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 <br>Announced in 2016, Gym is an open-source Python library developed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://svn.youshengyun.com:3000) research, making published research study more easily reproducible [24] [144] while providing users with a simple user interface for interacting with these environments. In 2022, new advancements of Gym have actually been moved to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>[Released](https://www.hijob.ca) in 2018, Gym Retro is a [platform](https://lets.chchat.me) for support knowing (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on [enhancing representatives](https://code.flyingtop.cn) to solve single tasks. Gym Retro provides the ability to generalize between games with similar concepts however different appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have understanding of how to even walk, but are offered the objectives of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial [knowing](http://www.forwardmotiontx.com) procedure, the representatives find out how to adapt to altering conditions. When a representative is then removed from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to [stabilize](https://www.dadam21.co.kr) in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might produce an intelligence "arms race" that could increase an [agent's capability](https://media.labtech.org) 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](https://git.novisync.com) utilized in the competitive five-on-five video 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](https://executiverecruitmentltd.co.uk) took place at The International 2017, the yearly best championship competition for the game, where Dendi, an expert  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 discovered by playing against itself for two weeks of actual time, and that the knowing software application was an action in the direction of developing software that can manage complex jobs like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots learn 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 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 were able to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 [exhibit matches](https://git.wheeparam.com) against expert players, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 total games in a four-day open online competition,  [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GemmaJenson1) winning 99.4% of those games. [165]
 <br>OpenAI 5's mechanisms in Dota 2's bot player shows the obstacles of [AI](https://78.47.96.161:3000) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated the usage of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses device learning to train a Shadow Hand, a [human-like robot](https://git.iidx.ca) hand, to control physical things. [167] It finds out completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by using domain randomization, a simulation approach which exposes the learner to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB electronic cameras to permit the robotic to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI showed that Dactyl could 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 model. OpenAI did this by improving the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of producing gradually more difficult environments. ADR varies from manual [domain randomization](http://repo.z1.mastarjeta.net) by not requiring a human to define 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://clujjobs.com) designs developed by OpenAI" to let developers contact it for "any English language [AI](https://geoffroy-berry.fr) job". [170] [171]
 <br>Text generation<br>
 <br>The company has popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's original GPT design ("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 revealed how a generative design of language could obtain world knowledge and procedure long-range reliances 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 without supervision transformer language model and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative versions initially launched to the general public. The full variation of GPT-2 was not immediately launched due to concern about possible abuse, including applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 presented a considerable threat.<br>
 <br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several websites [host interactive](http://101.43.248.1843000) demonstrations of different instances of GPT-2 and other [transformer designs](https://www.frigorista.org). [178] [179] [180]
 <br>GPT-2's authors argue without supervision language models to be general-purpose students, highlighted by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not additional 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 a minimum of 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186]
 <br>OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and could [generalize](https://usvs.ms) the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]
 <br>GPT-3 dramatically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the basic capability constraints of predictive language designs. [187] Pre-training 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  [pediascape.science](https://pediascape.science/wiki/User:KristanLightfoot) issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month complimentary 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://begild.top:8418) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in [personal](https://git.joystreamstats.live) beta. [194] According to OpenAI, the design can create working code in over a dozen shows languages, a lot of effectively in Python. [192]
 <br>Several problems with problems, style flaws and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has been implicated of producing copyrighted code, with no author attribution or license. [197]
 <br>OpenAI announced that they would stop support 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 or image inputs. [199] They announced that the updated innovation 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, evaluate 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 using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also [capable](https://repo.correlibre.org) of taking images as input on ChatGPT. [202] OpenAI has declined to reveal different technical details and statistics about GPT-4, such as the exact size of the model. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI announced and launched GPT-4o, which can [process](http://park7.wakwak.com) and create text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision criteria, setting brand-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 changing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million [input tokens](https://nepaxxtube.com) and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be particularly useful for enterprises, startups and developers seeking to automate services with [AI](https://gitlab.payamake-sefid.com) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been [designed](https://hyperwrk.com) to take more time to consider their responses, resulting in greater accuracy. These designs are particularly efficient 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 thinking model. OpenAI likewise unveiled o3-mini, a [lighter](https://krazzykross.com) and much faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecoms services service provider O2. [215]
 <br>Deep research study<br>
 <br>Deep research study is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached an accuracy 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 similarity](https://sossphoto.com) 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 develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can create images of practical things ("a stained-glass window with a picture of a blue strawberry") in addition to things that do not exist in reality ("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 variation of the model with more [realistic outcomes](https://medicalstaffinghub.com). [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new fundamental system for converting 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 effective model better able to generate images from complicated descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the 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 generate videos based upon short detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can [generate videos](https://theindietube.com) with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.<br>
 <br>Sora's advancement group called it after the Japanese word for "sky", to symbolize its "limitless creative capacity". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that purpose, however did not expose the number or the exact sources of the videos. [223]
 <br>OpenAI demonstrated some Sora-created [high-definition videos](http://47.100.3.2093000) to the public on February 15, 2024, stating that it could produce videos as much as one minute long. It likewise shared a technical report highlighting the approaches utilized to train the design, and the design's abilities. [225] It acknowledged some of its shortcomings, including battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but noted that they should have been cherry-picked and may not represent Sora's normal output. [225]
 <br>Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have shown significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to generate [practical video](http://8.138.140.943000) from text descriptions, mentioning its prospective to change storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly 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 design. [228] It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language recognition. [229]
 <br>Music generation<br>
 <br>MuseNet<br>
 <br>Released in 2019,  [wiki.myamens.com](http://wiki.myamens.com/index.php/User:LoreenErtel66) MuseNet is a deep neural net trained to forecast 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 start fairly however then fall under chaos the longer it plays. [230] [231] In pop culture,  [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:FlorianHoutz6) initial applications of this tool were used as early as 2020 for the web 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 produce music with vocals. After training on 1.2 million samples, the system accepts a category,  [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:Margareta19E) artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the songs "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a significant space" between Jukebox and human-generated music. The Verge stated "It's highly impressive, even if the results seem like mushy versions of songs that might feel familiar", while Business [Insider](https://pattonlabs.com) specified "surprisingly, a few of the resulting songs are memorable and sound genuine". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI released the Debate Game, which teaches makers to discuss toy problems in front of a human judge. The function is to research study whether such an approach may help in auditing [AI](https://www.ignitionadvertising.com) decisions and in establishing explainable [AI](https://grace4djourney.com). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was developed to analyze the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, various versions of Inception, and different [variations](https://grace4djourney.com) of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, [ChatGPT](https://youtubegratis.com) is an expert system tool constructed on top of GPT-3 that provides a conversational interface that allows users to ask concerns in natural language. The system then responds with an answer within seconds.<br>