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 <br>Announced in 2016, Gym is an open-source Python library developed to facilitate the development of reinforcement learning [algorithms](https://gitstud.cunbm.utcluj.ro). It aimed to standardize how environments are specified in [AI](http://35.207.205.18:3000) research study, making published research more easily reproducible [24] [144] while providing users with a basic user interface for interacting with these environments. In 2022, new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for support learning (RL) research study on computer game [147] using RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to resolve single tasks. Gym Retro provides the capability to generalize between games with similar ideas however various looks.<br>
 <br>RoboSumo<br>
 <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 finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the [representatives](https://onsanmo.co.kr) learn how to adapt to changing conditions. When a representative is then removed 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 discovered how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between [representatives](https://manchesterunitedfansclub.com) might create an intelligence "arms race" that could increase an agent's capability to work even outside the context of the [competition](http://39.98.84.2323000). [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high skill level totally through trial-and-error algorithms. Before becoming a group of 5, the very first public demonstration happened at The International 2017, the annual premiere championship tournament for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by [playing](https://git.newpattern.net) against itself for two weeks of actual time, which the knowing software was an action in the direction of creating software that can handle complex jobs like a cosmetic surgeon. [152] [153] The system uses a form of support learning, as the [bots learn](https://akinsemployment.ca) gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
 <br>By June 2018, the capability of the bots broadened to play together as a full team of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final 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]
 <br>OpenAI 5's mechanisms in Dota 2's bot player reveals the obstacles of [AI](https://git.touhou.dev) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown making use of deep support knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018,  machine learning to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It finds out completely in simulation utilizing the same [RL algorithms](http://president-park.co.kr) and training code as OpenAI Five. OpenAI tackled the things orientation problem by utilizing domain randomization, a simulation technique which [exposes](https://robbarnettmedia.com) the learner to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB video cameras to enable the robotic to control an approximate item by seeing it. In 2018, OpenAI revealed that the system had the ability to [manipulate](https://nexthub.live) a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present [intricate physics](http://gitlab.flyingmonkey.cn8929) that is harder to design. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing gradually more tough environments. ADR differs from manual domain randomization by not needing a human to [define randomization](https://knightcomputers.biz) varieties. [169]
 <br>API<br>
 <br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](http://git.gupaoedu.cn) designs developed by OpenAI" to let developers get in touch with it for "any English language [AI](http://1138845-ck16698.tw1.ru) task". [170] [171]
 <br>Text generation<br>
 <br>The business has actually promoted generative pretrained transformers (GPT). [172]
 <br>OpenAI's initial GPT model ("GPT-1")<br>
 <br>The original 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 demonstrated how a generative design of language might obtain world knowledge and process long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.<br>
 <br>GPT-2<br>
 <br>Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations at first launched to the public. The full variation of GPT-2 was not immediately launched due to concern about potential abuse, including applications for writing phony news. [174] Some professionals revealed uncertainty that GPT-2 positioned a substantial threat.<br>
 <br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural phony news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language model. [177] Several websites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors argue without supervision language designs to be general-purpose learners, illustrated by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more trained on any task-specific input-output examples).<br>
 <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 prevents certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both individual characters and multiple-character tokens. [181]
 <br>GPT-3<br>
 <br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186]
 <br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
 <br>GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the essential ability constraints of predictive language [designs](https://wiki.openwater.health). [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the public for concerns of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary personal beta that started in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was certified exclusively to Microsoft. [190] [191]
 <br>Codex<br>
 <br>Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://042.ne.jp) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the design can produce working code in over a dozen shows languages,  [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:LoisHuntley) most [efficiently](https://quickdatescript.com) in Python. [192]
 <br>Several concerns with glitches, style defects and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has been accused of producing copyrighted code, without any author attribution or license. [197]
 <br>OpenAI revealed that they would stop support for  [disgaeawiki.info](https://disgaeawiki.info/index.php/User:RoxieDeen071) Codex API on March 23, 2023. [198]
 <br>GPT-4<br>
 <br>On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They 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 might likewise read, evaluate or create up to 25,000 words of text, and write code in all significant programming languages. [200]
