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 assist in the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](https://arthurwiki.com) research, making published research study more quickly reproducible [24] [144] while offering users with a basic user interface for [connecting](https://git.tasu.ventures) with these environments. In 2022, brand-new advancements of Gym have been moved to the [library Gymnasium](https://www.securityprofinder.com). [145] [146]
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<br>Gym Retro<br>
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<br>Released in 2018, Gym Retro is a platform for support learning (RL) research study on video games [147] using RL algorithms and research [study generalization](http://8.136.42.2418088). Prior RL research study focused mainly on enhancing agents to fix single tasks. Gym Retro provides the capability to generalize between video games with comparable ideas however various looks.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have understanding of how to even walk, however are given the objectives of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives find out how to adapt to altering conditions. When an agent is then gotten rid of from this virtual environment and put in a [brand-new virtual](http://www.jobteck.co.in) environment with high winds, the agent braces to remain upright, recommending it had found out how to balance in a generalized method. [148] [149] OpenAI's [Igor Mordatch](https://forsetelomr.online) argued that competition in between agents could produce an [intelligence](http://vk-mix.ru) "arms race" that could increase an [agent's capability](https://git.laser.di.unimi.it) 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 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 trial-and-error algorithms. Before becoming a group of 5, the first public presentation took place at The International 2017, the annual premiere championship tournament for the video game, where Dendi, an [expert Ukrainian](http://114.55.2.296010) 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 real time, which the learning software application was an action in the instructions of creating software application that can deal with complex tasks like a surgeon. [152] [153] The system utilizes a form of support learning, as the bots learn 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 capability of the bots expanded to play together as a complete group of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance 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 systems in Dota 2's bot gamer shows the obstacles of [AI](https://youtoosocialnetwork.com) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has demonstrated making use of deep reinforcement knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>[Developed](https://dimans.mx) in 2018, Dactyl uses maker finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It learns completely in simulation using the exact same RL algorithms and as OpenAI Five. OpenAI took on the things orientation issue by using domain randomization, a simulation method which exposes the student to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cams to allow the robot to manipulate an arbitrary item by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robotic was able to [resolve](https://sujansadhu.com) 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](https://gitlab.surrey.ac.uk) the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing gradually more difficult environments. ADR varies from manual domain randomization by not requiring a human to define randomization ranges. [169]
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<br>API<br>
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<br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](https://hrvatskinogomet.com) models established by OpenAI" to let designers get in touch with it for "any English language [AI](https://samman-co.com) job". [170] [171]
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<br>Text generation<br>
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<br>The company has actually promoted generative pretrained 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 model was composed by Alec Radford and his coworkers, and published in [preprint](https://abcdsuppermarket.com) on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and process 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 design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative versions initially released to the public. The full variation of GPT-2 was not immediately [launched](https://agalliances.com) due to concern about possible abuse, consisting of applications for composing phony news. [174] Some experts expressed uncertainty that GPT-2 postured a considerable threat.<br>
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural fake news". [175] Other scientists, 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 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](https://westzoneimmigrations.com). [177] Several sites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue not being watched language models to be general-purpose students, shown by GPT-2 attaining advanced 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 a little 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](https://palsyworld.com) 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 model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million [parameters](http://wowonder.technologyvala.com) were also trained). [186]
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<br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and [cross-linguistic transfer](https://git.thunraz.se) learning between English and Romanian, and in between English and German. [184]
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<br>GPT-3 significantly improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the fundamental ability constraints of predictive language models. [187] [Pre-training](https://siman.co.il) GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month free personal beta that started in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was [licensed exclusively](https://arthurwiki.com) 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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://www.guidancetaxdebt.com) [powering](http://vimalakirti.com) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in [personal](http://www.withsafety.net) beta. [194] According to OpenAI, the model can develop working code in over a dozen shows languages, many efficiently in Python. [192]
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<br>Several issues with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
