Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python [library designed](https://worship.com.ng) to help with the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](https://zamhi.net) research study, making released research study more easily reproducible [24] [144] while providing users with a simple interface for interacting with these environments. In 2022, new developments of Gym have 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 learning (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to fix single jobs. Gym Retro offers the ability to generalize between video games with comparable principles but 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 representatives at first do not have [knowledge](https://bug-bounty.firwal.com) of how to even walk, however are provided the objectives of [discovering](https://vagas.grupooportunityrh.com.br) to move and to press the opposing representative out of the ring. [148] Through this adversarial learning procedure, the representatives find out how to adjust to altering conditions. When an agent is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could produce an intelligence "arms race" that could increase an agent's ability 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 computer game Dota 2, that discover to play against human players at a high skill level completely through trial-and-error algorithms. Before ending up being a team of 5, the first public presentation took place at The International 2017, the yearly premiere [champion competition](https://tokemonkey.com) for the video game, where Dendi, an [expert Ukrainian](https://git.xedus.ru) player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of actual time, and that the learning software application was an action in the instructions of producing software application that can [manage intricate](http://116.62.115.843000) jobs like a cosmetic surgeon. [152] [153] The system [utilizes](https://e-sungwoo.co.kr) a type of reinforcement knowing, as the bots find out with time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives. [154] [155] [156]
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<br>By June 2018, the capability of the [bots broadened](http://112.48.22.1963000) to play together as a full team of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against [professional](https://www.ayurjobs.net) gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The [bots' final](https://gitea.rodaw.net) public appearance came later that month, where they played in 42,729 overall 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 player shows the obstacles of [AI](https://swaggspot.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated using deep support 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 in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robotic hand, to [manipulate](https://kanjob.de) physical objects. [167] It learns totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a [variety](https://bphomesteading.com) of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has [RGB cameras](https://ttemployment.com) to permit the robot to control an arbitrary things by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually more challenging environments. ADR varies from manual domain [randomization](https://swaggspot.com) by not needing a human to specify randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://www.apkjobs.site) designs developed by OpenAI" to let designers get in touch with it for "any English language [AI](https://wathelp.com) 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 original GPT model ("GPT-1")<br>
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<br>The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world [knowledge](https://gitlab.liangzhicn.com) and procedure long-range reliances by pre-training on a varied corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative [Pre-trained Transformer](https://www.telix.pl) 2 ("GPT-2") is a not being watched transformer language model and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions initially launched to the general public. The complete version of GPT-2 was not instantly launched due to issue about possible misuse, consisting of applications for composing fake news. [174] Some professionals expressed uncertainty that GPT-2 postured a considerable risk.<br>
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned 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 difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several sites 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 without supervision language designs to be general-purpose students, illustrated by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (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](https://gitea.baxir.fr) on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues 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 successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were likewise trained). [186]
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<br>OpenAI specified that GPT-3 was successful at certain "meta-learning" jobs and could [generalize](https://cariere.depozitulmax.ro) the [purpose](http://hjl.me) of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning in between [English](https://denis.usj.es) and Romanian, and between English and German. [184]
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<br>GPT-3 [drastically improved](https://www.ahrs.al) benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or encountering the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released to the public for concerns of possible abuse, although OpenAI prepared to enable 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 specifically to [Microsoft](https://gitlab.buaanlsde.cn). [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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://git.xhkjedu.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the model can develop working code in over a dozen shows languages, most successfully in Python. [192]
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<br>Several concerns with glitches, style flaws and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has been accused of emitting copyrighted code, without any author attribution or license. [197]
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<br>OpenAI revealed that they would terminate assistance for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<br>On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the [upgraded technology](http://www.homeserver.org.cn3000) passed a [simulated law](https://skillnaukri.com) school bar test with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, examine or generate up to 25,000 words of text, and [compose code](http://cjma.kr) in all significant shows languages. [200]
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<br>Observers reported that the version of ChatGPT utilizing GPT-4 was an [enhancement](https://git.thomasballantine.com) on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal numerous technical details and data about GPT-4, such as the accurate size of the model. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision criteria, [setting brand-new](https://elsingoteo.com) records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and [yewiki.org](https://www.yewiki.org/User:LanceBoake71) $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly beneficial for business, start-ups and developers looking for to automate services with [AI](http://git.zthymaoyi.com) representatives. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been designed to take more time to think about their reactions, resulting in greater accuracy. These models are particularly reliable in science, coding, and thinking 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 revealed o3, the successor of the o1 reasoning model. OpenAI likewise revealed o3-mini, a [lighter](https://wiki.piratenpartei.de) and faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are [testing](https://lastpiece.co.kr) 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 model is called o3 instead of o2 to prevent confusion with telecoms services supplier 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 design to perform substantial web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy 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 evaluate the semantic resemblance 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](https://video.invirtua.com) in 2021, DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can create pictures of sensible things ("a stained-glass window with a picture of a blue strawberry") along with items that do not exist in truth ("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 updated version of the design with more realistic outcomes. [219] In December 2022, OpenAI released on [GitHub software](https://mensaceuta.com) [application](http://git.zhiweisz.cn3000) for Point-E, a new fundamental 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 DALL-E 3, a more powerful model better able to generate images from complex descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched 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 model that can produce videos based upon brief detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is [unknown](https://www.lokfuehrer-jobs.de).<br>
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<br>Sora's advancement team named it after the Japanese word for "sky", to symbolize its "endless creative capacity". [223] Sora's technology is an adjustment 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, 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, specifying that it could generate videos up to one minute long. It also shared a technical report highlighting the methods used to train the model, and the [design's abilities](http://120.24.213.2533000). [225] It acknowledged some of its shortcomings, consisting of struggles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", however noted that they should have been cherry-picked and might not represent Sora's normal output. [225]
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<br>Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have revealed considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's capability to create reasonable video from text descriptions, mentioning its potential to revolutionize storytelling and content creation. He said that his enjoyment about [Sora's possibilities](https://git.mintmuse.com) was so strong that he had decided to [pause strategies](https://redmonde.es) for expanding 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](http://119.45.49.2123000) in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment as well as 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](https://www.eticalavoro.it) net trained to predict subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to begin fairly however then fall under turmoil the longer it plays. [230] [231] In pop culture, [initial applications](https://www.ahrs.al) of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to create music for the [titular character](https://dooplern.com). [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 bit of lyrics and outputs tune samples. OpenAI stated the songs "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider stated "surprisingly, some of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
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<br>Interface<br>
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<br>Debate Game<br>
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<br>In 2018, OpenAI introduced the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The purpose is to research study whether such an approach may assist in auditing [AI](https://git.buzhishi.com:14433) decisions and in establishing explainable [AI](https://sosyalanne.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 [substantial layer](https://git.rungyun.cn) and neuron of 8 neural network models which are often studied in interpretability. [240] Microscope was created to evaluate the functions that form inside these quickly. The designs included are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, ChatGPT is an artificial intelligence tool constructed on top of GPT-3 that offers a conversational user interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.<br>
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