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<br>Announced in 2016, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) Gym is an open-source Python library designed to facilitate the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](http://gitlab.digital-work.cn) research study, making released research study more quickly reproducible [24] [144] while offering users with a simple user interface for engaging with these environments. In 2022, new [developments](http://47.105.104.2043000) 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 knowing (RL) research on video games [147] utilizing RL algorithms and research study generalization. [Prior RL](https://git.pleasantprogrammer.com) research study focused mainly on optimizing agents to solve single tasks. Gym Retro provides the capability to generalize in between games with comparable principles but 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 agents at first do not have knowledge of how to even walk, but are provided the objectives of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adjust to altering conditions. When an agent is then eliminated from this virtual environment and placed 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 method. [148] [149] OpenAI's Igor Mordatch argued that [competition](https://rca.co.id) between agents could develop an intelligence "arms race" that could increase a representative's ability to operate even outside the context of the [competitors](http://8.217.113.413000). [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 computer game Dota 2, that find out to play against human players at a high ability level entirely through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation occurred at The International 2017, the yearly premiere championship competition for the video game, where Dendi, an [expert Ukrainian](https://gitea.sprint-pay.com) gamer, lost against a bot in a live one-on-one 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 real time, which the knowing software was an action in the instructions of developing software application that can handle intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a kind of [support](http://47.108.161.783000) knowing, as the bots find out over time by [playing](https://www.jobseeker.my) against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the capability of the bots broadened to play together as a full team of 5, and they had the ability to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The [International](http://120.48.7.2503000) 2018, OpenAI Five played in two exhibit matches against professional players, however ended up losing both [video games](https://sunriji.com). [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer shows the difficulties of [AI](https://git.isatho.me) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown the usage of deep support 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 utilizes maker discovering to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It discovers completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a variety of [experiences](https://asteroidsathome.net) rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB cams to enable the robotic to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system was able 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 had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating progressively harder environments. ADR varies from manual domain randomization by not needing 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 brand-new [AI](https://git-web.phomecoming.com) models developed by OpenAI" to let [designers](https://git.k8sutv.it.ntnu.no) get in touch with it for "any English language [AI](http://101.42.41.254:3000) job". [170] [171]
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<br>Text generation<br>
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<br>The company has promoted generative pretrained transformers (GPT). [172]
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<br>OpenAI's initial GPT design ("GPT-1")<br>
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<br>The initial 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 demonstrated how a generative model of language might obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative variations at first launched to the general public. The complete version of GPT-2 was not instantly launched due to issue about potential misuse, including applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 posed a substantial danger.<br>
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<br>In response to GPT-2, [wiki.whenparked.com](https://wiki.whenparked.com/User:Earnest95D) the Allen Institute for Artificial Intelligence responded with a tool to spot "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language model. [177] Several sites host interactive [presentations](https://local.wuanwanghao.top3000) of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue without supervision language designs to be general-purpose students, shown by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design 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](https://ransomware.design) in Reddit submissions with a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. 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 a not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million [criteria](http://1.14.71.1033000) were also trained). [186]
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<br>OpenAI stated that GPT-3 prospered at certain "meta-learning" jobs and might generalize the function of a [single input-output](https://lr-mediconsult.de) pair. The GPT-3 release paper offered examples of [translation](https://lab.chocomart.kz) and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184]
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<br>GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the essential capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for issues of possible abuse, although OpenAI planned to permit 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. [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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://wiki.vst.hs-furtwangen.de) 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 [produce](https://code.3err0.ru) working code in over a lots shows languages, the majority of effectively in Python. [192]
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<br>Several concerns with problems, style flaws and security vulnerabilities were pointed out. [195] [196]
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<br>GitHub Copilot has been accused of discharging copyrighted code, with no author attribution or license. [197]
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<br>OpenAI announced that they would terminate 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of [accepting text](https://speeddating.co.il) or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar test with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise read, analyze or generate approximately 25,000 words of text, and write code in all major shows languages. [200]
