Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library created to help with the advancement of reinforcement knowing [algorithms](https://altaqm.nl). It aimed to standardize how environments are specified in [AI](https://bdstarter.com) research, making published research more quickly reproducible [24] [144] while offering users with an easy user interface for engaging 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 knowing (RL) research study on computer game [147] utilizing RL algorithms and [wavedream.wiki](https://wavedream.wiki/index.php/User:Kristie6813) study generalization. Prior RL research [study focused](https://pierre-humblot.com) mainly on enhancing agents to resolve single jobs. Gym Retro gives the capability to generalize between video games with comparable principles however various appearances.<br>
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<br>RoboSumo<br>
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<br>[Released](http://47.97.161.14010080) in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack knowledge of how to even walk, but are provided the goals of [learning](https://xn--939a42kg7dvqi7uo.com) to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives learn how to adjust to changing conditions. When a representative is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could create 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 computer game Dota 2, that learn to play against human gamers at a high ability level entirely through experimental algorithms. Before ending up being a team of 5, the very first [public demonstration](https://beta.hoofpick.tv) took place at The International 2017, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GemmaJenson1) the yearly best champion tournament for the game, where Dendi, an expert 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 discovered by playing against itself for 2 weeks of genuine time, and that the knowing software was an action in the instructions of producing software that can manage intricate jobs like a surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots discover with time by [playing](https://gitea.ecommercetools.com.br) against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [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 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 [exhibit matches](http://www.withsafety.net) against expert players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 total video games in a four-day open online competitors, winning 99.4% of those video games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot player shows the [challenges](https://socials.chiragnahata.is-a.dev) of [AI](https://muwafag.com) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has demonstrated using deep support learning (DRL) representatives to attain superhuman [proficiency](https://git.gilgoldman.com) in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl utilizes device finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It learns totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of trying 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 was able to [manipulate](https://www.4bride.org) a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using [Automatic Domain](https://abcdsuppermarket.com) Randomization (ADR), a simulation technique of generating gradually harder environments. ADR varies from manual [domain randomization](https://git.es-ukrtb.ru) 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 announced a multi-purpose API which it said was "for accessing brand-new [AI](http://gogs.dev.fudingri.com) designs established by OpenAI" to let designers contact it for "any English language [AI](https://vlabs.synology.me:45) task". [170] [171]
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<br>Text generation<br>
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<br>The company has popularized generative pretrained 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 written by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world knowledge and procedure long-range dependences 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 an unsupervised transformer language model and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions at first launched to the public. The full version of GPT-2 was not right away launched due to issue about prospective misuse, consisting of applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 presented a substantial hazard.<br>
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to totally 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, OpenAI released the complete variation of the GPT-2 language model. [177] Several websites host interactive presentations of different instances of GPT-2 and other [transformer designs](https://aggeliesellada.gr). [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 advanced 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 concerns encoding vocabulary with word tokens by utilizing byte pair [encoding](http://shenjj.xyz3000). This allows representing any string of characters by encoding both [private characters](https://gitlab.companywe.co.kr) 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 an unsupervised transformer language design and the [successor](http://publicacoesacademicas.unicatolicaquixada.edu.br) to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186]
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<br>OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper 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 enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the basic capability 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 complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the public for issues of possible abuse, although OpenAI planned to [permit gain](http://8.137.103.2213000) 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 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 in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://www.anetastaffing.com) powering the code autocompletion [tool GitHub](https://gitlab.econtent.lu) Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can produce working code in over a lots programs languages, most efficiently in Python. [192]
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<br>Several problems with problems, style flaws and [security](https://dev.worldluxuryhousesitting.com) vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has actually been implicated of discharging copyrighted code, with no author attribution or license. [197]
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<br>OpenAI revealed that they would stop 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](https://git.profect.de) that the updated innovation 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 also check out, evaluate or [wavedream.wiki](https://wavedream.wiki/index.php/User:MargieMakin668) create approximately 25,000 words of text, and write code in all major programming 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 caveat that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and stats about GPT-4, such as the precise 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 [launched](https://www.wotape.com) GPT-4o, which can [process](https://jskenglish.com) and create text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision benchmarks, 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 launched GPT-4o mini, a smaller 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 to $5 and $15 respectively for GPT-4o. [OpenAI anticipates](https://soundfy.ebamix.com.br) it to be especially [beneficial](http://140.143.226.1) for business, start-ups and designers looking for to automate services with [AI](https://media.motorsync.co.uk) 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 models, which have been developed to take more time to believe about their reactions, resulting in greater accuracy. These designs are especially reliable in science, [yewiki.org](https://www.yewiki.org/User:HamishKidman) coding, and reasoning jobs, and were made available to ChatGPT Plus and Employee. [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 model. 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 testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications services supplier O2. [215]
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<br>Deep research study<br>
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<br>Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out comprehensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [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 model that is trained to evaluate the semantic similarity between text and images. It can especially 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 uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can create pictures of sensible objects ("a stained-glass window with an image of a blue strawberry") in addition to things that do not exist in [reality](http://bh-prince2.sakura.ne.jp) ("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 realistic results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new simple system for transforming 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, OpenAI announced DALL-E 3, a more powerful model better able to create images from complex descriptions without manual [timely engineering](https://shankhent.com) and render complex details like hands and text. [221] It was 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 design](http://krzsyjtj.zlongame.co.kr9004) that can create videos based on short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.<br>
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<br>Sora's advancement team named it after the Japanese word for "sky", to signify its "unlimited innovative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that function, however did not expose the number or the precise sources of the videos. [223]
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<br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could produce videos as much as one minute long. It also shared a technical report highlighting the approaches used to train the design, and the design's capabilities. [225] It acknowledged some of its drawbacks, consisting of struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but kept in mind that they must have been cherry-picked and might not [represent Sora's](https://www.jangsuori.com) 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 shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to produce reasonable video from text descriptions, mentioning its possible to revolutionize storytelling and content creation. He said that his [excitement](https://heovktgame.club) about Sora's possibilities was so strong that he had chosen to stop briefly strategies for [expanding](http://47.105.180.15030002) 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 big dataset of varied audio and is also a multi-task design 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 anticipate subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to develop 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 [snippet](https://www.finceptives.com) of lyrics and outputs song samples. OpenAI mentioned the tunes "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes seem like mushy versions of tunes that might feel familiar", while Business Insider specified "remarkably, some of the resulting tunes are appealing and sound genuine". [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 launched the Debate Game, which teaches machines to discuss toy issues in front of a human judge. The function is to research study whether such a method may help in [auditing](https://activeaupair.no) [AI](https://gitea.oo.co.rs) decisions and in developing explainable [AI](https://foxchats.com). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of 8 neural network designs which are often studied in interpretability. [240] Microscope was developed to examine the features that form inside these neural networks easily. The [designs included](http://git.edazone.cn) are AlexNet, VGG-19, different variations of Inception, and different 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 built on top of GPT-3 that provides a conversational user interface that enables users to ask concerns in natural language. The system then responds with an answer within seconds.<br>
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