras to allow the robotic to control an [arbitrary](https://1.214.207.4410333) things by seeing it. In 2018, OpenAI showed that the system had the ability 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 robot was able to fix the puzzle 60% of the time. [Objects](https://endhum.com) like the Rubik's Cube present complicated physics that is harder to design. 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 needing a human to define randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](http://wiki.pokemonspeedruns.com) designs developed by OpenAI" to let developers contact it for "any English language [AI](http://101.200.33.64:3000) job". [170] [171]
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<br>Text generation<br>
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<br>The company has promoted 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 composed by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world knowledge and procedure long-range dependences 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 model and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative variations initially launched to the public. The full version of GPT-2 was not instantly launched due to concern about possible misuse, consisting of applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 [positioned](https://gitea.gai-co.com) a significant risk.<br>
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<br>In [reaction](https://seedvertexnetwork.co.ke) to GPT-2, the Allen Institute for [Artificial Intelligence](https://kyigit.kyigd.com3000) reacted with a tool to find "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to absolutely 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 released the complete version of the GPT-2 language model. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue without [supervision language](https://streaming.expedientevirtual.com) models to be general-purpose students, illustrated by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not additional trained on any task-specific input-output examples).<br>
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<br>The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns encoding [vocabulary](http://www.colegio-sanandres.cl) with word tokens by utilizing byte pair encoding. This permits 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 an unsupervised transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were likewise trained). [186]
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<br>OpenAI mentioned that GPT-3 was successful 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](https://giaovienvietnam.vn) and cross-linguistic transfer knowing 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 warned that such [scaling-up](https://takesavillage.club) of language models might be approaching or experiencing 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 tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away launched to the general public for [links.gtanet.com.br](https://links.gtanet.com.br/vernon471078) concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month complimentary private beta that started in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified exclusively to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has actually furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://visualchemy.gallery) 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 create working code in over a dozen programs languages, a lot of [effectively](https://marcosdumay.com) in Python. [192]
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<br>Several concerns with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
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<br>GitHub Copilot has been implicated of releasing copyrighted code, without any author attribution or license. [197]
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<br>OpenAI announced 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 revealed the release of [Generative Pre-trained](https://gitlab.informicus.ru) Transformer 4 (GPT-4), [efficient](https://code.lanakk.com) in accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar examination 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, evaluate or generate approximately 25,000 words of text, and compose code in all significant programming languages. [200]
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<br>Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 [retained](https://thedatingpage.com) a few of the problems with earlier [revisions](https://letustalk.co.in). [201] GPT-4 is also [capable](https://video.invirtua.com) of taking images as input on [ChatGPT](https://gitlab.radioecca.org). [202] OpenAI has decreased to expose different technical details and data about GPT-4, such as the accurate size of the design. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision standards, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the [Massive Multitask](http://swwwwiki.coresv.net) 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 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 anticipates it to be especially useful for business, start-ups and developers looking for to automate services with [AI](https://acetamide.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 been developed to take more time to think about their actions, leading to higher accuracy. These models are particularly efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
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<br>o3<br>
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<br>On December 20, 2024, OpenAI revealed o3, the successor of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecoms services supplier O2. [215]
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<br>Deep research<br>
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<br>Deep research study is an agent established by OpenAI, [unveiled](http://git.zhongjie51.com) on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached a [precision](https://electroplatingjobs.in) 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 examine the semantic similarity in between text and images. It can significantly be used 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 in 2021, DALL-E is a Transformer design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language 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 images of realistic objects ("a stained-glass window with an image of a blue strawberry") as well as things that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, [pediascape.science](https://pediascape.science/wiki/User:LoriHsu3056) 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 realistic outcomes. [219] In December 2022, OpenAI [released](https://quikconnect.us) on GitHub software for Point-E, a new [fundamental](http://git.gonstack.com) 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 better able to produce images from intricate descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was [released](https://www.koumii.com) to the general 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 create videos based upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.<br>
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<br>Sora's development team named it after the Japanese word for "sky", to represent its "unlimited imaginative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for that purpose, but did not reveal the number or the specific sources of the videos. [223]
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<br>OpenAI demonstrated some [Sora-created high-definition](http://101.42.41.2543000) videos to the general public on February 15, 2024, mentioning that it might produce videos approximately one minute long. It likewise shared a technical report highlighting the techniques utilized to train the design, and the design's capabilities. [225] It acknowledged a few of its shortcomings, consisting of battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but kept in mind that they should have been cherry-picked and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:FranklinBreillat) might not represent Sora's [normal output](https://beta.talentfusion.vn). [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually revealed considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to produce reasonable video from text descriptions, citing its potential to reinvent storytelling and material development. He said that his [excitement](https://47.100.42.7510443) about Sora's possibilities was so strong that he had decided to pause 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 in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment along with 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 in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to begin fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized 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 produce music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the tunes "show regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" and that "there is a considerable space" between Jukebox and human-generated music. The Verge mentioned "It's technically excellent, even if the results seem like mushy versions of tunes that may feel familiar", while Business Insider stated "remarkably, a few of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
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<br>User interfaces<br>
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<br>Debate Game<br>
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<br>In 2018, OpenAI introduced the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The purpose is to research whether such a technique may help in auditing [AI](https://neejobs.com) decisions and in developing explainable [AI](http://t93717yl.bget.ru). [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 8 neural network designs which are frequently studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various [versions](https://divsourcestaffing.com) of Inception, and various versions of [CLIP Resnet](http://jerl.zone3000). [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, ChatGPT is a [synthetic intelligence](https://www.celest-interim.fr) tool developed on top of GPT-3 that provides a conversational user interface that permits users to ask questions in natural language. The system then reacts with an answer within seconds.<br>
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