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 <br>Announced in 2016, Gym is an open-source Python [library](http://103.197.204.1623025) created to assist in the development of support knowing [algorithms](https://git.coalitionofinvisiblecolleges.org). It aimed to standardize how environments are specified in [AI](https://git.aiadmin.cc) research, making released research more quickly reproducible [24] [144] while offering users with an easy user interface for connecting with these environments. In 2022, new developments of Gym have been transferred to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018,  [forum.pinoo.com.tr](http://forum.pinoo.com.tr/profile.php?id=1324171) Gym Retro is a platform for support learning (RL) research study on computer game [147] utilizing RL algorithms and [study generalization](https://swahilihome.tv). Prior RL research study focused mainly on optimizing agents to solve single tasks. Gym Retro offers the capability to generalize between video 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 initially do not have understanding of how to even stroll, however are given the goals of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the agents learn how to adapt to altering conditions. When an agent is then removed 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 method. [148] [149] OpenAI's Igor Mordatch argued that competition in between [representatives](https://git.hichinatravel.com) might develop an intelligence "arms race" that might increase a representative's capability to function even outside the context of the competition. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human players at a high skill level entirely through [experimental algorithms](http://101.35.184.1553000). Before ending up being a team of 5, the first public presentation took place at The International 2017, the annual best champion tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of actual time, and that the knowing software was a step in the direction of creating software that can deal with complicated jobs like a cosmetic surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots discover gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156]
 <br>By June 2018, the ability of the bots expanded to play together as a complete team of 5, and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert players, 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 exhibit match in San Francisco. [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 overall [video games](https://han2.kr) in a four-day open online competitors, winning 99.4% of those games. [165]
 <br>OpenAI 5['s mechanisms](http://120.46.37.2433000) in Dota 2's bot player reveals the obstacles of [AI](https://tyciis.com) systems in multiplayer online [battle arena](https://network.janenk.com) (MOBA) games and how OpenAI Five has actually demonstrated using deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand,  [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:GemmaJenson1) a human-like robot hand, to control physical objects. [167] It finds out totally in simulation utilizing the same RL algorithms and [training](https://pittsburghpenguinsclub.com) code as OpenAI Five. OpenAI tackled the things orientation issue by using domain randomization, a simulation method which exposes the student to a variety of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB electronic cameras to permit the robot to manipulate an approximate object by seeing it. In 2018, OpenAI revealed that the system was able to [manipulate](https://localjobpost.com) a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the [Rubik's Cube](https://116.203.22.201) introduce complex [physics](http://dev.shopraves.com) that is harder to design. OpenAI did this by [improving](http://51.15.222.43) the [robustness](https://www.etymologiewebsite.nl) of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing progressively harder environments. ADR varies from manual domain randomization by not needing a human to define 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](http://41.111.206.175:3000) designs established by OpenAI" to let developers contact it for "any English language [AI](https://gogs.yaoxiangedu.com) job". [170] [171]
 <br>Text generation<br>
 <br>The company 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](https://gitea.mrc-europe.com) was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language could obtain world knowledge and process long-range [dependences](https://demo.playtubescript.com) 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 not being watched transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative [variations initially](https://www.diltexbrands.com) released to the public. The full variation of GPT-2 was not right away launched due to concern about potential abuse, consisting of applications for writing fake news. [174] Some specialists revealed uncertainty that GPT-2 positioned a considerable risk.<br>
 <br>In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural phony news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation 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 version of the GPT-2 language design. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
 <br>GPT-2's authors argue unsupervised language models to be general-purpose students, shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not further 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](https://git.lodis.se) in Reddit submissions with a minimum of 3 upvotes. It prevents 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 model and the successor to GPT-2. [182] [183] [184] OpenAI stated 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 full version of GPT-2 (although GPT-3 models with as few as 125 million specifications were likewise trained). [186]
 <br>OpenAI specified that GPT-3 prospered at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184]
 <br>GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the basic ability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away launched to the general public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid [cloud API](https://firstamendment.tv) after a two-month complimentary personal beta that started in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191]
 <br>Codex<br>
