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<br>Announced in 2016, Gym is an open-source Python library created to assist in the development of support learning algorithms. It aimed to standardize how environments are specified in [AI](https://blablasell.com) research study, making published research study more quickly reproducible [24] [144] while providing users with an easy interface for engaging with these environments. In 2022, brand-new developments of Gym have 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 study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing agents to fix single tasks. Gym Retro offers the ability to generalize in between video games with similar ideas but different looks.<br> |
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<br>RoboSumo<br> |
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack knowledge of how to even stroll, but are given the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives find out how to adapt to altering conditions. When a representative is then eliminated from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could produce an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the [competitors](http://8.134.237.707999). [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 video game Dota 2, that find out to play against human gamers at a high skill level completely through trial-and-error algorithms. Before ending up being a team of 5, the first public presentation occurred at The International 2017, the annual premiere champion [tournament](http://121.40.81.1163000) 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 discovered by playing against itself for two weeks of actual time, and that the learning software application was an action in the direction of creating software application that can manage intricate tasks like a cosmetic surgeon. [152] [153] The system uses a type 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 eliminating an opponent and taking map goals. [154] [155] [156] |
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<br>By June 2018, the ability of the bots expanded to play together as a complete team of 5, and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public [appearance](http://gpra.jpn.org) came later on that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games. [165] |
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<br>OpenAI 5 in Dota 2's bot player shows the challenges of [AI](https://netgork.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep support 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 items. [167] It finds out completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a [simulation](https://git.paaschburg.info) technique which exposes the learner to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB cameras to allow the robot to control an approximate object by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI demonstrated that Dactyl could fix a [Rubik's Cube](https://cbfacilitiesmanagement.ie). The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by [utilizing Automatic](https://remnanthouse.tv) Domain Randomization (ADR), a simulation method of creating gradually harder environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [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](http://gitlab.flyingmonkey.cn:8929) models developed by OpenAI" to let designers contact it for "any English language [AI](https://yaseen.tv) task". [170] [171] |
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<br>Text generation<br> |
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<br>The company has promoted generative pretrained transformers (GPT). [172] |
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<br>OpenAI's original GPT design ("GPT-1")<br> |
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<br>The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model of language could obtain world [understanding](http://hanbitoffice.com) and process long-range dependencies 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 an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions at first released to the public. The full version of GPT-2 was not instantly launched due to concern about possible abuse, including applications for composing fake news. [174] Some professionals expressed uncertainty that GPT-2 postured a significant danger.<br> |
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<br>In response to GPT-2, the Allen Institute for Artificial Intelligence [reacted](http://47.109.153.573000) with a tool to identify "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the innovation 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 released the complete version of the GPT-2 [language](http://47.93.16.2223000) model. [177] Several websites host interactive demonstrations of various 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, illustrated by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 [zero-shot tasks](https://meetpit.com) (i.e. the design was not further trained on any task-specific input-output examples).<br> |
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<br>The corpus it was trained on, called WebText, contains a little 40 gigabytes of text from [URLs shared](https://rassi.tv) in Reddit submissions with at least 3 upvotes. It prevents 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 mentioned that the full variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were likewise trained). [186] |
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<br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184] |
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<br>GPT-3 drastically improved [benchmark outcomes](https://sound.co.id) over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or coming across the essential capability constraints of predictive language models. [187] Pre-training GPT-3 required numerous 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 model was not instantly [released](http://git.huxiukeji.com) to the general public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was licensed 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 actually furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://webheaydemo.co.uk) 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 create working code in over a dozen shows languages, most successfully in Python. [192] |
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<br>Several problems with glitches, design defects and security vulnerabilities were pointed out. [195] [196] |
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<br>GitHub Copilot has been accused of discharging copyrighted code, with no author attribution or license. [197] |
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<br>OpenAI announced that they would terminate support 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 revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the [upgraded innovation](http://106.14.65.137) 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 might also read, examine or generate approximately 25,000 words of text, and write code in all significant programming languages. [200] |
