Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](https://gitlab.profi.travel) research, making published research study more quickly reproducible [24] [144] while supplying users with an easy interface for engaging with these environments. In 2022, [brand-new advancements](http://106.52.134.223000) of Gym have actually been relocated to the library Gymnasium. [145] [146]
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<br>Gym Retro<br>
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<br>Released in 2018, Gym Retro is a platform for support learning (RL) research 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 concepts however 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 knowledge of how to even stroll, but are given the [objectives](https://chhng.com) of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adjust to changing conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the [agent braces](https://salesupprocess.it) to remain upright, recommending it had actually discovered how to balance in a [generalized method](https://git.buckn.dev). [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could develop an intelligence "arms race" that could increase a representative's capability 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 team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high ability level entirely through trial-and-error algorithms. Before becoming a group of 5, the first public demonstration occurred at The International 2017, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Mel899356127290) the annual best championship competition for the game, where Dendi, a professional [Ukrainian](http://investicos.com) 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 found out by playing against itself for 2 weeks of actual time, and that the learning software application was an action in the direction of developing software application that can manage intricate tasks like a cosmetic surgeon. [152] [153] The system uses a type of [reinforcement](https://www.dutchsportsagency.com) knowing, 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 eliminating an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the [ability](https://superblock.kr) of the bots broadened to play together as a full team of 5, and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the [reigning](https://projob.co.il) world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those games. [165]
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<br>OpenAI 5['s mechanisms](https://gitlab.ngser.com) in Dota 2's bot player reveals the challenges of [AI](http://dating.instaawork.com) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown the usage of deep reinforcement learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It discovers totally in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a variety of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB electronic cameras to permit the robotic to manipulate an arbitrary things by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
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<br>In 2019, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:Freeman0349) OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by enhancing the robustness of Dactyl to perturbations by [utilizing Automatic](http://git.rabbittec.com) Domain Randomization (ADR), a simulation method of producing progressively more challenging environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
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<br>API<br>
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<br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://vishwakarmacommunity.org) models developed by OpenAI" to let developers contact it for "any English language [AI](https://git.prayujt.com) task". [170] [171]
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<br>Text generation<br>
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<br>The business has promoted generative pretrained transformers (GPT). [172]
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<br>[OpenAI's initial](https://testgitea.cldevops.de) GPT model ("GPT-1")<br>
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<br>The initial paper on [generative pre-training](http://121.5.25.2463000) of a [transformer-based language](https://natgeophoto.com) model was composed by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world knowledge and process long-range reliances by pre-training on a diverse corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions at first released to the public. The complete version of GPT-2 was not instantly launched due to [concern](http://www.jedge.top3000) about potential misuse, including applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 positioned a considerable threat.<br>
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<br>In reaction 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 innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language design. [177] Several websites host interactive demonstrations of various instances 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 learners, illustrated by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further trained on any task-specific input-output examples).<br>
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<br>The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both individual 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](http://www.grandbridgenet.com82) [a] Transformer 3 (GPT-3) is a not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186]
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<br>[OpenAI mentioned](https://www.yanyikele.com) that GPT-3 was successful at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) cross-linguistic transfer knowing in between English and Romanian, and in between [English](https://ivytube.com) and German. [184]
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<br>GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the basic capability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, 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 released to the general public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was 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](http://39.98.153.250:9080) powering the [code autocompletion](http://git.befish.com) tool GitHub Copilot. [193] In August 2021, an API was released in [private](http://124.192.206.823000) beta. [194] According to OpenAI, the model can [develop](https://subamtv.com) working code in over a lots shows languages, the majority of efficiently in Python. [192]
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<br>Several concerns with glitches, design defects and security vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has been implicated of discharging copyrighted code, without any author attribution or license. [197]
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<br>OpenAI revealed that they would stop 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 revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar test with a score around the [leading](http://202.164.44.2463000) 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise read, evaluate or produce up to 25,000 words of text, and write code in all significant shows 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 model, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose different technical details and stats about GPT-4, such as the precise size of the design. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision criteria, setting brand-new [records](http://yijichain.com) in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) [standard compared](https://git.uzavr.ru) to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller variation of GPT-4o changing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, [compared](http://appleacademy.kr) to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly helpful for business, start-ups and developers seeking to automate services with [AI](https://0miz2638.cdn.hp.avalon.pw:9443) 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 models, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ArlenKershaw) which have actually been developed to take more time to think about their responses, leading to higher accuracy. These designs are especially efficient in science, coding, and reasoning 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 likewise unveiled o3-mini, a lighter and quicker variation of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]
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<br>Deep research<br>
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<br>Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
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<br>Image classification<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the [semantic similarity](http://39.99.224.279022) between text and images. It can notably be utilized for image classification. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, DALL-E is a Transformer design that images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can develop pictures of sensible things ("a stained-glass window with an image of a blue strawberry") in addition to objects that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.<br>
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<br>DALL-E 2<br>
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<br>In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the design with more practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new rudimentary system for converting a [text description](https://sebeke.website) into a 3-dimensional model. [220]
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<br>DALL-E 3<br>
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<br>In September 2023, OpenAI revealed DALL-E 3, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:WilliamHolyfield) a more powerful model better able to create images from intricate descriptions without manual timely engineering and render complicated details like hands and text. [221] It was launched to the general public as a [ChatGPT](https://www.paknaukris.pro) 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 on short detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of created videos is unidentified.<br>
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<br>Sora's development team called it after the Japanese word for "sky", to represent its "endless imaginative capacity". [223] Sora's innovation is an adaptation of the [innovation](https://ransomware.design) behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos accredited for that purpose, but did not reveal the number or the specific 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, stating that it might create videos up to one minute long. It also shared a technical report [highlighting](https://47.100.42.7510443) the methods used to train the design, and the model's abilities. [225] It acknowledged some of its shortcomings, including struggles replicating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they must have been cherry-picked and might not represent Sora's typical output. [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to produce practical video from text descriptions, mentioning its prospective to change storytelling and [material creation](https://charin-issuedb.elaad.io). He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause 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 acknowledgment model. [228] It is trained on a large dataset of varied audio and is likewise a multi-task design that can perform multilingual speech acknowledgment in addition to 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 to anticipate subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start fairly however 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 psychological 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 generate music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and [outputs tune](https://git.polycompsol.com3000) samples. OpenAI specified the tunes "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" which "there is a considerable space" in between Jukebox and human-generated music. The Verge specified "It's technically excellent, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider stated "surprisingly, some of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
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<br>User interfaces<br>
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<br>Debate Game<br>
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<br>In 2018, OpenAI introduced the Debate Game, which teaches devices to dispute toy problems in front of a human judge. The purpose is to research whether such an approach may help in [auditing](http://43.139.182.871111) [AI](http://101.34.66.244:3000) choices and in establishing explainable [AI](https://prantle.com). [237] [238]
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<br>Microscope<br>
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<br>[Released](http://1.14.105.1609211) in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are frequently 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 versions 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 an expert system tool constructed on top of GPT-3 that offers a conversational user interface that allows users to ask questions in natural language. The system then responds with a response within seconds.<br>
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