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<br>Announced in 2016, Gym is an open-source Python library designed to assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://git.logicp.ca) research study, making published research study more easily reproducible [24] [144] while providing users with a simple user interface for interacting with these environments. In 2022, 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 reinforcement learning (RL) research on computer game [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to resolve single jobs. Gym Retro provides the capability to [generalize](https://rabota.newrba.ru) in between games with similar principles however various looks.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives [initially](http://flexchar.com) lack knowledge of how to even walk, but are given the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adjust to changing conditions. When a representative is then removed from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might create an intelligence "arms race" that might increase an agent's capability to work even outside the context of the [competitors](http://xn--289an1ad92ak6p.com). [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 video game Dota 2, that learn to play against human players at a high ability level totally through experimental algorithms. Before becoming a team of 5, the first public demonstration happened at The International 2017, the yearly premiere championship [competition](https://somo.global) for the video 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 learned by playing against itself for two weeks of genuine time, and that the [knowing software](http://git.fmode.cn3000) was a step in the instructions of producing software that can handle complicated tasks like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots learn with time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
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<br>By June 2018, the capability of the bots expanded to play together as a complete team of 5, and they were able to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert gamers, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated 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 appearance came later on that month, where they played in 42,729 overall video 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 of [AI](https://professionpartners.co.uk) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown making use of deep reinforcement knowing (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 uses maker learning to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers [totally](https://powerstack.co.in) in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by utilizing domain randomization, a simulation approach which exposes the learner to a range of [experiences](http://video.firstkick.live) instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB electronic cameras to allow the robotic to [manipulate](https://4stour.com) an approximate 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 [robotic](http://60.204.229.15120080) was able to fix the puzzle 60% of the time. Objects 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 using [Automatic Domain](http://123.57.66.463000) Randomization (ADR), a simulation approach of generating progressively more challenging environments. ADR differs from manual domain randomization by not requiring a human to specify randomization ranges. [169]
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<br>API<br>
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<br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://www.dadam21.co.kr) designs developed by OpenAI" to let [developers](https://tobesmart.co.kr) call on it for "any English language [AI](https://sugardaddyschile.cl) task". [170] [171]
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<br>Text generation<br>
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<br>The [company](https://wamc1950.com) has actually [promoted generative](https://cchkuwait.com) pretrained transformers (GPT). [172]
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<br>OpenAI's [initial GPT](https://sb.mangird.com) design ("GPT-1")<br>
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<br>The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and [released](http://www.vokipedia.de) in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world [understanding](https://www.infiniteebusiness.com) and process long-range reliances 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 follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative variations initially released to the general public. The complete variation of GPT-2 was not instantly launched due to issue about potential abuse, including applications for composing phony news. [174] Some experts expressed uncertainty that GPT-2 postured a substantial risk.<br>
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake news". [175] Other researchers, [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:Tina69B840440) such as Jeremy Howard, warned of "the innovation 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 released the complete variation of the GPT-2 language design. [177] Several sites host interactive presentations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue not being watched language models to be [general-purpose](http://39.101.134.269800) students, illustrated by GPT-2 attaining cutting edge accuracy 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns encoding [vocabulary](https://www.iqbagmarket.com) 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 an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were likewise trained). [186]
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<br>OpenAI mentioned that GPT-3 [succeeded](https://whoosgram.com) at certain "meta-learning" tasks and might generalize the function of a [single input-output](https://taelimfwell.com) pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
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<br>GPT-3 dramatically improved benchmark results over GPT-2. OpenAI warned that such [scaling-up](https://intgez.com) of language models might be approaching or coming across the fundamental capability constraints of predictive language models. [187] [Pre-training](http://120.77.2.937000) GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched to the general public for issues of possible abuse, although [OpenAI prepared](http://101.42.90.1213000) to enable 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 specifically to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://www.xcoder.one) 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 [produce](https://noblessevip.com) working code in over a lots programs languages, a lot of efficiently in Python. [192]
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<br>Several problems with glitches, design flaws and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has been accused of releasing copyrighted code, without any author attribution or license. [197]
