commit
55c546067e
@ -0,0 +1,76 @@
|
||||
<br>Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](https://clousound.com) research, [gratisafhalen.be](https://gratisafhalen.be/author/conradpatte/) making published research more quickly reproducible [24] [144] while offering users with a simple user interface for interacting with these environments. In 2022, 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 knowing (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to fix single tasks. [Gym Retro](http://47.107.29.613000) provides the ability to generalize in between games with similar concepts however various appearances.<br>
|
||||
<br>RoboSumo<br>
|
||||
<br>Released in 2017, RoboSumo is a virtual world where [humanoid metalearning](http://www.andreagorini.it) robotic representatives initially do not have understanding of how to even walk, however are provided the goals of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents learn how to adjust to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could develop an intelligence "arms race" that could increase a representative's ability to work even outside the context of the competitors. [148]
|
||||
<br>OpenAI 5<br>
|
||||
<br>OpenAI Five is a group of 5 utilized in the competitive five-on-five video game Dota 2, that find out to play against human players at a high [skill level](https://git.es-ukrtb.ru) entirely through trial-and-error algorithms. Before ending up being a group of 5, the very first public demonstration occurred at The International 2017, the yearly best champion competition for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by [playing](https://gitea.evo-labs.org) against itself for two weeks of actual time, and that the learning software application was a step in the [instructions](https://78.47.96.1613000) of developing software that can handle intricate tasks like a surgeon. [152] [153] The system utilizes a type of reinforcement learning, as the bots find out gradually by playing against themselves numerous times a day for months, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:EarlMouton55) and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
|
||||
<br>By June 2018, the capability of the bots broadened to play together as a full team of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against [professional](https://techtalent-source.com) players, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 total games in a four-day open online competitors, [winning](https://git.camus.cat) 99.4% of those games. [165]
|
||||
<br>OpenAI 5's systems in Dota 2's bot gamer shows the difficulties of [AI](https://mulkinflux.com) systems in [multiplayer online](http://120.77.205.309998) fight arena (MOBA) video games and how OpenAI Five has actually shown using deep reinforcement learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
|
||||
<br>Dactyl<br>
|
||||
<br>Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It learns completely 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 approach which exposes the student to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having [movement tracking](https://www.letsauth.net9999) electronic cameras, also has RGB cams to permit the robot to control an arbitrary things by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
|
||||
<br>In 2019, [it-viking.ch](http://it-viking.ch/index.php/User:GennieRedman012) OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing gradually more challenging environments. ADR differs from manual domain randomization by not [requiring](https://www.diekassa.at) a human to specify randomization varieties. [169]
|
||||
<br>API<br>
|
||||
<br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](http://httelecom.com.cn:3000) designs established by OpenAI" to let designers contact it for "any English language [AI](http://120.55.164.234:3000) job". [170] [171]
|
||||
<br>Text generation<br>
|
||||
<br>The [business](http://motojic.com) has actually popularized generative pretrained transformers (GPT). [172]
|
||||
<br>OpenAI's initial GPT design ("GPT-1")<br>
|
||||
<br>The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his colleagues, and [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and procedure long-range dependencies by pre-training on a varied corpus with long stretches of contiguous text.<br>
|
||||
<br>GPT-2<br>
|
||||
<br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the successor to [OpenAI's initial](https://www.ourstube.tv) GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just minimal demonstrative versions initially released to the general public. The complete version of GPT-2 was not immediately released due to concern about prospective misuse, consisting of applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 posed a substantial threat.<br>
|
||||
<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, cautioned of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer models. [178] [179] [180]
|
||||
<br>GPT-2's authors argue not being watched language designs to be general-purpose learners, 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 additional trained on any task-specific input-output examples).<br>
|
||||
<br>The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 [upvotes](http://git.bzgames.cn). It avoids certain problems encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both private characters and multiple-character tokens. [181]
|
||||
<br>GPT-3<br>
|
||||
<br>First explained in May 2020, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million [criteria](https://xevgalex.ru) were also trained). [186]
|
||||
<br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]
|
||||
<br>GPT-3 considerably enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or encountering the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, [compared](http://git.anitago.com3000) to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly launched to the general public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189]
|
||||
<br>On September 23, 2020, GPT-3 was licensed solely to Microsoft. [190] [191]
|
||||
<br>Codex<br>
|
||||
<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://blueroses.top:8888) 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 produce working code in over a dozen programming languages, most effectively in Python. [192]
|
||||
<br>Several problems with problems, style defects and security vulnerabilities were cited. [195] [196]
|
||||
<br>GitHub Copilot has actually been implicated of producing copyrighted code, with no author attribution or license. [197]
