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<br>Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](https://paxlook.com) research, making released research study more easily reproducible [24] [144] while offering users with an easy interface for [connecting](http://47.119.128.713000) with these environments. In 2022, new advancements 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](https://callingirls.com) in 2018, Gym Retro is a platform for [reinforcement learning](http://101.51.106.216) (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to resolve single tasks. Gym Retro offers the ability to generalize in between games with comparable concepts however 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 robotic agents initially do not have knowledge of how to even walk, but are offered the goals of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives discover how to adjust to altering conditions. When an agent is then removed from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, recommending it had learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might develop an intelligence "arms race" that could increase a representative's ability to function 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 used in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high ability level entirely through experimental algorithms. Before becoming a group of 5, the first public presentation took place at The International 2017, the annual best champion competition for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of actual time, and that the learning software application was a step in the direction of creating software that can manage complicated tasks like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots discover gradually by playing against themselves hundreds of times a day for months, and [it-viking.ch](http://it-viking.ch/index.php/User:MiriamMcVilly60) are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156] |
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<br>By June 2018, the capability of the bots expanded to play together as a complete group 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 exhibition matches against professional gamers, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world [champions](https://samman-co.com) of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 overall [video games](https://dztrader.com) in a four-day open online competitors, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2912735) winning 99.4% of those video games. [165] |
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<br>OpenAI 5's mechanisms in Dota 2's bot player shows the challenges of [AI](http://www.thekaca.org) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown making use of deep reinforcement learning (DRL) agents to attain superhuman skills in Dota 2 matches. [166] |
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<br>Dactyl<br> |
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<br>Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It learns totally in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB video cameras to allow the robotic to manipulate an arbitrary item by seeing it. In 2018, OpenAI revealed 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 fix a Rubik's Cube. The robot had the [ability](https://bd.cane-recruitment.com) to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating progressively more hard 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 which it said was "for accessing brand-new [AI](https://git.buckn.dev) models developed by OpenAI" to let developers contact it for "any English language [AI](https://intunz.com) 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 original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed 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 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 without supervision transformer language model and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions initially released to the public. The full variation of GPT-2 was not instantly launched due to issue about possible abuse, [including applications](http://139.199.191.273000) for writing fake news. [174] Some specialists revealed [uncertainty](https://gogs.jublot.com) that GPT-2 posed a significant hazard.<br> |
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<br>In reaction to GPT-2, the Allen [Institute](https://gitea.dusays.com) for Artificial Intelligence [reacted](https://empleos.dilimport.com) with a tool to spot "neural fake news". [175] Other scientists, such as Jeremy Howard, warned 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 difficult to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language design. [177] Several [sites host](https://www.jobseeker.my) interactive demonstrations of different instances of GPT-2 and other transformer models. [178] [179] [180] |
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<br>GPT-2's authors argue not being watched language designs to be [general-purpose](http://47.109.24.444747) learners, highlighted by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model 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 of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns 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 a without [supervision transformer](https://almanyaisbulma.com.tr) language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion specifications, [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 few as 125 million criteria were likewise trained). [186] |
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<br>OpenAI stated that GPT-3 was successful at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave 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 considerably enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or experiencing the basic capability constraints of [predictive language](http://47.110.52.1323000) designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the public for concerns 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 certified specifically to Microsoft. [190] [191] |
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<br>Codex<br> |
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<br>Announced in mid-2021, [disgaeawiki.info](https://disgaeawiki.info/index.php/User:TaniaSallee9) Codex is a descendant of GPT-3 that has furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://git.slegeir.com) [powering](https://www.thehappyservicecompany.com) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was [released](http://20.198.113.1673000) in private beta. [194] According to OpenAI, the design can develop working code in over a [dozen programs](http://gitlab.ileadgame.net) languages, the majority of successfully in Python. [192] |
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<br>Several concerns with glitches, style flaws and security vulnerabilities were cited. [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 stop 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](http://wiki.myamens.com) 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the updated innovation passed a simulated law school bar exam 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 read, evaluate or create as much as 25,000 words of text, and write code in all significant programs 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 model, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal numerous technical details and statistics 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 launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision criteria, setting brand-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 released GPT-4o mini, a smaller version of GPT-4o changing GPT-3.5 Turbo on the ChatGPT interface. Its [API costs](http://gpis.kr) $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly useful for enterprises, startups and developers seeking to automate services with [AI](http://tesma.co.kr) representatives. [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 designed to take more time to think of their reactions, resulting in higher accuracy. These designs are especially effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [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 successor of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and much faster variation of OpenAI o3. As of 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, safety and security scientists had the chance to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid confusion with [telecoms services](https://starfc.co.kr) provider O2. [215] |
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<br>Deep research study<br> |
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<br>Deep research study 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 browsing, data analysis, and synthesis, [it-viking.ch](http://it-viking.ch/index.php/User:RosieGilliland) providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching 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 Pre-training) is a design that is [trained](https://yooobu.com) to analyze the semantic similarity in between text and images. It can notably 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 model that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can [develop images](https://gitea.v-box.cn) of sensible items ("a stained-glass window with an image of a blue strawberry") along with objects 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 realistic results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new basic 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 design much better able to generate images from complicated descriptions without manual prompt engineering and render intricate 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 generate videos based upon brief [detailed prompts](http://78.108.145.233000) [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce 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](http://taesungco.net) team called it after the Japanese word for "sky", to symbolize its "unlimited imaginative potential". [223] Sora's innovation is an adaptation of the [innovation](https://doop.africa) behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos licensed for that function, but did not reveal the number or the exact sources of the videos. [223] |
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<br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could produce videos as much as one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:Randal50P9974608) the model's capabilities. [225] It acknowledged some of its drawbacks, including battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but noted that they should have been cherry-picked and might not represent Sora's typical output. [225] |
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<br>Despite uncertainty from some [scholastic leaders](https://try.gogs.io) following Sora's public demo, significant entertainment-industry figures have actually shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to create [practical video](https://www.nepaliworker.com) from text descriptions, mentioning its prospective to revolutionize storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly strategies 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 model. [228] It is trained on a big dataset of diverse audio and is likewise a multi-task design that can carry out [multilingual speech](https://socialpix.club) acknowledgment in addition to 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 predict subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web mental 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 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 conventional chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a substantial space" in between Jukebox and human-generated music. The Verge specified "It's highly excellent, even if the results sound like mushy variations of tunes that might feel familiar", while Business Insider stated "remarkably, 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 introduced the Debate Game, which teaches machines to dispute toy problems in front of a human judge. The purpose is to research whether such a technique may assist in auditing [AI](http://hoenking.cn:3000) choices and in establishing explainable [AI](https://sudanre.com). [237] [238] |
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<br>Microscope<br> |
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<br>Released in 2020, Microscope [239] is a [collection](https://azaanjobs.com) of [visualizations](https://git.chir.rs) of every significant layer and neuron of eight neural network models which are frequently studied in interpretability. [240] Microscope was created to examine the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various variations 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 an expert system tool built on top of GPT-3 that supplies a conversational user interface that allows users to ask concerns in natural language. The system then reacts with an answer within seconds.<br> |
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