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 <br>Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](http://git.bzgames.cn) research, making published research more easily reproducible [24] [144] while supplying users with an easy interface for interacting with these environments. In 2022, brand-new developments 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 reinforcement learning (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing representatives to resolve single tasks. Gym Retro provides the capability to generalize between video games with comparable concepts but various looks.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first do not have understanding of how to even walk, however are provided the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adapt to altering conditions. When an agent is then [removed](https://gogs.eldarsoft.com) from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor [Mordatch argued](https://evove.io) that competitors between agents could produce an intelligence "arms race" that might increase an agent's ability to work even outside the context of the competition. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five [video game](https://sjee.online) Dota 2, that find out to play against human gamers at a high ability level completely through experimental [algorithms](https://ka4nem.ru). Before becoming a group of 5, the first public presentation occurred at The International 2017, the annual premiere champion tournament for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of actual time, which the learning software was an action in the instructions of producing software application that can [handle complex](http://gungang.kr) jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of reinforcement knowing, as the bots discover over time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking [map goals](https://git.pyme.io). [154] [155] [156]
 <br>By June 2018, the ability of the bots expanded to play together as a complete group of 5, and they had the ability to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 [exhibition matches](http://47.92.159.28) against expert gamers, however 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](http://39.99.224.279022) match in San Francisco. [163] [164] The bots' last public appearance came later 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 systems in Dota 2's bot player shows the difficulties of [AI](https://git.ascarion.org) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep reinforcement knowing (DRL) representatives 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 manipulate physical items. [167] It learns completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by utilizing domain randomization, a simulation technique which exposes the  to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having [movement tracking](https://farmjobsuk.co.uk) cams, likewise has RGB cameras to permit the robot to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl could 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 [complex physics](https://elmerbits.com) that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating gradually more tough environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
 <br>API<br>
 <br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://www.jobseeker.my) models established by OpenAI" to let designers contact it for "any English language [AI](https://zurimeet.com) task". [170] [171]
 <br>Text generation<br>
 <br>The company has actually promoted generative pretrained transformers (GPT). [172]
 <br>OpenAI's original GPT design ("GPT-1")<br>
 <br>The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world knowledge and procedure long-range reliances by pre-training on a varied corpus with long [stretches](https://pakallnaukri.com) of contiguous text.<br>
 <br>GPT-2<br>
 <br>Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative variations at first released to the general public. The complete variation of GPT-2 was not instantly launched due to concern about potential abuse, including applications for composing phony news. [174] Some professionals expressed uncertainty that GPT-2 postured a substantial danger.<br>
 <br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural phony news". [175] Other scientists,  [disgaeawiki.info](https://disgaeawiki.info/index.php/User:KelleG0472) such as Jeremy Howard, warned 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 complete version of the GPT-2 language model. [177] Several sites host interactive presentations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors argue not being watched language models to be general-purpose learners, highlighted by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not additional trained on any task-specific input-output examples).<br>
 <br>The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both private 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 design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186]
 <br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" jobs and might 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 between English and German. [184]
 <br>GPT-3 [dramatically enhanced](http://120.48.7.2503000) benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or coming across the basic ability constraints of predictive language designs. [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 full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the general public for concerns of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month totally free private beta that began in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191]
 <br>Codex<br>
 <br>Announced in mid-2021, Codex is a descendant of GPT-3 that has actually additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://www.litehome.top) powering the [code autocompletion](http://39.98.153.2509080) tool GitHub Copilot. [193] In August 2021, an API was [released](https://www.schoenerechner.de) in personal beta. [194] According to OpenAI, the model can develop working code in over a dozen shows languages, most successfully in Python. [192]
 <br>Several issues with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has been implicated of releasing copyrighted code, with no author attribution or license. [197]
 <br>OpenAI announced that they would cease assistance 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), efficient in accepting text or image inputs. [199] They revealed that the upgraded technology passed a [simulated law](https://droidt99.com) 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 also check out, analyze or create as much as 25,000 words of text, and compose code in all significant programs languages. [200]
