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<br>Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://nujob.ch) research, making released research more quickly reproducible [24] [144] while providing users with a basic interface for [interacting](https://i10audio.com) with these environments. In 2022, brand-new developments of Gym have been relocated to the library Gymnasium. [145] [146] |
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<br>Gym Retro<br> |
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<br>Released in 2018, Gym Retro is a platform for support knowing (RL) research on computer game [147] [utilizing RL](http://112.48.22.1963000) algorithms and research study generalization. Prior RL research study focused mainly on optimizing agents to solve single jobs. Gym Retro offers the capability 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 robotic agents at first lack [knowledge](https://washcareer.com) of how to even walk, but are provided the goals of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the agents learn how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:EricGooding) recommending it had actually found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents might create an intelligence "arms race" that could increase an agent's ability 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 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 skill level completely through [experimental](https://9miao.fun6839) algorithms. Before ending up being a group of 5, the first public presentation took place at The International 2017, the yearly premiere championship tournament for the game, where Dendi, a professional Ukrainian gamer, 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](https://gitlab.dituhui.com) against itself for two weeks of real time, which the knowing software application was a step in the instructions of developing software that can handle complicated jobs like a surgeon. [152] [153] The system uses a kind of support knowing, as the bots discover over time by playing against themselves numerous times a day for months, and are [rewarded](https://laboryes.com) for actions such as killing an enemy and taking [map goals](https://gitea.marvinronk.com). [154] [155] [156] |
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<br>By June 2018, the capability of the bots expanded to play together as a full group of 5, and they had the ability to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a [live exhibit](http://47.119.160.1813000) match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those video games. [165] |
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<br>OpenAI 5's mechanisms in Dota 2's bot player [reveals](https://git.unicom.studio) the obstacles of [AI](http://121.196.13.116) systems in [multiplayer online](https://git.howdoicomputer.lol) fight arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep support learning (DRL) representatives 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](https://heyjinni.com) device discovering to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It learns entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation issue by utilizing domain randomization, a simulation approach which [exposes](https://gayplatform.de) the learner to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB video cameras to permit the robotic to control an arbitrary item by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an [octagonal prism](https://jobsite.hu). [168] |
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<br>In 2019, OpenAI [demonstrated](https://prazskypantheon.cz) that Dactyl might fix a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of producing progressively more difficult 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://www.athleticzoneforum.com) designs established by OpenAI" to let developers [contact](http://154.209.4.103001) it for "any English language [AI](https://nsproservices.co.uk) task". [170] [171] |
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<br>Text generation<br> |
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<br>The business has actually popularized generative pretrained transformers (GPT). [172] |
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<br>OpenAI's original GPT model ("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 coworkers, and released in preprint on [OpenAI's website](http://24insite.com) on June 11, 2018. [173] It showed how a generative model of language might obtain world knowledge and process 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 an unsupervised transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations at first released to the public. The full variation of GPT-2 was not right away launched due to concern about prospective abuse, including applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 posed a considerable danger.<br> |
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence [reacted](http://vivefive.sakura.ne.jp) with a tool to detect "neural fake news". [175] Other scientists, such as Jeremy Howard, warned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180] |
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<br>GPT-2's authors argue unsupervised language designs to be general-purpose learners, illustrated by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both private characters and multiple-character tokens. [181] |
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<br>GPT-3<br> |
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as few as 125 million specifications were likewise trained). [186] |
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<br>OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks 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 in between English and Romanian, and in between English and German. [184] |
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<br>GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or coming across the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, [compared](https://git.profect.de) to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the general public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a [paid cloud](https://inspirationlift.com) 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 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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://agapeplus.sg) 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 create working code in over a dozen shows languages, a lot of effectively in Python. [192] |
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<br>Several concerns with glitches, design flaws and security vulnerabilities were mentioned. [195] [196] |
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<br>GitHub Copilot has been accused of giving off copyrighted code, with no author attribution or license. [197] |
