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 <br>Announced in 2016, Gym is an open-source Python library designed to assist in the development of support learning algorithms. It aimed to standardize how environments are specified in [AI](https://dimension-gaming.nl) research, making published research more quickly reproducible [24] [144] while providing users with a simple user interface for engaging with these environments. In 2022, brand-new developments of Gym have been moved to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for [support knowing](https://git.mista.ru) (RL) research study on video games [147] utilizing RL algorithms and research study generalization. [Prior RL](https://www.lakarjobbisverige.se) research study focused mainly on enhancing agents to solve single tasks. Gym Retro provides the capability to generalize in between games with similar concepts however various appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have understanding of how to even stroll, but are provided the [objectives](https://enitajobs.com) of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the [representatives learn](http://gitlab.abovestratus.com) how to adjust to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had discovered how to balance in a generalized way. [148] [149] [OpenAI's Igor](http://forum.moto-fan.pl) Mordatch argued that competition between representatives could develop an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competition. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that learn to play against human players at a high ability level totally through trial-and-error algorithms. Before ending up being a team of 5, the very first public demonstration occurred at The International 2017, the yearly best championship competition for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually matchup. [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 learning software application was a step in the instructions of developing software application that can deal with complicated jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of reinforcement knowing, as the bots learn with time 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 capability of the bots expanded to play together as a full group of 5, and they were able to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional gamers, however wound up losing both 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. [163] [164] The bots' last public look came later on that month, where they played in 42,729 total games in a four-day open online competitors, [winning](https://gitlab.econtent.lu) 99.4% of those video games. [165]
 <br>OpenAI 5's mechanisms in Dota 2's bot player shows the obstacles of [AI](http://115.29.202.246:8888) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated making use of [deep support](https://lovetechconsulting.net) learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses maker discovering to train a Shadow Hand, a [human-like robotic](http://wiki.iurium.cz) hand, to manipulate physical things. [167] It finds out completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of [attempting](https://m1bar.com) to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, also has RGB cameras to permit the robot to control an approximate things by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce [complicated physics](https://git.gqnotes.com) that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of producing progressively more hard environments. ADR varies from manual domain randomization by not needing a human to specify [randomization ranges](https://edujobs.itpcrm.net). [169]
 <br>API<br>
 <br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://cats.wiki) designs established by OpenAI" to let developers contact it for "any English language [AI](https://sistemagent.com:8081) task". [170] [171]
 <br>Text generation<br>
 <br>The business has actually popularized generative pretrained transformers (GPT). [172]
 <br>[OpenAI's initial](https://138.197.71.160) GPT design ("GPT-1")<br>
 <br>The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and process 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 a not being watched transformer language design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative variations initially released to the general public. The full variation of GPT-2 was not right away released due to concern about potential misuse, consisting of applications for writing fake news. [174] Some professionals expressed uncertainty that GPT-2 presented a significant danger.<br>
 <br>In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language design. [177] Several sites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors argue unsupervised language [designs](https://223.130.175.1476501) to be general-purpose students, highlighted by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot tasks (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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain [issues encoding](http://180.76.133.25316300) vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both specific 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 design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were also trained). [186]
 <br>OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the [function](http://124.221.255.92) of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and in between [English](http://8.129.8.58) and German. [184]
 <br>GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or [experiencing](http://code.bitahub.com) the basic ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous 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 right away [released](http://47.101.131.2353000) to the public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month totally free private beta that began 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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://98.27.190.224) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the design can produce working code in over a dozen shows languages, most efficiently in Python. [192]
 <br>Several concerns with glitches, design flaws and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has actually been accused of producing copyrighted code, without any author attribution or license. [197]
 <br>OpenAI announced that they would stop 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), efficient in accepting text or image inputs. [199] They [revealed](https://legatobooks.com) that the upgraded technology passed a simulated law school bar test with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, examine or create up to 25,000 words of text, and write code in all significant programming languages. [200]
 <br>Observers reported that the model of ChatGPT utilizing 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 modifications. [201] GPT-4 is likewise [efficient](https://git.berezowski.de) in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose numerous technical details and statistics about GPT-4, such as the exact size of the design. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text,  [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision criteria, setting 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 sized version 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](https://wiki.atlantia.sca.org) it to be especially useful for business, start-ups and designers looking for to automate services with [AI](https://git.ivabus.dev) 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 think about their actions, causing higher accuracy. These models are especially effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [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 also revealed o3-mini, a lighter and [faster variation](https://wiki.armello.com) of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the [opportunity](http://famedoot.in) to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecoms companies O2. [215]
 <br>Deep research<br>
 <br>Deep research study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the [abilities](https://realestate.kctech.com.np) of [OpenAI's](https://theneverendingstory.net) o3 model to carry out comprehensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [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 examine the semantic resemblance between text and images. It can notably 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 version of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can produce images of realistic items ("a stained-glass window with an image of a blue strawberry") in addition to items that do not exist in reality ("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 model with more realistic results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new fundamental system for transforming a text description into a 3-dimensional design. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI revealed DALL-E 3, a more powerful model better able to generate images from complicated descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video model that can create videos based on short detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br>
 <br>Sora's advancement team called it after the Japanese word for "sky", to symbolize its "unlimited innovative capacity". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using  along with [copyrighted videos](https://gitea.ashcloud.com) accredited for that purpose, however did not expose the number or the specific sources of the videos. [223]
 <br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might generate videos as much as one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the model's abilities. [225] It acknowledged some of its drawbacks, [consisting](https://mediawiki.hcah.in) of struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but kept in mind that they should have been cherry-picked and might not represent Sora's normal output. [225]
 <br>Despite uncertainty from some [academic leaders](http://www.brightching.cn) following Sora's public demo, significant entertainment-industry figures have actually shown significant interest in the technology's capacity. In an interview, actor/[filmmaker Tyler](http://cloud-repo.sdt.services) Perry expressed his astonishment at the innovation's ability to generate sensible video from text descriptions, citing its potential to change storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to [pause strategies](http://121.5.25.2463000) 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 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 identification. [229]
 <br>Music generation<br>
 <br>MuseNet<br>
 <br>Released in 2019, [MuseNet](https://getstartupjob.com) is a deep neural net trained to predict subsequent [musical notes](https://gayplatform.de) in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to start fairly however then fall into mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to develop 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 genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "reveal regional musical coherence [and] follow conventional chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable space" in between Jukebox and  [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:JewellMoffett6) human-generated music. The Verge specified "It's technically impressive, even if the results seem like mushy versions of songs that might feel familiar", while Business Insider mentioned "remarkably, a few of the resulting tunes are memorable and sound genuine". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, [OpenAI released](http://47.244.181.255) the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research whether such an approach may help in auditing [AI](https://ipmanage.sumedangkab.go.id) decisions and in developing explainable [AI](https://tube.leadstrium.com). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of eight neural network designs which are often studied in interpretability. [240] Microscope was [developed](https://15.164.25.185) to analyze the features that form inside these neural networks quickly. The [designs](https://radi8tv.com) included are AlexNet, VGG-19, different versions of Inception, and various versions of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is an expert system [tool developed](https://my-estro.it) on top of GPT-3 that offers a conversational interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.<br>