 <br>Announced in mid-2021, Codex is a descendant of GPT-3 that has actually furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://www.etymologiewebsite.nl) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the design can create working code in over a dozen programming languages, a lot of efficiently in Python. [192]
 <br>Several concerns with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has been accused of producing copyrighted code, with no author attribution or license. [197]
 <br>OpenAI announced that they would discontinue assistance for Codex API on March 23, 2023. [198]
 <br>GPT-4<br>
 <br>On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar test 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 as much as 25,000 words of text, and compose code in all significant shows languages. [200]
 <br>Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and statistics about GPT-4, such as the precise size of the design. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI revealed and [released](http://101.36.160.14021044) GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision benchmarks, setting new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark [compared](http://81.70.24.14) to 86.5% by GPT-4. [207]
 <br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized variation of GPT-4o changing GPT-3.5 Turbo on the ChatGPT interface. Its [API costs](https://gitea.bone6.com) $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be especially beneficial for enterprises, start-ups and designers seeking to automate services with [AI](https://hinh.com) agents. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been designed to take more time to consider their reactions, leading to greater precision. These models are especially efficient in science, coding, and reasoning tasks, and were made available to ChatGPT 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](https://repo.maum.in) o3, the follower of the o1 reasoning design. OpenAI likewise revealed o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are [checking](https://www.allclanbattles.com) o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications services company 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 abilities of OpenAI's o3 design to carry out substantial web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) [standard](http://www.yfgame.store). [120]
 <br>Image category<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 between text and images. It can notably be used for image classification. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>[Revealed](https://heyanesthesia.com) in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can [develop images](http://www.jimtangyh.xyz7002) of [reasonable items](https://b52cum.com) ("a stained-glass window with an image of a blue strawberry") as well as items 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 announced DALL-E 2, an upgraded version of the model with more realistic results. [219] In December 2022, OpenAI released on GitHub  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 revealed DALL-E 3, a more powerful model much better able to generate images from complex descriptions without manual timely engineering and render complicated details like hands and text. [221] It was released to the general 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 upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.<br>
 <br>Sora's development group called it after the Japanese word for "sky", to symbolize its "endless innovative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system [utilizing publicly-available](https://whotube.great-site.net) videos as well as copyrighted videos accredited for that purpose, but did not reveal the number or the precise sources of the videos. [223]
 <br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could produce videos approximately one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the model's capabilities. [225] It acknowledged some of its drawbacks, including battles replicating complicated physics. [226] Will [Douglas Heaven](https://gitea.viamage.com) of the MIT Technology Review called the presentation videos "excellent", 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 substantial interest in the innovation's potential. In an interview,  [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) actor/filmmaker Tyler Perry revealed his astonishment at the [technology's ability](http://119.3.9.593000) to create [reasonable video](https://www.angevinepromotions.com) from text descriptions, mentioning its [potential](https://www.outletrelogios.com.br) to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause plans for broadening his Atlanta-based movie studio. [227]
 <br>Speech-to-text<br>
 <br>Whisper<br>
 <br>Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of varied audio and is likewise a multi-task design that can carry out multilingual speech acknowledgment along with 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 forecast subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly but then fall into [turmoil](https://chat.app8station.com) the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the internet 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 genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "reveal local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs do not have "familiar larger 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 outstanding, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are appealing and sound legitimate". [234] [235] [236]
 <br>User interfaces<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI launched the Debate Game, which teaches makers to debate toy issues in front of a human judge. The function is to research study whether such a method may assist in auditing [AI](https://cvwala.com) decisions and in establishing explainable [AI](https://theindietube.com). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of 8 neural network models which are often studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks quickly. The designs 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 artificial intelligence tool developed on top of GPT-3 that provides a conversational interface that allows users to ask questions in natural language. The system then reacts with a response within seconds.<br>