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 <br>Announced in 2016, Gym is an open-source Python library created to assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](https://git.rootfinlay.co.uk) research, making released research more quickly reproducible [24] [144] while offering users with a basic interface for communicating with these environments. In 2022, new [advancements](http://120.77.213.1393389) of Gym have been moved to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for support knowing (RL) research study on video games [147] using [RL algorithms](https://gitea.winet.space) and study generalization. Prior RL research study focused mainly on enhancing agents to fix single jobs. Gym Retro provides the ability to generalize between video games with [comparable](http://git.yoho.cn) principles but different looks.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, [RoboSumo](https://tiktack.socialkhaleel.com) is a virtual world where humanoid metalearning robotic agents initially lack knowledge of how to even stroll, however are offered the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the [representatives](https://www.joinyfy.com) learn how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents might create an intelligence "arms race" that might increase an agent's ability to operate even outside the context of the competition. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that discover to play against human players at a high ability level completely through experimental algorithms. Before ending up being a team of 5, the very first public demonstration occurred at The International 2017, the annual premiere championship competition for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually 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, which the knowing software application was an action in the direction of creating software application that can [manage complicated](http://git.365zuoye.com) jobs like a cosmetic surgeon. [152] [153] The system utilizes a form of support knowing, as the bots discover with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
 <br>By June 2018,  [it-viking.ch](http://it-viking.ch/index.php/User:VilmaVann66544) the ability 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 players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world [champions](https://soundfy.ebamix.com.br) 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 that month, where they played in 42,729 overall games in a [four-day](https://www.olindeo.net) open online competition, winning 99.4% of those games. [165]
 <br>OpenAI 5's mechanisms in Dota 2's bot player shows the obstacles of [AI](https://forum.freeadvice.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep support [knowing](https://gitea.thisbot.ru) (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses machine learning to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It discovers completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by using domain randomization, a simulation approach which exposes the learner to a variety of [experiences](https://cristianoronaldoclub.com) instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cams to permit the robotic to [control](http://47.244.181.255) an arbitrary things by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by improving the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of generating gradually more hard [environments](https://basedwa.re). ADR varies from manual domain randomization by not requiring a human to [define randomization](https://thecodelab.online) varieties. [169]
 <br>API<br>
 <br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://git.visualartists.ru) designs developed by OpenAI" to let designers call on it for "any English language [AI](http://g-friend.co.kr) job". [170] [171]
 <br>Text generation<br>
 <br>The company has actually [promoted generative](https://asg-pluss.com) pretrained transformers (GPT). [172]
 <br>OpenAI's initial GPT design ("GPT-1")<br>
 <br>The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language could obtain world [understanding](https://cristianoronaldoclub.com) and procedure long-range dependencies by pre-training on a varied corpus with long stretches of contiguous text.<br>
 <br>GPT-2<br>
 <br>Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations at first launched to the general public. The complete variation of GPT-2 was not instantly released due to concern about potential misuse, consisting of applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 posed a [substantial threat](http://1.14.71.1033000).<br>
 <br>In response to GPT-2, the Allen Institute for Artificial Intelligence [reacted](http://107.182.30.1906000) with a tool to detect "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language design. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further trained on any task-specific input-output examples).<br>
 <br>The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows [representing](https://mypetdoll.co.kr) any string of characters by encoding both specific characters and multiple-character tokens. [181]
 <br>GPT-3<br>
 <br>First  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 full variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were likewise trained). [186]
 <br>OpenAI mentioned that GPT-3 was [successful](https://kewesocial.site) at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
 <br>GPT-3 considerably enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the basic capability constraints of predictive language models. [187] Pre-training GPT-3 required numerous 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 right away released to the public for issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month free private beta that started in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191]
 <br>Codex<br>
 <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](http://git.ndjsxh.cn:10080) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the design can produce working code in over a lots programs languages, most effectively in Python. [192]
 <br>Several problems with glitches, style flaws and security vulnerabilities were cited. [195] [196]
 <br>GitHub Copilot has been implicated of discharging copyrighted code, with no author attribution or license. [197]
 <br>OpenAI revealed that they would cease support for Codex API on March 23, 2023. [198]
 <br>GPT-4<br>
 <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 technology passed a simulated law school bar examination 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 might likewise check out, evaluate or generate approximately 25,000 words of text, and compose code in all major shows languages. [200]
 <br>Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal different technical details and stats about GPT-4, such as the exact size of the model. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision criteria, setting new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207]
 <br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be especially beneficial for enterprises, startups and developers seeking to automate services with [AI](https://git.camus.cat) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been created to take more time to think of their actions, causing higher precision. These [designs](http://112.124.19.388080) are especially reliable in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
 <br>o3<br>
 <br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 [reasoning design](http://git.moneo.lv). OpenAI likewise revealed o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are [testing](https://www.social.united-tuesday.org) o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with [telecoms companies](https://openedu.com) O2. [215]
 <br>Deep research<br>
 <br>Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out extensive web surfing, data 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 a precision of 26.6 percent on HLE (Humanity's Last Exam) [benchmark](http://test-www.writebug.com3000). [120]
 <br>Image category<br>
 <br>CLIP<br>
 <br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic similarity in between text and images. It can significantly be utilized for image category. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>Revealed in 2021, DALL-E is a Transformer model that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can produce pictures of realistic objects ("a stained-glass window with an image of a blue strawberry") as well as things that do not exist in reality ("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 variation of the model with more practical results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new fundamental system for transforming 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 design much better able to create images from intricate 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 feature in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video model that can create videos based on brief detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.<br>
 <br>Sora's advancement group called it after the Japanese word for "sky", to signify its "limitless imaginative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that purpose, however did not reveal the number or the precise sources of the videos. [223]
 <br>[OpenAI demonstrated](http://wiki-tb-service.com) some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could generate videos approximately one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged a few of its imperfections, including struggles simulating [intricate physics](https://git.jerrita.cn). [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but noted that they must have been cherry-picked and might not [represent Sora's](https://redsocial.cl) typical output. [225]
 <br>Despite uncertainty from some academic leaders following Sora's public demonstration, notable entertainment-industry figures have revealed substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to generate realistic video from text descriptions, mentioning its possible to revolutionize storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly prepare for broadening his Atlanta-based motion picture studio. [227]
 <br>Speech-to-text<br>
 <br>Whisper<br>
 <br>Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task design that can carry out multilingual speech [recognition](https://scode.unisza.edu.my) as well as speech translation and language recognition. [229]
 <br>Music generation<br>
 <br>MuseNet<br>
 <br>Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were [utilized](http://damoa8949.com) as early as 2020 for the internet mental thriller Ben Drowned to produce music for the titular character. [232] [233]
 <br>Jukebox<br>
 <br>Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "show local musical coherence [and] follow traditional chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider specified "remarkably, some of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI introduced the Debate Game, which teaches makers to debate toy issues in front of a human judge. The purpose is to research whether such a method may assist in auditing [AI](https://www.ksqa-contest.kr) [decisions](https://freelyhelp.com) and in developing explainable [AI](http://120.46.37.243:3000). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, different versions of Inception, and various versions 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 supplies a conversational user [interface](https://social.stssconstruction.com) that allows users to ask concerns in natural language. The system then responds with a response within seconds.<br>