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<br>Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of support learning algorithms. It aimed to [standardize](http://8.134.38.1063000) how environments are defined in [AI](https://git.tissue.works) research study, making released research study more easily reproducible [24] [144] while supplying users with a simple user interface for connecting with these environments. In 2022, brand-new developments of Gym have been transferred to the library Gymnasium. [145] [146] |
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<br>Gym Retro<br> |
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<br>Released in 2018, [surgiteams.com](https://surgiteams.com/index.php/User:MagaretMccurry6) Gym Retro is a platform for support learning (RL) research on video games [147] using RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to resolve single tasks. Gym Retro offers the ability to generalize in between video games with comparable principles however various appearances.<br> |
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<br>RoboSumo<br> |
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning [robot agents](https://git.toolhub.cc) at first do not have understanding of how to even walk, however are given the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the agents find out how to adapt to changing conditions. When a representative is then eliminated from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually discovered how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might develop an intelligence "arms race" that could increase a representative's capability 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 group of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high ability level completely through experimental algorithms. Before becoming a team of 5, the very first public demonstration took place at The International 2017, the annual premiere championship competition for the video game, where Dendi, a professional 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 found out by playing against itself for 2 weeks of actual time, and that the knowing software was a step in the direction of developing software application that can deal with complex jobs like a surgeon. [152] [153] The system uses a kind of support learning, as the bots learn gradually by [playing](https://trulymet.com) against themselves numerous times a day for months, and are [rewarded](http://www.grainfather.eu) for actions such as killing an enemy and taking map objectives. [154] [155] [156] |
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<br>By June 2018, the capability of the bots broadened to play together as a complete group of 5, and they had the ability 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 professional gamers, but wound up losing both [video games](http://gitea.zyimm.com). [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' 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 games. [165] |
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<br>OpenAI 5's systems in Dota 2's bot player shows the challenges of [AI](https://www.thehappyservicecompany.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated making use of deep reinforcement learning (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 utilizes machine finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out totally in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by using domain randomization, a simulation approach which exposes the student to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB video cameras to permit the robot to control an approximate object 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 demonstrated that Dactyl might fix a [Rubik's Cube](https://startuptube.xyz). The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing progressively harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [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](http://code.exploring.cn) models developed by OpenAI" to let developers contact it for "any English language [AI](https://meet.globalworshipcenter.com) job". [170] [171] |
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<br>Text generation<br> |
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<br>The business has [promoted generative](https://streaming.expedientevirtual.com) pretrained transformers (GPT). [172] |
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<br>OpenAI's initial GPT design ("GPT-1")<br> |
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<br>The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language could obtain world knowledge and procedure long-range reliances 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 follower to [OpenAI's original](http://code.qutaovip.com) GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions at first released to the public. The full version of GPT-2 was not instantly launched due to concern about potential misuse, consisting of applications for [composing phony](http://47.113.125.2033000) news. [174] Some specialists revealed 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](https://www.loupanvideos.com) with a tool to discover "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to absolutely 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 released the complete version of the GPT-2 language model. [177] Several sites host interactive presentations of various 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](https://oakrecruitment.uk) models to be general-purpose students, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not additional trained on any task-specific input-output examples).<br> |
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<br>The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 [upvotes](https://git.soy.dog). It avoids certain concerns encoding vocabulary with word tokens by utilizing byte [pair encoding](http://expand-digitalcommerce.com). 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 [a] Transformer 3 (GPT-3) is an unsupervised transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were likewise trained). [186] |
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<br>OpenAI specified that GPT-3 was successful at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave [examples](http://wowonder.technologyvala.com) of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184] |
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<br>GPT-3 dramatically enhanced [benchmark outcomes](https://coatrunway.partners) over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1074946) coming across the [fundamental ability](https://openedu.com) constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 design. [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 planned to enable gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was licensed exclusively to [Microsoft](https://vidy.africa). [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://git.i2edu.net) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the model can create working code in over a lots programming languages, the majority of successfully in Python. [192] |
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<br>Several issues with problems, style defects and security vulnerabilities were pointed out. [195] [196] |
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<br>GitHub Copilot has actually been accused of emitting copyrighted code, with no author attribution or license. [197] |
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<br>OpenAI revealed that they would stop assistance for Codex API on March 23, 2023. [198] |
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<br>GPT-4<br> |
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<br>On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the upgraded innovation passed a simulated law school bar examination 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 could also read, analyze or [generate](https://radicaltarot.com) up to 25,000 words of text, and write code in all major shows languages. [200] |
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<br>Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal different technical details and statistics about GPT-4, such as the precise 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 launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision criteria, setting brand-new [records](https://git.revoltsoft.ru) in audio speech acknowledgment 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 launched GPT-4o mini, a smaller sized variation of GPT-4o replacing 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](https://git.guildofwriters.org) to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly helpful for business, start-ups and developers seeking to automate services with [AI](https://ourehelp.com) 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 consider their reactions, causing greater accuracy. These models are especially efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211] |
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<br>o3<br> |
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<br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecoms services O2. [215] |
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<br>Deep research study<br> |
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<br>Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy 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 design that is trained to evaluate the semantic resemblance between text and images. It can especially 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, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can develop images of reasonable items ("a stained-glass window with an image of a blue strawberry") as well as objects 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 design with more practical outcomes. [219] In December 2022, OpenAI released on GitHub software [application](https://code.cypod.me) for Point-E, a brand-new fundamental system for transforming 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 complicated descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched 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](https://andonovproltd.com) that can [produce videos](https://www.klartraum-wiki.de) based on brief detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of generated videos is [unidentified](https://ifairy.world).<br> |
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<br>Sora's development team named it after the Japanese word for "sky", to represent its "unlimited 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 utilizing publicly-available videos in addition to copyrighted videos accredited for that function, but did not expose 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, mentioning that it might create videos as much as one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the model's abilities. [225] It acknowledged a few of its imperfections, consisting of battles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", however kept in mind that they must have been cherry-picked and may not represent Sora's common output. [225] |
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<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry [figures](https://avicii.blog) have shown significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's capability to produce sensible video from text descriptions, citing its prospective to revolutionize storytelling and content creation. He said that his [enjoyment](https://gitlab-zdmp.platform.zdmp.eu) about Sora's possibilities was so strong that he had chosen to stop briefly prepare for expanding 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 big dataset of varied audio and is also a multi-task model that can perform multilingual speech acknowledgment 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 predict subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly but then fall under turmoil the longer it plays. [230] [231] In popular culture, [wiki.whenparked.com](https://wiki.whenparked.com/User:LetaX2026348693) initial applications of this tool were utilized as early as 2020 for the web psychological 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 category, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "reveal regional musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" which "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's highly remarkable, even if the results seem like mushy variations of tunes that may feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting songs are memorable and sound genuine". [234] [235] [236] |
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<br>User user interfaces<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 debate toy problems in front of a human judge. The function is to research study whether such a technique might assist in auditing [AI](https://lifestagescs.com) decisions and [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/halleybodin) in establishing explainable [AI](https://muwafag.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 neuron of 8 neural network [designs](https://x-like.ir) which are typically studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The [designs consisted](https://basedwa.re) of are AlexNet, VGG-19, different variations of Inception, [ratemywifey.com](https://ratemywifey.com/author/orvalming2/) 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 an [artificial intelligence](http://git.jetplasma-oa.com) tool constructed on top of GPT-3 that offers a conversational interface that allows users to ask concerns in natural language. The system then responds with an answer within seconds.<br> |
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