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 <br>Announced in 2016, Gym is an open-source Python library created to facilitate the development of support learning algorithms. It aimed to standardize how [environments](https://gitea.daysofourlives.cn11443) are specified in [AI](http://122.51.17.90:2000) research, making released research study more easily reproducible [24] [144] while [supplying](https://edge1.co.kr) users with a basic user interface for connecting with these environments. In 2022, new developments 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 reinforcement knowing (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing representatives to resolve single tasks. Gym Retro gives the capability to generalize in between games with comparable concepts but different appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first do not have understanding of how to even stroll, but are given the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process,  [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/halleybodin) the agents discover how to adjust to altering conditions. When an agent is then eliminated from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could produce an intelligence "arms race" that might 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 5 OpenAI-curated bots used in the [competitive five-on-five](http://13.209.39.13932421) [video game](https://wiki.idealirc.org) Dota 2, that find out to play against human gamers at a high ability level totally through experimental algorithms. Before becoming a team of 5, the first public demonstration happened at The International 2017, the yearly premiere champion competition 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 found out by playing against itself for 2 weeks of genuine time, and that the knowing software application was an action in the instructions of creating software that can handle complicated jobs like a cosmetic surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots discover in 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 expanded to play together as a full group of 5, and they were able to defeat groups of amateur and [semi-professional players](https://sadegitweb.pegasus.com.mx). [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional players, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the game at the time, 2:0 in a live exhibition match in [San Francisco](https://gitlab.kicon.fri.uniza.sk). [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 total [video games](http://116.198.225.843000) in a four-day open online competitors, winning 99.4% of those . [165]
 <br>OpenAI 5['s systems](https://friendify.sbs) in Dota 2's bot gamer shows the difficulties of [AI](http://www.dahengsi.com:30002) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated the use of deep reinforcement learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl utilizes machine discovering to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It discovers entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by using domain randomization, a simulation approach which exposes the learner to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cams to allow the robotic to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system was able to [manipulate](https://video-sharing.senhosts.com) a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by [improving](http://114.55.54.523000) the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of creating gradually [harder environments](http://222.85.191.975000). ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
 <br>API<br>
 <br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://gogs.fytlun.com) designs developed by OpenAI" to let designers call on it for "any English language [AI](https://git.bugwc.com) job". [170] [171]
 <br>Text generation<br>
 <br>The company has actually popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's original 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 associates,  [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11861831) and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world knowledge and procedure long-range dependencies by pre-training on a diverse 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 model and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative variations initially [launched](https://git.mae.wtf) to the public. The complete variation of GPT-2 was not right away launched due to issue about possible misuse, including applications for composing phony news. [174] Some specialists revealed uncertainty that GPT-2 positioned a considerable hazard.<br>
 <br>In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural fake news". [175] Other scientists, such as Jeremy Howard, warned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language model. [177] Several [sites host](https://foke.chat) interactive demonstrations of various instances of GPT-2 and other [transformer models](http://47.121.121.1376002). [178] [179] [180]
 <br>GPT-2's authors argue not being watched language models to be general-purpose learners, shown by GPT-2 attaining state-of-the-art accuracy 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](https://aravis.dev) 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 a without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were likewise trained). [186]
 <br>OpenAI specified 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 knowing in between English and Romanian, and  [surgiteams.com](https://surgiteams.com/index.php/User:LatanyaZiegler) between English and German. [184]
 <br>GPT-3 significantly improved benchmark results 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 designs. [187] [Pre-training](https://careers.ecocashholdings.co.zw) GPT-3 required several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [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 prepared to enable gain access to through a paid cloud API after a two-month free personal 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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://grainfather.asia) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can create working code in over a lots programming languages, the majority of effectively in Python. [192]
 <br>Several concerns with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has been implicated of discharging copyrighted code, with no author attribution or license. [197]
 <br>OpenAI announced that they would terminate support for Codex API on March 23, 2023. [198]
 <br>GPT-4<br>
 <br>On March 14, 2023, [OpenAI revealed](https://git.desearch.cc) the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the updated innovation passed a simulated law [school bar](https://git.serenetia.com) exam with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, analyze or produce approximately 25,000 words of text, and compose code in all major programs languages. [200]
 <br>Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has declined to reveal numerous technical details and data about GPT-4, such as the precise size of the design. [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 benchmarks, setting brand-new records in [audio speech](https://www.9iii9.com) recognition 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 released GPT-4o mini, a smaller sized variation of GPT-4o changing 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 helpful for business, start-ups and developers looking for to automate services with [AI](http://mangofarm.kr) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been developed to take more time to think of their reactions, leading to greater precision. These models are particularly reliable in science, coding, and reasoning 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 revealed o3, the follower of the o1 thinking model. OpenAI also unveiled o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid confusion with telecoms companies O2. [215]
 <br>Deep research<br>
 <br>Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the [abilities](http://120.48.7.2503000) of OpenAI's o3 model to perform comprehensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [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 evaluate 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 utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can produce pictures of [reasonable objects](https://ipen.com.hk) ("a stained-glass window with a picture of a blue strawberry") along with things that do not exist in truth ("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 announced DALL-E 2, an upgraded version of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new rudimentary system for transforming a text description into a 3-dimensional model. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI announced DALL-E 3, a more powerful design much better able to produce images from intricate descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was launched to the public as a ChatGPT Plus feature in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video design that can create videos based on brief detailed prompts [223] as well as extend existing videos forwards or in [reverse](https://music.afrisolentertainment.com) in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The [optimum length](https://git.lab.evangoo.de) of created videos is unknown.<br>
 <br>[Sora's development](https://20.112.29.181) team called it after the Japanese word for "sky", to signify its "endless innovative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 [text-to-image design](https://git.bwt.com.de). [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos licensed for that purpose, however did not expose the number or the exact 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 likewise shared a [technical report](http://93.104.210.1003000) highlighting the approaches utilized to train the design,  [surgiteams.com](https://surgiteams.com/index.php/User:CathleenMadison) and the model's capabilities. [225] It acknowledged a few of its drawbacks, including battles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they should have been cherry-picked and might not represent Sora's common output. [225]
 <br>Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually shown considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to create practical video from text descriptions, citing its potential to change storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause strategies 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 recognition model. [228] It is trained on a big dataset of [varied audio](https://wathelp.com) and is also a multi-task design that can carry out multilingual speech recognition as well as 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 forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet mental 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 genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the songs "show local musical coherence [and] follow traditional chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the outcomes sound like mushy variations of tunes that might feel familiar", while Business Insider mentioned "remarkably, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI released the Debate Game, which teaches devices to debate toy issues in front of a human judge. The purpose is to research study whether such a technique might assist in auditing [AI](http://shiningon.top) decisions and in developing explainable [AI](https://sharingopportunities.com). [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 typically studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, different variations of Inception, and different versions 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 interface that allows users to ask concerns in natural language. The system then reacts with an answer within seconds.<br>