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<br>Observers reported that the version of ChatGPT using GPT-4 was an [enhancement](http://042.ne.jp) on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal numerous technical details and data about GPT-4, such as the accurate size of the design. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI revealed and [released](http://47.100.72.853000) GPT-4o, which can process and create text, images and audio. [204] GPT-4o [attained state-of-the-art](http://git.jaxc.cn) results in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) [criteria compared](https://noinai.com) to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user [interface](https://103.1.12.176). Its API costs $0.15 per million input tokens and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:IvyCano5125640) $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be especially beneficial for enterprises, startups and designers looking for to automate services with [AI](http://skyfffire.com:3000) 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 created to take more time to consider their actions, leading to greater precision. These designs are especially effective in science, coding, and thinking tasks, and were made available to [ChatGPT](http://gnu5.hisystem.com.ar) Plus and Team members. [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 [successor](http://47.108.140.33) of the o1 thinking design. OpenAI also unveiled o3-mini, a lighter and [quicker](https://jobs.web4y.online) version of OpenAI o3. Since December 21, 2024, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:FlorianHoutz6) this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:SashaGuidi) 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 telecommunications providers O2. [215]
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<br>Deep research study<br>
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<br>Deep research is a representative established by OpenAI, revealed on February 2, 2025. It [leverages](https://playtube.app) the abilities of [OpenAI's](https://gitlab.chabokan.net) o3 design to [perform substantial](https://boonbac.com) web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [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 analyze the semantic similarity in between text and images. It can notably be utilized for image classification. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, [oeclub.org](https://oeclub.org/index.php?title=User:JulietaD31) DALL-E is a Transformer design that develops images from . [218] DALL-E uses 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 a sad capybara") and generate corresponding images. It can create images of practical objects ("a stained-glass window with a picture of a blue strawberry") in addition to objects that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.<br>
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<br>DALL-E 2<br>
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<br>In April 2022, OpenAI announced DALL-E 2, an updated variation of the model with more realistic results. [219] In December 2022, OpenAI released 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 announced DALL-E 3, a more powerful model much better able to produce images from complicated descriptions 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 model that can generate videos based on short detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is [unknown](https://firstcanadajobs.ca).<br>
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<br>Sora's development team called it after the Japanese word for "sky", to signify its "endless creative capacity". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that function, 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 videos to the public on February 15, 2024, stating that it might create videos approximately one minute long. It also shared a technical report highlighting the [methods](https://cyberbizafrica.com) used to train the design, and the design's capabilities. [225] It acknowledged some of its drawbacks, including struggles mimicing intricate physics. [226] Will [Douglas Heaven](https://www.koumii.com) of the MIT Technology Review called the presentation videos "excellent", but kept in mind that they must have been cherry-picked and might not represent Sora's common output. [225]
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<br>Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to produce sensible video from text descriptions, mentioning its prospective to change storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause prepare 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 acknowledgment model. [228] It is trained on a large dataset of varied audio and is also a multi-task model that can carry out multilingual speech acknowledgment in addition to 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 forecast subsequent musical notes in MIDI music files. It can produce tunes with 10 [instruments](http://gpra.jpn.org) in 15 styles. According to The Verge, a song created by MuseNet tends to start fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet psychological thriller Ben [Drowned](https://careers.synergywirelineequipment.com) to create music for the titular character. [232] [233]
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<br>Jukebox<br>
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<br>Released in 2020, Jukebox is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs song [samples](https://901radio.com). OpenAI specified the songs "reveal regional musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial space" in between Jukebox and human-generated music. The Verge mentioned "It's technically impressive, even if the outcomes sound like mushy versions of songs that might feel familiar", while [Business](https://gitlab.steamos.cloud) [Insider stated](http://39.98.116.22230006) "remarkably, a few of the resulting tunes are catchy 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 devices to [discuss toy](https://gitea.deprived.dev) problems in front of a human judge. The function is to research study whether such a technique might assist in auditing [AI](http://45.67.56.214:3030) choices and in establishing explainable [AI](https://ddsbyowner.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 significant layer and nerve cell of 8 neural network designs which are often [studied](https://git.epochteca.com) in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various versions of Inception, and different variations of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, [ChatGPT](http://xrkorea.kr) is an artificial intelligence tool constructed on top of GPT-3 that supplies a conversational user interface that permits users to ask questions in natural language. The system then responds with a response within seconds.<br>
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