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 <br>Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://119.29.169.157:8081) research, making published research study more easily reproducible [24] [144] while providing users with a basic interface for connecting with these environments. In 2022, brand-new developments of Gym have been transferred to the [library Gymnasium](https://goodprice-tv.com). [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for support learning (RL) research on video games [147] using RL algorithms and research study generalization. Prior RL research  mainly on optimizing agents to fix single jobs. Gym Retro gives the ability to generalize between video games with comparable principles but various appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first do not have [understanding](https://git.pt.byspectra.com) of how to even walk, however are offered the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives discover how to adapt to changing conditions. When an agent is then removed from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered how to balance in a generalized method. [148] [149] OpenAI's Igor [Mordatch](https://social.web2rise.com) argued that competitors in between agents could create an intelligence "arms race" that might increase an agent's ability to work even outside the context of the competitors. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2,  [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:MauricioDoughert) that discover to play against human players at a high skill level completely through trial-and-error algorithms. Before ending up being a group of 5, the first public demonstration occurred at The International 2017, the annual best championship competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for two weeks of actual time, which the learning software application was a step in the instructions of developing software application that can deal with complicated jobs like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots discover in time by playing against themselves numerous times a day for months, and are [rewarded](https://spaceballs-nrw.de) for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
 <br>By June 2018, the [ability](https://www.hammerloop.com) of the bots broadened to play together as a complete group 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 2 exhibition matches against professional players, however ended up losing both [video games](https://community.cathome.pet). [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the game at the time, 2:0 in a [live exhibition](https://club.at.world) match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those games. [165]
 <br>OpenAI 5's systems in Dota 2's bot player reveals the obstacles of [AI](http://120.79.75.202:3000) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated using deep support learning (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl utilizes machine discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It discovers entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cameras to enable the robotic to manipulate an arbitrary things by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an [octagonal prism](https://gitea.jessy-lebrun.fr). [168]
 <br>In 2019, OpenAI showed that Dactyl could solve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by [improving](https://www.yewiki.org) the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of creating gradually more tough environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
 <br>API<br>
 <br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://saathiyo.com) models established by OpenAI" to let designers call on it for "any English language [AI](https://taelimfwell.com) task". [170] [171]
 <br>Text generation<br>
 <br>The business has popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's initial 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 colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding and procedure long-range dependencies by pre-training on a varied 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 to [OpenAI's initial](https://rabota-57.ru) GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions initially launched to the general public. The complete variation of GPT-2 was not instantly launched due to concern about possible misuse, including applications for composing fake news. [174] Some specialists expressed uncertainty that GPT-2 posed a significant threat.<br>
 <br>In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake 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 drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language design. [177] Several [websites](https://movie.nanuly.kr) host interactive demonstrations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors argue unsupervised language designs to be general-purpose students, shown by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not additional trained on any task-specific input-output examples).<br>
 <br>The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from [URLs shared](http://dcmt.co.kr) in Reddit submissions with at least 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by using 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 an unsupervised transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million specifications 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 between English and Romanian, and in between English and German. [184]
 <br>GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or encountering the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, 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 launched to the public for issues of possible abuse, although OpenAI planned to permit gain access to through a [paid cloud](https://gitlab.iue.fh-kiel.de) API after a two-month complimentary private beta that started in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was certified 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://git.taokeapp.net:3000) 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 produce working code in over a lots programs languages, many [efficiently](https://newborhooddates.com) in Python. [192]
 <br>Several issues with glitches, style flaws and security vulnerabilities were mentioned. [195] [196]
 <br>GitHub Copilot has actually been accused of producing copyrighted code, without any author [attribution](https://git.xutils.co) or license. [197]
 <br>OpenAI announced that they would [discontinue support](http://www.colegio-sanandres.cl) for Codex API 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), capable of [accepting text](https://www.dataalafrica.com) or image inputs. [199] They announced that the updated innovation passed a simulated law school bar test 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, evaluate or generate as much as 25,000 words of text, and write code in all significant programs languages. [200]
 <br>Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal numerous technical details and data about GPT-4, such as the accurate size of the model. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o [attained cutting](https://shareru.jp) edge results in voice, multilingual, and vision benchmarks, setting new records in audio speech 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 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](http://www.jacksonhampton.com3000) it to be particularly beneficial for enterprises, startups and developers looking for to [automate services](https://leicestercityfansclub.com) with [AI](http://60.209.125.238:20010) agents. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been designed to take more time to think of their reactions, leading to greater precision. These designs are particularly reliable 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 unveiled o3, the successor of the o1 reasoning design. OpenAI likewise revealed o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and [security researchers](https://groupeudson.com) 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 an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive web browsing, information analysis, and synthesis, providing 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) benchmark. [120]
 <br>Image classification<br>
 <br>CLIP<br>
 <br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze the [semantic resemblance](https://git.citpb.ru) in between text and images. It can significantly be utilized for image classification. [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 utilizes a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can produce pictures of realistic objects ("a stained-glass window with an image 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 updated variation of the design with more sensible results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new fundamental system for transforming 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 powerful model much better able to generate images from intricate descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released to the general public as a ChatGPT Plus [feature](https://fototik.com) 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 triggers [223] along with [extend existing](https://wiki.contextgarden.net) videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br>
 <br>Sora's advancement team named it after the Japanese word for "sky", to symbolize its "limitless imaginative potential". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos licensed for that purpose, however did not expose the number or the precise sources of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might create videos approximately one minute long. It also shared a technical report highlighting the approaches used to train the model, and the model's capabilities. [225] It acknowledged a few of its shortcomings, including battles mimicing intricate physics. [226] Will [Douglas Heaven](https://www.speedrunwiki.com) of the MIT Technology Review called the presentation videos "outstanding", however noted that they must have been [cherry-picked](https://dlya-nas.com) and may not represent Sora's typical output. [225]
 <br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually shown substantial interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to generate realistic video from text descriptions, citing its [potential](http://git.bkdo.net) to change storytelling and content production. He said that his [excitement](https://wiki.idealirc.org) about Sora's possibilities was so strong that he had decided to pause strategies 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 recognition model. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can perform multilingual speech acknowledgment along with speech translation and [language recognition](https://gitlab.xfce.org). [229]
 <br>Music generation<br>
 <br>MuseNet<br>
 <br>Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall into turmoil 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](https://git.progamma.com.ua) to create 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](http://121.5.25.2463000) on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a significant space" in between Jukebox and human-generated music. The Verge specified "It's highly remarkable, even if the outcomes sound like mushy variations of songs that might feel familiar", while Business Insider stated "surprisingly, some of the resulting songs are catchy 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 discuss toy problems in front of a human judge. The function is to research whether such a method may assist in auditing [AI](https://git.flyfish.dev) decisions and in developing explainable [AI](https://vagyonor.hu). [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](https://git-dev.xyue.zip8443) models which are often studied in interpretability. [240] Microscope was developed to evaluate the functions that form inside these [neural networks](https://heyjinni.com) easily. The models consisted of 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 expert system [tool constructed](http://60.205.104.1793000) on top of GPT-3 that supplies a [conversational](http://plus.ngo) user interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.<br>