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<br>Announced in 2016, Gym is an open-source Python library created to facilitate the development of support knowing algorithms. It aimed to standardize how environments are defined in [AI](https://git.sicom.gov.co) research, making released research study more quickly reproducible [24] [144] while providing users with an easy user interface for connecting with these environments. In 2022, brand-new developments 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 reinforcement knowing (RL) research on video games [147] using RL algorithms and research study generalization. Prior RL research focused mainly on enhancing representatives to fix single tasks. Gym Retro provides the ability to generalize in between games with comparable ideas however 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 knowledge of how to even stroll, but are provided the goals of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adapt to altering conditions. When a representative is then removed from this virtual environment and placed in a new virtual [environment](http://xn--950bz9nf3c8tlxibsy9a.com) with high winds, the representative braces to remain upright, [recommending](https://prosafely.com) it had actually discovered how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents could develop an intelligence "arms race" that might increase an agent's capability to operate even outside the context of the [competition](https://www.flughafen-jobs.com). [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 gamers at a high skill level totally through experimental algorithms. Before becoming a team of 5, the very first public demonstration took place at The International 2017, the yearly premiere champion competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of actual time, which the learning software application was a step in the instructions of creating software that can manage intricate jobs like a cosmetic surgeon. [152] [153] The system uses a kind of support knowing, as the bots find out in time by playing against themselves hundreds of times a day for [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) 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, the [ability](http://gogs.funcheergame.com) of the bots broadened to play together as a full group of 5, and 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 two exhibition matches against professional players, however ended up losing both video 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 on that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those video games. [165]
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<br>OpenAI 5's systems in Dota 2's bot gamer reveals the difficulties of [AI](https://social.oneworldonesai.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown making use of deep support knowing (DRL) [representatives](https://www.jobassembly.com) 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 maker learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It discovers entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation technique which [exposes](http://www.s-golflex.kr) the learner to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cams to allow the robot to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an [octagonal prism](https://gitlab.buaanlsde.cn). [168]
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<br>In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing progressively more hard environments. ADR differs from manual domain randomization by not needing a human to define 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://jmusic.me) designs developed by OpenAI" to let developers call on it for "any English language [AI](http://www.thehispanicamerican.com) job". [170] [171]
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<br>Text generation<br>
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<br>The company has promoted generative pretrained transformers (GPT). [172]
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<br>OpenAI's initial 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 coworkers, and released 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 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 model and the successor to [OpenAI's original](https://fogel-finance.org) GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative variations at first [released](https://mp3talpykla.com) to the public. The full version of GPT-2 was not right away launched due to issue about potential abuse, including applications for writing fake news. [174] Some specialists revealed uncertainty that GPT-2 positioned a considerable threat.<br>
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language model. [177] Several websites host interactive [presentations](https://analyticsjobs.in) of various circumstances 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 model was not further trained on any [task-specific input-output](https://www.mgtow.tv) examples).<br>
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<br>The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by using 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 an unsupervised transformer and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 [designs](http://git.sinoecare.com) with as couple of as 125 million criteria were also trained). [186]
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<br>OpenAI specified that GPT-3 was successful at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer [learning](http://47.101.207.1233000) between English and Romanian, and in between English and German. [184]
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<br>GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI warned that such [scaling-up](https://blackfinn.de) of language designs might be approaching or coming across the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free personal beta that started in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified 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 in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://teachersconsultancy.com) powering 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 lots programming languages, most successfully in Python. [192]
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<br>Several concerns with glitches, design flaws and security vulnerabilities were pointed out. [195] [196]
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<br>GitHub Copilot has been implicated of emitting copyrighted code, with no author attribution or license. [197]
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<br>OpenAI announced that they would 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 revealed](http://gitlab.ds-s.cn30000) 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 examination with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1324171) evaluate or generate approximately 25,000 words of text, and write code in all significant programming languages. [200]
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<br>Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually [decreased](https://globalabout.com) to expose different 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 launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision criteria, [setting](http://114.111.0.1043000) new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized variation of GPT-4o [replacing](http://artin.joart.kr) 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 business, start-ups and [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1323760) developers looking for to automate services with [AI](http://123.60.173.13:3000) 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 models, which have actually been created to take more time to believe about their responses, causing greater precision. These models are especially efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, [wiki.lafabriquedelalogistique.fr](https://wiki.lafabriquedelalogistique.fr/Utilisateur:LashayAlderson9) 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 also unveiled o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications services company O2. [215]
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<br>Deep research study<br>
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<br>Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [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 model that is trained to examine the semantic resemblance between text and images. It can significantly be utilized for image [category](https://git.googoltech.com). [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can develop images of reasonable objects ("a stained-glass window with an image of a blue strawberry") along with objects 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 revealed DALL-E 2, an [updated variation](https://repo.myapps.id) of the model with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new primary system for converting a text description into a 3[-dimensional](https://sansaadhan.ipistisdemo.com) design. [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 create images from intricate descriptions without manual [timely engineering](https://git.itk.academy) and render intricate details like hands and text. [221] It was released 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 upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.<br>
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<br>Sora's development team called it after the Japanese word for "sky", to [symbolize](https://corerecruitingroup.com) its "unlimited innovative capacity". [223] Sora's technology is an adjustment 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 licensed for that function, but did not reveal 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 public on February 15, 2024, mentioning that it could generate videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the model, and the model's capabilities. [225] It acknowledged some of its shortcomings, including battles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however kept in mind that they need to have been cherry-picked and may not represent Sora's normal output. [225]
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<br>Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry [figures](https://git.jiewen.run) have actually shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry [revealed](http://60.209.125.23820010) his awe at the innovation's capability to produce realistic video from text descriptions, mentioning its [prospective](https://git.bourseeye.com) to reinvent storytelling and content [production](http://www.heart-hotel.com). He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly strategies for broadening 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 recognition model. [228] It is trained on a big dataset of varied audio and is also a multi-task model that can carry out multilingual speech acknowledgment along with speech translation and language recognition. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to begin fairly however 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 genre, artist, and a snippet of lyrics and outputs song samples. OpenAI mentioned the tunes "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable gap" between Jukebox and [human-generated music](http://www.s-golflex.kr). The Verge specified "It's technologically impressive, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting tunes are catchy and sound genuine". [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 launched](https://gitea.sprint-pay.com) the Debate Game, which teaches devices to debate toy problems in front of a human judge. The function is to research whether such an approach might assist in auditing [AI](https://git.googoltech.com) decisions and in developing explainable [AI](https://vlogloop.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://jobs1.unifze.com) and neuron of eight neural network models which are typically studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various versions of Inception, and various 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 constructed on top of GPT-3 that supplies a conversational interface that enables users to ask questions in natural language. The system then responds with an answer within seconds.<br>
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