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 <br>Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of support learning algorithms. It aimed to standardize how environments are defined in [AI](http://193.200.130.186:3000) research study, making published research more easily reproducible [24] [144] while providing users with an easy interface for connecting with these environments. In 2022, new advancements of Gym have actually been transferred to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing agents to fix single tasks. Gym Retro provides the capability to generalize between video games with similar ideas however different appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack knowledge of how to even walk, however are provided the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents find out how to adjust to altering conditions. When a [representative](https://gl.ignite-vision.com) is then removed from this virtual environment and positioned in a new virtual environment with high winds, the [representative](https://alumni.myra.ac.in) braces to remain upright, recommending it had learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could develop an intelligence "arms race" that might increase a [representative's capability](https://lms.digi4equality.eu) to operate even outside the context of the competitors. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human players at a high ability level entirely through  algorithms. Before ending up being a group of 5, the very first public presentation took place at The International 2017, the annual best champion competition for the game, where Dendi, an [expert Ukrainian](http://120.25.165.2073000) player,  [surgiteams.com](https://surgiteams.com/index.php/User:Darnell83J) lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of actual time, and that the [knowing software](https://git.lona-development.org) was a step in the instructions of producing software application that can deal with complex jobs like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots discover in time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
 <br>By June 2018, the ability of the bots expanded to play together as a full group of 5, and they had the ability to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players,  [wiki.asexuality.org](https://wiki.asexuality.org/w/index.php?title=User_talk:AngelitaR70) however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the game at the time, 2:0 in a live exhibition match in [San Francisco](https://gitea.robertops.com). [163] [164] The bots' last public look came later 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 systems in Dota 2's bot player reveals the obstacles of [AI](https://git.vhdltool.com) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated making use of deep reinforcement learning (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a [human-like robotic](http://101.200.127.153000) hand, to control physical items. [167] It learns totally in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB video cameras to enable the robotic to control 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 solve a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by [enhancing](https://kibistudio.com57183) the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually harder environments. ADR differs from manual domain randomization by not requiring a human to specify randomization varieties. [169]
 <br>API<br>
 <br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](https://one2train.net) models developed by OpenAI" to let designers [contact](https://git.ycoto.cn) it for "any English language [AI](http://repo.fusi24.com:3000) job". [170] [171]
 <br>Text generation<br>
 <br>The [business](https://xhandler.com) has 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 design was written by Alec Radford and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language might obtain world understanding and procedure long-range reliances 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 a without supervision transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions at first launched to the general public. The full version of GPT-2 was not right away released due to concern about prospective abuse, including applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 posed a substantial threat.<br>
 <br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several websites [host interactive](https://git.yqfqzmy.monster) presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors argue not being watched language designs to be general-purpose students, shown by GPT-2 attaining state-of-the-art [accuracy](https://git.li-yo.ts.net) 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain [issues encoding](https://deprezyon.com) vocabulary with word tokens by utilizing byte pair [encoding](https://deadlocked.wiki). This allows 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 not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 [contained](https://dreamcorpsllc.com) 175 billion criteria, [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 couple of as 125 million parameters were likewise trained). [186]
 <br>OpenAI specified that GPT-3 prospered at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and between [English](https://followmypic.com) and German. [184]
 <br>GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or coming across the essential ability constraints of [predictive language](http://40th.jiuzhai.com) models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model 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 free private beta that began in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was licensed solely to Microsoft. [190] [191]
 <br>Codex<br>
 <br>Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://gitlab.awcls.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 shows languages, the majority of successfully in Python. [192]
 <br>Several concerns with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
 <br>[GitHub Copilot](https://saga.iao.ru3043) has actually been accused of giving off copyrighted code, without any author attribution or license. [197]
 <br>OpenAI announced that they would terminate assistance for  [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DessieLundstrom) 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 or image inputs. [199] They announced that the updated innovation passed a simulated law school [bar exam](https://talentup.asia) 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 check out, analyze or generate approximately 25,000 words of text, and write code in all significant programs languages. [200]
 <br>Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined to expose numerous technical details and data about GPT-4, such as the precise size of the model. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition 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 version of GPT-4o [replacing](https://git.obo.cash) GPT-3.5 Turbo on the ChatGPT user 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 enterprises, start-ups and designers seeking to automate services with [AI](https://massivemiracle.com) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been created to take more time to consider their reactions, causing higher accuracy. These models are especially efficient in science, coding, and thinking jobs, and were made available to [ChatGPT](https://mychampionssport.jubelio.store) Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211]
 <br>o3<br>
 <br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing 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 rather than o2 to prevent confusion with telecoms providers O2. [215]
 <br>Deep research study<br>
 <br>Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of [OpenAI's](https://gitstud.cunbm.utcluj.ro) o3 design to carry out extensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [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 examine the semantic resemblance between text and images. It can notably be used for image category. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>Revealed in 2021, DALL-E is a Transformer design that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can create pictures of practical items ("a stained-glass window with an image of a blue strawberry") along with items 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 upgraded version of the design with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new primary system for transforming a text description into a 3-dimensional model. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI revealed DALL-E 3, a more powerful 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 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 triggers [223] as well as extend existing videos [forwards](http://1.119.152.2304026) or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.<br>
 <br>Sora's development team named it after the Japanese word for "sky", to signify its "unlimited creative capacity". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 [text-to-image](https://git.lewd.wtf) design. [225] [OpenAI trained](https://www.cvgods.com) the system utilizing publicly-available videos as well as copyrighted videos licensed for that function, however did not reveal 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, mentioning that it could produce videos as much as one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged some of its imperfections, consisting of struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", however kept in mind that they must 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 significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to produce reasonable video from text descriptions, mentioning its possible to revolutionize storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had actually decided to stop briefly 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 design. [228] It is trained on a large dataset of varied audio and is also a multi-task design that can carry out multilingual speech acknowledgment 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](https://arbeitsschutz-wiki.de) 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 begin fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned to [produce music](http://git.attnserver.com) 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](http://103.197.204.1633025) a genre, artist, and a [snippet](https://jobboat.co.uk) of lyrics and outputs song samples. OpenAI stated the tunes "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" which "there is a significant gap" in between Jukebox and human-generated music. The Verge mentioned "It's technically remarkable, even if the results seem like mushy variations of songs that might feel familiar", while Business Insider stated "remarkably, some of the resulting tunes are catchy 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 issues in front of a human judge. The function is to research whether such an approach might assist in auditing [AI](https://24cyber.ru) choices and in developing explainable [AI](https://ysa.sa). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was created to evaluate the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions 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 permits users to ask concerns in [natural language](https://www.gabeandlisa.com). The system then reacts with a response within seconds.<br>