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 <br>Announced in 2016, Gym is an open-source Python library developed to help with the development of reinforcement learning algorithms. It aimed to [standardize](https://manchesterunitedfansclub.com) how [environments](http://101.132.100.8) are defined in [AI](http://119.3.70.207:5690) research, making released research more quickly reproducible [24] [144] while supplying users with a simple interface for engaging with these environments. In 2022, new developments of Gym have actually been relocated to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for [reinforcement knowing](http://47.119.128.713000) (RL) research study on video games [147] using RL algorithms and research study generalization. [Prior RL](http://gitea.ucarmesin.de) research study focused mainly on enhancing agents to solve single jobs. Gym Retro gives the ability to generalize in between video games with comparable ideas but various appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack understanding of how to even stroll, however are provided the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process,  [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:SIAMaryellen) the agents discover how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might develop an intelligence "arms race" that could increase an agent's ability to function even outside the context of the competitors. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a team of five OpenAI-curated bots [utilized](https://ahlamhospitalityjobs.com) in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high skill level totally through experimental algorithms. Before becoming a group of 5, the first public presentation took place at The [International](https://git.alexavr.ru) 2017, the annual best champion tournament for  [oeclub.org](https://oeclub.org/index.php/User:CarltonEichmann) the video game, where Dendi, a professional 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 discovered by playing against itself for 2 weeks of genuine time, and that the learning software application was a step in the direction of producing software application that can manage complex tasks like a surgeon. [152] [153] The system utilizes a kind of support learning, as the bots discover with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
 <br>By June 2018, the ability of the [bots expanded](https://snapfyn.com) to play together as a complete team of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The [International](https://www.jigmedatse.com) 2018, OpenAI Five played in two exhibition matches against expert players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall games in a [four-day](https://git.elder-geek.net) open online competition, winning 99.4% of those games. [165]
 <br>OpenAI 5's systems in Dota 2's bot player reveals the challenges of [AI](https://jobsdirect.lk) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown making use of deep support learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by utilizing domain randomization, a simulation method which exposes the student to a variety of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB video cameras to permit the robot to manipulate an arbitrary things 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 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 intricate physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method 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 revealed a multi-purpose API which it said was "for accessing new [AI](https://git.revoltsoft.ru) designs developed by OpenAI" to let developers contact it for "any English language [AI](http://124.223.100.38:3000) task". [170] [171]
 <br>Text generation<br>
 <br>The company has actually promoted 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 written by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative design of language might obtain world knowledge and procedure long-range dependencies by pre-training on a varied corpus with long [stretches](http://git.papagostore.com) 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](https://crmthebespoke.a1professionals.net) to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions at first released to the public. The complete variation of GPT-2 was not right away launched due to concern about potential abuse, including applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 positioned a considerable hazard.<br>
 <br>In response 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, warned of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors argue not being watched language models to be general-purpose students, highlighted by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more trained on any task-specific input-output examples).<br>
 <br>The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 [upvotes](https://followingbook.com). It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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](http://170.187.182.1213000) 3 (GPT-3) is an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated 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](http://106.14.125.169) 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" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]
 <br>GPT-3 significantly improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the basic ability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of 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 immediately launched to the general public for issues of possible abuse, although OpenAI planned to permit 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 specifically to Microsoft. [190] [191]
 <br>Codex<br>
 <br>Announced in mid-2021, Codex is a descendant of GPT-3 that has actually additionally been [trained](https://gitea.ruwii.com) on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://gitlab.mints-id.com) 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 produce working code in over a dozen shows languages, a lot of efficiently in Python. [192]
 <br>Several issues with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has been implicated of emitting copyrighted code, with no author attribution or license. [197]
 <br>OpenAI announced that they would discontinue support 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](https://massivemiracle.com) Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar exam 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 create approximately 25,000 words of text, and write code in all major programming languages. [200]