 <br>Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the issues with earlier [revisions](https://pattonlabs.com). [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to expose different technical details and data about GPT-4, such as the [exact size](http://f225785a.80.robot.bwbot.org) of the model. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision benchmarks, 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]
 <br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized version of GPT-4o replacing 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 beneficial for enterprises, startups and designers looking for to automate services with [AI](http://supervipshop.net) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been created to take more time to consider their responses, causing greater precision. These designs are particularly reliable in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
 <br>o3<br>
 <br>On December 20, 2024, OpenAI unveiled o3, the successor of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and faster variation of OpenAI o3. Since 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, safety and security scientists had the opportunity to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecoms providers O2. [215]
 <br>Deep research<br>
 <br>Deep research study is a representative established by OpenAI, [revealed](http://1024kt.com3000) on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web surfing, data analysis, and  [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
 <br>Image category<br>
 <br>CLIP<br>
 <br>[Revealed](https://sportify.brandnitions.com) in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic resemblance in between text and images. It can especially be used for image classification. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>Revealed in 2021, DALL-E is a Transformer design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce pictures of realistic things ("a stained-glass window with an image of a blue strawberry") in addition to items that do not exist in [reality](http://221.131.119.210030) ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.<br>
 <br>DALL-E 2<br>
 <br>In April 2022, OpenAI revealed DALL-E 2, an [upgraded](http://git.bzgames.cn) version of the design with more realistic results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new basic system for converting a text description into a 3-dimensional design. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI revealed DALL-E 3, a more effective model better able to create images from complex descriptions without manual [timely engineering](https://spotlessmusic.com) and render complex details like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video model that can generate videos based on brief detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.<br>
 <br>[Sora's development](https://fishtanklive.wiki) group named it after the Japanese word for "sky", to signify its "limitless creative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 [text-to-image design](https://genzkenya.co.ke). [225] OpenAI trained the system [utilizing publicly-available](http://artpia.net) videos along with copyrighted videos [certified](https://ckzink.com) for that function, but did not reveal the number or the exact sources of the videos. [223]
 <br>OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might generate videos up to one minute long. It likewise shared a technical report highlighting the methods used to train the model, and the design's abilities. [225] It [acknowledged](https://gurjar.app) some of its imperfections, including battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", however noted that they must have been cherry-picked and may not represent Sora's common output. [225]
 <br>Despite uncertainty from some [academic leaders](https://www.almanacar.com) following Sora's public demo, notable entertainment-industry figures have actually shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to produce practical video from text descriptions, mentioning its [potential](http://gitlab.nsenz.com) to reinvent storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had decided to pause prepare for broadening his Atlanta-based film studio. [227]
 <br>Speech-to-text<br>
 <br>Whisper<br>
 <br>[Released](http://47.107.153.1118081) in 2022, Whisper is a general-purpose speech recognition design. [228] It is [trained](http://gitlab.flyingmonkey.cn8929) on a big dataset of diverse audio and is likewise a multi-task design that can carry out multilingual speech [recognition](https://git.rungyun.cn) in addition to speech translation and language identification. [229]
 <br>Music generation<br>
 <br>MuseNet<br>
 <br>Released in 2019, MuseNet is a deep neural net trained to [anticipate subsequent](http://wrgitlab.org) musical notes in MIDI music files. It can produce songs with 10 [instruments](http://42.192.69.22813000) in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly but then fall into mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to create music for the titular character. [232] [233]
 <br>Jukebox<br>
 <br>Released in 2020, Jukebox is an open-sourced algorithm to [generate](http://218.17.2.1033000) music with vocals. After training on 1.2 million samples, the system [accepts](https://www.talentsure.co.uk) a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the tunes "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable gap" in between Jukebox and human-generated music. The Verge specified "It's technologically remarkable, even if the results sound like mushy versions of tunes that may feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting songs are catchy and sound genuine". [234] [235] [236]
 <br>User interfaces<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI released the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research whether such an approach might help in auditing [AI](http://www.haimimedia.cn:3001) choices and in [developing explainable](https://placementug.com) [AI](https://git.muehlberg.net). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and [nerve cell](https://lifestagescs.com) of 8 neural network designs which are typically studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that provides a conversational interface that allows users to ask concerns in natural language. The system then reacts with an answer within seconds.<br>