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 <br>Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](http://20.198.113.167:3000) research study, making released research more quickly reproducible [24] [144] while supplying users with an easy user interface for communicating with these environments. In 2022, new advancements of Gym have actually been [transferred](https://git-dev.xyue.zip8443) to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for support knowing (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing representatives to resolve single tasks. Gym Retro provides the capability to generalize in between video games with similar concepts however different looks.<br>
 <br>RoboSumo<br>
 <br>Released in 2017,  [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) RoboSumo is a virtual world where humanoid metalearning robot agents initially do not have understanding of how to even stroll, however are provided the objectives of [finding](http://touringtreffen.nl) out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the [representatives](https://git.kimcblog.com) find out how to adjust to changing conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could produce an intelligence "arms race" that might increase an agent's capability to function even outside the context of the competitors. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that find out to play against human players at a high skill level totally through experimental algorithms. Before becoming a team of 5, the very first public demonstration occurred at The International 2017, the annual best champion competition for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for two weeks of real time, which the learning software application was a step in the direction of creating software that can deal with intricate tasks like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots discover gradually by playing against themselves hundreds of times a day for months, and are rewarded for [actions](https://git.christophhagen.de) such as killing an opponent and taking map objectives. [154] [155] [156]
 <br>By June 2018, the capability of the [bots expanded](https://cozwo.com) to play together as a complete group of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against [professional](https://www.paknaukris.pro) gamers,  [raovatonline.org](https://raovatonline.org/author/alvaellwood/) but wound 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 exhibition match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those [video games](http://47.97.159.1443000). [165]
 <br>OpenAI 5's mechanisms in Dota 2's bot player reveals the difficulties of [AI](https://git.guildofwriters.org) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated using deep reinforcement learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl utilizes machine discovering to train a Shadow Hand, a [human-like robotic](https://letsstartjob.com) hand, to manipulate physical items. [167] It finds out totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the things [orientation issue](https://job.duttainnovations.com) by using domain randomization, a simulation technique which exposes the student to a variety of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB electronic cameras to allow the robot to control an arbitrary item by seeing it. In 2018, OpenAI revealed 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 had the ability 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 enhancing the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR),  [demo.qkseo.in](http://demo.qkseo.in/profile.php?id=995691) a simulation technique of producing progressively more challenging environments. ADR varies from manual domain randomization 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 new [AI](https://www.jungmile.com) designs developed by OpenAI" to let designers contact it for "any English language [AI](http://202.90.141.17:3000) task". [170] [171]
 <br>Text generation<br>
 <br>The company has promoted generative pretrained transformers (GPT). [172]
 <br>OpenAI's initial GPT design ("GPT-1")<br>
 <br>The initial paper on generative pre-training of a transformer-based language design 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](https://admithel.com) could obtain world knowledge and [procedure long-range](https://flowndeveloper.site) reliances 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 an unsupervised transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative [versions](http://www.grainfather.co.nz) initially launched to the general public. The complete version of GPT-2 was not right away released due to issue about potential misuse, including applications for composing fake news. [174] Some professionals revealed [uncertainty](https://origintraffic.com) that GPT-2 postured a considerable risk.<br>
 <br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence [responded](https://git.wisptales.org) with a tool to discover "neural phony news". [175] Other researchers, such as Jeremy Howard, cautioned of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language model. [177] Several websites host interactive [presentations](http://119.29.169.1578081) of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
 <br>GPT-2's authors argue not being watched language models to be general-purpose learners, shown by GPT-2 attaining advanced precision and [perplexity](https://www.uaehire.com) on 7 of 8 zero-shot tasks (i.e. the design was not additional trained on any task-specific [input-output](http://39.106.8.2463003) 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 a minimum of 3 upvotes. It prevents 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 3 (GPT-3) is an unsupervised transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned 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 complete version of GPT-2 (although GPT-3 designs with as few as 125 million parameters were also trained). [186]
 <br>OpenAI stated 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 dramatically enhanced benchmark results over GPT-2. OpenAI warned that such [scaling-up](https://gogs.tyduyong.com) of language designs might be approaching or experiencing the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month totally free personal beta that started in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was [certified exclusively](https://ezworkers.com) 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 on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://git.protokolla.fi) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the model can [develop](https://taar.me) working code in over a dozen shows languages, many effectively in Python. [192]
