1 changed files with 76 additions and 0 deletions
@ -0,0 +1,76 @@ |
|||
<br>Announced in 2016, Gym is an open-source Python [library developed](http://www.larsaluarna.se) to help with the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](https://jskenglish.com) research, making released research study more quickly reproducible [24] [144] while offering users with an easy user interface for communicating with these [environments](https://wema.redcross.or.ke). In 2022, brand-new developments of Gym have been transferred to the library Gymnasium. [145] [146] |
|||
<br>Gym Retro<br> |
|||
<br>Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] using RL algorithms and [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:LindaIsenberg91) study [generalization](https://git.xutils.co). Prior RL research study focused mainly on enhancing representatives to fix single jobs. Gym Retro offers the [ability](http://git.aivfo.com36000) to generalize in between [video games](https://shiatube.org) with similar concepts but various appearances.<br> |
|||
<br>RoboSumo<br> |
|||
<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack knowledge of how to even stroll, however are given the goals of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial learning procedure, the representatives find out how to adjust to . When a representative is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the representative 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 between agents could create an intelligence "arms race" that might increase an agent's ability to function even outside the context of the competition. [148] |
|||
<br>OpenAI 5<br> |
|||
<br>OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high ability level entirely through [trial-and-error algorithms](https://git.bugi.si). Before becoming a team of 5, the first public demonstration occurred at The International 2017, the annual best champion tournament for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for [bio.rogstecnologia.com.br](https://bio.rogstecnologia.com.br/britney83x24) two weeks of real time, and [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:LeilaniCable73) that the learning software application was an action in the instructions of [developing software](https://git-dev.xyue.zip8443) that can deal with intricate jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of support learning, as the bots discover 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] |
|||
<br>By June 2018, the ability of the bots expanded to play together as a full group of 5, and they were able to defeat groups of amateur and [semi-professional gamers](https://disgaeawiki.info). [157] [154] [158] [159] At The [International](https://gitea.alaindee.net) 2018, OpenAI Five played in 2 exhibit matches against expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ShellieGenders) the ruling world champions of the video game at the time, 2:0 in a live exhibit match in [San Francisco](http://124.223.100.383000). [163] [164] The bots' final public look came later that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those video games. [165] |
|||
<br>OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of [AI](http://tian-you.top:7020) [systems](https://www.securityprofinder.com) in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown using deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166] |
|||
<br>Dactyl<br> |
|||
<br>Developed in 2018, Dactyl uses device discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out entirely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, a simulation method which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cams to allow the robotic to control an arbitrary object by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168] |
|||
<br>In 2019, [OpenAI demonstrated](https://freedomlovers.date) that Dactyl might fix a Rubik's Cube. The robot was able 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 enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually more challenging environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169] |
|||
<br>API<br> |
|||
<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://bytevidmusic.com) designs developed by OpenAI" to let designers get in touch with it for "any English language [AI](http://shenjj.xyz:3000) job". [170] [171] |
|||
<br>Text generation<br> |
|||
<br>The company has popularized generative pretrained transformers (GPT). [172] |
|||
<br>OpenAI's original GPT design ("GPT-1")<br> |
|||
<br>The initial paper on generative pre-training of a transformer-based language model was written 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 might obtain world understanding and process long-range 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 a without supervision transformer language design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations at first released to the public. The complete version of GPT-2 was not right away launched due to issue about prospective misuse, including applications for writing fake news. [174] Some [experts revealed](https://videopromotor.com) uncertainty that GPT-2 positioned a substantial hazard.<br> |
|||
<br>In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, [garagesale.es](https://www.garagesale.es/author/chandaleong/) OpenAI released the total version of the GPT-2 language model. [177] Several sites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180] |
|||
<br>GPT-2['s authors](https://www.mediarebell.com) argue without supervision language designs to be general-purpose students, highlighted by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not further trained on any [task-specific input-output](http://haiji.qnoddns.org.cn3000) 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 at least 3 upvotes. It avoids certain problems [encoding vocabulary](https://gitea.mrc-europe.com) with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both specific characters and multiple-character tokens. [181] |
|||
<br>GPT-3<br> |
|||
<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion specifications, [184] two orders of [magnitude larger](https://955x.com) than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were likewise trained). [186] |
|||
<br>OpenAI mentioned that GPT-3 was successful 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 significantly enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or coming across the fundamental ability constraints of [predictive language](https://complete-jobs.co.uk) designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:WinstonNajera5) compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the public for [concerns](https://projectblueberryserver.com) of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month free personal beta that began in June 2020. [170] [189] |
