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 advancement of support knowing algorithms. It aimed to standardize how environments are specified in [AI](https://xotube.com) research, making released research study more easily reproducible [24] [144] while offering users with a simple interface for engaging 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 knowing (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to solve single tasks. Gym Retro provides the [capability](https://cielexpertise.ma) to generalize in between games with similar ideas but 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 robot representatives initially do not have understanding of how to even stroll, but are offered the goals of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents discover how to adapt to altering conditions. When a representative is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could produce an intelligence "arms race" that could increase an agent's ability to work even outside the context of the competitors. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a group of 5 OpenAI-curated bots used in the [competitive five-on-five](http://git.ningdatech.com) computer game Dota 2, that find out to play against human gamers at a high skill level completely through experimental algorithms. Before ending up being a team of 5, the very first public presentation took place at The International 2017, the annual best champion competition for the game, where Dendi, an expert Ukrainian 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 found out by playing against itself for two weeks of genuine time, which the knowing software application was a step in the instructions of producing software that can handle intricate jobs like a surgeon. [152] [153] The system utilizes a form of reinforcement knowing, as the bots learn gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
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<br>By June 2018, [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) the ability of the bots expanded to play together as a complete group of 5, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) they had the ability to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional gamers, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 total 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 shows the challenges of [AI](https://gitea.ws.adacts.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown 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 in 2018, Dactyl uses [device finding](https://suomalainennaikki.com) out to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It learns totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by using domain randomization, a simulation method which exposes the learner to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having [movement tracking](https://skytube.skyinfo.in) cams, likewise has RGB video cameras to allow the robot to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. [Objects](https://www.ataristan.com) like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by [improving](http://git.permaviat.ru) the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating progressively harder environments. ADR differs from manual domain randomization by not requiring 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 new [AI](https://git.eisenwiener.com) models developed by OpenAI" to let designers call on it for "any English language [AI](https://app.deepsoul.es) job". [170] [171]
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<br>Text generation<br>
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<br>The [company](http://okosg.co.kr) has actually promoted generative pretrained [transformers](https://www.cvgods.com) (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 written by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of [language](http://git.guandanmaster.com) could obtain world understanding and procedure long-range dependences by pre-training on a varied corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative variations initially released to the public. The full version of GPT-2 was not instantly released due to concern about prospective abuse, including applications for writing fake news. [174] Some specialists revealed uncertainty that GPT-2 postured a substantial risk.<br>
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language model. [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 unsupervised language designs to be general-purpose learners, illustrated by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (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://175.178.153.226) on, called WebText, contains slightly 40 [gigabytes](https://wiki.dulovic.tech) of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by using encoding. This allows 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 design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version 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 designs with as couple of as 125 million criteria were also trained). [186]
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<br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the [purpose](https://bnsgh.com) of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer [knowing](https://sosmed.almarifah.id) in between English and Romanian, and in between English and German. [184]
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<br>GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=12072110) coming across the essential ability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, [compared](https://njspmaca.in) to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the public for [concerns](https://revinr.site) of possible abuse, although OpenAI planned to allow 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](https://animployment.com). [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://128.199.125.93:3000) powering the code autocompletion 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 shows languages, most efficiently in Python. [192]
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<br>Several concerns with glitches, style defects and security vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has been accused of producing copyrighted code, with no author attribution or license. [197]
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<br>OpenAI announced 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the upgraded innovation passed a simulated law 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 might likewise read, [evaluate](https://gitea.linkensphere.com) or produce as much as 25,000 words of text, and compose code in all significant shows languages. [200]
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<br>Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined to expose various technical details and data 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](https://starleta.xyz) and create text, images and audio. [204] GPT-4o attained state-of-the-art [outcomes](https://jobsinethiopia.net) in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI [launched](https://git.eisenwiener.com) GPT-4o mini, a smaller version 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 anticipates it to be especially useful for enterprises, startups and designers looking for to automate services with [AI](http://gitlabhwy.kmlckj.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 actually been developed to take more time to consider their actions, causing higher precision. These designs are particularly efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:NolanBrito91) Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
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<br>o3<br>
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<br>On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and [quicker variation](https://git.kawen.site) of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with [telecoms providers](http://104.248.138.208) O2. [215]
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<br>Deep research study<br>
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<br>Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out extensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and [Python tools](https://avicii.blog) made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
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<br>Image category<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic resemblance in between text and images. It can significantly 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 model](https://gitea.eggtech.net) that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can create images of realistic things ("a stained-glass window with an image of a blue strawberry") as well as items 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 announced DALL-E 2, an upgraded variation of the design with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new simple system for transforming 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, OpenAI announced DALL-E 3, a more effective design much better able to [produce](https://gitea.aventin.com) images from intricate descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was released 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 design that can create videos based upon short detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.<br>
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<br>Sora's advancement team called it after the Japanese word for "sky", to symbolize its "limitless imaginative capacity". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos [certified](http://platform.kuopu.net9999) for that purpose, but did not reveal the number or the precise sources of the videos. [223]
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<br>OpenAI demonstrated some [Sora-created high-definition](https://wiki.dulovic.tech) videos to the public on February 15, 2024, specifying that it might create videos up to one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the design's abilities. [225] It acknowledged a few of its drawbacks, consisting of battles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but kept in mind that they should have been cherry-picked and might not represent Sora's common output. [225]
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<br>Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have revealed substantial interest in the technology's capacity. In an interview, actor/filmmaker [Tyler Perry](https://gitea.lelespace.top) revealed his awe at the innovation's capability to create [reasonable video](https://git.xjtustei.nteren.net) from text descriptions, citing its potential to revolutionize storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly strategies for expanding his Atlanta-based movie 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 design. [228] It is trained on a large dataset of varied audio and is likewise a multi-task design that can carry out multilingual speech recognition as well as speech translation and language recognition. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to start fairly but then fall into mayhem the longer it plays. [230] [231] In pop culture, [preliminary applications](https://www.so-open.com) of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to produce 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 tune samples. OpenAI stated the tunes "reveal regional musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" which "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's technologically excellent, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider mentioned "remarkably, a few of the resulting songs are memorable 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 launched the Debate Game, which teaches machines to dispute toy problems in front of a human judge. The purpose is to research whether such an approach might assist in auditing [AI](https://repo.correlibre.org) decisions and in developing explainable [AI](http://grainfather.asia). [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 neuron of eight neural network models which are often studied in interpretability. [240] Microscope was developed to examine the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various variations 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 a synthetic intelligence tool developed on top of GPT-3 that offers a conversational interface that permits users to ask concerns in natural language. The system then reacts with an answer within seconds.<br>
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