Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library developed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](https://git.danomer.com) research, making released research more quickly reproducible [24] [144] while [providing](https://git.micg.net) users with a simple interface for connecting with these environments. In 2022, new advancements of Gym have actually been moved to the library Gymnasium. [145] [146]
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<br>Gym Retro<br>
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<br>Released in 2018, Gym Retro is a platform for support learning (RL) research study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing representatives to solve [single tasks](https://socialsnug.net). Gym Retro gives the capability to generalize between games with similar ideas however different [appearances](https://systemcheck-wiki.de).<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 initially do not have knowledge of how to even stroll, [yewiki.org](https://www.yewiki.org/User:GeorgiannaBottom) but are offered the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the agents learn how to adjust to changing conditions. When an agent is then removed from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might produce an intelligence "arms race" that might increase a representative'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 utilized in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high [skill level](http://178.44.118.232) completely through trial-and-error algorithms. Before ending up being a group of 5, the first public presentation occurred at The International 2017, the [annual premiere](http://gitlab.lecanal.fr) champion 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 explained that the bot had actually discovered by playing against itself for two weeks of real time, and that the learning software application was a step in the instructions of producing software that can deal with complex tasks like a surgeon. [152] [153] The system uses a type of support knowing, as the bots learn gradually by playing against themselves hundreds of 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 ability of the bots expanded to play together as a full team of 5, and they were able to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against [professional](https://sansaadhan.ipistisdemo.com) gamers, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions 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 competitors, winning 99.4% of those video games. [165]
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<br>OpenAI 5's systems in Dota 2's bot player reveals the obstacles of [AI](https://forum.elaivizh.eu) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown the use of deep support learning (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl utilizes maker [finding](https://git.the9grounds.com) out to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It learns entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, likewise has [RGB cams](http://43.139.182.871111) to enable the robot to manipulate an [arbitrary item](http://47.106.205.1408089) by seeing it. In 2018, [OpenAI revealed](http://47.100.81.115) 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 could resolve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to [perturbations](http://47.76.141.283000) by using Automatic Domain Randomization (ADR), a simulation technique of generating gradually more difficult 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://web.zqsender.com) designs established by OpenAI" to let developers get in touch with it for "any English language [AI](https://gitea.namsoo-dev.com) task". [170] [171]
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<br>Text generation<br>
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<br>The company has [promoted generative](https://equijob.de) pretrained transformers (GPT). [172]
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<br>OpenAI's original GPT design ("GPT-1")<br>
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<br>The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of [language](https://jotshopping.com) could obtain world knowledge and procedure long-range dependencies 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 only limited demonstrative variations at first launched to the public. The complete version of GPT-2 was not instantly launched due to concern about potential misuse, including applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 postured a considerable danger.<br>
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence [responded](https://wikitravel.org) with a tool to find "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation to absolutely 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 complete version of the GPT-2 language model. [177] Several sites host interactive presentations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue without supervision language designs to be general-purpose students, shown by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more 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 a minimum of 3 [upvotes](https://asw.alma.cl). It avoids certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both private 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 without supervision transformer language design and the [follower](https://dev.nebulun.com) to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186]
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<br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of [translation](https://soundfy.ebamix.com.br) and cross-linguistic transfer knowing in between [English](https://intgez.com) and Romanian, and between English and German. [184]
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<br>GPT-3 drastically enhanced benchmark results over GPT-2. [OpenAI warned](https://avicii.blog) that such scaling-up of language models might be approaching or coming across the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the general public for issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a personal beta that began in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified solely 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 in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://47.119.175.5:3000) [powering](http://hrplus.com.vn) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can produce working code in over a dozen programming languages, the majority of effectively in Python. [192]
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<br>Several problems with problems, style defects and security vulnerabilities were pointed out. [195] [196]
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<br>GitHub Copilot has been accused of discharging copyrighted code, without any author attribution or license. [197]
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<br>OpenAI revealed that they would cease support for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<br>On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the upgraded innovation passed a simulated law school bar exam 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 check out, [examine](https://gitea.carmon.co.kr) or create as much as 25,000 words of text, and write code in all major shows languages. [200]
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<br>Observers reported that the version of ChatGPT using GPT-4 was an [enhancement](http://media.nudigi.id) on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise [efficient](https://try.gogs.io) in taking images as input on ChatGPT. [202] OpenAI has decreased to expose numerous technical details and statistics about GPT-4, such as the exact 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 modern results in voice, multilingual, and vision criteria, setting brand-new [records](https://careers.express) 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 version of GPT-4o [replacing](https://git.hitchhiker-linux.org) 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 useful for enterprises, startups and developers looking for [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:LDTLela8982) to automate services with [AI](http://60.250.156.230:3000) 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 designs, which have been created to take more time to believe about their reactions, causing greater precision. These models are particularly reliable in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Staff member. [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 unveiled o3, the successor of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid [confusion](http://dnd.achoo.jp) with telecommunications companies O2. [215]
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<br>Deep research study<br>
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<br>Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform extensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached an [accuracy](https://scholarpool.com) 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 notably 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 creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret natural [language inputs](https://selfloveaffirmations.net) (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create [matching](http://gitlab.marcosurrey.de) images. It can develop images of reasonable objects ("a stained-glass window with an image of a blue strawberry") along with items 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 version of the model with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new rudimentary system for converting 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 announced DALL-E 3, a more powerful model better able to create images from [intricate descriptions](http://jenkins.stormindgames.com) without manual timely engineering and render complex details like hands and text. [221] It was [released](https://bandbtextile.de) to the general 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 produce [videos based](http://162.14.117.2343000) upon brief [detailed prompts](https://gst.meu.edu.jo) [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.<br>
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<br>Sora's development group named it after the Japanese word for "sky", to represent its "limitless innovative potential". [223] Sora's innovation is an adjustment of the [innovation](https://spillbean.in.net) behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that purpose, however did not expose the number or the exact sources of the videos. [223]
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<br>OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it might produce videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the design, and the model's capabilities. [225] It acknowledged a few of its drawbacks, including struggles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", however noted 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, notable entertainment-industry figures have actually revealed substantial interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to create [reasonable](http://39.108.83.1543000) video from text descriptions, mentioning its possible to revolutionize storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to pause prepare for broadening 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 in 2022, Whisper is a [general-purpose speech](https://www.xcoder.one) acknowledgment design. [228] It is trained on a big dataset of [varied audio](https://www.pkjobs.store) and is also a multi-task design that can perform 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://drapia.org) net trained to anticipate subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to start fairly however then fall into turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web psychological 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 genre, artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a significant gap" between Jukebox and human-generated music. The Verge mentioned "It's highly outstanding, even if the results seem like mushy variations of tunes that might feel familiar", while Business Insider stated "remarkably, a few of the resulting songs are catchy and sound genuine". [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 makers to discuss toy problems in front of a human judge. The purpose is to research whether such a technique might assist in auditing [AI](http://47.112.200.206:3000) choices and in establishing explainable [AI](https://jobs.superfny.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 significant layer and neuron of eight [neural network](http://106.14.140.713000) models which are frequently studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different variations 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 a synthetic intelligence tool developed on top of GPT-3 that provides a conversational interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.<br>
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