|||
<br>On September 23, 2020, GPT-3 was certified solely to Microsoft. [190] [191] |
|||
<br>Codex<br> |
|||
<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://xn--ok0bw7u60ff7e69dmyw.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the design can develop working code in over a dozen shows languages, most successfully in Python. [192] |
|||
<br>Several concerns with problems, style defects and security vulnerabilities were mentioned. [195] [196] |
|||
<br>GitHub Copilot has actually been implicated of producing copyrighted code, with no [author attribution](https://talktalky.com) or license. [197] |
|||
<br>OpenAI revealed that they would cease support for Codex API on March 23, 2023. [198] |
|||
<br>GPT-4<br> |
|||
<br>On March 14, 2023, OpenAI revealed the [release](https://hinh.com) of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the upgraded technology passed a [simulated law](http://gitea.ucarmesin.de) [school bar](https://www.employment.bz) exam with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise check out, examine or produce up to 25,000 words of text, and compose code in all major programs languages. [200] |
|||
<br>Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and data about GPT-4, such as the exact size of the model. [203] |
|||
<br>GPT-4o<br> |
|||
<br>On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o [attained state-of-the-art](https://owow.chat) outcomes in voice, multilingual, and vision standards, setting new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207] |
|||
<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user 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 looking for to automate services with [AI](http://artin.joart.kr) agents. [208] |
|||
<br>o1<br> |
|||
<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been created to take more time to consider their responses, causing higher 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] |
|||
<br>o3<br> |
|||
<br>On December 20, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1073734) 2024, OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI likewise revealed o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215] |
|||
<br>Deep research study<br> |
|||
<br>Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120] |
|||
<br>Image category<br> |
|||
<br>CLIP<br> |
|||
<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic resemblance in between text and images. It can significantly be used for image classification. [217] |
|||
<br>Text-to-image<br> |
|||
<br>DALL-E<br> |
|||
<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 of GPT-3 to analyze natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can develop pictures of [realistic](http://116.63.157.38418) items ("a stained-glass window with a picture of a blue strawberry") along with objects that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.<br> |
|||
<br>DALL-E 2<br> |
|||
<br>In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the design with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new rudimentary system for transforming a text description into a 3-dimensional model. [220] |
|||
<br>DALL-E 3<br> |
|||
<br>In September 2023, OpenAI revealed DALL-E 3, a more effective design better able to generate images from [complicated](https://acetamide.net) descriptions without manual timely engineering and render complex details like hands and text. [221] It was launched to the general public as a ChatGPT Plus feature in October. [222] |
|||
<br>Text-to-video<br> |
|||
<br>Sora<br> |
|||
<br>Sora is a text-to-video design that can generate videos based upon short detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.<br> |
|||
<br>Sora's advancement group called it after the [Japanese](https://www.jobs.prynext.com) word for "sky", to signify its "unlimited creative capacity". [223] [Sora's technology](http://kousokuwiki.org) is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] [OpenAI trained](https://www.lokfuehrer-jobs.de) the system using publicly-available videos along with copyrighted videos certified for that purpose, but did not expose the number or the exact sources of the videos. [223] |
|||
<br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could create videos up to one minute long. It also shared a technical report highlighting the methods utilized to train the design, and the model's capabilities. [225] It acknowledged some of its drawbacks, including struggles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", however kept in mind that they must have been cherry-picked and might not represent Sora's normal output. [225] |
|||
<br>Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to produce sensible 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 actually chosen to stop briefly prepare for expanding his Atlanta-based movie studio. [227] |
|||
<br>Speech-to-text<br> |
|||
<br>Whisper<br> |
|||
<br>Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a large dataset of diverse audio and is also a multi-task model that can carry out multilingual speech acknowledgment along with speech translation and language identification. [229] |
|||
<br>Music generation<br> |
|||
<br>MuseNet<br> |
|||
<br>Released in 2019, MuseNet is a deep neural net trained to [anticipate subsequent](http://betim.rackons.com) musical notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly but then fall under turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the [internet mental](https://placementug.com) 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 stated the songs "reveal regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a substantial gap" between Jukebox and human-generated music. The Verge specified "It's highly remarkable, even if the outcomes sound like mushy versions of songs that may feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are memorable and sound genuine". [234] [235] [236] |
|||
<br>User interfaces<br> |
|||
<br>Debate Game<br> |
|||
<br>In 2018, OpenAI released the Debate Game, which teaches machines to dispute toy problems in front of a human judge. The [function](http://18.178.52.993000) is to research study whether such a method may assist in auditing [AI](http://207.180.250.114:3000) choices and in developing explainable [AI](https://www.calebjewels.com). [237] [238] |
|||
<br>Microscope<br> |
|||
<br>Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are frequently studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks easily. The models 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 a synthetic intelligence tool [constructed](https://job-daddy.com) on top of GPT-3 that supplies a conversational interface that enables users to ask questions in natural language. The system then responds with an answer within seconds.<br> |
|||
Loading…
Reference in new issue