 <br>Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to expose different technical details and stats about GPT-4, such as the exact size of the design. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision standards, setting brand-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 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  [yewiki.org](https://www.yewiki.org/User:Nichol9618) $15 respectively for GPT-4o. OpenAI expects it to be particularly beneficial for business, startups and developers seeking to automate services with [AI](https://www.klartraum-wiki.de) agents. [208]
 <br>o1<br>
 <br>On September 12,  [garagesale.es](https://www.garagesale.es/author/marcyschwar/) 2024, OpenAI launched the o1-preview and o1-mini designs, which have been designed to take more time to consider their actions, leading to higher accuracy. These models are particularly reliable in science, coding, and thinking tasks, and were made available to [ChatGPT](https://flixtube.info) Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]
 <br>o3<br>
 <br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking design. OpenAI likewise revealed o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this model is not available for  [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:FannyMaki7) public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to avoid confusion with [telecommunications services](https://git.kitgxrl.gay) supplier O2. [215]
 <br>Deep research study<br>
 <br>Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out comprehensive web surfing, information analysis, and synthesis, providing detailed reports within a [timeframe](https://accc.rcec.sinica.edu.tw) of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
 <br>Image category<br>
 <br>CLIP<br>
 <br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic similarity between text and images. It can significantly be used for image category. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>[Revealed](https://redefineworksllc.com) 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 handbag formed like a pentagon" or "an isometric view of a sad capybara") and [produce matching](https://www.genbecle.com) images. It can develop images of realistic things ("a stained-glass window with a picture of a blue strawberry") as well as items 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>
 <br>DALL-E 2<br>
 <br>In April 2022, OpenAI revealed DALL-E 2, an updated variation of the model with more practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new simple system for converting a text description into a 3-dimensional model. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI revealed DALL-E 3, a more [powerful design](https://complete-jobs.co.uk) better able to produce images from complex descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the general public as a [ChatGPT](https://my.buzztv.co.za) Plus function in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video design that can generate videos based upon brief detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with [resolution](http://101.34.39.123000) up to 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.<br>
 <br>Sora's advancement team named it after the Japanese word for "sky", to signify its "limitless imaginative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that purpose, however did not expose the number or  [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:MiquelAer064) the exact sources of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it might create videos as much as one minute long. It also shared a technical report highlighting the methods used to train the model, and the model's abilities. [225] It [acknowledged](http://39.105.203.1873000) some of its drawbacks,  [raovatonline.org](https://raovatonline.org/author/angelicadre/) including struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable",  [wiki.snooze-hotelsoftware.de](https://wiki.snooze-hotelsoftware.de/index.php?title=Benutzer:ClaireNovak) however noted that they should have been cherry-picked and might not represent Sora's normal output. [225]
 <br>Despite uncertainty from some academic leaders following [Sora's public](https://szmfettq2idi.com) demo, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to produce sensible video from text descriptions, citing its potential to [revolutionize storytelling](http://114.115.138.988900) and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause strategies for expanding his [Atlanta-based film](http://60.204.229.15120080) studio. [227]
 <br>Speech-to-text<br>
 <br>Whisper<br>
 <br>Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is [trained](https://gl.cooperatic.fr) on a big dataset of diverse audio and is also a multi-task design that can carry out multilingual speech recognition along with 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 musical notes in MIDI music files. It can produce songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start fairly however then fall under turmoil the longer it plays. [230] [231] In pop 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]
 <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 category, artist, and a bit of lyrics and outputs tune [samples](https://trabajosmexico.online). OpenAI specified the songs "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" and that "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's highly remarkable, even if the outcomes seem like mushy variations of songs that may feel familiar", while Business Insider specified "remarkably, some of the resulting songs are appealing and sound legitimate". [234] [235] [236]
 <br>User user interfaces<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI launched 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 may assist in [auditing](https://gitea.gm56.ru) [AI](https://www.ksqa-contest.kr) choices and in establishing explainable [AI](https://kryza.network). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network models which are frequently studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various variations of Inception, and various versions of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, [ChatGPT](https://gogocambo.com) is a synthetic intelligence tool constructed on top of GPT-3 that provides a conversational user interface that allows users to ask questions in natural language. The system then reacts with a response within seconds.<br>