Add The Verge Stated It's Technologically Impressive
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<br>Announced in 2016, Gym is an open-source Python library created to help with the advancement of reinforcement knowing [algorithms](https://www.oemautomation.com8888). It aimed to standardize how environments are defined in [AI](https://playtube.ann.az) research, making released research study more easily reproducible [24] [144] while supplying users with an easy user interface for interacting with these environments. In 2022, new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]
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<br>Gym Retro<br>
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<br>Released in 2018, Gym Retro is a platform for support learning (RL) research on computer game [147] utilizing RL algorithms and research [study generalization](https://gitea.alaindee.net). Prior RL research focused mainly on enhancing agents to resolve single tasks. Gym Retro offers the capability to generalize in between video games with similar principles however various 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 lack knowledge of how to even stroll, but are given the goals of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents discover how to adjust to changing conditions. When an agent is then gotten rid of from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could create 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 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 ability level totally through trial-and-error algorithms. Before becoming a team of 5, the very first public presentation occurred at The International 2017, the annual best championship tournament for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of genuine time, and that the learning software application was an action in the direction of developing software application that can deal with complicated jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of [support](http://39.100.93.1872585) knowing, as the bots learn with time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the capability of the bots broadened to play together as a full team of 5, and they were able to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The [International](https://shiapedia.1god.org) 2018, OpenAI Five played in two exhibit matches against expert gamers, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the [reigning](https://git.ascarion.org) world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 total video 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](http://skupra-nat.uamt.feec.vutbr.cz30000) in Dota 2's bot gamer shows the obstacles of [AI](https://igazszavak.info) [systems](https://git.becks-web.de) in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown making use of deep support knowing (DRL) representatives to attain superhuman [proficiency](http://47.99.132.1643000) in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl utilizes device finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It learns entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation approach which [exposes](https://git.andy.lgbt) the learner to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has [RGB cameras](https://okoskalyha.hu) to enable the robotic to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an [octagonal prism](https://89.22.113.100). [168]
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<br>In 2019, OpenAI showed that Dactyl could solve a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing progressively more challenging environments. ADR differs from manual domain randomization by not requiring a human to specify randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI announced a [multi-purpose API](https://xn--114-2k0oi50d.com) which it said was "for accessing new [AI](http://www.book-os.com:3000) designs developed by OpenAI" to let developers contact it for "any English language [AI](http://123.207.206.135:8048) job". [170] [171]
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<br>Text generation<br>
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<br>The company has popularized generative pretrained transformers (GPT). [172]
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<br>OpenAI's original 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://nextjobnepal.com) by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding and procedure long-range dependences by pre-training on a diverse 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 successor to [OpenAI's initial](http://bhnrecruiter.com) GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations initially launched to the public. The complete variation of GPT-2 was not right away launched due to issue about potential abuse, [consisting](https://oerdigamers.info) of applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 posed a significant danger.<br>
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to completely 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 total version of the GPT-2 language model. [177] Several websites host interactive demonstrations of different circumstances 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 modern accuracy and perplexity on 7 of 8 zero-shot jobs (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 somewhat 40 gigabytes of text from [URLs shared](https://git.wyling.cn) in Reddit submissions with at least 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both specific characters and multiple-character tokens. [181]
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<br>GPT-3<br>
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is an unsupervised transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were also trained). [186]
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<br>OpenAI stated that GPT-3 was successful at certain "meta-learning" tasks and could generalize the of a single input-output pair. The GPT-3 release paper offered examples of [translation](http://gitlab.unissoft-grp.com9880) and [cross-linguistic transfer](https://git.prime.cv) knowing in between English and Romanian, and in between English and German. [184]
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<br>GPT-3 [considerably improved](https://gitlab-zdmp.platform.zdmp.eu) benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or encountering the essential ability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately [released](https://gitlab.rlp.net) to the general public for issues of possible abuse, although OpenAI prepared to permit 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 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 on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://blackfinn.de) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the design can develop working code in over a dozen shows languages, a lot of efficiently in Python. [192]
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<br>Several concerns with glitches, style flaws and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has actually been accused of emitting copyrighted code, without any author attribution or license. [197]
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<br>OpenAI announced that they would terminate 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 the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the updated innovation passed a simulated law school bar exam with a score around the top 10% of [test takers](https://www.flytteogfragttilbud.dk). (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also read, examine or create up to 25,000 words of text, and compose code in all major shows languages. [200]
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<br>Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on [ChatGPT](https://deprezyon.com). [202] OpenAI has [decreased](https://juryi.sn) to expose different 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 and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision standards, setting new records in audio speech acknowledgment 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 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 particularly useful for business, startups and developers looking for to [automate services](https://jobs.ofblackpool.com) with [AI](http://xiaomaapp.top: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 developed to take more time to think about their reactions, resulting in higher 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, 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 revealed o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications services supplier O2. [215]
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<br>Deep research study<br>
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<br>Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform comprehensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, it reached an [accuracy](https://git.epochteca.com) of 26.6 percent on HLE (Humanity's Last Exam) [standard](https://centerfairstaffing.com). [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](https://culturaitaliana.org) Pre-training) is a model that is trained to evaluate the semantic resemblance 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 model that creates images from [textual descriptions](https://social.oneworldonesai.com). [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can develop images of practical items ("a stained-glass window with an image of a blue strawberry") in addition to 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 updated version of the design with more practical outcomes. [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://vts-maritime.com) 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 design much better able to [produce](http://1.94.30.13000) images from complex descriptions without manual prompt engineering and render complex details like hands and text. [221] It was launched to the 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 produce videos based on short detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The [optimum length](https://jobs.salaseloffshore.com) of created videos is unidentified.<br>
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<br>Sora's development group called it after the Japanese word for "sky", to signify its "limitless imaginative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system [utilizing publicly-available](http://release.rupeetracker.in) videos in addition to copyrighted videos licensed for that purpose, but did not expose the number or the precise sources 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 produce videos up to one minute long. It likewise shared a technical report highlighting the approaches [utilized](http://git.nuomayun.com) to train the model, and the model's abilities. [225] It acknowledged a few of its drawbacks, consisting of struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but noted that they should have been [cherry-picked](http://51.75.64.148) and may not represent Sora's normal output. [225]
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<br>Despite [uncertainty](http://66.112.209.23000) from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have shown considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to create sensible video from text descriptions, mentioning its prospective to change storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly plans for expanding 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 recognition design. [228] It is trained on a big dataset of varied audio and is also a [multi-task model](https://surreycreepcatchers.ca) 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 net trained to forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a tune produced by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to produce music for the titular character. [232] [233]
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<br>Jukebox<br>
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<br>Released in 2020, Jukebox is an open-sourced algorithm to produce 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 stated the tunes "show regional musical coherence [and] follow traditional chord patterns" but [acknowledged](https://www.airemploy.co.uk) that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a significant space" between Jukebox and human-generated music. The Verge mentioned "It's technically remarkable, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider mentioned "remarkably, some of the resulting tunes are appealing and sound legitimate". [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 introduced 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://gitea.dokm.xyz) decisions and in developing explainable [AI](http://git.sinoecare.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 [considerable layer](https://lms.jolt.io) and nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:Natisha96L) and various versions of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>[Launched](https://social.ppmandi.com) in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that supplies a conversational user interface that allows users to ask questions in natural language. The system then reacts with a response within seconds.<br>
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