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<br>Announced in 2016, Gym is an [open-source Python](http://101.34.211.1723000) library designed to help with the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://117.71.100.222:3000) research, making published research more quickly reproducible [24] [144] while offering users with a basic user interface for [connecting](http://39.106.223.11) with these environments. In 2022, new advancements 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 support knowing (RL) research on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing representatives to fix single tasks. Gym Retro provides the capability to [generalize](https://paknoukri.com) between games with similar principles however different appearances.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have understanding of how to even walk, however are given the goals of [finding](http://bertogram.com) out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the agents learn how to adapt to changing conditions. When a representative is then eliminated from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might [develop](https://labz.biz) an intelligence "arms race" that might increase an agent's capability to function even outside the context of the competitors. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high skill level completely through experimental algorithms. Before becoming a group of 5, the very first public presentation happened at The International 2017, the annual premiere championship competition for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg [Brockman](http://107.172.157.443000) explained that the bot had found out by playing against itself for two weeks of real time, which the knowing software was a step in the direction of creating software application that can manage complex jobs like a surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots learn with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the capability of the bots broadened to play together as a full group of 5, and they were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The [International](https://notitia.tv) 2018, OpenAI Five played in two exhibition matches against [professional](http://27.128.240.723000) gamers, however wound up losing both video 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 exhibition match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those video games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot player reveals the challenges of [AI](https://jobs.ezelogs.com) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown the use of deep reinforcement learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robot hand, to manipulate [physical items](https://gogs.greta.wywiwyg.net). [167] It learns completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by using domain randomization, a simulation technique which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB video cameras to enable the robot to control an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of creating gradually more hard environments. ADR varies from manual domain randomization by not requiring 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://easy-career.com) designs established by OpenAI" to let developers get in touch with it for "any English language [AI](https://pediascape.science) task". [170] [171]
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<br>Text generation<br>
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<br>The company has actually popularized generative pretrained transformers (GPT). [172]
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<br>OpenAI's initial GPT model ("GPT-1")<br>
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<br>The [initial paper](https://sossdate.com) on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model 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 model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations [initially launched](https://gitlab.innive.com) to the public. The full variation of GPT-2 was not immediately launched due to concern about possible abuse, [consisting](http://47.98.226.2403000) of applications for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 positioned a substantial 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 fake news". [175] Other scientists, such as Jeremy Howard, warned of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language design. [177] Several sites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 [zero-shot tasks](https://git.synz.io) (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](http://27.154.233.18610080) on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both specific characters and multiple-character tokens. [181]
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<br>GPT-3<br>
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as few as 125 million specifications were likewise trained). [186]
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<br>OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and in between [English](http://www.engel-und-waisen.de) and German. [184]
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<br>GPT-3 significantly improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or [encountering](https://talento50zaragoza.com) the essential capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, [compared](https://selfloveaffirmations.net) to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the public for concerns of possible abuse, although OpenAI prepared to permit gain 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 exclusively 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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://144.123.43.138:2023) powering the [code autocompletion](http://jenkins.stormindgames.com) tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the design can develop working code in over a lots programming languages, a lot of efficiently in Python. [192]
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<br>Several problems with glitches, design flaws and security vulnerabilities were pointed out. [195] [196]
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<br>GitHub Copilot has actually been implicated of producing copyrighted code, with no author attribution or license. [197]
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<br>OpenAI announced that they would cease 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 updated technology passed a simulated law school bar test with a score around the top 10% of . (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise check out, analyze or produce up to 25,000 words of text, and compose 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, [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:JulianeStoker8) with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous [technical](https://pediascape.science) details and stats 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 text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision criteria, setting 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 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 it to be especially helpful for enterprises, startups and developers seeking to automate [services](https://git.cloud.krotovic.com) with [AI](http://xn--o39aoby1e85nw4rx0fwvcmubsl71ekzf4w4a.kr) agents. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been created to take more time to think of their reactions, leading to greater precision. These designs are particularly [reliable](https://gitea.star-linear.com) in science, coding, and reasoning jobs, and were made available to [ChatGPT](https://www.ontheballpersonnel.com.au) 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 model. OpenAI likewise unveiled o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, [yewiki.org](https://www.yewiki.org/User:TrevorMcConnell) this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]
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<br>Deep research study<br>
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<br>Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform extensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 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 classification<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic similarity in between text and images. It can significantly be used 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 develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to [translate natural](https://git.xhkjedu.com) language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can develop images of practical objects ("a stained-glass window with a picture of a blue strawberry") as well as objects that do not exist in truth ("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 revealed DALL-E 2, an upgraded version of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new fundamental system for converting 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, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=12036058) OpenAI announced DALL-E 3, a more effective model much better able to create images from complex descriptions without manual prompt engineering and render complicated details 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 model that can create videos based on brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with [resolution](https://www.ssecretcoslab.com) as much as 1920x1080 or [disgaeawiki.info](https://disgaeawiki.info/index.php/User:MyrnaKeefer) 1080x1920. The maximal length of created videos is unidentified.<br>
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<br>Sora's advancement group called it after the Japanese word for "sky", to signify its "unlimited imaginative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos certified for that purpose, but did not expose the number or the exact 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, specifying that it could create videos approximately one minute long. It also shared a technical report [highlighting](http://forum.altaycoins.com) the approaches [utilized](https://raovatonline.org) to train the design, and the design's capabilities. [225] It acknowledged some of its shortcomings, consisting of struggles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but noted that they must have been cherry-picked and may not represent Sora's common output. [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually shown significant interest in the innovation's [potential](http://charge-gateway.com). In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to produce practical video from text descriptions, citing its possible to transform storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause strategies 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](https://dsspace.co.kr) design. [228] It is trained on a big dataset of [varied audio](https://gitlab.tiemao.cloud) and is also a multi-task model that can perform multilingual speech recognition along with speech translation and language recognition. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall under mayhem the longer it plays. [230] [231] In pop culture, initial 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 create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song [samples](https://gitea.ecommercetools.com.br). OpenAI stated the songs "show regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" which "there is a substantial space" between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the outcomes sound like mushy variations of songs that may feel familiar", while Business Insider specified "remarkably, some of the resulting tunes are appealing 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 released the Debate Game, which teaches devices to [discuss toy](http://111.9.47.10510244) issues in front of a human judge. The purpose is to research whether such a method may help in auditing [AI](http://101.43.112.107:3000) decisions and in developing explainable [AI](https://genzkenya.co.ke). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, Microscope [239] is a [collection](https://social.ppmandi.com) of visualizations of every [substantial layer](https://sttimothysignal.org) and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various 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 developed on top of GPT-3 that offers a conversational interface that enables users to ask questions in natural language. The system then reacts with a response within seconds.<br>
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