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 <br>Announced in 2016, Gym is an open-source Python library designed to help with the advancement of reinforcement knowing algorithms. It aimed to [standardize](https://droidt99.com) how environments are defined in [AI](https://www.app.telegraphyx.ru) research, making released research more quickly reproducible [24] [144] while offering users with a basic user interface for connecting with these environments. In 2022, brand-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 knowing (RL) research on computer game [147] using RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to fix single jobs. Gym Retro provides the ability to generalize in between video games with comparable principles but different looks.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack understanding of how to even walk, however are given the objectives of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives find out how to adjust 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 agent braces to remain upright, [recommending](https://git.logicp.ca) it had actually learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could create an intelligence "arms race" that could increase an agent's ability to function even outside the context of the competitors. [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 learn to play against human gamers at a high skill level totally through experimental algorithms. Before ending up being a group of 5, the very first public presentation occurred at The International 2017, the annual best champion competition for the game, where Dendi, an expert 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.juxiong.net) against itself for 2 weeks of real time, and that the learning software application was an action in the instructions of producing software that can manage intricate jobs like a cosmetic surgeon. [152] [153] The system uses a type of support knowing, as the bots find out gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map objectives. [154] [155] [156]
 <br>By June 2018, the ability of the bots expanded to play together as a full team of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those video games. [165]
 <br>OpenAI 5's systems in Dota 2's bot player shows the obstacles of [AI](https://digital-field.cn:50443) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep support knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>[Developed](https://mulkinflux.com) in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It learns completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB cams to permit the robot to control an approximate things by seeing it. In 2018, OpenAI revealed that the system had the ability to [control](http://114.115.138.988900) a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI showed that Dactyl could resolve a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by [enhancing](https://code.smolnet.org) the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR),  [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:MXJDwight00410) a simulation method of generating gradually more hard environments. ADR varies from manual domain randomization by not requiring a human to define randomization ranges. [169]
 <br>API<br>
 <br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](https://demo.wowonderstudio.com) designs developed by OpenAI" to let designers call on it for "any English language [AI](https://qademo2.stockholmitacademy.org) task". [170] [171]
 <br>Text generation<br>
 <br>The business has popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's original GPT model ("GPT-1")<br>
 <br>The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world understanding and procedure long-range reliances by pre-training on a diverse corpus with long stretches of adjoining text.<br>
 <br>GPT-2<br>
 <br>[Generative](https://gogs.dev.dazesoft.cn) Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the follower to [OpenAI's original](https://www.ieo-worktravel.com) GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions initially launched to the public. The complete version of GPT-2 was not instantly launched due to concern about prospective misuse, consisting of applications for composing phony news. [174] Some experts expressed uncertainty that GPT-2 presented a substantial risk.<br>
 <br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total version 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 not being watched language models to be general-purpose learners, shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 [zero-shot tasks](http://makerjia.cn3000) (i.e. the design was not more trained on any task-specific input-output examples).<br>
 <br>The corpus it was trained on, called WebText, contains slightly 40  of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This [permits representing](https://www.top5stockbroker.com) any string of characters by encoding both [specific characters](https://gogs.dev.dazesoft.cn) 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 follower to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full [variation](http://sopoong.whost.co.kr) of GPT-2 (although GPT-3 models with as few as 125 million parameters were also trained). [186]
 <br>OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184]
 <br>GPT-3 drastically enhanced [benchmark outcomes](http://git.szchuanxia.cn) over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or coming across the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, [compared](https://careers.ecocashholdings.co.zw) to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released to the public for [concerns](https://tobesmart.co.kr) of possible abuse, although OpenAI prepared 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](https://git.profect.de) powering the [code autocompletion](http://plethe.com) tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the design can create working code in over a lots programming languages, most successfully in Python. [192]
 <br>Several problems with glitches, style flaws and security vulnerabilities were cited. [195] [196]
 <br>GitHub Copilot has actually been implicated of discharging copyrighted code, without any author attribution or license. [197]
 <br>OpenAI revealed that they would discontinue assistance for Codex API on March 23, 2023. [198]
 <br>GPT-4<br>
 <br>On March 14, 2023, [OpenAI revealed](http://193.105.6.1673000) the release of Generative Pre-trained Transformer 4 (GPT-4), capable of [accepting text](http://oj.algorithmnote.cn3000) or image inputs. [199] They announced that the updated technology passed a simulated law school bar examination with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also read, examine or generate up to 25,000 words of text, and compose code in all significant shows languages. [200]
 <br>Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the problems with earlier [modifications](http://159.75.133.6720080). [201] GPT-4 is likewise efficient in taking images as input on [ChatGPT](http://121.40.209.823000). [202] OpenAI has decreased to expose numerous 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 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 criteria, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard 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 particularly useful for business, startups and developers seeking to automate services with [AI](https://supremecarelink.com) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been developed to take more time to think of their actions, causing higher accuracy. These [designs](https://www.meetgr.com) are especially effective in science, coding, and thinking jobs, 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 follower of the o1 thinking design. OpenAI also revealed o3-mini, a [lighter](https://gitlab.dituhui.com) and faster version of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are evaluating 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 model is called o3 rather than o2 to prevent confusion with telecoms services provider O2. [215]
 <br>Deep research study<br>
 <br>Deep research study 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 browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With [searching](https://impactosocial.unicef.es) and Python tools allowed, it reached an accuracy 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 model that is trained to evaluate the semantic resemblance 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 design that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can develop images of practical objects ("a stained-glass window with an image of a blue strawberry") in addition to things that do not exist in truth ("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 updated version of the model with more sensible results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new primary system for transforming a [text description](https://www.armeniapedia.org) into a 3-dimensional model. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI revealed DALL-E 3, a more effective model much better able to produce 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 function 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 short detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br>
 <br>[Sora's advancement](http://www.sleepdisordersresource.com) team named it after the Japanese word for "sky", to signify its "limitless creative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI [trained](https://azaanjobs.com) the system using publicly-available videos in addition to copyrighted videos certified for that function, however did not reveal the number or the specific sources of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it might create videos as much as one minute long. It also shared a technical report highlighting the methods used to train the design, and the design's capabilities. [225] It acknowledged a few of its drawbacks, including struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but kept in mind that they need to have been cherry-picked and may not represent Sora's normal output. [225]
 <br>Despite [uncertainty](http://59.57.4.663000) from some academic leaders following Sora's public demonstration, notable entertainment-industry figures have shown [substantial](http://lophas.com) interest in the technology's capacity. In an interview, actor/filmmaker Tyler [Perry expressed](http://63.141.251.154) his awe at the innovation's capability to produce [reasonable video](https://git.xiaoya360.com) from text descriptions, citing its possible to reinvent storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause plans for broadening 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 design. [228] It is trained on a large dataset of varied audio and is also a [multi-task design](https://gamehiker.com) that can perform 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 styles. According to The Verge, a tune created by MuseNet tends to begin fairly however then fall into chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the web 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 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 specified the songs "show local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable space" between Jukebox and human-generated music. The Verge specified "It's highly outstanding, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are appealing and sound genuine". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI introduced the Debate Game, which teaches devices to dispute toy problems in front of a human judge. The function is to research whether such a method might assist in auditing [AI](http://114.132.230.24:180) decisions and in establishing explainable [AI](https://www.groceryshopping.co.za). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of 8 neural network models which are frequently studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various variations of Inception, and various variations of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is an artificial intelligence tool constructed on top of GPT-3 that supplies a conversational user interface that enables users to ask concerns in natural language. The system then responds with an answer within seconds.<br>