|
||||
<br>OpenAI announced that they would stop assistance for Codex API on March 23, 2023. [198]
|
||||
<br>GPT-4<br>
|
||||
<br>On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar test with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, evaluate or create up to 25,000 words of text, and write code in all significant programming languages. [200]
|
||||
<br>Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined 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 results 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) criteria compared to 86.5% by GPT-4. [207]
|
||||
<br>On July 18, 2024, OpenAI released GPT-4o mini, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:EloisaRowe77) a smaller sized version of GPT-4o changing GPT-3.5 Turbo on the ChatGPT interface. Its API costs $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly beneficial for business, startups and designers seeking to automate services with [AI](https://lazerjobs.in) representatives. [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 think of their responses, causing higher precision. These models are especially effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
|
||||
<br>o3<br>
|
||||
<br>On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and faster version of OpenAI o3. Since 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, security and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecoms services [company](https://goodinfriends.com) O2. [215]
|
||||
<br>Deep research study<br>
|
||||
<br>Deep research study is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty 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) criteria. [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 similarity between text and images. It can significantly be utilized for image category. [217]
|
||||
<br>Text-to-image<br>
|
||||
<br>DALL-E<br>
|
||||
<br>Revealed in 2021, DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E 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 an unfortunate capybara") and generate corresponding images. It can produce pictures of practical objects ("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>
|
||||
<br>DALL-E 2<br>
|
||||
<br>In April 2022, OpenAI revealed DALL-E 2, an updated version of the design with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new simple system for transforming a text description into a 3-dimensional design. [220]
|
||||
<br>DALL-E 3<br>
|
||||
<br>In September 2023, OpenAI revealed DALL-E 3, a more effective design better able to create images from complex 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]
|
||||
<br>Text-to-video<br>
|
||||
<br>Sora<br>
|
||||
<br>Sora is a text-to-video design that can generate videos based upon brief detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of created videos is unknown.<br>
|
||||
<br>Sora's development group named it after the Japanese word for "sky", to represent its "endless creative capacity". [223] Sora's technology is an [adjustment](http://116.236.50.1038789) of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that function, however did not expose the number or the precise sources of the videos. [223]
|
||||
<br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could generate videos as much as one minute long. It also shared a technical report highlighting the approaches utilized to train the design, and the design's capabilities. [225] It acknowledged some of its imperfections, [consisting](http://104.248.138.208) of battles mimicing complicated physics. [226] Will Douglas Heaven of the MIT [Technology Review](https://gitlab.chabokan.net) called the [presentation](https://code.dsconce.space) videos "remarkable", however kept in mind that they need to have been cherry-picked and may not represent Sora's [typical output](https://beta.talentfusion.vn). [225]
|
||||
<br>Despite uncertainty from some [academic leaders](https://dev.ncot.uk) following Sora's public demo, significant entertainment-industry figures have actually shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's capability to create [reasonable](https://fotobinge.pincandies.com) video from text descriptions, mentioning its prospective to revolutionize storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to pause plans for broadening his Atlanta-based motion picture studio. [227]
|
||||
<br>Speech-to-text<br>
|
||||
<br>Whisper<br>
|
||||
<br>Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task design that can carry out multilingual speech acknowledgment along with speech translation and language recognition. [229]
|
||||
<br>Music generation<br>
|
||||
<br>MuseNet<br>
|
||||
<br>Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, a song produced 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 web psychological thriller Ben Drowned to develop music for the titular character. [232] [233]
|
||||
<br>Jukebox<br>
|
||||
<br>Released in 2020, Jukebox is an open-sourced algorithm to create 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. OpenAI mentioned the songs "show regional musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" which "there is a significant gap" between Jukebox and [human-generated music](http://git.sysoit.co.kr). The Verge mentioned "It's technologically remarkable, even if the results seem like mushy variations of tunes that might feel familiar", while [Business Insider](https://getquikjob.com) stated "remarkably, a few of the resulting songs are memorable and sound legitimate". [234] [235] [236]
|
||||
<br>User interfaces<br>
|
||||
<br>Debate Game<br>
|
||||
<br>In 2018, [OpenAI launched](http://gitlab.ileadgame.net) the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The function is to research whether such a technique may help in auditing [AI](http://8.137.54.213:9000) choices and in establishing explainable [AI](https://bvbborussiadortmundfansclub.com). [237] [238]
|
||||
<br>Microscope<br>
|
||||
<br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight [neural network](https://travel-friends.net) designs which are often studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, different versions of Inception, and different variations of CLIP Resnet. [241]
|
||||
<br>ChatGPT<br>
|
||||
<br>Launched in November 2022, ChatGPT is an artificial intelligence tool built on top of GPT-3 that offers a conversational user interface that enables users to ask concerns in natural language. The system then reacts with a response within seconds.<br>
|
||||
Loading…
Reference in new issue