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 <br>Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of reinforcement learning [algorithms](http://www.mouneyrac.com). It aimed to standardize how environments are specified in [AI](https://xn--114-2k0oi50d.com) research, making published research more quickly reproducible [24] [144] while supplying users with a simple user interface for interacting with these environments. In 2022, new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for  [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) reinforcement learning (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to resolve single jobs. Gym Retro provides the ability to generalize between games with [comparable concepts](https://dash.bss.nz) however different appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot [agents initially](https://git.collincahill.dev) lack understanding of how to even stroll, but are provided the objectives of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could produce an intelligence "arms race" that might increase an agent's capability to work even outside the context of the competitors. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high ability level totally through experimental algorithms. Before becoming a group of 5, the first public demonstration took place at The International 2017, the yearly best championship tournament for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of actual time, and that the learning software application was a step in the direction of creating software application that can deal with intricate jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement knowing, 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 capability of the [bots expanded](http://xintechs.com3000) to play together as a full team of 5, and they were able to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the [reigning](https://manilall.com) world [champions](http://vimalakirti.com) of the game at the time, 2:0 in a [live exhibit](http://148.66.10.103000) match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall video games in a [four-day](http://gitlab.lvxingqiche.com) open online competitors, winning 99.4% of those games. [165]
 <br>OpenAI 5's systems in Dota 2's bot player shows the obstacles of [AI](https://4kwavemedia.com) systems in [multiplayer online](https://www.ayuujk.com) battle arena (MOBA) games and how OpenAI Five has actually shown using deep reinforcement learning (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>[Developed](https://www.assistantcareer.com) in 2018, Dactyl uses maker finding out to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It learns entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by using domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB video cameras to allow the robot to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating progressively more challenging environments. ADR varies from manual domain [randomization](https://www.graysontalent.com) by not requiring 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 brand-new [AI](https://www.graysontalent.com) models established by OpenAI" to let developers call on it for "any English language [AI](http://101.231.37.170:8087) job". [170] [171]
 <br>Text generation<br>
 <br>The business has promoted 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 model was written by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world understanding and process long-range dependences 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 an unsupervised transformer language design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted [demonstrative versions](https://www.meditationgoodtip.com) initially launched to the general public. The complete variation of GPT-2 was not right away released due to concern about possible misuse, including applications for [composing](http://www.heart-hotel.com) phony news. [174] Some specialists expressed uncertainty that GPT-2 presented a significant risk.<br>
 <br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to completely 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 released the complete version of the GPT-2 language model. [177] Several websites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
 <br>GPT-2's authors argue not being watched language designs to be [general-purpose](http://www.xyais.com) students, highlighted by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (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](http://dev.ccwin-in.com3000) pair encoding. This permits [representing](http://47.114.82.1623000) any string of [characters](https://allcollars.com) 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 design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were also trained). [186]
 <br>OpenAI specified that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
 <br>GPT-3 dramatically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a [paid cloud](https://mediawiki.hcah.in) API after a two-month complimentary private beta that began in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was [certified exclusively](http://xintechs.com3000) 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://www.iway.lk) 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 develop working code in over a dozen shows languages, many efficiently in Python. [192]
 <br>Several concerns with glitches, design flaws and security vulnerabilities were cited. [195] [196]
 <br>GitHub Copilot has actually been accused of discharging 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 announced the release of Generative Pre-trained 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 could likewise check out, analyze or generate approximately 25,000 words of text, and compose code in all significant programming languages. [200]
 <br>Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose different technical details and stats 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 GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern outcomes in voice, multilingual, and vision benchmarks, setting brand-new [records](https://recruitment.econet.co.zw) 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 variation of GPT-4o replacing GPT-3.5 Turbo on the [ChatGPT interface](https://sosyalanne.com). 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 anticipates it to be particularly beneficial for business, startups and designers seeking to automate services with [AI](http://60.205.104.179:3000) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1[-preview](https://chemitube.com) and o1-mini models, which have been designed to take more time to think about their actions, leading to higher accuracy. These designs are especially efficient in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
 <br>o3<br>
 <br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking design. OpenAI likewise revealed o3-mini, a lighter and [quicker variation](https://giaovienvietnam.vn) of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and [security scientists](https://jobstoapply.com) had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with [telecoms companies](https://103.1.12.176) O2. [215]
 <br>Deep research study<br>
 <br>Deep research study is a [representative established](https://healthcarestaff.org) by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out [comprehensive web](https://demo.pixelphotoscript.com) surfing, information analysis, and synthesis, delivering [detailed reports](https://humped.life) within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
 <br>Image classification<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 especially be utilized for image classification. [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]  a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can [produce images](https://rna.link) of sensible things ("a stained-glass window with an image of a blue strawberry") as well as 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 updated version of the design with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new rudimentary system for converting a text description into a 3[-dimensional](https://realestate.kctech.com.np) design. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI revealed DALL-E 3, a more powerful model better able to produce images from complicated descriptions without manual prompt engineering and render [complex details](https://idemnaposao.rs) like hands and text. [221] It was launched 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 generate videos based upon brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.<br>
 <br>Sora's development group named it after the Japanese word for "sky", to represent its "endless imaginative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that purpose, however did not reveal the number or the specific sources of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it might create videos approximately one minute long. It also shared a technical report highlighting the approaches used to train the model, and the design's capabilities. [225] It acknowledged some of its shortcomings, consisting of battles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but kept in mind that they need to have been cherry-picked and may not represent Sora's normal output. [225]
 <br>Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have shown substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to create realistic video from text descriptions, citing its prospective to revolutionize storytelling and content production. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause prepare 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](https://sujansadhu.com) acknowledgment model. [228] It is trained on a big dataset of diverse audio and is likewise a [multi-task](https://gigsonline.co.za) design that can carry out multilingual speech recognition in addition to speech translation and language identification. [229]
 <br>Music generation<br>
 <br>MuseNet<br>
 <br>Released in 2019, MuseNet is a deep neural net [trained](http://www.fasteap.cn3000) to forecast subsequent musical notes in MIDI music files. It can create tunes 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 pop culture, preliminary applications of this tool were utilized as early as 2020 for the web mental 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](https://www.yohaig.ng) to create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song samples. [OpenAI mentioned](https://recruitment.econet.co.zw) the tunes "show regional musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a significant gap" in between Jukebox and human-generated music. The Verge stated "It's technologically impressive, even if the outcomes sound like mushy variations of songs that may feel familiar", while Business Insider stated "surprisingly, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI introduced the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The purpose is to research whether such a method may help in auditing [AI](https://notitia.tv) choices and in establishing explainable [AI](https://travelpages.com.gh). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network models which are frequently studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different variations of Inception, and various variations of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that supplies a conversational user interface that allows users to ask concerns in natural language. The system then reacts with an answer within seconds.<br>