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<br>Announced in 2016, Gym is an open-source Python library created to help with the [development](http://101.43.112.1073000) of support knowing algorithms. It aimed to standardize how environments are defined in [AI](https://radicaltarot.com) research study, making released research study more easily reproducible [24] [144] while supplying users with a basic user interface for interacting with these environments. In 2022, brand-new developments of Gym have actually been moved to the [library Gymnasium](http://dev.catedra.edu.co8084). [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 research study generalization. Prior RL research focused mainly on optimizing agents to resolve single tasks. Gym Retro provides the ability to generalize in between video games with comparable 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 walk, but are provided the goals of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives discover how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might create 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 team of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that find out to play against [human gamers](https://socialcoin.online) at a high skill level entirely through experimental algorithms. Before ending up being a group of 5, the very first public demonstration took place at The International 2017, the annual best champion tournament for [gratisafhalen.be](https://gratisafhalen.be/author/maribelbugd/) the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of real time, and that the [knowing software](http://47.105.104.2043000) application was an action in the instructions of creating software that can handle intricate jobs like a surgeon. [152] [153] The system utilizes a kind of support knowing, as the bots discover gradually by playing against themselves hundreds of times a day for months, and 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 teams of amateur and semi-professional players. [157] [154] [158] [159] At The [International](https://online-learning-initiative.org) 2018, OpenAI Five played in 2 exhibition matches against professional players, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions 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 on that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those games. [165] |
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer reveals the [challenges](https://edu.shpl.ru) of [AI](http://gitlab.andorsoft.ad) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown using deep support learning (DRL) representatives 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 utilizes machine finding out to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It finds out entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by utilizing domain randomization, a [simulation](https://www.etymologiewebsite.nl) technique which exposes the learner to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB electronic [cameras](http://chkkv.cn3000) to allow the robotic to control an approximate things by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI [demonstrated](https://tocgitlab.laiye.com) that Dactyl might solve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing gradually more challenging environments. ADR differs from manual domain randomization by not [requiring](https://connect.taifany.com) 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://gitlab.freedesktop.org) models developed by OpenAI" to let designers call on it for "any English language [AI](https://koubry.com) 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 model ("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](https://eurosynapses.giannistriantafyllou.gr) on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and procedure long-range reliances by pre-training on a varied corpus with long stretches of adjoining text.<br> |
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<br>GPT-2<br> |
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<br>Generative [Pre-trained Transformer](https://ubereducation.co.uk) 2 ("GPT-2") is a without supervision transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just minimal demonstrative versions initially released to the public. The complete variation of GPT-2 was not instantly launched due to issue about possible abuse, including applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 postured a considerable risk.<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 scientists, such as Jeremy Howard, alerted of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180] |
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<br>GPT-2's authors argue without supervision language designs to be general-purpose students, highlighted by GPT-2 attaining modern 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 slightly 40 gigabytes of text from URLs shared 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](https://git.ipmake.me) 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 not being watched transformer [language](https://youtubegratis.com) model and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger 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 also trained). [186] |
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<br>OpenAI mentioned that GPT-3 [succeeded](http://whai.space3000) at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer [knowing](https://git-dev.xyue.zip8443) between [English](https://www.freeadzforum.com) and Romanian, and between [English](https://www.sociopost.co.uk) and German. [184] |
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<br>GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across the essential ability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the general public for issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month free personal beta that began in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was certified solely 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://www.passadforbundet.se) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can produce working code in over a lots programming languages, the majority of effectively in Python. [192] |
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<br>Several concerns with problems, style flaws and [security](https://vooxvideo.com) [vulnerabilities](https://git.getmind.cn) were cited. [195] [196] |
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<br>[GitHub Copilot](http://211.159.154.983000) has been implicated of giving off copyrighted code, with no 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar exam with a rating 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 generate approximately 25,000 words of text, and write code in all significant programming languages. [200] |
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<br>Observers reported that the version of [ChatGPT](http://103.197.204.1633025) using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose different [technical details](http://ieye.xyz5080) and stats about GPT-4, such as the exact size of the design. [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 generate text, images and audio. [204] GPT-4o [attained modern](http://47.107.29.613000) results in voice, multilingual, and vision standards, setting new records in audio speech recognition and [translation](https://git.li-yo.ts.net). [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 sized version of GPT-4o changing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million [input tokens](https://viddertube.com) 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, start-ups and developers seeking to automate services with [AI](https://git.augustogunsch.com) 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 consider their reactions, leading to greater precision. These designs are particularly reliable in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211] |
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<br>o3<br> |
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<br>On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning design. OpenAI also [unveiled](https://pattondemos.com) o3-mini, a [lighter](https://www.eadvisor.it) and faster version of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, [security](https://adsall.net) and security researchers 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<br> |
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<br>Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform substantial web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120] |
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<br>Image classification<br> |
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<br>CLIP<br> |
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic resemblance between text and images. It can notably be used 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](https://git.toolhub.cc) in 2021, DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version 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 generate corresponding images. It can create images of reasonable objects ("a stained-glass window with a picture of a blue strawberry") along with objects 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 of the model with more realistic results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new simple 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 powerful model much better able to produce images from complex descriptions without manual timely engineering and render intricate details like hands and text. [221] It was released to the general 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 model that can produce videos based upon short detailed prompts [223] along with extend existing [videos forwards](https://privamaxsecurity.co.ke) or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br> |
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<br>Sora's development team named it after the Japanese word for "sky", to signify its "endless innovative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] [OpenAI trained](https://git.coalitionofinvisiblecolleges.org) the system utilizing publicly-available videos as well as copyrighted videos certified for that function, however did not expose the number or the precise sources of the videos. [223] |
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<br>OpenAI demonstrated some [Sora-created high-definition](http://47.103.91.16050903) videos to the public on February 15, 2024, stating that it might produce videos approximately one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the model's abilities. [225] It [acknowledged](http://154.9.255.1983000) a few of its drawbacks, consisting of battles mimicing complicated [physics](http://git.hongtusihai.com). [226] Will Douglas Heaven of the MIT Technology Review called the [presentation](http://18.178.52.993000) videos "excellent", however kept in mind that they should have been [cherry-picked](https://tribetok.com) and might not represent Sora's normal output. [225] |
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have shown [considerable](http://git.szmicode.com3000) interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's capability to produce realistic video from text descriptions, citing its prospective to transform storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had 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 recognition design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task design that can carry out multilingual speech acknowledgment 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 predict subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to begin fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web psychological 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](https://www.rotaryjobmarket.com) with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the tunes "reveal local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge stated "It's technically impressive, even if the results sound like mushy variations of songs that may feel familiar", while Business Insider [mentioned](https://www.homebasework.net) "remarkably, a few of the resulting songs are memorable 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 launched the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The function is to research whether such an approach may assist in auditing [AI](https://vidy.africa) decisions and in establishing explainable [AI](https://gitlab.kicon.fri.uniza.sk). [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 and nerve cell of 8 neural network designs which are often studied in interpretability. [240] Microscope was created to analyze the features that form inside these neural networks quickly. The models included are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241] |
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<br>ChatGPT<br> |
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<br>Launched in November 2022, ChatGPT is an artificial intelligence tool built on top of GPT-3 that offers a conversational [interface](https://www.telewolves.com) that enables users to ask concerns in natural language. The system then reacts with a response within seconds.<br> |
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