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 <br>Announced in 2016, Gym is an [open-source Python](https://www.telewolves.com) library developed to help with the advancement of support learning algorithms. It aimed to standardize how environments are specified in [AI](https://git.whitedwarf.me) research study, making published research study more easily reproducible [24] [144] while providing users with an easy interface for interacting with these environments. In 2022,  [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:DomingaEspinoza) new advancements of Gym have actually been moved to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to solve single jobs. Gym Retro provides the capability to generalize between games with comparable ideas however various appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, [RoboSumo](http://sehwaapparel.co.kr) is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even walk, however are provided the [objectives](http://gitpfg.pinfangw.com) of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives find out how to adapt to changing conditions. When a representative is then removed from this [virtual environment](http://yanghaoran.space6003) and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor  [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:FidelPurser3) Mordatch argued that competitors between agents might [produce](http://115.236.37.10530011) an intelligence "arms race" that could 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 five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high skill level completely through trial-and-error algorithms. Before ending up being a team of 5, the first [public presentation](https://gitlab.vp-yun.com) happened at The International 2017, the yearly best champion competition for the game, where Dendi, a [professional](https://newvideos.com) Ukrainian player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of [genuine](https://youtubegratis.com) time, and that the learning software was an action in the direction of producing software that can manage complicated jobs like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots find out gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and  [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:KeithSpina077) taking map goals. [154] [155] [156]
 <br>By June 2018, the capability of the bots expanded to play together as a complete team of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG,  [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2768920) the ruling world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 total video games in a four-day open online competitors, winning 99.4% of those video games. [165]
 <br>OpenAI 5's mechanisms in Dota 2's bot player shows the obstacles of [AI](http://shenjj.xyz:3000) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown making use of deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It learns completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by utilizing domain randomization, a simulation method which exposes the [learner](https://gamberonmusic.com) to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB [cameras](https://git.bbh.org.in) to permit the robotic to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an [octagonal prism](https://tnrecruit.com). [168]
 <br>In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robot had the [ability](http://wdz.imix7.com13131) to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating gradually more difficult environments. ADR varies from manual domain randomization by not requiring a human to define randomization varieties. [169]
 <br>API<br>
 <br>In June 2020, OpenAI announced a [multi-purpose API](https://git.magicvoidpointers.com) which it said was "for accessing brand-new [AI](https://cruyffinstitutecareers.com) designs established by OpenAI" to let [developers](https://www.virsocial.com) get in touch with it for "any English language [AI](http://git.permaviat.ru) task". [170] [171]
 <br>Text generation<br>
 <br>The company has promoted generative pretrained transformers (GPT). [172]
 <br>OpenAI's initial GPT model ("GPT-1")<br>
 <br>The [initial paper](https://ruofei.vip) 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 website on June 11, 2018. [173] It revealed how a generative model of language could obtain world knowledge and procedure long-range reliances by [pre-training](http://charge-gateway.com) on a diverse corpus with long stretches of contiguous text.<br>
 <br>GPT-2<br>
 <br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language model and the [follower](https://likemochi.com) to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions at first released to the public. The complete version of GPT-2 was not immediately launched due to concern about prospective misuse, including applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 [positioned](https://tageeapp.com) a considerable risk.<br>
 <br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other researchers, 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 drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 [language design](https://zomi.watch). [177] Several websites host interactive presentations of different circumstances 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 students, shown by GPT-2 attaining modern precision and  [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:RusselEdler299) perplexity on 7 of 8 zero-shot tasks (i.e. the design was not further trained on any task-specific input-output examples).<br>
 <br>The corpus it was trained on,  [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:CodyKane8892) called WebText, contains slightly 40 [gigabytes](http://gitlab.unissoft-grp.com9880) of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids 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 an unsupervised transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million criteria were likewise trained). [186]
 <br>OpenAI specified that GPT-3 prospered at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