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<br>Observers reported that the iteration 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 problems with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and statistics about GPT-4, such as the [exact size](http://406.gotele.net) of the model. [203] |
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<br>GPT-4o<br> |
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<br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207] |
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<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller 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 anticipates it to be particularly beneficial for business, start-ups and designers seeking to automate services with [AI](https://library.kemu.ac.ke) representatives. [208] |
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<br>o1<br> |
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<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been created to take more time to think about their actions, resulting in greater accuracy. These designs are especially efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed 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 reasoning design. OpenAI likewise revealed o3-mini, a [lighter](https://git.devinmajor.com) and faster variation of OpenAI o3. As of December 21, 2024, this model 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 researchers had the opportunity to obtain early access to these models. [214] The model is called o3 instead of 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 study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web browsing, information analysis, and synthesis, providing detailed reports 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) 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 analyze the semantic similarity between text and images. It can significantly be used for image classification. [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 design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and create corresponding images. It can develop pictures of practical [objects](https://gogs.artapp.cn) ("a stained-glass window with an image of a blue strawberry") in addition to things that do not exist in truth ("a cube with the texture of a porcupine"). Since 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 updated version of the design with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new fundamental system for transforming a text description into a 3-dimensional model. [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 design better able to generate images from intricate descriptions without manual prompt engineering and [it-viking.ch](http://it-viking.ch/index.php/User:JasonHeavener88) render intricate details like hands and text. [221] It was launched 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 create videos based upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.<br> |
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<br>Sora's advancement group called it after the Japanese word for "sky", to symbolize its "limitless creative 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 videos in addition to copyrighted videos licensed for that function, however did not reveal the number or the [specific sources](https://arlogjobs.org) of the videos. [223] |
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<br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might create videos up to one minute long. It also shared a technical report highlighting the methods used to train the model, and the design's abilities. [225] It acknowledged a few of its imperfections, including battles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but kept in mind that they must have been cherry-picked and may not represent Sora's [normal output](https://git.tesinteractive.com). [225] |
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<br>Despite [uncertainty](https://heyanesthesia.com) from some academic leaders following Sora's public demo, significant entertainment-industry figures have actually shown significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to produce realistic video from text descriptions, citing its potential to revolutionize storytelling and content production. He said that his enjoyment about [Sora's possibilities](https://git.juxiong.net) was so strong that he had chosen to stop briefly strategies for expanding his Atlanta-based motion picture 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 [recognition](https://thenolugroup.co.za) design. [228] It is trained on a big dataset of [varied audio](https://ubuntushows.com) and is likewise a multi-task design that can perform multilingual speech acknowledgment along with 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 predict subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly however then fall into mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to produce music for the [titular character](https://freeads.cloud). [232] [233] |
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<br>Jukebox<br> |
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<br>[Released](http://valueadd.kr) 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 bit of lyrics and outputs song samples. OpenAI specified the songs "reveal local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" and that "there is a significant gap" between Jukebox and human-generated music. The Verge specified "It's highly outstanding, even if the outcomes seem like mushy versions of songs that might feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are appealing and sound genuine". [234] [235] [236] |
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<br>Interface<br> |
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<br>Debate Game<br> |
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<br>In 2018, [OpenAI introduced](http://h2kelim.com) the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The purpose is to research whether such a technique may assist in auditing [AI](https://corevacancies.com) decisions and in establishing explainable [AI](https://frce.de). [237] [238] |
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<br>Microscope<br> |
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<br>[Released](https://vooxvideo.com) in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network models which are often studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various 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 is an artificial intelligence tool built on top of GPT-3 that provides a conversational user [interface](https://sound.co.id) that permits users to ask questions in [natural language](https://lekoxnfx.com4000). The system then reacts with a [response](https://crossroad-bj.com) within seconds.<br> |
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