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<br>Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of [reinforcement learning](https://www.webthemes.ca) algorithms. It aimed to standardize how environments are specified in [AI](http://51.75.64.148) research, making published research study more quickly reproducible [24] [144] while providing users with a basic user interface for connecting with these environments. In 2022, [brand-new advancements](https://careers.webdschool.com) 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 learning (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to solve single jobs. Gym Retro provides the ability to generalize in between video games with comparable principles however various 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 robot agents initially lack understanding of how to even stroll, but are provided the goals of discovering to move and [it-viking.ch](http://it-viking.ch/index.php/User:MagnoliaDarcy3) to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents find out how to adjust to altering conditions. When a representative is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents might produce an intelligence "arms race" that might increase a representative's ability to work 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 group of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high [ability](https://www.2dudesandalaptop.com) level completely through experimental algorithms. Before becoming a team of 5, the very first public demonstration took place at The [International](https://executiverecruitmentltd.co.uk) 2017, the [annual premiere](https://u-hired.com) championship tournament for the game, where Dendi, a professional Ukrainian 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 learned by playing against itself for 2 weeks of actual time, and that the learning software application was an action in the direction of producing software that can deal with complex tasks like a [cosmetic surgeon](http://jobasjob.com). [152] [153] The system [utilizes](https://jktechnohub.com) a kind of support knowing, as the bots find out over time by playing against themselves hundreds of times a day for months, and are rewarded for actions 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 broadened to play together as a full group of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 overall games in a four-day open online competition, [winning](http://lophas.com) 99.4% of those games. [165] |
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<br>OpenAI 5's systems in Dota 2's bot player reveals the challenges of [AI](https://hcp.com.gt) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown making use of deep support learning (DRL) representatives 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 uses device discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It discovers completely in simulation using the very same [RL algorithms](http://www.xn--80agdtqbchdq6j.xn--p1ai) and training code as OpenAI Five. OpenAI tackled the item orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB electronic cameras to enable the robot to manipulate an approximate item by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI demonstrated that Dactyl might fix a . The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce [complicated physics](http://195.58.37.180) that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating gradually harder environments. ADR differs from manual domain randomization by not requiring 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](http://mpowerstaffing.com) designs established by OpenAI" to let designers get in touch with it for "any English language [AI](http://www.hxgc-tech.com:3000) task". [170] [171] |
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<br>Text generation<br> |
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<br>The business has actually popularized generative [pretrained](https://workmate.club) transformers (GPT). [172] |
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<br>OpenAI's original GPT design ("GPT-1")<br> |
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<br>The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his coworkers, and published in [preprint](http://www5a.biglobe.ne.jp) on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world understanding and process long-range dependences by pre-training on a varied corpus with long stretches of adjoining 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 successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative variations initially launched to the public. The complete variation of GPT-2 was not right away released due to issue about potential abuse, including applications for composing phony news. [174] Some specialists revealed uncertainty that GPT-2 posed a considerable risk.<br> |
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence [reacted](https://church.ibible.hk) with a tool to discover "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, [pediascape.science](https://pediascape.science/wiki/User:Faith61S993) OpenAI released the complete version of the GPT-2 language design. [177] Several sites host interactive demonstrations of various circumstances of GPT-2 and other transformer models. [178] [179] [180] |
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<br>GPT-2's authors argue not being watched language models to be general-purpose students, highlighted by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by [utilizing byte](https://alllifesciences.com) 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 not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of [magnitude larger](http://210.236.40.2409080) than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as few as 125 million criteria were also trained). [186] |
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<br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer [knowing](https://git.manu.moe) in between English and Romanian, and between English and German. [184] |
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<br>GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or encountering the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, [compared](https://93.177.65.216) to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched 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 free private beta that started 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](https://mulkinflux.com) of GPT-3 that has furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://arlogjobs.org) 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 lots programs languages, a lot of successfully in Python. [192] |
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<br>Several issues with problems, design defects and security vulnerabilities were cited. [195] [196] |
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<br>GitHub Copilot has actually been accused of discharging copyrighted code, with no author [attribution](https://linkin.commoners.in) 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 revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar test 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 might likewise check out, evaluate or produce approximately 25,000 words of text, and compose code in all major programs languages. [200] |
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<br>Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose numerous [technical details](https://samisg.eu8443) 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 revealed and released GPT-4o, which can [process](https://www.2dudesandalaptop.com) and produce text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision standards, setting brand-new [records](https://cats.wiki) in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) [standard](https://maibuzz.com) 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 version of GPT-4o [replacing](http://git.kdan.cc8865) GPT-3.5 Turbo on the ChatGPT user interface. Its [API costs](http://94.224.160.697990) $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 helpful for enterprises, start-ups and designers seeking to automate services with [AI](https://giaovienvietnam.vn) representatives. [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 designs, which have actually been designed to take more time to think of their reactions, resulting in higher accuracy. These designs are especially reliable in science, [oeclub.org](https://oeclub.org/index.php/User:Hazel19N87329656) coding, and thinking tasks, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211] |
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<br>o3<br> |
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<br>On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking design. OpenAI also unveiled o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215] |
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<br>Deep research study<br> |
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<br>Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform extensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, 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 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 used 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 uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can produce pictures of realistic things ("a stained-glass window with a picture of a blue strawberry") in addition to items 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 revealed](http://142.93.151.79) DALL-E 2, an upgraded variation of the model with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new simple system for converting a text description into a 3[-dimensional](http://2.47.57.152) 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 effective design better able to create images from intricate descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was launched to the general public as a ChatGPT Plus feature in October. [222] |
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<br>Text-to-video<br> |
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<br>Sora<br> |
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<br>Sora is a text-to-video model that can create videos based on short detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce videos with [resolution](https://tapeway.com) as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.<br> |
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<br>Sora's development group named it after the Japanese word for "sky", to [represent](https://git.novisync.com) its "limitless creative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to [copyrighted videos](http://www5a.biglobe.ne.jp) accredited for [oeclub.org](https://oeclub.org/index.php/User:YSTWinston) that function, but did not expose the number or the specific sources of the videos. [223] |
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<br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could generate videos approximately one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the model's capabilities. [225] It acknowledged a few of its shortcomings, including battles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", however noted that they should have been cherry-picked and might not represent Sora's common output. [225] |
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<br>Despite [uncertainty](https://aubameyangclub.com) from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have revealed significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's capability to produce realistic video from text descriptions, mentioning its possible to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly 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 large dataset of varied audio and is also a multi-task model that can perform 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](https://dronio24.com) in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a [song generated](http://admin.youngsang-tech.com) by MuseNet tends to begin fairly however then fall into mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to develop music for the [titular](https://jobs.ethio-academy.com) 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](https://www.kayserieticaretmerkezi.com) with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Fawn85X3571217) outputs tune samples. OpenAI stated the tunes "show local musical coherence [and] follow traditional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" which "there is a considerable space" between Jukebox and human-generated music. The Verge specified "It's highly remarkable, even if the results sound like mushy variations of tunes that might feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting tunes are catchy 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 the Debate Game, which teaches machines to discuss toy problems in front of a human judge. The function is to research study whether such a technique may assist in auditing [AI](https://git.lgoon.xyz) choices and in developing explainable [AI](https://jobboat.co.uk). [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 [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:NicolasTeichelma) nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different variations of Inception, 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 artificial intelligence tool built on top of GPT-3 that offers a conversational interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.<br> |
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