 <br>GPT-3 dramatically enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or coming across the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed several 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 instantly released to the general public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month totally free personal beta that began in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was [licensed exclusively](http://8.129.8.58) 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](https://sea-crew.ru) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in [private](https://mediawiki.hcah.in) beta. [194] According to OpenAI, the model can produce working code in over a dozen programs languages, a lot of effectively in Python. [192]
 <br>Several problems with glitches, design flaws and security vulnerabilities were cited. [195] [196]
 <br>GitHub Copilot has been accused of releasing copyrighted code, with no author attribution or license. [197]
 <br>OpenAI revealed that they would discontinue assistance for [Codex API](http://62.234.217.1373000) 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), efficient in accepting text or image inputs. [199] They announced that the upgraded innovation 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 up to 25,000 words of text, and write code in all major programs languages. [200]
 <br>Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose various technical details and stats about GPT-4, such as the [precise size](https://vieclamangiang.net) 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 cutting](https://www.viewtubs.com) edge 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]
 <br>On July 18, 2024, OpenAI released 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 anticipates it to be especially useful for business, start-ups and developers looking for to automate services with [AI](https://www.xtrareal.tv) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been designed to take more time to think about their reactions, causing greater accuracy. These models are especially efficient in science, coding, and reasoning jobs, 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 thinking design. OpenAI likewise [unveiled](https://edtech.wiki) o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are [evaluating](https://e-sungwoo.co.kr) o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]
 <br>Deep research<br>
 <br>Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
 <br>Image classification<br>
 <br>CLIP<br>
 <br>Revealed 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 notably be used for image classification. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>Revealed in 2021, DALL-E is a Transformer model 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 bag shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can create pictures of practical things ("a stained-glass window with an image of a blue strawberry") along with 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 version of the model with more practical results. [219] In December 2022,  [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:TracieCoats00) OpenAI released on GitHub software application for Point-E, a new simple system for converting a text description into a 3-dimensional design. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI announced DALL-E 3, a more effective model much better able to create 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 Plus [feature](https://www.jccer.com2223) in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video design that can produce videos based on brief [detailed prompts](https://geniusactionblueprint.com) [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 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 represent its "endless creative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 [text-to-image](https://git.coalitionofinvisiblecolleges.org) model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos certified for that purpose, but did not expose the number or the [specific sources](https://jobs.fabumama.com) of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might generate videos approximately one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, including struggles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but kept in mind that they must have been cherry-picked and may not represent Sora's normal output. [225]
 <br>Despite uncertainty from some scholastic leaders following Sora's public demo, [notable entertainment-industry](https://careerworksource.org) figures have [revealed](https://phoebe.roshka.com) significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's capability to create [practical](http://185.254.95.2413000) video from text descriptions, mentioning its prospective to reinvent storytelling and content production. He said that his enjoyment about [Sora's possibilities](http://files.mfactory.org) was so strong that he had chosen to stop briefly plans for expanding his Atlanta-based film studio. [227]
 <br>Speech-to-text<br>
 <br>Whisper<br>
 <br>Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition in addition to 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 [anticipate subsequent](http://47.108.78.21828999) musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to start fairly however then fall into mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were used 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 create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs song samples. OpenAI mentioned the songs "show regional musical coherence [and] follow conventional chord patterns" however [acknowledged](http://www.chinajobbox.com) that the songs do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a significant space" between Jukebox and human-generated music. The Verge mentioned "It's highly excellent, even if the results sound like mushy versions of tunes that might feel familiar", while Business Insider stated "remarkably, some of the resulting songs are memorable and sound legitimate". [234] [235] [236]
 <br>User interfaces<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI launched the Debate Game, which teaches devices to discuss toy problems in front of a human judge. The purpose is to research whether such a technique may assist in auditing [AI](http://175.178.113.220:3000) decisions and in developing explainable [AI](https://salesupprocess.it). [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 models which are often studied in interpretability. [240] Microscope was  to examine the features that form inside these neural networks easily. The designs 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 expert system tool developed on top of GPT-3 that provides a conversational interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.<br>