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<br>Announced in 2016, Gym is an open-source Python library developed to help with the advancement of support knowing algorithms. It aimed to standardize how environments are specified in [AI](https://gitlab.xfce.org) research study, making released research more easily reproducible [24] [144] while offering users with a simple user interface for interacting with these environments. In 2022, new advancements of Gym have actually been moved 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 focused mainly on optimizing agents to solve single tasks. Gym Retro gives the ability to generalize in between video games with [comparable](https://git.declic3000.com) 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 agents at first do not have understanding of how to even stroll, but are given the objectives of finding out to move and to press the opposing agent out of the ring. [148] Through this [adversarial](http://187.216.152.1519999) learning process, the agents discover how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might 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 group of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high skill level completely through trial-and-error algorithms. Before ending up being a group of 5, the first public presentation took place at The International 2017, the annual premiere champion competition for the game, where Dendi, an expert Ukrainian player, 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](https://younetwork.app) against itself for two weeks of actual time, which the knowing software was an action in the instructions of developing software that can manage intricate tasks like a surgeon. [152] [153] The system utilizes a type of support knowing, as the bots learn in 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 objectives. [154] [155] [156] |
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<br>By June 2018, the capability of the bots broadened to play together as a full group of 5, and they were able to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert gamers, however ended up losing both [video games](https://inamoro.com.br). [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs 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 video games in a four-day open online competitors, winning 99.4% of those video games. [165] |
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<br>OpenAI 5's systems in Dota 2's bot player shows the obstacles of [AI](https://git.poloniumv.net) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated using deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166] |
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<br>Dactyl<br> |
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<br>Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robotic hand, to control physical objects. [167] It finds out completely in simulation using the exact same RL algorithms and [training](https://saopaulofansclub.com) code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation method which exposes the learner to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB cams to permit the robot to control an [approximate](https://git.spitkov.hu) things by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168] |
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<br>In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robot had the [ability](http://tobang-bangsu.co.kr) to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by [enhancing](https://careerjunction.org.in) the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating gradually more tough environments. ADR varies from manual domain randomization by not requiring a human to specify 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 new [AI](https://gitea.qianking.xyz:3443) designs developed by OpenAI" to let developers call on it for "any English language [AI](https://losangelesgalaxyfansclub.com) task". [170] [171] |
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<br>Text generation<br> |
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<br>The company has actually 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 original 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 website on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding and process long-range reliances by pre-training on a diverse corpus with long stretches of adjoining text.<br> |
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<br>GPT-2<br> |
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<br>Generative [Pre-trained Transformer](https://www.e-vinil.ro) 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative variations initially released to the public. The complete variation of GPT-2 was not instantly released due to concern about potential misuse, including applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 positioned a significant threat.<br> |
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<br>In action to GPT-2, the Allen Institute for [Artificial Intelligence](https://tweecampus.com) reacted with a tool to detect "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted 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 sites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [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 advanced 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> |
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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 at least 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both individual 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 design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion specifications, [184] two orders of than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186] |
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<br>OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and might generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184] |
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<br>GPT-3 dramatically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or coming across the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, [compared](http://8.134.61.1073000) to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a [two-month totally](https://superblock.kr) free personal beta that began in June 2020. [170] [189] |
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<br>On September 23, 2020, GPT-3 was certified solely to [Microsoft](https://git.fpghoti.com). [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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://116.204.119.171:3000) 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 produce working code in over a lots shows languages, many effectively in Python. [192] |
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<br>Several problems with glitches, design defects and security vulnerabilities were mentioned. [195] [196] |
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<br>GitHub Copilot has actually been implicated of discharging copyrighted code, with no author attribution or license. [197] |