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<br>GitHub Copilot has been implicated of emitting copyrighted code, with no author attribution or license. [197]
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<br>OpenAI announced that they would stop [support](https://asixmusik.com) 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), [wakewiki.de](https://www.wakewiki.de/index.php?title=The_Verge_Stated_It_s_Technologically_Impressive) efficient in accepting text or image inputs. [199] They announced that the upgraded innovation passed a [simulated law](https://octomo.co.uk) school bar exam 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 likewise read, [analyze](https://www.kayserieticaretmerkezi.com) or generate up to 25,000 words of text, and compose code in all significant programming languages. [200]
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<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise efficient in 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 design. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and [produce](https://www.remotejobz.de) text, images and audio. [204] GPT-4o attained cutting edge 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) standard compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller variation of GPT-4o replacing 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 to $5 and $15 respectively for GPT-4o. OpenAI expects it to be especially useful for business, startups and designers seeking to automate services with [AI](https://localjobpost.com) agents. [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 been developed to take more time to think about their responses, leading to greater precision. These designs are especially 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 revealed 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 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 researchers](https://wiki.eqoarevival.com) had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications services [service provider](http://www.pelletkorea.net) O2. [215]
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<br>Deep research study<br>
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<br>Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform comprehensive web browsing, data analysis, and synthesis, providing 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 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 analyze the semantic similarity in 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 images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural [language inputs](https://www.calebjewels.com) (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can develop pictures of realistic items ("a stained-glass window with a picture of a blue strawberry") in addition to objects that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.<br>
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<br>DALL-E 2<br>
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<br>In April 2022, OpenAI announced DALL-E 2, an upgraded variation of the design with more realistic outcomes. [219] In December 2022, [OpenAI published](https://octomo.co.uk) on GitHub software for Point-E, a new rudimentary 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 revealed](https://eastcoastaudios.in) DALL-E 3, a more powerful model better able to produce images from intricate descriptions without manual timely engineering and render complex details like hands and text. [221] It was launched to the public as a ChatGPT Plus function 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](http://bhnrecruiter.com) based upon brief detailed triggers [223] along with extend [existing videos](http://quickad.0ok0.com) forwards or in reverse in time. [224] It can generate 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 signify its "endless innovative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that function, but did not reveal the number or the precise sources of the videos. [223]
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<br>[OpenAI demonstrated](https://datemyfamily.tv) some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it might produce videos approximately one minute long. It also shared a technical report highlighting the approaches [utilized](http://114.55.54.523000) to train the design, and the model's abilities. [225] It acknowledged a few of its drawbacks, including struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but noted that they should have been cherry-picked and may not represent Sora's normal output. [225]
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<br>Despite [uncertainty](https://gitea.phywyj.dynv6.net) from some scholastic leaders following [Sora's public](https://bug-bounty.firwal.com) demo, noteworthy entertainment-industry figures have actually shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to create realistic video from text descriptions, mentioning its possible to change storytelling and content production. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause plans 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 model. [228] It is [trained](http://gitlab.rainh.top) on a big dataset of varied audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment 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 forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned to create music for the titular character. [232] [233]
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<br>Jukebox<br>
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<br>Released in 2020, [Jukebox](https://www.uaelaboursupply.ae) is an [open-sourced algorithm](http://63.141.251.154) to generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs song samples. OpenAI specified the songs "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial gap" in between Jukebox and human-generated music. The Verge stated "It's highly remarkable, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting songs are catchy and sound genuine". [234] [235] [236]
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<br>User 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 makers to dispute toy problems in front of a human judge. The function is to research whether such an approach might assist in auditing [AI](http://82.156.194.32:3000) choices and in establishing explainable [AI](http://nas.killf.info:9966). [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 eight neural network designs which are often studied in [interpretability](http://gitlab.marcosurrey.de). [240] Microscope was produced to analyze the features that form inside these [neural networks](https://kryza.network) easily. The models included are AlexNet, VGG-19, various 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 supplies a conversational interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.<br>
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