 <br>Several problems with problems, design flaws and security vulnerabilities were cited. [195] [196]
 <br>GitHub Copilot has actually been accused of emitting copyrighted code, without any author attribution or license. [197]
 <br>OpenAI revealed 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), efficient in accepting text or image inputs. [199] They revealed that the updated innovation passed a simulated law school bar examination 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 likewise check out, analyze or generate up to 25,000 words of text, and write code in all major programs languages. [200]
 <br>Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has [decreased](https://starfc.co.kr) to expose various technical details and statistics about GPT-4, such as the accurate size of the model. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, [OpenAI revealed](http://xn--ok0b74gbuofpaf7p.com) and  [disgaeawiki.info](https://disgaeawiki.info/index.php/User:SusieGoodwin) released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision criteria, setting new records in audio speech acknowledgment and [translation](https://body-positivity.org). [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 launched GPT-4o mini, a smaller sized version of GPT-4o changing 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 it to be especially beneficial for business, start-ups and developers looking for to automate services with [AI](http://gitea.smartscf.cn:8000) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been developed to take more time to consider their actions, causing greater accuracy. These designs are especially efficient 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 revealed o3, the follower of the o1 thinking design. OpenAI likewise unveiled o3-mini, a lighter and much [faster variation](http://40th.jiuzhai.com) of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]
 <br>Deep research<br>
 <br>Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It [leverages](http://turtle.tube) the capabilities of [OpenAI's](https://jobstaffs.com) o3 model to carry out comprehensive web surfing, information analysis, and synthesis, delivering detailed [reports](https://nbc.co.uk) within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) [benchmark](http://101.200.181.61). [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 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 design that produces 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 an unfortunate capybara") and generate corresponding images. It can develop pictures of realistic things ("a stained-glass window with a picture of a blue strawberry") in addition to things that do not exist in truth ("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 variation of the design with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new basic system for converting a text description into a 3-dimensional design. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI announced DALL-E 3, a more powerful model much better able to create images from complicated descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was released to the public as a ChatGPT Plus [feature](https://praca.e-logistyka.pl) in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video model that can produce videos based upon short detailed triggers [223] along with [extend existing](https://quikconnect.us) videos forwards or in reverse in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.<br>
 <br>Sora's development [team named](http://git.mvp.studio) it after the Japanese word for "sky", to symbolize its "limitless imaginative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that purpose, but did not reveal the number or the specific sources of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, [mentioning](http://159.75.133.6720080) that it might create videos approximately one minute long. It likewise shared a technical report highlighting the approaches utilized to train the design, and the design's capabilities. [225] It acknowledged a few of its imperfections, including struggles mimicing complicated physics. [226] Will  of the MIT Technology Review called the demonstration videos "outstanding", but noted that they should 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, noteworthy entertainment-industry figures have shown substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler [Perry expressed](http://120.201.125.1403000) his astonishment at the technology's ability to generate sensible video from text descriptions, citing its prospective to transform storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly plans for broadening his Atlanta-based motion picture 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 big dataset of diverse audio and is likewise a multi-task model that can perform multilingual speech recognition 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 predict subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to start fairly but then fall under turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web 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 generate 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 specified the songs "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" which "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the results seem like mushy variations of songs that might feel familiar", while Business Insider specified "surprisingly, some of the resulting tunes are catchy and sound genuine". [234] [235] [236]
 <br>User user interfaces<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI launched the Debate Game, which teaches devices to debate toy issues in front of a human judge. The purpose is to research whether such an approach may assist in auditing [AI](http://jobshut.org) choices and in establishing explainable [AI](http://8.134.237.70:7999). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of 8 neural network designs which are typically studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and different variations of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is an expert system tool built on top of GPT-3 that provides a conversational user interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.<br>