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<br>Announced in 2016, Gym is an [open-source Python](http://vimalakirti.com) library created to assist in the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://128.199.175.152:9000) research, making published research study more easily reproducible [24] [144] while providing users with a simple interface for interacting with these environments. In 2022, new developments of Gym have actually been transferred 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 learning (RL) research on video games [147] utilizing RL algorithms and research [study generalization](https://wkla.no-ip.biz). Prior RL research focused mainly on optimizing representatives to resolve single jobs. Gym Retro provides the capability to generalize between video games with similar concepts however various 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 representatives at first lack understanding of how to even stroll, however are offered the objectives of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents find out how to adapt to changing conditions. When a representative is then removed from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, recommending it had actually discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents could develop an intelligence "arms race" that could [increase](https://tubevieu.com) an agent's capability to function even outside the context of the competitors. [148] |
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<br>OpenAI 5<br> |
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<br>OpenAI Five is a team of five OpenAI-curated bots used 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 algorithms. Before ending up being a group of 5, the very first public presentation happened at The International 2017, the yearly premiere champion competition for the game, where Dendi, an [expert Ukrainian](https://video.emcd.ro) 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 discovered by playing against itself for 2 weeks of actual time, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Discussion_utilisateur:JaimeGalway) which the knowing software was an action in the direction of [developing software](http://45.45.238.983000) application that can handle complicated jobs like a surgeon. [152] [153] The system uses a kind of support learning, as the bots find out in time by playing against themselves hundreds of 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](https://git.bwt.com.de) of the bots expanded to play together as a complete group of 5, and they were able to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against [professional](https://git.snaile.de) gamers, however ended up losing both video 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 match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those games. [165] |
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer reveals the obstacles of [AI](http://219.150.88.234:33000) [systems](https://openedu.com) in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown using deep support learning (DRL) agents 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 uses machine learning to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It learns completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:KatherinL70) a simulation technique which exposes the learner to a range of [experiences](https://gitlab.t-salon.cc) instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB video cameras to permit the robot to control an approximate item by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI showed that Dactyl could resolve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to [perturbations](http://git.meloinfo.com) by utilizing Automatic Domain Randomization (ADR), a simulation method of generating progressively more hard environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169] |
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<br>API<br> |
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://git.whitedwarf.me) designs developed by OpenAI" to let designers get in touch with it for "any English language [AI](https://gitea.belanjaparts.com) task". [170] [171] |
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<br>Text generation<br> |
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<br>The company has popularized generative [pretrained](https://droomjobs.nl) 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 [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Guadalupe7674) published in [preprint](https://vydiio.com) on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and process long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.<br> |
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<br>GPT-2<br> |
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the follower to [OpenAI's initial](https://medicalrecruitersusa.com) GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative versions initially launched to the public. The complete variation of GPT-2 was not right away released due to concern about potential abuse, including applications for composing phony news. [174] Some specialists expressed uncertainty that GPT-2 postured a significant threat.<br> |
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language model. [177] Several websites host interactive presentations of various instances of GPT-2 and other transformer designs. [178] [179] [180] |
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<br>GPT-2's authors argue without supervision language models to be general-purpose learners, highlighted by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not additional 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 concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both specific 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](https://repo.gusdya.net) design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the full 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 couple of 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 between English and [wavedream.wiki](https://wavedream.wiki/index.php/User:TammieRaposo6) Romanian, and between English and German. [184] |
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<br>GPT-3 [considerably improved](http://peterlevi.com) [benchmark outcomes](https://investsolutions.org.uk) over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the fundamental capability constraints of [predictive language](https://www.oscommerce.com) models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not [instantly](http://47.90.83.1323000) launched to the general public for issues of possible abuse, although OpenAI prepared to [permit gain](https://pipewiki.org) access to through a paid cloud API after a two-month totally free 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 in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://topstours.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the design can create working code in over a lots programming languages, many efficiently in Python. [192] |
