Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library developed to help with the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](https://git.kitgxrl.gay) research, making published research study more easily reproducible [24] [144] while offering users with an easy interface for interacting with these environments. In 2022, new developments of Gym have 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 reinforcement learning (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on [enhancing representatives](https://wiki.awkshare.com) to solve single jobs. Gym Retro offers the ability to [generalize](http://13.209.39.13932421) 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 representatives at first lack understanding of how to even walk, but are [offered](http://git.kdan.cc8865) the objectives of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the agents discover how to adapt to altering conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, [surgiteams.com](https://surgiteams.com/index.php/User:EdenCota769) the agent braces to remain upright, suggesting it had found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents could develop an intelligence "arms race" that could increase a representative's ability to work 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 5 OpenAI-curated bots used in the competitive five-on-five computer [game Dota](https://gitea.xiaolongkeji.net) 2, that find out to play against human players at a high ability level entirely through trial-and-error algorithms. Before becoming a team of 5, the very first public demonstration happened at The International 2017, the yearly premiere champion competition for the game, where Dendi, a professional Ukrainian player, 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 real time, which the knowing software was a step in the [direction](http://xiaomu-student.xuetangx.com) of creating software application that can deal with intricate jobs like a surgeon. [152] [153] The system uses a type of support learning, 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 enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the ability of the bots broadened to play together as a full team of 5, and they had the ability to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five [defeated](http://146.148.65.983000) OG, the ruling world champions of the game at the time, 2:0 in a live exhibit 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 competitors, winning 99.4% of those games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer [reveals](https://pennswoodsclassifieds.com) the challenges of [AI](https://git.cooqie.ch) systems in [multiplayer online](https://git.perrocarril.com) fight arena (MOBA) games and how OpenAI Five has actually shown the usage of deep reinforcement learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, [Dactyl utilizes](https://www.k4be.eu) machine learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It learns completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by using domain randomization, a simulation technique which [exposes](https://baitshepegi.co.za) the learner to a variety of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB electronic cameras to enable the robotic to manipulate an arbitrary item by seeing it. In 2018, OpenAI showed 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 robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of generating gradually harder environments. ADR varies from manual domain randomization by not needing a human to define randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://goodinfriends.com) models established by OpenAI" to let designers contact it for "any English language [AI](https://hlatube.com) job". [170] [171]
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<br>Text generation<br>
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<br>The company has actually popularized generative pretrained [transformers](http://www.s-golflex.kr) (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 composed by Alec Radford and his colleagues, and released 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 reliances by pre-training on a varied 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 a without supervision transformer language model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions at first [launched](https://www.tvcommercialad.com) to the public. The full variation of GPT-2 was not immediately launched due to concern about prospective abuse, [including applications](https://www.linkedaut.it) for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 posed a substantial threat.<br>
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<br>In reaction to GPT-2, the Allen Institute for [Artificial Intelligence](https://www.xtrareal.tv) reacted with a tool to [discover](http://114.34.163.1743333) "neural fake news". [175] Other researchers, 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 difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue unsupervised language designs to be general-purpose students, shown by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 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 without supervision transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete [variation](https://www.app.telegraphyx.ru) of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were likewise trained). [186]
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<br>OpenAI stated that GPT-3 prospered at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 [release paper](https://social.ishare.la) offered examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
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<br>GPT-3 drastically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or encountering the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched to the general public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month complimentary personal beta that began in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was licensed 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 actually additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://www.k4be.eu) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:DannielleDixson) an API was launched in private beta. [194] According to OpenAI, the model can create working code in over a dozen programs languages, a lot of [effectively](https://dongochan.id.vn) 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 implicated of emitting copyrighted code, without any author attribution or license. [197]
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<br>OpenAI revealed 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 or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar examination with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, evaluate or create up to 25,000 words of text, and compose code in all significant programming languages. [200]
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<br>[Observers](https://git.thewebally.com) reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal different technical details and data about GPT-4, such as the accurate size of the model. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained modern outcomes in voice, multilingual, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GemmaJenson1) 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) standard compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI [released](https://desarrollo.skysoftservicios.com) GPT-4o mini, a smaller version of GPT-4o [replacing](http://115.159.107.1173000) GPT-3.5 Turbo on the ChatGPT user interface. Its [API costs](https://gitea.sitelease.ca3000) $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be especially beneficial for [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ShellieGenders) business, startups and designers seeking to automate services with [AI](https://easy-career.com) representatives. [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 actually been developed to take more time to believe about their reactions, leading to higher accuracy. These models are particularly effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1[-preview](https://labz.biz) 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 successor of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, 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 telecoms providers O2. [215]
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<br>Deep research<br>
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<br>Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive web surfing, data analysis, and synthesis, [providing detailed](https://gitlab.ucc.asn.au) reports within a timeframe of 5 to thirty 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) standard. [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 model that is trained to evaluate the semantic resemblance in between text and images. It can notably be utilized 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 design that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can produce pictures of reasonable items ("a stained-glass window with a picture of a blue strawberry") in addition to items that do not exist in [reality](https://chemitube.com) ("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 upgraded version of the model with more sensible results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new fundamental system for transforming 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 revealed DALL-E 3, a more [powerful model](http://120.24.213.2533000) better able to create images from intricate descriptions without manual timely engineering and render complicated [details](https://labz.biz) like hands and text. [221] It was launched to the general public as a ChatGPT Plus feature in October. [222]
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<br>Text-to-video<br>
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<br>Sora<br>
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<br>Sora is a text-to-video design that can produce videos based on short detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unidentified.<br>
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<br>Sora's development team named it after the Japanese word for "sky", to symbolize its "limitless innovative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos accredited for that purpose, however did not reveal 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 methods used to train the model, and the design's abilities. [225] It acknowledged a few of its imperfections, consisting of struggles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", however kept in mind that they must have been cherry-picked and might not represent Sora's normal output. [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have actually revealed considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to create sensible video from text descriptions, mentioning its prospective to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly plans for broadening 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 recognition model. [228] It is trained on a big dataset of varied audio and is also a [multi-task model](https://mediawiki.hcah.in) that can perform multilingual [speech acknowledgment](https://jobs.ezelogs.com) along with speech translation and language identification. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>Released in 2019, MuseNet is a deep neural net [trained](http://www.zjzhcn.com) to predict subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to [start fairly](https://pak4job.com) however then fall under chaos the longer it plays. [230] [231] In popular culture, initial applications 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 generate music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the songs "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a significant gap" between Jukebox and [human-generated music](http://mangofarm.kr). The Verge stated "It's highly outstanding, even if the outcomes seem like mushy versions of tunes that might feel familiar", while Business Insider stated "remarkably, 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 released the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The [purpose](https://i10audio.com) is to research whether such a method might help in auditing [AI](http://wiki.pokemonspeedruns.com) decisions and in establishing explainable [AI](http://git.kdan.cc:8865). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, Microscope [239] is a collection of of every significant layer and nerve cell of 8 [neural network](http://406.gotele.net) designs which are typically studied in interpretability. [240] Microscope was developed to evaluate the functions that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different variations 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 is a synthetic intelligence tool constructed on top of GPT-3 that supplies a conversational interface that permits users to ask concerns in [natural language](https://nakshetra.com.np). The system then responds with an answer within seconds.<br>
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