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<br>Announced in 2016, Gym is an open-source Python library designed to facilitate the development of support learning algorithms. It aimed to standardize how environments are specified in [AI](https://gitlab.alpinelinux.org) research study, making released research study more quickly reproducible [24] [144] while providing users with a basic interface for interacting with these environments. In 2022, brand-new developments of Gym have been relocated 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 knowing (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to resolve single tasks. Gym Retro gives the ability to generalize between games with comparable ideas however different appearances.<br> |
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<br>RoboSumo<br> |
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<br>Released in 2017, RoboSumo is a [virtual](http://personal-view.com) world where humanoid metalearning robot agents initially lack understanding of how to even stroll, but are given the goals of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents could produce an intelligence "arms race" that might increase an [agent's ability](https://www.goodbodyschool.co.kr) 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 five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against [human gamers](https://tuxpa.in) at a high skill level entirely through [experimental algorithms](https://uniondaocoop.com). Before ending up being a group of 5, the first public presentation took place at The International 2017, the annual best championship tournament for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:HaleyCaperton) 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 learned by playing against itself for 2 weeks of actual time, and that the learning software application was an action in the direction of developing software that can deal with complex jobs like a surgeon. [152] [153] The system uses a form of support knowing, as the bots discover in time by playing against themselves numerous 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 expanded to play together as a full team of 5, and [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:RosarioHairston) they were able to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, but ended 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 exhibition match in San Francisco. [163] [164] The bots' last [public appearance](https://git.fanwikis.org) came later on that month, where they played in 42,729 total games in a [four-day](https://hatchingjobs.com) open online competitors, winning 99.4% of those [video games](https://git.bwnetwork.us). [165] |
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer reveals the challenges of [AI](https://jobidream.com) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated the usage of deep reinforcement knowing (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 utilizes maker finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It finds out completely in simulation using the very same RL algorithms and [training code](https://ruofei.vip) as OpenAI Five. OpenAI took on the item orientation issue by using domain randomization, a simulation method which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB electronic cameras to enable the robot to control an arbitrary item by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by improving the [robustness](https://cagit.cacode.net) of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating progressively harder environments. ADR differs from manual domain randomization by not needing a human to define randomization ranges. [169] |
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<br>API<br> |
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<br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](https://kahps.org) models developed by OpenAI" to let developers contact it for "any English language [AI](https://epspatrolscv.com) task". [170] [171] |
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<br>Text generation<br> |
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<br>The business has [promoted generative](https://www.keeloke.com) pretrained transformers (GPT). [172] |
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<br>OpenAI's initial GPT model ("GPT-1")<br> |
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<br>The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and [published](http://park8.wakwak.com) in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model of language might obtain world understanding and procedure 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 design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative variations initially released to the general public. The complete variation of GPT-2 was not right away launched due to concern about potential misuse, consisting of applications for [oeclub.org](https://oeclub.org/index.php/User:EloiseLaflamme8) composing fake news. [174] Some experts expressed uncertainty that GPT-2 presented a considerable threat.<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 scientists, such as Jeremy Howard, cautioned 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 impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several [websites](https://usvs.ms) host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180] |
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<br>GPT-2's authors argue unsupervised language models to be general-purpose students, shown by GPT-2 attaining advanced 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing 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](https://10-4truckrecruiting.com) [a] Transformer 3 (GPT-3) is an unsupervised transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full 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 stated that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184] |
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<br>GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or encountering the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month totally free personal 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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://lasvegasibs.ae) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the design can develop working code in over a dozen programs languages, a lot of efficiently in Python. [192] |
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<br>Several issues with glitches, style flaws and security vulnerabilities were pointed out. [195] [196] |
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<br>GitHub Copilot has been accused of discharging copyrighted code, without any author attribution or license. [197] |
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<br>OpenAI announced that they would terminate 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), efficient in accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar test with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also read, examine or generate up to 25,000 words of text, and write code in all significant programs languages. [200] |
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<br>Observers reported that the version of ChatGPT using GPT-4 was an [improvement](https://macphersonwiki.mywikis.wiki) on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal various technical details and data 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 revealed and launched GPT-4o, which can process and [generate](https://git.xedus.ru) text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision benchmarks, records in audio speech recognition and translation. [205] [206] It scored 88.7% on the [Massive Multitask](https://matchmaderight.com) Language Understanding (MMLU) criteria 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 [yewiki.org](https://www.yewiki.org/User:CindaNangle760) GPT-4o. OpenAI anticipates it to be especially useful for enterprises, start-ups and designers looking for to automate services with [AI](http://xintechs.com:3000) representatives. [208] |
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<br>o1<br> |
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<br>On September 12, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:CecilaDalgarno) 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been developed to take more time to think about their actions, causing higher precision. These designs are especially effective in science, coding, and thinking 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 revealed o3, the successor of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with [telecoms companies](https://www.9iii9.com) O2. [215] |
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<br>Deep research<br> |
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<br>Deep research study is a representative established by OpenAI, revealed on February 2, 2025. It leverages the abilities of [OpenAI's](https://gitea.tmartens.dev) o3 design to perform substantial web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [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 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 utilizes a 12-billion-parameter variation of GPT-3 to analyze [natural language](https://executiverecruitmentltd.co.uk) inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can create images of sensible items ("a stained-glass window with an image of a blue strawberry") in addition to items 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 updated version of the model with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new fundamental 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 announced DALL-E 3, a more effective model much better able to generate images from complicated descriptions without manual timely engineering and [render intricate](https://crossdark.net) details 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 design that can generate videos based upon short detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The [optimum length](https://wiki.lspace.org) of produced videos is unknown.<br> |
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<br>Sora's development team called it after the Japanese word for "sky", to symbolize its "limitless innovative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos accredited for that function, but did not reveal the number or the exact sources of the videos. [223] |
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<br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it might produce videos approximately one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, including battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but kept in mind that they must have been cherry-picked and might not represent Sora's [typical](https://sugarmummyarab.com) output. [225] |
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<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have shown substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to generate [practical video](http://43.136.17.1423000) from text descriptions, citing its potential to transform storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually 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](https://repo.globalserviceindonesia.co.id) recognition design. [228] It is trained on a large dataset of diverse audio and is also a multi-task model that can [perform](http://120.79.211.1733000) multilingual speech [recognition](http://116.205.229.1963000) 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 is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to develop 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 outputs tune samples. OpenAI specified the tunes "reveal regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a considerable space" in between Jukebox and human-generated music. The Verge stated "It's highly excellent, even if the results seem like mushy variations of tunes that might feel familiar", while Business Insider specified "remarkably, some of the resulting songs are catchy 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 machines to debate toy issues in front of a human judge. The [purpose](https://splink24.com) is to research whether such a technique may help in auditing [AI](https://talentmatch.somatik.io) choices and in developing explainable [AI](https://funnyutube.com). [237] [238] |
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<br>Microscope<br> |
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<br>[Released](http://git.qhdsx.com) in 2020, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=1009888) Microscope [239] is a collection of visualizations of every substantial layer and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was created to evaluate the [features](http://121.36.37.7015501) that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various 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 an artificial intelligence tool developed on top of GPT-3 that offers a conversational user interface that allows users to ask questions in natural language. The system then responds with a response within seconds.<br> |
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