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<br>Announced in 2016, Gym is an open-source Python library [designed](https://955x.com) to facilitate the advancement of reinforcement learning [algorithms](http://release.rupeetracker.in). It aimed to standardize how [environments](https://test1.tlogsir.com) are specified in [AI](https://www.ynxbd.cn:8888) research study, making published research study more easily reproducible [24] [144] while offering users with an easy interface for communicating with these environments. 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] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to resolve single tasks. Gym Retro offers the capability to generalize in between games with similar concepts however different 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 lack knowledge of how to even stroll, but are provided the objectives of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial learning procedure, [surgiteams.com](https://surgiteams.com/index.php/User:AshliLent31607) the agents discover how to adapt to changing 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, recommending it had learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might produce an intelligence "arms race" that could increase an agent's capability 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 5 OpenAI-curated bots utilized in the competitive five-on-five [video game](https://git.junzimu.com) Dota 2, that find out to play against human players at a high ability level totally through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation took place at The International 2017, the annual premiere championship competition for 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 actually found out by playing against itself for two weeks of actual time, which the learning software application was an action in the direction of developing software application that can handle complicated tasks like a [cosmetic surgeon](http://szyg.work3000). [152] [153] The system uses a type of support learning, as the bots learn in time by playing against themselves numerous times a day for months, and are rewarded for [actions](https://www.roednetwork.com) such as eliminating an opponent and taking map goals. [154] [155] [156] |
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<br>By June 2018, the capability of the bots expanded to play together as a full team of 5, and they had the ability to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling 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 competitors, winning 99.4% of those games. [165] |
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<br>OpenAI 5's systems in Dota 2's bot player reveals the obstacles of [AI](https://andyfreund.de) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually [demonstrated](https://git.vhdltool.com) making use of [deep support](http://forum.infonzplus.net) learning (DRL) representatives to attain superhuman proficiency in Dota 2 . [166] |
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<br>Dactyl<br> |
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<br>Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It learns entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking [electronic](http://api.cenhuy.com3000) cameras, also has RGB electronic cameras to allow the robot to control an arbitrary things by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI showed that Dactyl could resolve a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present 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 generating gradually more tough environments. ADR differs 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 [revealed](http://www.hcmis.cn) a multi-purpose API which it said was "for accessing new [AI](https://git.protokolla.fi) designs developed by OpenAI" to let developers call on it for "any English language [AI](https://git.slegeir.com) job". [170] [171] |
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<br>Text generation<br> |
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<br>The [business](https://www.pkgovtjobz.site) has promoted generative pretrained transformers (GPT). [172] |
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<br>OpenAI's initial GPT model ("GPT-1")<br> |
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<br>The original paper on generative [pre-training](https://www.jobzpakistan.info) of a transformer-based language model was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a [generative design](https://www.stmlnportal.com) of language could obtain world knowledge and process long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.<br> |
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<br>GPT-2<br> |
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<br>[Generative](https://nbc.co.uk) Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative versions at first launched to the general public. The full variation of GPT-2 was not right away launched due to concern about prospective misuse, consisting of applications for writing phony news. [174] Some professionals expressed uncertainty that GPT-2 positioned a substantial risk.<br> |
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language design. [177] Several [websites host](https://salesupprocess.it) interactive presentations of various circumstances of GPT-2 and other transformer models. [178] [179] [180] |
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<br>GPT-2's authors [argue unsupervised](http://140.82.32.174) language designs to be general-purpose learners, shown by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design 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 at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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](http://archmageriseswiki.com) [a] Transformer 3 (GPT-3) is a not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 [contained](http://116.198.225.843000) 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were also trained). [186] |
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<br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper provided 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 enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or experiencing the basic ability constraints of predictive language models. [187] Pre-training GPT-3 needed several 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 instantly released to the general public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month complimentary personal beta that started in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was 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](http://hybrid-forum.ru) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can produce working code in over a dozen shows languages, most successfully in Python. [192] |
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<br>Several problems with glitches, [style defects](https://rubius-qa-course.northeurope.cloudapp.azure.com) and security vulnerabilities were cited. [195] [196] |
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<br>GitHub Copilot has been implicated of releasing copyrighted code, with no author attribution or license. [197] |
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<br>OpenAI announced that they would stop assistance for [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TabithaWithers0) 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 announced that the upgraded innovation passed a simulated law school bar examination with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, evaluate or generate approximately 25,000 words of text, and compose code in all significant programming languages. [200] |
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<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose different technical details and statistics 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 announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision standards, setting new records in [audio speech](https://duniareligi.com) recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) [standard compared](https://www.jobzpakistan.info) 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 replacing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be particularly [beneficial](https://dev-members.writeappreviews.com) for enterprises, start-ups and developers looking for to [automate services](https://dongawith.com) with [AI](http://110.41.143.128:8081) agents. [208] |
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<br>o1<br> |
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<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been created to take more time to believe about their actions, resulting in greater accuracy. 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 model](https://29sixservices.in). OpenAI also unveiled o3-mini, a [lighter](https://jvptube.net) and faster version of OpenAI o3. Since December 21, 2024, this design 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 models. [214] The model is called o3 instead of 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 a representative established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, it reached a precision 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](https://www.chinami.com) 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 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 model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can produce pictures of realistic objects ("a stained-glass window with a picture of a blue strawberry") as well as things that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.<br> |
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<br>DALL-E 2<br> |
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<br>In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the design with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new rudimentary system for [converting](http://web.joang.com8088) 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 revealed DALL-E 3, a more effective design much better able to create images from intricate descriptions without manual [prompt engineering](http://119.29.169.1578081) and render intricate details like hands and text. [221] It was launched to the 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 model that can produce videos based on brief detailed triggers [223] as well as extend existing videos forwards or in [reverse](https://gitea.sync-web.jp) in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br> |
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<br>Sora's development group called it after the Japanese word for "sky", to represent its "limitless imaginative potential". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that purpose, however did not reveal the number or the precise 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 could generate videos up to one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the model's abilities. [225] It acknowledged a few of its shortcomings, including struggles simulating complex physics. [226] Will Douglas Heaven of the MIT [Technology](http://121.36.62.315000) Review called the presentation videos "remarkable", but noted that they must have been cherry-picked and may not represent Sora's typical output. [225] |
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<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to create realistic video from text descriptions, citing its possible to reinvent storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause strategies 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 design. [228] It is trained on a big dataset of diverse audio and is also a multi-task design that can carry out multilingual speech acknowledgment along with 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 forecast subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 designs. According to The Verge, a tune created by MuseNet tends to begin fairly however then fall into turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to produce 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 song samples. OpenAI mentioned the tunes "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a significant space" between Jukebox and human-generated music. The Verge specified "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>Interface<br> |
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<br>Debate Game<br> |
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<br>In 2018, [OpenAI introduced](http://ev-gateway.com) the Debate Game, which teaches devices to debate toy problems in front of a human judge. The function is to research whether such an approach may assist in auditing [AI](https://botcam.robocoders.ir) decisions and in establishing explainable [AI](https://www.philthejob.nl). [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 [considerable layer](http://47.107.29.613000) and nerve cell of 8 neural network models which are typically studied in interpretability. [240] Microscope was created to [examine](https://www.lingualoc.com) the features that form inside these neural networks easily. The models included are AlexNet, VGG-19, various [variations](https://gl.ignite-vision.com) of Inception, [wiki.whenparked.com](https://wiki.whenparked.com/User:LetaX2026348693) 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 built on top of GPT-3 that supplies a conversational interface that allows users to ask concerns in natural language. The system then reacts with a response within seconds.<br> |
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