 <br>Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to expose various 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 and generate text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision standards, setting brand-new records in audio speech acknowledgment and [translation](https://avicii.blog). [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
 <br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized variation of GPT-4o replacing 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 especially useful for business, startups and designers seeking to automate services with [AI](https://dubaijobzone.com) agents. [208]
 <br>o1<br>
 <br>On September 12, 2024, [OpenAI launched](https://culturaitaliana.org) the o1-preview and o1-mini models, which have been developed to take more time to consider their reactions, causing greater precision. These models are especially efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Team members. [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 design. OpenAI likewise unveiled o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are [testing](https://repos.ubtob.net) o3 and o3-mini. [212] [213] Until January 10, 2025, safety 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 telecommunications services company O2. [215]
 <br>Deep research study<br>
 <br>Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform extensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools enabled, it reached an accuracy 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 analyze the semantic similarity between text and images. It can especially be used for  [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:IrwinCambage) image classification. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>Revealed in 2021, DALL-E is a Transformer design that creates images from [textual descriptions](https://ransomware.design). [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can produce pictures of reasonable things ("a stained-glass window with a picture of a blue strawberry") along with things that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021,  [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:AndreThorp2) no API or code is available.<br>
 <br>DALL-E 2<br>
 <br>In April 2022, OpenAI announced DALL-E 2, an [upgraded](http://yhxcloud.com12213) version of the design with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new rudimentary system for converting 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 effective model much better able to produce 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 brief detailed prompts [223] along with extend existing videos forwards or in reverse in time. [224] It can [produce videos](https://coding.activcount.info) with resolution as much as 1920x1080 or 1080x1920. The maximal length of created videos is [unidentified](https://git.devinmajor.com).<br>
 <br>Sora's advancement team called it after the Japanese word for "sky", to symbolize its "limitless innovative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system [utilizing publicly-available](http://work.diqian.com3000) videos along with copyrighted videos certified for that purpose, however 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,  [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2847718) specifying that it could produce videos approximately one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the design's abilities. [225] It acknowledged a few of its shortcomings, including struggles mimicing complex physics. [226] Will Douglas Heaven of the MIT [Technology Review](http://e-kou.jp) called the presentation videos "excellent", but noted that they need to have been cherry-picked and may not represent Sora's typical output. [225]
 <br>Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have actually revealed considerable interest in the [technology's](http://106.52.242.1773000) potential. In an interview, actor/filmmaker Tyler Perry [expressed](http://git.jcode.net) his awe at the innovation's ability to create reasonable video from text descriptions, mentioning its prospective to transform storytelling and content development. He said that his enjoyment about [Sora's possibilities](http://git.youkehulian.cn) was so strong that he had actually chosen to stop briefly prepare for expanding his Atlanta-based movie 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 design that can carry out multilingual speech recognition along with speech translation and language identification. [229]
 <br>Music generation<br>
 <br>MuseNet<br>
 <br>Released in 2019, [MuseNet](https://dokuwiki.stream) is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can [produce](http://thinkwithbookmap.com) songs with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to start fairly however then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web 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](https://167.172.148.934433) with vocals. After [training](https://fcschalke04fansclub.com) on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs song samples. OpenAI stated the songs "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" which "there is a substantial gap" between Jukebox and [human-generated music](https://1.214.207.4410333). The Verge stated "It's technologically outstanding, even if the results seem like mushy variations of songs that might feel familiar", while [Business Insider](https://phoebe.roshka.com) mentioned "remarkably, a few of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
 <br>User interfaces<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI introduced the Debate Game, which  to discuss toy issues in front of a human judge. The purpose is to research whether such an approach may help in auditing [AI](http://www5f.biglobe.ne.jp) choices and in establishing explainable [AI](https://git.sommerschein.de). [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 typically studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, and different versions of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, [ChatGPT](http://45.67.56.2143030) is an [artificial intelligence](https://se.mathematik.uni-marburg.de) tool built on top of GPT-3 that provides a conversational interface that enables users to ask questions in natural language. The system then responds with an answer within seconds.<br>