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<br>Announced in 2016, Gym is an open-source Python library created to facilitate the development of support knowing [algorithms](https://www.medicalvideos.com). It aimed to standardize how environments are defined in [AI](https://git.jamarketingllc.com) research, making published research study more quickly reproducible [24] [144] while providing users with a simple user interface for communicating with these environments. In 2022, brand-new advancements of Gym have actually been transferred to the library Gymnasium. [145] [146]
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<br>Gym Retro<br>
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<br>Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to resolve single jobs. Gym Retro provides the capability to generalize between video games with similar ideas however various looks.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, [RoboSumo](https://beautyteria.net) is a virtual world where humanoid metalearning robotic representatives at first lack understanding of how to even stroll, but are offered the goals of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives learn how to adjust to changing conditions. When an agent is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents could develop an intelligence "arms race" that might increase an agent's ability to function even outside the context of the competition. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, [yewiki.org](https://www.yewiki.org/User:ElenaGrenda45) that [discover](http://62.210.71.92) to play against human players at a high ability level entirely through experimental algorithms. Before ending up being a group of 5, the first public presentation occurred at The International 2017, the annual premiere 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 found out by playing against itself for two weeks of genuine time, which the learning software was an action in the instructions of creating software that can deal with complex jobs like a surgeon. [152] [153] The system uses a form of support learning, as the bots discover in time by playing against themselves numerous times a day for months, and are rewarded for [actions](http://223.68.171.1508004) such as eliminating an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the ability of the bots broadened to play together as a full group of 5, and they had the to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those video games. [165]
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<br>OpenAI 5's mechanisms in Dota 2's bot gamer [reveals](https://eelam.tv) the difficulties of [AI](http://git.nuomayun.com) systems in multiplayer online fight arena (MOBA) [video games](https://alllifesciences.com) and how OpenAI Five has actually shown using deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl uses machine learning to train a Shadow Hand, a human-like robotic hand, to control physical objects. [167] It discovers entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation method which exposes the learner to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB [cameras](https://ravadasolutions.com) 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]
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<br>In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex 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 approach of creating progressively more challenging environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](https://138.197.71.160) models developed by OpenAI" to let designers contact it for "any English language [AI](http://gogs.kexiaoshuang.com) job". [170] [171]
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<br>Text generation<br>
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<br>The business has promoted generative pretrained transformers (GPT). [172]
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<br>OpenAI's initial GPT design ("GPT-1")<br>
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<br>The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative design of language could obtain world understanding and procedure long-range dependences by pre-training on a varied corpus with long stretches of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions at first released to the general public. The complete version of GPT-2 was not instantly released due to issue about possible misuse, including applications for writing phony news. [174] Some professionals revealed uncertainty that GPT-2 posed a substantial threat.<br>
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<br>In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural phony news". [175] Other scientists, 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 drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language model. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue unsupervised language designs to be general-purpose students, shown by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not further trained on any [task-specific input-output](http://8.140.205.1543000) examples).<br>
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<br>The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain issues encoding 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]
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<br>GPT-3<br>
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as few as 125 million criteria were likewise trained). [186]
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<br>OpenAI stated that GPT-3 was [successful](http://201.17.3.963000) at certain "meta-learning" jobs and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184]
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<br>GPT-3 drastically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or encountering the essential capability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, [compared](https://gitlab.grupolambda.info.bo) 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 to the public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month totally free private beta that started in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://20.112.29.181) 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 working code in over a lots shows languages, many effectively in Python. [192]
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<br>Several concerns with problems, style defects and security vulnerabilities were pointed out. [195] [196]
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<br>[GitHub Copilot](https://gitlab.iue.fh-kiel.de) has actually been accused of [producing copyrighted](https://kcshk.com) code, with no author attribution or license. [197]
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<br>OpenAI revealed that they would stop assistance for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<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 upgraded innovation passed a simulated law school bar examination 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 likewise read, [surgiteams.com](https://surgiteams.com/index.php/User:LatanyaZiegler) examine or produce approximately 25,000 words of text, and [compose code](https://v-jobs.net) in all major programs languages. [200]