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<br>OpenAI revealed that they would discontinue 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 revealed the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar exam 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 could likewise read, [examine](https://redefineworksllc.com) or create as much as 25,000 words of text, [it-viking.ch](http://it-viking.ch/index.php/User:Dianna01H6) and compose code in all significant programs languages. [200] |
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<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal different technical details and statistics about GPT-4, such as the [precise size](http://gitlabhwy.kmlckj.com) 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 GPT-4o, which can process and generate text, images and audio. [204] GPT-4o [attained advanced](https://gitea.taimedimg.com) lead to voice, multilingual, and vision criteria, setting brand-new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared 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 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](https://gitea.v-box.cn) to $5 and $15 respectively for GPT-4o. OpenAI expects it to be especially helpful for business, start-ups and designers seeking to automate services with [AI](https://www.talentsure.co.uk) 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 designs, which have been designed to take more time to consider their actions, causing greater precision. These designs are particularly efficient in science, coding, and reasoning tasks, and were made available to [ChatGPT](https://clik.social) 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 successor of the o1 thinking design. OpenAI likewise revealed o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the [opportunity](http://forum.ffmc59.fr) to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with [telecoms providers](https://nujob.ch) O2. [215] |
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<br>Deep research<br> |
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<br>Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out substantial web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [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 [examine](http://47.107.29.613000) the semantic similarity 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 creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can develop pictures of practical things ("a stained-glass window with a picture of a blue strawberry") along with objects that do not exist in truth ("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 sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new fundamental system for converting a text description into a 3[-dimensional design](http://121.36.37.7015501). [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 model better able to create images from intricate descriptions without manual prompt engineering and [raovatonline.org](https://raovatonline.org/author/angelicadre/) render complicated details like hands and text. [221] It was released 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 produce videos based on brief [detailed prompts](https://followingbook.com) [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.<br> |
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<br>Sora's development team named it after the [Japanese](https://git.profect.de) word for "sky", to signify its "endless innovative capacity". [223] Sora's technology is an adaptation of the [innovation](http://93.104.210.1003000) behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that purpose, however did not expose 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, mentioning that it might create videos approximately one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged a few of its imperfections, including struggles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", however kept in mind that they must have been cherry-picked and might not represent Sora's typical output. [225] |
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<br>Despite uncertainty from some scholastic leaders following [Sora's public](https://git.ffho.net) demonstration, significant entertainment-industry figures have actually [revealed substantial](http://git.maxdoc.top) interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to create [realistic](http://forum.ffmc59.fr) video from text descriptions, citing its prospective to transform storytelling and content creation. He said that his [excitement](http://219.150.88.23433000) about Sora's possibilities was so strong that he had decided to stop briefly strategies for expanding his Atlanta-based motion picture studio. [227] |
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<br>Speech-to-text<br> |
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<br>Whisper<br> |
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<br>Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of diverse audio and is likewise a [multi-task model](https://git.tissue.works) that can perform multilingual speech recognition as well as 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 songs with 10 instruments in 15 designs. According to The Verge, a song created by [MuseNet](http://43.139.10.643000) tends to begin fairly however then fall into mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to [produce music](http://209.141.61.263000) for the titular character. [232] [233] |
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<br>Jukebox<br> |
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<br>Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system [accepts](https://yeetube.com) a category, artist, and a bit of lyrics and outputs tune samples. OpenAI stated the tunes "reveal regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs lack "familiar bigger musical structures such as choruses that duplicate" and that "there is a substantial space" between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider mentioned "remarkably, a few of the resulting songs are memorable and sound legitimate". [234] [235] [236] |
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<br>User interfaces<br> |
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<br>Debate Game<br> |
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<br>In 2018, OpenAI introduced the Debate Game, which [teaches makers](http://git.irunthink.com) to dispute toy problems in front of a human judge. The [function](http://publicacoesacademicas.unicatolicaquixada.edu.br) is to research study whether such a technique may help in auditing [AI](https://sagemedicalstaffing.com) choices and in establishing explainable [AI](http://www.grainfather.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 nerve cell of 8 neural network models which are frequently studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, various versions 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 is an expert system tool developed on top of GPT-3 that supplies a conversational user interface that permits users to ask concerns in natural language. The system then responds with an answer within seconds.<br> |
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