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<br>OpenAI revealed that they would cease support for [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:WendellAnthon10) 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), [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1095188) efficient in accepting text or image inputs. [199] They announced that the [updated technology](http://hjl.me) 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 also check out, analyze or generate up to 25,000 words of text, and compose code in all significant programs languages. [200] |
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<br>Observers reported that the iteration of ChatGPT using GPT-4 was an [improvement](https://mp3talpykla.com) on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose numerous technical details and data about GPT-4, such as the accurate 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 released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision benchmarks, [setting](http://minority2hire.com) new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) [benchmark](https://www.jobexpertsindia.com) [compared](https://allcallpro.com) 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 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 particularly beneficial for business, start-ups and developers seeking to automate services with [AI](http://xintechs.com:3000) agents. [208] |
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<br>o1<br> |
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<br>On September 12, 2024, [OpenAI released](https://www.oscommerce.com) the o1-preview and o1-mini designs, which have been developed to take more time to think about their reactions, leading to greater precision. These models are particularly reliable in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced 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 model. OpenAI also unveiled o3-mini, a lighter and much [faster variation](http://ufidahz.com.cn9015) of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these [designs](https://itconsulting.millims.com). [214] The design is called o3 instead of o2 to prevent confusion with telecommunications services provider O2. [215] |
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<br>Deep research<br> |
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<br>Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out substantial web browsing, information analysis, and synthesis, delivering detailed [reports](https://jobskhata.com) within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120] |
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<br>Image classification<br> |
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<br>CLIP<br> |
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic resemblance in between text and images. It can notably be utilized 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 design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to [interpret natural](http://hjl.me) language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce pictures of realistic objects ("a stained-glass window with a picture of a blue strawberry") in addition to objects 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> |
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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 model with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new fundamental system for converting 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, [gratisafhalen.be](https://gratisafhalen.be/author/redajosephs/) OpenAI announced DALL-E 3, a more effective design better able to create images from [intricate descriptions](https://ideezy.com) without manual timely engineering and render intricate details like hands and text. [221] It was [released](http://ufidahz.com.cn9015) 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 upon brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unknown.<br> |
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<br>Sora's development team named it after the Japanese word for "sky", to represent its "unlimited 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 along with copyrighted videos accredited for that function, however did not reveal the number or the precise 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, specifying that it might generate videos up to one minute long. It likewise shared a technical report highlighting the [methods](http://125.43.68.2263001) used to train the model, and the design's abilities. [225] It acknowledged some of its imperfections, consisting of struggles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but noted that they should have been cherry-picked and may not represent Sora's common output. [225] |
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<br>Despite uncertainty from some scholastic leaders following Sora's public demo, notable 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 technology's ability to produce practical video from text descriptions, citing its prospective to transform storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause plans for broadening 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](https://www.yaweragha.com) recognition design. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can carry out multilingual speech recognition as well as speech translation and language recognition. [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](http://jialcheerful.club3000) to forecast subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a tune created 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 internet mental 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 generate music with vocals. After training on 1.2 million samples, the system [accepts](http://git.andyshi.cloud) a genre, artist, and a bit of lyrics and outputs tune [samples](https://git.starve.space). OpenAI mentioned the tunes "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a substantial space" in between Jukebox and human-generated music. The Verge mentioned "It's technically excellent, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are memorable and sound legitimate". [234] [235] [236] |
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<br>User interfaces<br> |
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<br>Debate Game<br> |
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<br>In 2018, OpenAI introduced the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research whether such a [technique](http://47.114.82.1623000) may assist in auditing [AI](https://git.xaviermaso.com) decisions and in developing explainable [AI](http://sintec-rs.com.br). [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 nerve cell of 8 neural network models which are often studied in interpretability. [240] Microscope was developed to analyze the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, [wakewiki.de](https://www.wakewiki.de/index.php?title=Benutzer:FranchescaMbx) and different versions of [CLIP Resnet](http://keenhome.synology.me). [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 offers a conversational interface that permits users to ask questions in [natural language](https://bertlierecruitment.co.za). The system then reacts with a response within seconds.<br> |
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