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<br>Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](https://beautyteria.net) research, making published research more easily reproducible [24] [144] while providing users with an easy interface for engaging with these environments. In 2022, new advancements of Gym have actually 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 knowing](https://git.fafadiatech.com) (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to resolve single jobs. Gym Retro provides the ability to generalize in between games with similar ideas but different 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 representatives initially lack understanding of how to even stroll, however are offered the goals of discovering to move and to press the opposing agent out of the ring. [148] Through this [adversarial learning](https://sb.mangird.com) procedure, the agents discover how to adjust to changing conditions. When a [representative](https://www.medexmd.com) is then eliminated from this [virtual environment](https://eurosynapses.giannistriantafyllou.gr) and put in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might create an intelligence "arms race" that could increase an agent's capability to function even outside the context of the [competition](https://duyurum.com). [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 Dota 2, that learn to play against human gamers at a high ability level totally through experimental algorithms. Before ending up being a team of 5, the very first public demonstration happened at The International 2017, the annual premiere 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 found out by playing against itself for two weeks of actual time, and that the learning software was an action in the direction of producing software that can manage complex jobs like a [cosmetic surgeon](https://tricityfriends.com). [152] [153] The system uses a form of support learning, 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 objectives. [154] [155] [156] |
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<br>By June 2018, the ability 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 professional players, but ended up losing both [video games](https://code.thintz.com). [160] [161] [162] In April 2019, OpenAI Five beat 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 that month, where they played in 42,729 total 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 gamer reveals the obstacles of [AI](https://www.stmlnportal.com) systems in multiplayer online [battle arena](https://git.owlhosting.cloud) (MOBA) games and how OpenAI Five has shown making use of deep support learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166] |
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<br>Dactyl<br> |
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<br>Developed in 2018, Dactyl utilizes device learning to train a Shadow Hand, a [human-like robot](https://www.anetastaffing.com) hand, to control physical things. [167] It finds out entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB video cameras to allow the [robotic](http://47.104.65.21419206) to manipulate an arbitrary item by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an [octagonal prism](http://bc.zycoo.com3000). [168] |
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<br>In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The [robotic](https://bytes-the-dust.com) had the ability 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 enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing progressively [harder environments](https://skytechenterprisesolutions.net). ADR varies from manual domain randomization by not requiring a human to define randomization varieties. [169] |
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<br>API<br> |
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](http://wiki.iurium.cz) models developed by OpenAI" to let designers contact it for "any English language [AI](https://git.genowisdom.cn) task". [170] [171] |
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<br>Text generation<br> |
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<br>The business has actually popularized generative [pretrained](https://truthbook.social) transformers (GPT). [172] |
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<br>OpenAI's original GPT model ("GPT-1")<br> |
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<br>The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world understanding and procedure long-range dependencies 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 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative variations initially released to the public. The full version of GPT-2 was not instantly released due to concern about potential abuse, [consisting](http://kuzeydogu.ogo.org.tr) of applications for composing phony news. [174] Some experts expressed uncertainty that GPT-2 presented a significant 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 phony news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language model. [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 unsupervised language models to be general-purpose learners, highlighted by GPT-2 attaining cutting edge accuracy 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](http://107.172.157.443000) of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain problems 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 [a] Transformer 3 (GPT-3) is a without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186] |
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<br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184] |
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<br>GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the essential ability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the general public for concerns of possible abuse, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) 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 licensed solely 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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://git.yoho.cn) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can produce working code in over a lots programming languages, most efficiently in Python. [192] |
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<br>Several problems with glitches, style defects and security vulnerabilities were pointed out. [195] [196] |
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<br>GitHub Copilot has been [accused](https://git.numa.jku.at) of emitting copyrighted code, without any author attribution or license. [197] |
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<br>OpenAI announced 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), efficient in [accepting text](https://busanmkt.com) or image inputs. [199] They announced that the updated technology passed a simulated law school bar examination with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, [analyze](https://musixx.smart-und-nett.de) or create up to 25,000 words of text, and write code in all major programs languages. [200] |
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<br>Observers reported that the version of ChatGPT using GPT-4 was an [enhancement](http://109.195.52.923000) on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier [revisions](http://8.137.58.203000). [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various technical details and stats 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 launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision standards, setting brand-new records in audio speech recognition and [translation](http://caxapok.space). [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 released GPT-4o mini, a smaller sized 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 particularly helpful for enterprises, start-ups and designers seeking to automate services with [AI](https://copyrightcontest.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 designed to take more time to consider their responses, causing greater precision. These designs are especially efficient in science, coding, and reasoning tasks, and were made available to [ChatGPT](https://gitea.tgnotify.top) 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 thinking model. OpenAI also revealed o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:DewayneKeen425) 2024, this model is not available for public usage. According to OpenAI, they are checking 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 design is called o3 instead of o2 to avoid confusion with [telecoms providers](https://goodprice-tv.com) O2. [215] |
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<br>Deep research study<br> |
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<br>Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of [OpenAI's](https://pioneerayurvedic.ac.in) o3 model to perform substantial web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools 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, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:MaeLinderman7) CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic resemblance in between text and images. It can significantly 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. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce images of practical items ("a stained-glass window with a picture 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 version of the model with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new basic 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 revealed DALL-E 3, a more powerful model better able to generate images from [intricate](https://82.65.204.63) [descriptions](http://code.exploring.cn) without manual prompt engineering and render complicated 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 design that can create videos based on short detailed [prompts](http://xn--9t4b21gtvab0p69c.com) [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.<br> |
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<br>Sora's development group named it after the Japanese word for "sky", to represent its "unlimited imaginative potential". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos certified for that function, but did not expose the number or the exact sources of the videos. [223] |
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<br>OpenAI demonstrated some Sora-created [high-definition videos](https://iinnsource.com) to the public on February 15, 2024, mentioning that it could generate videos up to one minute long. It also shared a technical report highlighting the [techniques](https://rubius-qa-course.northeurope.cloudapp.azure.com) used to train the design, and the design's capabilities. [225] It acknowledged some of its imperfections, including struggles [simulating complicated](https://iinnsource.com) physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they need to have been cherry-picked and might not represent Sora's normal output. [225] |
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<br>Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have revealed substantial interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to produce realistic video from text descriptions, mentioning its possible to transform storytelling and content production. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause plans for broadening 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 model. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can carry out 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](https://git.healthathome.com.np) in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall into mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the internet mental 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](https://bizad.io) to generate music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the songs "show 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" which "there is a considerable space" in between Jukebox and human-generated music. The Verge mentioned "It's highly impressive, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are memorable and sound genuine". [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 launched the Debate Game, which teaches machines to dispute toy problems in front of a human judge. The purpose is to research study whether such a technique may help in auditing [AI](https://git.qiucl.cn) decisions and in establishing explainable [AI](https://git.christophhagen.de). [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 and neuron of eight neural network models which are typically studied in interpretability. [240] [Microscope](http://modulysa.com) was created to analyze the features that form inside these neural networks easily. The models included are AlexNet, VGG-19, various variations of Inception, and various versions of CLIP Resnet. [241] |
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<br>ChatGPT<br> |
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<br>Launched in November 2022, [ChatGPT](https://aggm.bz) is an artificial intelligence tool developed on top of GPT-3 that provides a conversational interface that permits users to ask concerns in natural language. The system then responds with an answer within seconds.<br> |
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