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<br>Announced in 2016, Gym is an open-source Python library created to assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](http://112.112.149.146:13000) research, making released research more easily reproducible [24] [144] while providing users with a simple interface for interacting with these [environments](http://git.szchuanxia.cn). In 2022, brand-new advancements 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 support learning (RL) research study on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to resolve single tasks. Gym Retro gives the ability to generalize between games with comparable principles but various appearances.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, [wavedream.wiki](https://wavedream.wiki/index.php/User:SiobhanMcGlinn5) RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack understanding of how to even walk, however are given the objectives of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adjust to altering conditions. When a representative is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor [Mordatch](https://baripedia.org) argued that competition in between agents could develop an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competitors. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a team of five OpenAI-curated bots [utilized](https://casajienilor.ro) in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high skill level entirely through trial-and-error algorithms. Before ending up being a group of 5, the very first public demonstration occurred at The International 2017, the yearly best championship tournament for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of genuine time, and that the learning software was a step in the direction of creating software that can deal with intricate jobs like a surgeon. [152] [153] The system uses a type of support knowing, as the bots discover gradually by playing against themselves numerous 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 capability 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 two exhibit matches against professional gamers, 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 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 competition, winning 99.4% of those video games. [165]
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<br>OpenAI 5's systems in Dota 2's bot gamer shows the [difficulties](https://jobidream.com) of [AI](https://kittelartscollege.com) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown the use of deep support learning (DRL) agents 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 device learning to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It discovers completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a variety of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB electronic cameras to enable the robotic to manipulate an approximate object by seeing it. In 2018, OpenAI revealed 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 fix a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by [utilizing Automatic](https://cruzazulfansclub.com) Domain [Randomization](http://taesungco.net) (ADR), a simulation approach of producing progressively more tough environments. ADR varies from manual domain randomization by not [requiring](http://codaip.co.kr) a human to define randomization ranges. [169]
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<br>API<br>
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<br>In June 2020, [OpenAI revealed](https://gitlab-dev.yzone01.com) a multi-purpose API which it said was "for accessing brand-new [AI](https://thestylehitch.com) models established by OpenAI" to let developers get in touch with it for "any English language [AI](https://empleos.dilimport.com) job". [170] [171]
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<br>Text generation<br>
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<br>The business has 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 original 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 site on June 11, 2018. [173] It revealed how a generative model of language could obtain world understanding and procedure long-range dependences by pre-training on a diverse corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations at first released to the general public. The full version of GPT-2 was not instantly launched due to concern about prospective misuse, consisting of applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 postured a significant hazard.<br>
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence [reacted](https://ssconsultancy.in) with a tool to detect "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language design. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue not being watched [language designs](https://gitea.chenbingyuan.com) to be general-purpose learners, shown by GPT-2 [attaining state-of-the-art](https://pedulidigital.com) precision and perplexity on 7 of 8 zero-shot jobs (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 avoids certain concerns encoding vocabulary with word tokens by using byte pair encoding. This permits 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 design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 [designs](https://code.dsconce.space) with as couple of as 125 million specifications were likewise trained). [186]
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<br>OpenAI specified that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and between [English](https://watch.bybitnw.com) and German. [184]
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<br>GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or encountering the basic capability constraints of predictive language designs. [187] [Pre-training](http://testyourcharger.com) GPT-3 needed a number of 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 model was not right away launched to the general public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid [cloud API](https://gitea.linkensphere.com) after a two-month totally free private beta that started in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was licensed specifically to [Microsoft](https://givebackabroad.org). [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](http://globalk-foodiero.com) powering the code autocompletion tool [GitHub Copilot](http://47.75.109.82). [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 programming languages, most effectively in Python. [192]
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<br>Several issues with problems, style defects and security vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has been accused of [discharging copyrighted](https://git.fanwikis.org) code, with no author attribution or license. [197]
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<br>OpenAI revealed that they would cease support for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<br>On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the [updated technology](http://krasnoselka.od.ua) 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 might likewise check out, analyze or produce up to 25,000 words of text, and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:LienGoshorn40) compose code in all major programming languages. [200]
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<br>Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has declined 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 released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting [edge lead](http://shiningon.top) to voice, multilingual, and vision criteria, setting brand-new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI GPT-4o mini, a smaller 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 expects it to be especially [beneficial](https://git.brass.host) for business, startups and designers seeking to automate services with [AI](https://pycel.co) representatives. [208]
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<br>o1<br>
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<br>On September 12, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been designed to take more time to consider their actions, resulting in higher accuracy. These designs are particularly reliable in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [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 unveiled o3, [surgiteams.com](https://surgiteams.com/index.php/User:KaseyDees635) the successor of the o1 reasoning design. OpenAI also unveiled o3-mini, [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:MichaelCrocker0) a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this design 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 scientists had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
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<br>Deep research<br>
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<br>Deep research is an [agent developed](http://47.114.82.1623000) by OpenAI, unveiled on February 2, 2025. It leverages the [capabilities](https://git.yuhong.com.cn) of OpenAI's o3 model to perform comprehensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached a precision 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, [wavedream.wiki](https://wavedream.wiki/index.php/User:WhitneyStead245) CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic similarity in between text and images. It can notably be utilized for image classification. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, DALL-E is a Transformer design that develops images from [textual descriptions](http://125.43.68.2263001). [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce pictures of practical items ("a stained-glass window with a picture of a blue strawberry") along with items that do not exist in reality ("a cube with the texture of a porcupine"). 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 announced DALL-E 2, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:LukeTijerina) an upgraded version of the model with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new primary 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 [powerful design](https://cruzazulfansclub.com) better able to generate images from complex descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the 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 on short detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.<br>
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<br>Sora's development group called it after the Japanese word for "sky", to [symbolize](http://git.dgtis.com) 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 using publicly-available videos along with copyrighted videos licensed for that function, but did not reveal the number or the specific 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, specifying that it might create videos up to one minute long. It likewise shared a technical report highlighting the methods used to train the design, and the model's capabilities. [225] It acknowledged a few of its shortcomings, consisting of battles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but kept in mind that they should have been cherry-picked and might not represent Sora's common output. [225]
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<br>Despite uncertainty from some academic leaders following [Sora's public](http://43.139.182.871111) demonstration, notable entertainment-industry figures have actually revealed substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's ability to produce [practical video](http://suvenir51.ru) from text descriptions, citing its possible to revolutionize storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly plans 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 [recognition](http://git.twopiz.com8888) model. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task design that can perform multilingual speech acknowledgment in addition to 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 predict subsequent [musical](http://otyjob.com) notes in [MIDI music](http://fatims.org) files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to start fairly however then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary 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 generate 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 songs "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" which "there is a significant gap" in between Jukebox and human-generated music. The Verge mentioned "It's highly impressive, even if the results sound like mushy versions of tunes that might feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are appealing 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 makers to dispute toy problems in front of a human judge. The function is to research study whether such an approach may help in auditing [AI](https://git.progamma.com.ua) [decisions](https://10-4truckrecruiting.com) and in [establishing explainable](http://47.119.128.713000) [AI](https://laviesound.com). [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 substantial layer and nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was produced to analyze the features that form inside these neural networks easily. The models included are AlexNet, VGG-19, different versions of Inception, and different variations of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, ChatGPT is an expert system [tool built](https://rhabits.io) on top of GPT-3 that supplies a conversational user interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.<br>
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