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<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal numerous technical details and statistics about GPT-4, such as the exact size of the model. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI announced and launched GPT-4o, which can [process](http://111.8.36.1803000) and generate text, images and audio. [204] GPT-4o attained state-of-the-art lead to 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) benchmark compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI launched GPT-4o mini, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:PHZIsis067429) a smaller variation 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 anticipates it to be especially helpful for business, start-ups and designers seeking to automate services with [AI](http://parasite.kicks-ass.org:3000) agents. [208]
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<br>o1<br>
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<br>On September 12, 2024, [OpenAI released](https://www.huntsrecruitment.com) the o1-preview and o1-mini designs, which have been designed to take more time to believe about their responses, causing higher accuracy. These models are particularly [effective](http://106.15.41.156) in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was [changed](https://desarrollo.skysoftservicios.com) by o1. [211]
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<br>o3<br>
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<br>On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI also 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 [evaluating](http://123.206.9.273000) 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 rather than o2 to prevent confusion with telecoms companies O2. [215]
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<br>Deep research study<br>
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<br>Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform extensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
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<br>Image category<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to evaluate the semantic resemblance in between text and images. It can notably be used for image category. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, DALL-E is a Transformer model that creates images from textual descriptions. [218] DALL-E uses a 12[-billion-parameter variation](https://social.instinxtreme.com) of GPT-3 to interpret natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can produce pictures of sensible items ("a stained-glass window with an image of a blue strawberry") along with things 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>
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<br>DALL-E 2<br>
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<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 brand-new rudimentary system for transforming a text description into a 3-dimensional design. [220]
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<br>DALL-E 3<br>
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<br>In September 2023, OpenAI revealed DALL-E 3, a more powerful model much better able to produce images from complicated descriptions without manual timely engineering and render complicated details like hands and text. [221] It was released to the public as a ChatGPT Plus feature in October. [222]
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<br>Text-to-video<br>
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<br>Sora<br>
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<br>Sora is a text-to-video design that can generate videos based on brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with [resolution](https://alapcari.com) as much as 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.<br>
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<br>Sora's development group named it after the Japanese word for "sky", to symbolize its "limitless imaginative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] [OpenAI trained](https://git.zyhhb.net) the system using publicly-available videos along with copyrighted videos accredited for that purpose, however did not expose the number or the exact sources of the videos. [223]
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<br>OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might create videos approximately one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the model's abilities. [225] It acknowledged some of its drawbacks, including battles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but noted that they must have been cherry-picked and may not represent Sora's normal output. [225]
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<br>Despite [uncertainty](http://101.34.66.2443000) from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have revealed substantial interest in the innovation's potential. In an interview, actor/[filmmaker Tyler](https://gitlab.kitware.com) Perry revealed his astonishment at the technology's ability to produce practical video from text descriptions, mentioning its prospective to change storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly prepare for expanding his Atlanta-based movie studio. [227]
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<br>Speech-to-text<br>
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<br>Whisper<br>
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<br>Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of varied audio and is also a multi-task model that can carry out multilingual speech [acknowledgment](http://stackhub.co.kr) as well as speech translation and language identification. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>Released in 2019, MuseNet is a deep neural net trained to [forecast subsequent](http://chillibell.com) 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 however then fall under turmoil the longer it plays. [230] [231] In popular culture, initial 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]
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<br>Jukebox<br>
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<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 category, artist, and a snippet of lyrics and outputs tune [samples](http://www.withsafety.net). OpenAI specified the songs "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" which "there is a significant gap" in between Jukebox and human-generated music. The Verge mentioned "It's technologically outstanding, even if the results sound like mushy versions of tunes that might feel familiar", while [Business Insider](http://47.75.109.82) specified "remarkably, some of the resulting tunes are memorable and sound genuine". [234] [235] [236]
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<br>User user interfaces<br>
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<br>Debate Game<br>
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<br>In 2018, OpenAI released the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The function is to research whether such a technique may assist in [auditing](https://homejobs.today) [AI](https://clubamericafansclub.com) choices and in establishing explainable [AI](http://szyg.work:3000). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are typically studied in interpretability. [240] Microscope was developed to analyze the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, different variations of Inception, and various versions of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, [ChatGPT](https://redmonde.es) is an expert system tool built on top of GPT-3 that provides a conversational user interface that enables users to ask concerns in natural language. The system then responds with a response within seconds.<br>
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