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<br>Announced in 2016, Gym is an open-source Python library created to help with the development of support learning algorithms. It aimed to standardize how environments are defined in [AI](https://2flab.com) research, making published research more easily reproducible [24] [144] while offering users with a basic interface for connecting 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 is a platform for support learning (RL) research on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on enhancing representatives to fix single jobs. Gym Retro gives the ability to generalize between video games with comparable ideas 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 robotic representatives initially do not have understanding of how to even stroll, but are given the goals of learning to move and to press the [opposing agent](http://doc.folib.com3000) out of the ring. [148] Through this adversarial knowing procedure, the agents learn how to adjust to altering conditions. When an agent is then gotten rid of from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could develop an intelligence "arms race" that could increase a representative's ability to operate 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 team of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that find out to play against human players at a high skill level totally through trial-and-error algorithms. Before ending up being a team of 5, the first public demonstration took place at The International 2017, the annual premiere champion competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of genuine time, and that the knowing software [application](https://www.nenboy.com29283) was an action in the instructions of producing software that can manage complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the bots learn in time by playing against themselves numerous times a day for months, and are rewarded for [actions](http://47.108.78.21828999) such as killing an enemy and taking map objectives. [154] [155] [156] |
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<br>By June 2018, the capability of the bots broadened to play together as a complete team of 5, and they were able to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five [defeated](https://gitea.adminakademia.pl) OG, the reigning world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 overall games in a four-day open online competitors, 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](https://workmate.club) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown using deep reinforcement 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 utilizes maker discovering to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It [discovers](http://dibodating.com) completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by utilizing domain randomization, a [simulation](https://git.profect.de) approach which exposes the learner to a variety of experiences instead of [attempting](https://media.motorsync.co.uk) to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB cameras to allow the robotic to control an approximate item 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 might solve a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing progressively more difficult environments. ADR differs from manual domain randomization by not requiring a human to define randomization [varieties](https://papersoc.com). [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://scholarpool.com) designs developed by OpenAI" to let designers call on it for "any English language [AI](https://plane3t.soka.ac.jp) job". [170] [171] |
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<br>Text generation<br> |
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<br>The company has actually promoted 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 written by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative design of language might obtain world knowledge and procedure long-range [reliances](https://aiviu.app) 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 initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just [limited demonstrative](https://gitea.dgov.io) versions at first released to the general public. The complete variation of GPT-2 was not instantly released due to concern about prospective abuse, including applications for composing fake news. [174] Some specialists revealed uncertainty that GPT-2 posed a substantial hazard.<br> |
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<br>In action to GPT-2, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:LeiaBuckley2869) the Allen Institute for Artificial Intelligence responded with a tool to identify "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other . [178] [179] [180] |
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<br>GPT-2's authors argue without supervision language designs to be [general-purpose](https://workmate.club) learners, shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain [concerns encoding](http://211.159.154.983000) vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both private characters and multiple-character tokens. [181] |
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<br>GPT-3<br> |
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186] |
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<br>OpenAI stated 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 gave examples of translation and cross-linguistic transfer knowing between [English](https://realmadridperipheral.com) and Romanian, and between English and German. [184] |
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<br>GPT-3 considerably improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or coming across the fundamental capability constraints 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 complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a [two-month free](https://hayhat.net) personal beta that began in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was licensed specifically to Microsoft. [190] [191] |
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<br>Codex<br> |
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<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has actually additionally been [trained](https://partyandeventjobs.com) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://oeclub.org) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the design can develop working code in over a dozen programs languages, most effectively in Python. [192] |
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<br>Several issues with glitches, design defects 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 discontinue support for Codex API on March 23, 2023. [198] |
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<br>GPT-4<br> |
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<br>On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the [upgraded innovation](https://git.profect.de) passed a simulated law school bar examination with a rating around the leading 10% of [test takers](https://dsspace.co.kr). (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also check out, examine or produce approximately 25,000 words of text, and compose code in all major programs languages. [200] |
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<br>Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is also [capable](https://gigsonline.co.za) of taking images as input on ChatGPT. [202] OpenAI has declined to reveal various technical details and data 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](https://matchpet.es) GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision criteria, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207] |
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<br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller version of GPT-4o changing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be especially useful for business, startups and developers seeking to automate services with [AI](https://tintinger.org) representatives. [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 been designed to take more time to think of their responses, causing higher precision. These designs are particularly reliable in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [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 revealed o3, the successor of the o1 reasoning model. OpenAI likewise revealed o3-mini, a lighter and faster version of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications services company O2. [215] |
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<br>Deep research study<br> |
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<br>Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It [leverages](https://git.andert.me) the capabilities of OpenAI's o3 design to [perform substantial](https://gitlab-mirror.scale.sc) web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120] |
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<br>Image category<br> |
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<br>CLIP<br> |
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<br>[Revealed](http://106.52.242.1773000) in 2021, CLIP ([Contrastive Language-Image](http://kuma.wisilicon.com4000) Pre-training) is a model that is trained to evaluate the semantic similarity 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 design that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can develop pictures of realistic objects ("a stained-glass window with a picture of a blue strawberry") along with things 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 revealed DALL-E 2, an upgraded variation of the model with more realistic results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new fundamental system for converting a text description into a 3[-dimensional design](https://www.celest-interim.fr). [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 generate images from complex descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released to the public as a ChatGPT Plus function in October. [222] |
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<br>Text-to-video<br> |
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<br>Sora<br> |
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<br>Sora is a text-to-video design that can produce videos based on short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of created videos is unknown.<br> |
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<br>Sora's advancement group named it after the Japanese word for "sky", to signify its "limitless imaginative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos certified for that purpose, however did not reveal the number or the precise sources of the videos. [223] |
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<br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could create videos approximately one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the model's capabilities. [225] It acknowledged a few of its shortcomings, consisting of struggles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but kept in mind that they need to have been cherry-picked and might not represent Sora's common output. [225] |
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, [notable entertainment-industry](http://git.hongtusihai.com) figures have revealed considerable interest in the innovation's capacity. In an interview, actor/[filmmaker Tyler](https://vieclam.tuoitrethaibinh.vn) Perry expressed his astonishment at the technology's ability to generate practical video from text descriptions, mentioning its possible to [revolutionize storytelling](https://mediascatter.com) and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly prepare for broadening his Atlanta-based motion picture 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](https://63game.top) model. [228] It is trained on a large dataset of varied audio and is also a multi-task model that can carry out multilingual speech recognition along with 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](https://partyandeventjobs.com) net trained to predict subsequent musical notes in MIDI music files. It can create 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 pop culture, initial applications of this tool were used 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](https://gitlab.rails365.net) of lyrics and outputs song samples. OpenAI stated the tunes "show local musical coherence [and] follow conventional chord patterns" but [acknowledged](https://gigsonline.co.za) that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable space" in between Jukebox and human-generated music. The Verge stated "It's technologically outstanding, even if the results sound like mushy variations of songs that may feel familiar", while Business Insider stated "surprisingly, some of the resulting songs 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 released the Debate Game, which teaches machines to debate toy problems in front of a human judge. The function is to research whether such an approach may assist in auditing [AI](http://wrgitlab.org) choices and in developing explainable [AI](http://travelandfood.ru). [237] [238] |
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<br>Microscope<br> |
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<br>[Released](https://www.facetwig.com) in 2020, Microscope [239] is a [collection](https://tempjobsindia.in) of visualizations of every substantial layer and nerve cell of eight neural network designs which are often studied in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks quickly. The designs included 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 developed on top of GPT-3 that offers a conversational interface that permits users to ask questions in natural language. The system then reacts with an answer within seconds.<br> |
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