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<br>Announced in 2016, Gym is an open-source Python library developed to help with the [advancement](https://video.igor-kostelac.com) of support knowing algorithms. It aimed to standardize how environments are specified in [AI](https://scode.unisza.edu.my) research, making published research more quickly reproducible [24] [144] while offering users with a simple user interface for interacting with these environments. In 2022, brand-new developments of Gym have been relocated 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 [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:KeithSpina077) support knowing (RL) research study on computer game [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to fix single tasks. Gym Retro provides the ability to generalize between video games with similar concepts 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 agents at first do not have understanding of how to even stroll, however are provided the goals of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could produce an intelligence "arms race" that might increase an agent's ability to function even outside the context of the competition. [148] |
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<br>OpenAI 5<br> |
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<br>OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five computer [game Dota](https://git.programming.dev) 2, that find out to play against human players at a high ability level entirely through experimental algorithms. Before becoming a team of 5, the very first public presentation occurred at The International 2017, the yearly best championship tournament for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg [Brockman](https://login.discomfort.kz) explained that the bot had learned by [playing](https://academia.tripoligate.com) against itself for 2 weeks of genuine time, which the knowing software application was an action in the direction of creating software application that can handle complicated tasks like a surgeon. [152] [153] The system utilizes a kind of reinforcement knowing, as the bots learn gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156] |
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<br>By June 2018, the ability of the bots broadened to play together as a full team of 5, and they were able to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later on 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 gamer reveals the challenges of [AI](https://ysa.sa) systems in [multiplayer online](https://www.ourstube.tv) fight arena (MOBA) games and how OpenAI Five has shown the usage of deep support learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166] |
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<br>Dactyl<br> |
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<br>Developed in 2018, Dactyl uses maker finding out to train a Shadow Hand, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:Rosalind2029) a human-like robot hand, to control physical things. [167] It discovers entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by using domain randomization, a [simulation technique](https://sahabatcasn.com) 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 cameras to allow the robot to manipulate an arbitrary things by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. [Objects](https://sc.e-path.cn) like the Rubik's Cube introduce 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 method of creating progressively more challenging environments. ADR differs 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 brand-new [AI](https://www.dadam21.co.kr) models developed by OpenAI" to let developers contact it for "any English language [AI](https://youtoosocialnetwork.com) task". [170] [171] |
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<br>Text generation<br> |
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<br>The business has 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 initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language might obtain world and procedure long-range dependences by pre-training on a varied corpus with long stretches of adjoining text.<br> |
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<br>GPT-2<br> |
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the [follower](https://sc.e-path.cn) to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions at first launched to the public. The full variation of GPT-2 was not instantly released due to concern about prospective misuse, consisting of applications for composing phony news. [174] Some specialists revealed uncertainty that GPT-2 presented a substantial danger.<br> |
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural phony news". [175] Other scientists, such as Jeremy Howard, cautioned of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several websites host interactive presentations 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 models to be general-purpose learners, illustrated by GPT-2 attaining cutting edge 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 on, called WebText, contains a little 40 [gigabytes](http://208.167.242.1503000) of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 an unsupervised transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned 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 variation of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were also trained). [186] |
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<br>OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184] |
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<br>GPT-3 dramatically improved benchmark results over GPT-2. [OpenAI cautioned](https://noxxxx.com) that such scaling-up of language designs could be [approaching](https://www.worlddiary.co) or coming across the basic ability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, [compared](https://medicalrecruitersusa.com) to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released to the public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month complimentary 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. [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 additionally been [trained](https://www.jungmile.com) on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://47.112.158.86:3000) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the model can create working code in over a lots programming languages, most efficiently in Python. [192] |
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<br>Several problems with problems, style flaws and security vulnerabilities were mentioned. [195] [196] |
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<br>GitHub Copilot has been accused of discharging copyrighted code, with no author attribution or license. [197] |
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<br>OpenAI revealed that they would discontinue 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](https://www.goodbodyschool.co.kr) or image inputs. [199] They revealed that the updated innovation passed a simulated law school bar test 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 might also read, evaluate or generate approximately 25,000 words of text, and compose code in all significant programming languages. [200] |
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<br>Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal numerous technical details and statistics about GPT-4, [it-viking.ch](http://it-viking.ch/index.php/User:Dianna01H6) such as the exact size of the model. [203] |
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<br>GPT-4o<br> |
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<br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision benchmarks, setting brand-new 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 variation of GPT-4o changing 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, [start-ups](https://ysa.sa) and developers seeking to automate services with [AI](https://actsfile.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 designs, which have been developed to take more time to think about their responses, resulting in higher precision. These designs are especially efficient 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 follower of the o1 thinking model. OpenAI also unveiled o3-mini, a lighter and quicker variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, [security](https://wiki.rolandradio.net) 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 companies O2. [215] |
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<br>Deep research<br> |
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<br>Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web browsing, information analysis, and [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:AndyDana123) synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [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 analyze the semantic resemblance between text and images. It can notably be [utilized](https://guyanajob.com) 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 in 2021, DALL-E is a Transformer design that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can produce images of practical objects ("a stained-glass window with an image of a blue strawberry") as well as things 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 upgraded variation of the design with more realistic results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new fundamental system for converting a text description into a 3[-dimensional](http://8.136.197.2303000) 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 powerful model much better able to produce images from intricate descriptions without manual prompt engineering and render complicated 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 generate videos based upon brief detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate 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 development group named it after the Japanese word for "sky", to symbolize its "unlimited innovative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos [certified](https://realmadridperipheral.com) for that function, however did not reveal the number or the [precise sources](http://208.167.242.1503000) of the videos. [223] |
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<br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might create videos up to one minute long. It likewise shared a technical report [highlighting](http://metis.lti.cs.cmu.edu8023) the techniques utilized to train the design, and the model's abilities. [225] It acknowledged some of its drawbacks, consisting of battles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they must 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 demo, noteworthy entertainment-industry figures have actually shown significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry [revealed](https://namesdev.com) his awe at the innovation's capability to create realistic video from text descriptions, citing its potential to transform storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly prepare for broadening his Atlanta-based motion picture studio. [227] |
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<br>Speech-to-text<br> |
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<br>Whisper<br> |
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<br>Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can carry out multilingual speech [recognition](https://denis.usj.es) in addition to speech translation and language [identification](https://job.firm.in). [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](http://revoltsoft.ru3000) 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 start fairly but then fall into chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to create 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 produce 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 songs "reveal local 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 gap" in between Jukebox and human-generated music. The Verge mentioned "It's highly remarkable, even if the results seem like mushy variations of songs that might feel familiar", while [Business](http://47.113.115.2393000) Insider stated "surprisingly, a few of the resulting tunes 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 debate toy problems in front of a human judge. The purpose is to research whether such a technique might help in auditing [AI](https://www.mapsisa.org) choices and in developing explainable [AI](https://sjee.online). [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 8 neural network designs which are frequently studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks easily. The designs consisted of 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 expert system tool built on top of GPT-3 that provides a conversational interface that enables users to ask [concerns](https://noblessevip.com) in natural language. The system then reacts with an answer within seconds.<br> |
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