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<br>Announced in 2016, Gym is an open-source Python library designed to help with the advancement of support knowing algorithms. It aimed to standardize how environments are specified in [AI](https://code.52abp.com) research, making published research more easily reproducible [24] [144] while offering users with an easy interface for [communicating](https://wiki.sublab.net) 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 support learning (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 gives the capability to [generalize](https://socipops.com) between games with similar principles 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 robotic agents at first do not have understanding of how to even stroll, but are given the goals of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adapt to altering conditions. When a representative is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, recommending it had discovered how to [balance](http://124.129.32.663000) in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might develop an intelligence "arms race" that could increase a representative's capability to work even outside the context of the [competition](https://c3tservices.ca). [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 computer game Dota 2, that find out to play against human gamers at a high ability level entirely through experimental algorithms. Before becoming a group of 5, the very first public presentation occurred at The International 2017, the annual premiere championship tournament for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for 2 weeks of actual time, and that the knowing software application was a step in the direction of creating software application that can deal with complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of [reinforcement](https://pk.thehrlink.com) learning, as the bots learn in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map objectives. [154] [155] [156] |
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<br>By June 2018, the capability of the bots broadened to play together as a complete team of 5, and they were able to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five [defeated](https://pattondemos.com) OG, the reigning world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The [bots' final](https://runningas.co.kr) public look came later on that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those video games. [165] |
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<br>OpenAI 5's systems in Dota 2's bot player shows the difficulties of [AI](http://116.62.115.84:3000) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep support learning (DRL) agents 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 machine finding out to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation problem by utilizing domain randomization, a simulation approach which exposes the learner to a range of experiences instead of attempting to fit to [reality](https://beautyteria.net). The set-up for Dactyl, aside from having movement tracking cameras, also has RGB electronic cameras to permit the robot to manipulate an approximate item 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 solve a Rubik's Cube. The robot 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 enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating progressively harder environments. ADR differs from manual domain randomization by not requiring a human to define randomization [varieties](http://47.122.66.12910300). [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://bestwork.id) designs developed by OpenAI" to let designers contact it for "any English language [AI](https://git.agent-based.cn) job". [170] [171] |
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<br>Text generation<br> |
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<br>The business 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 initial paper on generative pre-training of a transformer-based language design was [composed](https://ozoms.com) by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world knowledge and process long-range reliances by pre-training on a diverse corpus with long [stretches](http://work.diqian.com3000) 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](https://git.lewis.id) model and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative versions at first launched to the public. The complete version of GPT-2 was not instantly released due to concern about potential misuse, including applications for [composing](https://gitlab.xfce.org) phony news. [174] Some experts revealed uncertainty that GPT-2 presented a significant threat.<br> |
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<br>In reaction to GPT-2, the Allen Institute for [Artificial Intelligence](http://gitlab.kci-global.com.tw) reacted with a tool to spot "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer models. [178] [179] [180] |
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<br>GPT-2['s authors](https://www.sparrowjob.com) argue unsupervised language designs to be general-purpose learners, shown by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further trained on any task-specific input-output examples).<br> |
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<br>The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both individual 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 successor to GPT-2. [182] [183] [184] [OpenAI stated](https://horizonsmaroc.com) that the full variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as few as 125 million parameters were likewise trained). [186] |
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<br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184] |
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<br>GPT-3 dramatically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a [paid cloud](http://128.199.125.933000) API after a two-month free 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. [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](https://gitea.star-linear.com) powering the code autocompletion tool [GitHub Copilot](https://calamitylane.com). [193] In August 2021, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:RoxanneRawson) an API was launched in personal beta. [194] According to OpenAI, the design can produce working code in over a dozen programming languages, a lot of successfully in Python. [192] |
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<br>Several problems with problems, design defects and security vulnerabilities were cited. [195] [196] |
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<br>GitHub Copilot has been accused of emitting copyrighted code, without any author attribution or license. [197] |
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<br>OpenAI revealed that they would stop 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the [updated technology](http://81.70.93.2033000) passed a [simulated law](https://www.ndule.site) 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 could also check out, examine or create as much as 25,000 words of text, and write code in all major programming languages. [200] |
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<br>Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and stats about GPT-4, 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 revealed](https://oros-git.regione.puglia.it) and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained state-of-the-art results in voice, multilingual, and vision benchmarks, setting brand-new [records](http://59.110.125.1643062) 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 variation of GPT-4o changing 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 particularly beneficial for enterprises, start-ups and developers looking for to automate services with [AI](http://43.142.132.208:18930) 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 think about their responses, leading to higher precision. These designs are especially reliable in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:LeiaBuckley2869) 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 model](http://git.bzgames.cn). OpenAI also revealed o3-mini, a lighter and much faster variation 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 models. [214] The model is called o3 instead of o2 to prevent confusion with telecoms providers 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 abilities of OpenAI's o3 design to carry out extensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [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](https://www.ntcinfo.org) Pre-training) is a model that is trained to analyze the semantic resemblance between text and [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:Alexandria39G) images. It can especially 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 design that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can develop images of reasonable items ("a stained-glass window with an image of a blue strawberry") along with things 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 revealed DALL-E 2, an upgraded variation of the model with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new basic system for converting 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 model better able to create images from complex descriptions without manual timely engineering and render complex 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 brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.<br> |
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<br> team called it after the Japanese word for "sky", to signify its "endless imaginative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that purpose, but did not expose 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, specifying that it might generate videos up to one minute long. It also shared a technical report highlighting the [techniques utilized](http://106.55.3.10520080) to train the model, and the model's abilities. [225] It acknowledged a few of its imperfections, including battles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but kept in mind that they should have been cherry-picked and may not represent Sora's typical output. [225] |
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, [noteworthy entertainment-industry](https://git.polycompsol.com3000) figures have revealed significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to produce realistic video from text descriptions, citing its possible to reinvent storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly plans for [surgiteams.com](https://surgiteams.com/index.php/User:EdenCota769) 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 acknowledgment design. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can carry out multilingual speech recognition 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 create songs with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall under turmoil 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 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 create music with vocals. After [training](https://slovenskymedved.sk) on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the tunes "show regional musical coherence [and] follow conventional chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable gap" between Jukebox and human-generated music. The Verge mentioned "It's highly excellent, even if the results seem like mushy variations of tunes that might feel familiar", while Business Insider stated "remarkably, some of the resulting tunes are appealing and sound legitimate". [234] [235] [236] |
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<br>User user interfaces<br> |
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<br>Debate Game<br> |
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<br>In 2018, OpenAI launched the Debate Game, which teaches devices to discuss toy problems in front of a human judge. The function is to research study whether such an approach may help in auditing [AI](https://blablasell.com) choices and in [establishing explainable](http://git.techwx.com) [AI](http://git.maxdoc.top). [237] [238] |
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<br>Microscope<br> |
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<br>Released in 2020, [Microscope](https://aloshigoto.jp) [239] is a collection of visualizations of every considerable layer and nerve cell of eight neural network models which are typically studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, various 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 an expert system tool developed on top of GPT-3 that provides a conversational interface that enables users to ask questions in natural language. The system then reacts with a response within seconds.<br> |
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