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 <br>Announced in 2016, Gym is an open-source Python library [developed](https://git.becks-web.de) to assist in the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://4kwavemedia.com) research study, making released research study more quickly reproducible [24] [144] while offering users with a simple user interface for engaging with these environments. In 2022, brand-new developments of Gym have actually been transferred to the . [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on computer game [147] utilizing RL [algorithms](http://git.zhongjie51.com) and study generalization. Prior RL research study focused mainly on enhancing agents to solve single tasks. Gym Retro offers the ability to generalize in between games with similar principles however different appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even stroll, however are given the goals of [learning](https://gitlab.digineers.nl) to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adjust to altering conditions. When an agent is then gotten rid of from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, recommending it had discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could develop an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competitors. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high skill level entirely through experimental algorithms. Before ending up being a team of 5, the very first public demonstration happened at The International 2017, the annual best champion competition for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of real time, and that the knowing software was an action in the instructions of creating software that can handle complicated tasks like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots find out over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives. [154] [155] [156]
 <br>By June 2018, the capability of the bots broadened to play together as a complete team of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world [champions](https://82.65.204.63) of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 total video games in a [four-day](https://209rocks.com) open online competition, winning 99.4% of those video games. [165]
 <br>OpenAI 5's systems in Dota 2's bot player reveals the difficulties of [AI](https://crossdark.net) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown using deep support learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses [machine finding](https://gitea.qi0527.com) out to train a Shadow Hand, a [human-like robot](http://e-kou.jp) hand, to control physical objects. [167] It discovers totally in [simulation](https://storymaps.nhmc.uoc.gr) using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by using domain randomization, a simulation approach which exposes the student to a variety of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cameras to allow the robot to manipulate an arbitrary object by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl could fix a [Rubik's Cube](http://wowonder.technologyvala.com). The robot was able 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 technique of generating progressively more difficult [environments](https://www.lakarjobbisverige.se). ADR varies from manual domain randomization by not requiring a human to specify randomization varieties. [169]
 <br>API<br>
 <br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://talentsplendor.com) designs established by OpenAI" to let developers contact it for "any English language [AI](http://bryggeriklubben.se) job". [170] [171]
 <br>Text generation<br>
 <br>The company has popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's initial GPT model ("GPT-1")<br>
 <br>The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and procedure long-range dependences by pre-training on a varied corpus with long stretches of adjoining 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 initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions initially launched to the public. The full variation of GPT-2 was not instantly launched due to concern about possible abuse, including applications for writing fake news. [174] Some specialists expressed uncertainty that GPT-2 posed a significant danger.<br>
 <br>In response to GPT-2, the Allen [Institute](https://squishmallowswiki.com) for Artificial Intelligence reacted with a tool to discover "neural phony news". [175] Other scientists, such as Jeremy Howard, [alerted](http://101.132.73.143000) of "the innovation 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 released the total variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors [argue unsupervised](https://harborhousejeju.kr) [language models](https://git.yuhong.com.cn) to be general-purpose learners, highlighted by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 [zero-shot jobs](https://xevgalex.ru) (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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both specific 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 not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as couple of as 125 million [specifications](http://git.pushecommerce.com) were also trained). [186]
 <br>OpenAI stated that GPT-3 succeeded 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 in between English and German. [184]
 <br>GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand  [surgiteams.com](https://surgiteams.com/index.php/User:ArcherOchoa9) petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released to the general public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month free personal beta that started in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was certified solely to Microsoft. [190] [191]
 <br>Codex<br>
 <br>Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://git.zhongjie51.com) 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 create working code in over a dozen programming languages, the majority of successfully in Python. [192]
 <br>Several problems with problems, style defects and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has actually been implicated of giving off copyrighted code, without any author attribution or license. [197]
 <br>OpenAI revealed that they would cease assistance for Codex API on March 23, 2023. [198]
 <br>GPT-4<br>
 <br>On March 14, 2023, OpenAI revealed the [release](https://www.medicalvideos.com) of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the upgraded technology 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 check out, examine or create approximately 25,000 words of text, and write code in all major shows languages. [200]
 <br>Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and data about GPT-4, such as the exact size of the design. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced 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](https://pl.velo.wiki) 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](https://kewesocial.site) $0.15 per million input tokens and $0.60 per million output tokens, [compared](https://acrohani-ta.com) to $5 and $15 respectively for GPT-4o. OpenAI expects it to be especially beneficial for business, [start-ups](http://43.139.10.643000) and [developers seeking](https://git.xantxo-coquillard.fr) to automate services with [AI](http://www.szkis.cn:13000) agents. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been developed to take more time to think about their actions, causing greater accuracy. These models are particularly efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and  [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile