commit
621373b03d
1 changed files with 76 additions and 0 deletions
@ -0,0 +1,76 @@
@@ -0,0 +1,76 @@
|
||||
<br>Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of support learning algorithms. It aimed to standardize how environments are defined in [AI](https://aji.ghar.ku.jaldi.nai.aana.ba.tume.dont.tach.me) research, making released research more easily reproducible [24] [144] while supplying users with a simple interface for interacting with these environments. In 2022, brand-new developments of Gym have actually been moved to the library Gymnasium. [145] [146] |
||||
<br>Gym Retro<br> |
||||
<br>[Released](http://111.230.115.1083000) in 2018, Gym Retro is a [platform](http://106.52.121.976088) for support knowing (RL) research on computer game [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to resolve single tasks. Gym Retro offers the capability to generalize between games with comparable principles however various appearances.<br> |
||||
<br>RoboSumo<br> |
||||
<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack understanding of how to even stroll, however are offered the goals of learning to move and [wavedream.wiki](https://wavedream.wiki/index.php/User:VeroniqueBernhar) to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to changing conditions. When a representative is then removed from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents might create an intelligence "arms race" that could increase a representative's capability to operate even outside the context of the competitors. [148] |
||||
<br>OpenAI 5<br> |
||||
<br>OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human gamers at a high ability level completely through experimental algorithms. Before ending up being a group of 5, the very first public presentation took place at The International 2017, the yearly premiere championship tournament for the video 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 actually learned by playing against itself for two weeks of actual time, which the was a step in the instructions of developing software application that can handle complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement knowing, as the bots discover in time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156] |
||||
<br>By June 2018, the capability of the bots expanded to play together as a full group of 5, and they had the ability to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, however ended up losing both games. [160] [161] [162] In April 2019, [genbecle.com](https://www.genbecle.com/index.php?title=Utilisateur:JulieBrower730) OpenAI Five defeated OG, the ruling world champions of the [video game](https://deadreckoninggame.com) at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 overall video games in a [four-day](http://www.szkis.cn13000) open online competition, winning 99.4% of those [video games](https://derivsocial.org). [165] |
||||
<br>OpenAI 5['s systems](https://saopaulofansclub.com) in Dota 2's bot player shows the difficulties of [AI](https://sound.co.id) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated using deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166] |
||||
<br>Dactyl<br> |
||||
<br>Developed in 2018, Dactyl uses device learning to train a Shadow Hand, [ratemywifey.com](https://ratemywifey.com/author/ollieholtze/) a human-like robotic hand, to manipulate physical objects. [167] It discovers completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by [utilizing domain](https://meeting2up.it) randomization, a simulation method which exposes the student to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has RGB video cameras to allow the robotic to control an arbitrary things by seeing it. In 2018, [OpenAI revealed](https://noblessevip.com) that the system had the ability to control a cube and an octagonal prism. [168] |
||||
<br>In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The [robotic](https://activeaupair.no) had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate 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 creating gradually harder environments. ADR differs from manual domain randomization by not requiring a human to specify randomization ranges. [169] |
||||
<br>API<br> |
||||
<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](http://123.60.173.13:3000) models developed by OpenAI" to let designers contact it for "any English language [AI](http://git.airtlab.com:3000) job". [170] [171] |
||||
<br>Text generation<br> |
||||
<br>The business has actually promoted 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 design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and procedure long-range dependencies by pre-training on a diverse 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 original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions at first launched to the general public. The full version of GPT-2 was not immediately launched due to concern about potential misuse, including applications for writing phony news. [174] Some professionals expressed uncertainty that GPT-2 posed a considerable danger.<br> |
||||
<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other scientists, 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 drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language design. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180] |
||||
<br>GPT-2's authors argue unsupervised language models to be general-purpose learners, highlighted by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not more trained on any task-specific input-output examples).<br> |
||||
<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 prevents certain concerns encoding vocabulary 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 not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were likewise trained). [186] |
||||
<br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer [learning](https://jobs.ondispatch.com) between English and Romanian, and in between English and German. [184] |
||||
<br>GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or encountering the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the general public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month complimentary personal beta that began in June 2020. [170] [189] |
||||
<br>On September 23, 2020, GPT-3 was licensed exclusively 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://40.73.118.158) [powering](https://git.frugt.org) the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, [archmageriseswiki.com](http://archmageriseswiki.com/index.php/User:DaleneCollins99) the model can produce working code in over a lots programs languages, most successfully in Python. [192] |
||||
<br>Several problems with glitches, style defects and [security vulnerabilities](https://git.hitchhiker-linux.org) were pointed out. [195] [196] |
||||
<br>GitHub Copilot has been accused of giving off copyrighted code, with no author attribution or license. [197] |
||||
<br>OpenAI announced that they would discontinue support 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), efficient in accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar exam 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 likewise read, analyze or produce approximately 25,000 words of text, and write code in all significant programming languages. [200] |
||||
<br>Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise [efficient](http://hi-couplering.com) in taking images as input on ChatGPT. [202] OpenAI has declined to expose various technical details and statistics about GPT-4, such as the exact size of the model. [203] |
||||
<br>GPT-4o<br> |
||||
<br>On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision standards, setting brand-new records in audio speech acknowledgment and [translation](https://abcdsuppermarket.com). [205] [206] It scored 88.7% on the Massive Multitask [Language](https://ipen.com.hk) Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207] |
||||
<br>On July 18, 2024, OpenAI [released](https://git.privateger.me) 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 helpful for business, start-ups and designers seeking to automate services with [AI](https://test.gamesfree.ca) agents. [208] |
||||
<br>o1<br> |
||||
<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been developed to take more time to think of their actions, leading to greater precision. These designs are particularly reliable in science, coding, and reasoning jobs, and were made available to [ChatGPT](https://glhwar3.com) Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211] |
||||
<br>o3<br> |
||||
<br>On December 20, 2024, [OpenAI revealed](http://h.gemho.cn7099) o3, the follower of the o1 thinking design. OpenAI likewise [revealed](http://gitlab.dstsoft.net) o3-mini, a lighter and quicker version 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 scientists had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to prevent confusion with telecoms [providers](https://ubuntushows.com) 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](http://gite.limi.ink) o3 design to carry out extensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and [Python tools](http://dgzyt.xyz3000) made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [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 in between text and images. It can significantly be utilized 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 version of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can produce images of reasonable objects ("a stained-glass window with a picture of a blue strawberry") as well as items 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 announced DALL-E 2, an updated variation of the design with more sensible results. [219] In December 2022, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:TristanFlournoy) OpenAI published on GitHub software application for Point-E, a new basic system for converting 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 powerful design better able to produce images from complex descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched 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 model that can create videos based on brief detailed triggers [223] as well as extend existing [videos forwards](https://git.didi.la) or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of created videos is unidentified.<br> |
||||
<br>Sora's development team named it after the Japanese word for "sky", to symbolize its "unlimited imaginative potential". [223] Sora's innovation is an adjustment of the innovation behind the [DALL ·](https://travelpages.com.gh) E 3 text-to-image design. [225] [OpenAI trained](https://raovatonline.org) the system [utilizing publicly-available](https://sabiile.com) videos along with copyrighted videos licensed 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 general public on February 15, 2024, specifying that it could produce videos up to one minute long. It also shared a [technical report](https://hankukenergy.kr) highlighting the approaches utilized to train the design, and the model's capabilities. [225] It acknowledged some of its drawbacks, including struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but kept in mind that they must have been [cherry-picked](https://jobs.cntertech.com) and might not represent Sora's typical output. [225] |
||||
<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have actually shown significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's capability to create sensible video from text descriptions, citing its possible to revolutionize storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause strategies for broadening his Atlanta-based film 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 big dataset of varied audio and is likewise a multi-task design that can carry out [multilingual speech](https://betalk.in.th) acknowledgment as well as speech translation and language identification. [229] |
||||
<br>Music generation<br> |
||||
<br>MuseNet<br> |
||||
<br>Released in 2019, [MuseNet](https://mcn-kw.com) is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can [produce songs](https://dinle.online) with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall under chaos the longer it plays. [230] [231] In pop 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] |
||||
<br>Jukebox<br> |
||||
<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 song [samples](https://community.scriptstribe.com). OpenAI stated the songs "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" and that "there is a substantial space" in between Jukebox and [human-generated music](https://209rocks.com). The Verge stated "It's technically impressive, even if the outcomes seem like mushy variations of songs that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting tunes are catchy and sound genuine". [234] [235] [236] |
||||
<br>Interface<br> |
||||
<br>Debate Game<br> |
||||
<br>In 2018, OpenAI released the Debate Game, which teaches makers to debate toy issues in front of a [human judge](http://git.chaowebserver.com). The function is to research whether such a method may help in auditing [AI](https://git.privateger.me) decisions and in establishing explainable [AI](https://161.97.85.50). [237] [238] |
||||
<br>Microscope<br> |
||||
<br>Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network designs which are typically studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various versions of Inception, and different variations of CLIP Resnet. [241] |
||||
<br>ChatGPT<br> |
||||
<br>[Launched](http://111.8.36.1803000) in November 2022, ChatGPT is an expert system tool constructed 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