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<br>Several problems with problems, [style flaws](http://modulysa.com) and security vulnerabilities were pointed out. [195] [196] |
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<br>GitHub Copilot has been accused of discharging copyrighted code, without any author attribution or license. [197] |
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<br>OpenAI revealed 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 or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar examination with a rating around the leading 10% of . (By contrast, [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:CourtneyWardill) GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise read, examine or produce up to 25,000 words of text, and write code in all significant programming languages. [200] |
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<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and data about GPT-4, such as the exact size of the design. [203] |
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<br>GPT-4o<br> |
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<br>On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision criteria, setting new records in audio speech acknowledgment and [translation](http://git.irunthink.com). [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207] |
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<br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, [compared](http://upleta.rackons.com) to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be particularly helpful for enterprises, start-ups and developers looking for to automate services with [AI](https://wooshbit.com) agents. [208] |
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<br>o1<br> |
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<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been designed to take more time to think of their reactions, resulting in greater accuracy. These designs are particularly effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211] |
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<br>o3<br> |
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<br>On December 20, 2024, OpenAI unveiled o3, the successor of the o1 [thinking model](http://www.chinajobbox.com). OpenAI also revealed o3-mini, a lighter and faster version 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 instead of o2 to prevent confusion with telecoms services company O2. [215] |
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<br>Deep research study<br> |
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<br>Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of [OpenAI's](https://jobs.ahaconsultant.co.in) o3 model to carry out extensive web surfing, data analysis, and synthesis, delivering 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 examine the semantic similarity between text and images. It can significantly be used for image category. [217] |
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<br>Text-to-image<br> |
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<br>DALL-E<br> |
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<br>Revealed in 2021, DALL-E is a Transformer model that [develops](https://git.andreaswittke.de) images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can produce images of realistic objects ("a stained-glass window with an image of a blue strawberry") in addition to things that do not exist in [reality](http://repo.magicbane.com) ("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 announced DALL-E 2, an upgraded version of the design with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:KristianGlennie) a new basic system for converting 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, [wavedream.wiki](https://wavedream.wiki/index.php/User:JaysonSpahn7) OpenAI announced DALL-E 3, a more powerful design much better able to create images from complicated descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the 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 upon short detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.<br> |
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<br>Sora's advancement team called it after the Japanese word for "sky", to symbolize its "endless imaginative capacity". [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 certified 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 general public on February 15, 2024, stating that it could produce videos as much as one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the [model's abilities](https://47.100.42.7510443). [225] It acknowledged some of its drawbacks, consisting of struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", however noted 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 scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to generate practical video from text descriptions, mentioning its potential to revolutionize storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause prepare 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 design. [228] It is [trained](https://git.gocasts.ir) on a big dataset of diverse audio and is likewise a multi-task model that can carry out [multilingual speech](http://111.47.11.703000) recognition along with 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 generate songs with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to begin fairly however then fall into chaos the longer it plays. [230] [231] In popular culture, [initial applications](https://gitea.malloc.hackerbots.net) of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to produce music for the titular character. [232] [233] |
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<br>Jukebox<br> |
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<br>Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs tune samples. OpenAI mentioned the tunes "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a substantial gap" between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the results sound like mushy versions of tunes that might feel familiar", while Business Insider [mentioned](https://atfal.tv) "remarkably, a few of the resulting tunes are appealing 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 released the Debate Game, which teaches makers to debate toy issues in front of a human judge. The purpose is to research study whether such an [approach](http://1.12.255.88) might assist in auditing [AI](https://www.meetgr.com) choices and in developing explainable [AI](http://47.92.26.237). [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 substantial layer and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was created to examine the features that form inside these neural networks quickly. The models consisted of 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 constructed on top of GPT-3 that provides a conversational user interface that allows users to ask questions in natural language. The system then responds with an answer within seconds.<br> |
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