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 assist in the advancement of support knowing algorithms. It aimed to standardize how environments are defined in [AI](http://123.249.110.128:5555) research, making released research more quickly reproducible [24] [144] while supplying users with a basic interface for interacting with these environments. In 2022, 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](http://dev.ccwin-in.com3000) is a platform for [reinforcement learning](https://git.novisync.com) (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to resolve single jobs. Gym Retro offers the capability to generalize between games with comparable principles 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 representatives initially do not have knowledge of how to even stroll, however are offered the objectives of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had actually learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might produce an intelligence "arms race" that might increase a representative's ability to work even outside the context of the competitors. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high skill level entirely through experimental algorithms. Before ending up being a team of 5, the very first public presentation took place at The International 2017, the yearly premiere championship tournament for the game, where Dendi, an [expert Ukrainian](http://175.25.51.903000) player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=995691) CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of real time, and that the learning software was a step in the instructions of creating software that can manage complicated jobs like a cosmetic surgeon. [152] [153] The system uses a kind of support knowing, as the bots learn in time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent 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 group of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the 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 competition, 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](http://81.71.148.57:8080) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown using deep support knowing (DRL) agents 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 device discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It discovers totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation method which exposes the [learner](https://www.zapztv.com) to a variety of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB cams to allow the robotic to manipulate an arbitrary things by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by [utilizing Automatic](https://body-positivity.org) [Domain Randomization](https://barokafunerals.co.za) (ADR), a simulation method of creating gradually harder environments. ADR differs from manual domain [randomization](https://safeway.com.bd) by not requiring a human to specify randomization ranges. [169]
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<br>API<br>
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<br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://geniusactionblueprint.com) designs established by OpenAI" to let designers get in touch with it for "any English language [AI](https://git.lewis.id) task". [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 model was composed by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of [language](https://nextodate.com) might obtain world understanding and process long-range dependencies by pre-training on a diverse 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 an unsupervised transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions at first released to the public. The full version of GPT-2 was not immediately launched due to concern about potential misuse, consisting of applications for writing phony news. [174] Some professionals revealed uncertainty that GPT-2 posed a considerable threat.<br>
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence [responded](https://ejamii.com) with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, cautioned of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language model. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
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<br>GPT-2's authors argue unsupervised language models to be general-purpose learners, highlighted by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more trained on any task-specific input-output examples).<br>
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<br>The corpus it was trained on, called WebText, contains a little 40 [gigabytes](https://silverray.worshipwithme.co.ke) of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing 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 language design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were likewise trained). [186]
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<br>OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the function of a single input-output pair. The GPT-3 [release paper](https://careerconnect.mmu.edu.my) gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]
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<br>GPT-3 drastically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or coming across the basic ability [constraints](https://younetwork.app) of predictive language designs. [187] Pre-training GPT-3 required 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 was not instantly launched to the public for issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud 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 solely to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:BellaDenehy6165) 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://git.pleasantprogrammer.com) powering the [code autocompletion](https://edge1.co.kr) tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can [produce](https://gitea.bone6.com) working code in over a dozen programming languages, a lot of successfully in Python. [192]
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<br>Several concerns with problems, design flaws and security vulnerabilities were mentioned. [195] [196]
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<br>GitHub Copilot has been accused of producing copyrighted code, with no author attribution or license. [197]
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<br>OpenAI revealed that they would [discontinue support](https://surreycreepcatchers.ca) for Codex API on March 23, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:ShastaBoettcher) 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), efficient in accepting text 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 could likewise check out, evaluate 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 iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat 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 [declined](https://seekinternship.ng) to expose various technical details and data 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 announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision criteria, setting brand-new [records](http://43.138.57.2023000) in audio speech recognition 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 released GPT-4o mini, a smaller sized version of GPT-4o replacing 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 beneficial for enterprises, startups and developers seeking to automate services with [AI](https://picturegram.app) agents. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been created to take more time to consider their actions, causing greater precision. These designs are particularly [reliable](https://gitea.pi.cr4.live) in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Team members. [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 revealed o3, the follower of the o1 thinking model. OpenAI likewise unveiled o3-mini, a [lighter](https://gitlab.ui.ac.id) and quicker version of OpenAI o3. Since December 21, 2024, this model is not available for [public usage](http://101.35.184.1553000). According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215]
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<br>Deep research study<br>
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<br>Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out extensive web surfing, data analysis, and synthesis, providing detailed [reports](http://git.setech.ltd8300) within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
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<br>Image category<br>
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<br>CLIP<br>
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<br>[Revealed](http://engineerring.net) in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the [semantic resemblance](https://www.punajuaj.com) between text and images. It can especially be utilized 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 model that produces images from [textual](http://git.tederen.com) descriptions. [218] DALL-E utilizes a 12[-billion-parameter](https://git.uzavr.ru) variation of GPT-3 to [translate natural](http://39.99.134.1658123) language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can produce pictures of [practical](https://ruraltv.in) objects ("a stained-glass window with an image of a blue strawberry") in addition to objects that do not exist in truth ("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 variation of the model with more reasonable results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new primary system for transforming 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 [intricate descriptions](https://gitea.pi.cr4.live) without manual prompt engineering and render complex details like hands and text. [221] It was released 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 design that can generate videos based upon short detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.<br>
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<br>Sora's advancement group named it after the Japanese word for "sky", to represent its "limitless innovative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos in addition to [copyrighted videos](https://play.future.al) accredited for that function, but did not expose the number or the exact sources of the videos. [223]
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<br>OpenAI showed some [Sora-created high-definition](http://115.182.208.2453000) videos to the general public on February 15, 2024, mentioning that it could create videos as much as one minute long. It also shared a technical report highlighting the methods utilized to train the design, and the design's capabilities. [225] It acknowledged some of its drawbacks, consisting of struggles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but noted that they need to have been cherry-picked and may not represent Sora's common output. [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have shown significant interest in the innovation's [potential](https://howtolo.com). In an interview, actor/filmmaker Tyler Perry expressed his awe at the [innovation's ability](https://wino.org.pl) to create sensible video from text descriptions, [mentioning](https://youslade.com) its possible to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause plans for expanding 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 acknowledgment model. [228] It is trained on a big [dataset](https://gogs.kakaranet.com) of varied audio and is likewise a [multi-task design](https://abstaffs.com) that can carry out multilingual speech recognition along with speech translation and [language](https://git.wisptales.org) 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 anticipate subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to 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 stated the tunes "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge stated "It's highly outstanding, even if the outcomes seem like mushy variations of songs that may feel familiar", while Business [Insider](https://gitlab.healthcare-inc.com) specified "surprisingly, some of the resulting songs are memorable and sound genuine". [234] [235] [236]
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<br>Interface<br>
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<br>Debate Game<br>
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<br>In 2018, OpenAI launched the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The purpose is to research whether such a technique might assist in auditing [AI](https://interconnectionpeople.se) decisions and in developing explainable [AI](http://git.mvp.studio). [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 typically studied in interpretability. [240] Microscope was created to evaluate the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, different versions of Inception, and different variations of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, ChatGPT is a [synthetic intelligence](http://xingyunyi.cn3000) tool constructed on top of GPT-3 that provides a conversational user interface that permits users to ask concerns in natural language. The system then reacts with a response within seconds.<br>
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