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<br>Announced in 2016, Gym is an open-source Python library developed to help with the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](https://nepaxxtube.com) research, making released research more quickly reproducible [24] [144] while [providing](https://mediawiki.hcah.in) users with a simple user interface for communicating with these environments. In 2022, [gratisafhalen.be](https://gratisafhalen.be/author/rebbeca9609/) brand-new advancements of Gym have actually been [relocated](https://www.tvcommercialad.com) to the [library Gymnasium](https://www.tvcommercialad.com). [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](https://mmsmaza.in) [147] using RL algorithms and study generalization. Prior RL research [focused](https://smaphofilm.com) mainly on optimizing representatives to solve single jobs. [Gym Retro](https://www.referall.us) provides the ability to generalize between games with similar concepts but different looks.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, [RoboSumo](https://vidy.africa) is a virtual world where humanoid metalearning robotic [representatives initially](http://candidacy.com.ng) lack understanding of how to even stroll, but are provided the objectives of discovering to move and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:FlorianHoutz6) to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the [representatives discover](https://younghopestaffing.com) how to adapt to [changing conditions](http://8.141.155.1833000). When an agent is then eliminated from this virtual environment and [positioned](https://classtube.ru) in a new virtual environment with high winds, the representative braces to remain upright, [recommending](https://melaninbook.com) it had discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might develop an [intelligence](https://tv.lemonsocial.com) "arms race" that could increase a representative's ability to function 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 five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that learn to play against human players at a high skill level totally through trial-and-error algorithms. Before ending up being a team of 5, the first public presentation took place at The International 2017, the annual best championship tournament for the game, where Dendi, an expert [Ukrainian](https://phdjobday.eu) 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 found out by playing against itself for two weeks of actual time, which the learning software application was an action in the direction of creating software that can handle intricate tasks like a surgeon. [152] [153] The system utilizes 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 objectives. [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 were able to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:CharleyRudall29) but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 overall 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://boonbac.com) [systems](https://git.ombreport.info) in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It discovers totally in simulation using the exact same RL algorithms and [training code](https://lifeinsuranceacademy.org) as OpenAI Five. OpenAI took on the things orientation problem by [utilizing domain](https://git.thomasballantine.com) randomization, a simulation approach which exposes the student to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB cameras to permit the robotic to manipulate an arbitrary item by seeing it. In 2018, OpenAI showed that the system was able to [control](https://mypetdoll.co.kr) a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating progressively more tough environments. ADR varies 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](https://socialnetwork.cloudyzx.com) models established by OpenAI" to let designers call on it for "any English language [AI](https://social.web2rise.com) task". [170] [171]
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<br>Text generation<br>
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<br>The business has actually 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 initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding and process long-range dependencies 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 design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions at first released to the public. The complete version of GPT-2 was not immediately launched due to issue about prospective abuse, including applications for composing [fake news](https://www.yourtalentvisa.com). [174] Some experts expressed uncertainty that GPT-2 positioned a significant risk.<br>
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to completely 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 launched the complete version of the GPT-2 language design. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue without supervision language models to be general-purpose students, shown by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot jobs (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 somewhat 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 allows representing any string of characters by encoding 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 variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were also trained). [186]
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<br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples 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 dramatically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or experiencing the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:EmileBeyer396) the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the public for concerns of possible abuse, although OpenAI prepared to permit 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 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 on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://112.124.19.38:8080) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can develop working code in over a dozen shows languages, many successfully in Python. [192]
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<br>Several issues with problems, style defects and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has actually been implicated of producing copyrighted code, without any author attribution or license. [197]
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<br>OpenAI announced that they would stop 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), efficient in accepting text or image inputs. [199] They revealed 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 also read, analyze or create approximately 25,000 words of text, and compose code in all significant shows languages. [200]
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<br>Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 [retained](https://jobs.assist-staffing.com) some of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose different technical details and statistics about GPT-4, such as the exact 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 released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting [edge lead](https://www.huntsrecruitment.com) to voice, multilingual, and vision benchmarks, setting 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 sized 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 anticipates it to be particularly beneficial for enterprises, startups and designers looking for to automate services with [AI](https://git.ombreport.info) agents. [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 created to take more time to think of their responses, leading to greater accuracy. These designs are particularly effective 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, OpenAI revealed o3, the successor of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecoms companies O2. [215]
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<br>Deep research study<br>
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<br>Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out substantial web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and [wavedream.wiki](https://wavedream.wiki/index.php/User:LanSeyler65095) Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [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 model that is trained to analyze the semantic resemblance in between text and images. It can notably 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 produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can produce pictures of realistic items ("a stained-glass window with a picture of a blue strawberry") along with things 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 revealed DALL-E 2, an upgraded version of the design with more practical results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new fundamental system for converting a text description into a 3-dimensional model. [220]
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<br>DALL-E 3<br>
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<br>In September 2023, OpenAI announced DALL-E 3, a more powerful design much better able to produce images from complicated descriptions without manual prompt engineering and render [complex details](https://career.finixia.in) like hands and text. [221] It was released to the general 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 produce videos based upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of created videos is unknown.<br>
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<br>Sora's advancement team called it after the Japanese word for "sky", to signify its "unlimited innovative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that purpose, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:JohnetteTonkin7) but did not expose 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 public on February 15, 2024, [stating](http://git.cattech.org) that it could create videos up to one minute long. It likewise shared a technical report [highlighting](https://nurseportal.io) the approaches used to train the design, and the design's capabilities. [225] It acknowledged some of its imperfections, consisting of battles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but noted that they should have been cherry-picked and may not represent Sora's typical output. [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to create sensible video from text descriptions, citing its potential to change storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to stop briefly [strategies](https://eet3122salainf.sytes.net) for broadening his Atlanta-based movie 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 [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Marcy4075626057) is also a multi-task design that can perform multilingual [speech acknowledgment](http://dev.icrosswalk.ru46300) 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 [anticipate subsequent](https://ezworkers.com) musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to begin fairly but then fall under chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to 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 category, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the songs "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge [mentioned](https://hylpress.net) "It's highly remarkable, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider mentioned "remarkably, some 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 launched the Debate Game, which teaches devices to dispute toy problems in front of a human judge. The function is to research whether such an [approach](http://203.171.20.943000) might assist in auditing [AI](https://git.jzcscw.cn) decisions and in establishing explainable [AI](http://124.223.100.38:3000). [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 neuron of 8 neural network models which are typically studied in interpretability. [240] Microscope was developed to analyze the functions that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and various versions of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, ChatGPT is a synthetic intelligence tool developed on top of GPT-3 that offers a conversational interface that enables users to ask questions in natural language. The system then reacts with a response within seconds.<br>
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