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<br>OpenAI revealed that they would cease assistance for [Codex API](http://123.60.67.64) 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 [updated innovation](https://tenacrebooks.com) passed a simulated law school bar exam with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise check out, evaluate or create approximately 25,000 words of text, and write code in all major programming languages. [200]
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<br>Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise [capable](http://101.43.129.2610880) of taking images as input on ChatGPT. [202] OpenAI has actually [declined](http://www.zeil.kr) to reveal various [technical details](https://shinjintech.co.kr) and data about GPT-4, such as the accurate size of the model. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art results in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition and . [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 launched GPT-4o mini, a smaller variation of GPT-4o changing GPT-3.5 Turbo on the ChatGPT interface. Its [API costs](http://wecomy.co.kr) $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 business, start-ups and designers looking for to automate services with [AI](http://git.zltest.com.tw:3333) agents. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been developed to take more time to think of their reactions, leading to greater precision. These designs are particularly effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Staff member. [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 unveiled o3, the successor of the o1 reasoning model. OpenAI also revealed o3-mini, 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, security and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 instead of o2 to prevent confusion with telecoms companies O2. [215]
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<br>Deep research<br>
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<br>Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
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<br>Image category<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic resemblance in between text and images. It can significantly be utilized for image category. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can produce pictures of reasonable objects ("a stained-glass window with a picture of a blue strawberry") along with things that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.<br>
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<br>DALL-E 2<br>
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<br>In April 2022, OpenAI announced DALL-E 2, an updated variation of the model with more sensible results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new basic 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 revealed DALL-E 3, a more powerful design better able to produce images from [complex descriptions](https://rhcstaffing.com) without manual timely engineering and render complicated details like hands and text. [221] It was released 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 design that can generate videos based upon brief detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.<br>
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<br>Sora's advancement team named it after the Japanese word for "sky", to represent its "unlimited innovative potential". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that purpose, but did not expose the number or the precise sources of the videos. [223]
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<br>OpenAI demonstrated some [Sora-created high-definition](http://gitlab.ds-s.cn30000) videos to the general public on February 15, 2024, mentioning that it could produce videos as much as one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the model's abilities. [225] It acknowledged some of its drawbacks, consisting of battles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", however kept in mind that they should have been cherry-picked and might not [represent Sora's](https://video.igor-kostelac.com) common output. [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have revealed [considerable](http://1.12.255.88) interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to create reasonable video from text descriptions, mentioning its potential to [reinvent storytelling](https://ready4hr.com) and content production. He said that his excitement about Sora's possibilities was so strong that he had decided to pause prepare for expanding his Atlanta-based motion picture studio. [227]
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<br>Speech-to-text<br>
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<br>Whisper<br>
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<br>Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can carry out multilingual speech recognition 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 anticipate subsequent [musical notes](https://gitea.taimedimg.com) in MIDI music files. It can create songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to begin fairly however then fall under chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web psychological [thriller](https://git.jamarketingllc.com) Ben [Drowned](https://globalhospitalitycareer.com) 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 produce music with vocals. After training on 1.2 million samples, the system [accepts](https://gantnews.com) a genre, artist, and [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:VeraPeek6773) a bit of lyrics and outputs song samples. OpenAI mentioned the tunes "show local musical coherence [and] follow standard chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" and that "there is a significant space" in between Jukebox and human-generated music. The Verge mentioned "It's highly excellent, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting tunes are appealing 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 problems in front of a human judge. The purpose is to research whether such a method may help in auditing [AI](http://artsm.net) decisions and in establishing explainable [AI](https://git.aiadmin.cc). [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 neuron of eight neural network designs which are typically studied in interpretability. [240] Microscope was created to evaluate the [features](https://git.unicom.studio) that form inside these neural networks easily. The [models included](https://www.arztsucheonline.de) are AlexNet, VGG-19, various variations 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 expert system tool developed on top of GPT-3 that supplies a conversational interface that permits users to ask questions in natural language. The system then responds with a response within seconds.<br>
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