parent
8cbd929648
commit
03c642a730
1 changed files with 76 additions and 0 deletions
@ -0,0 +1,76 @@ |
||||
<br>Announced in 2016, Gym is an open-source Python library created to facilitate the development of support learning algorithms. It aimed to standardize how environments are defined in [AI](https://rootsofblackessence.com) research study, making published research study more quickly reproducible [24] [144] while supplying users with a basic interface for [connecting](https://git.fhlz.top) with these environments. In 2022, brand-new advancements of Gym have been moved 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 computer game [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing representatives to fix single jobs. Gym Retro provides the capability to generalize in between video games with comparable principles however different looks.<br> |
||||
<br>RoboSumo<br> |
||||
<br>Released in 2017, RoboSumo is a virtual world where [humanoid metalearning](https://gitlab.steamos.cloud) robot agents at first lack knowledge of how to even stroll, but are provided the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents learn how to adjust to altering conditions. When a representative is then eliminated from this virtual environment and put in a new virtual environment with high winds, the agent 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 competition between agents could create an intelligence "arms race" that might increase a representative's ability to function even outside the context of the competitors. [148] |
||||
<br>OpenAI 5<br> |
||||
<br>OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against [human gamers](https://git.arcbjorn.com) at a high skill level entirely through experimental algorithms. Before ending up being a team of 5, the very first public demonstration happened at The International 2017, the annual best championship competition for the game, where Dendi, a [professional Ukrainian](https://www.nas-store.com) gamer, lost against a bot in a live one-on-one [matchup](https://members.mcafeeinstitute.com). [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of actual time, and that the knowing software application was a step in the instructions of creating software application that can handle complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of support learning, as the bots find out gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156] |
||||
<br>By June 2018, the capability of the bots expanded to play together as a complete 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 exhibit matches against professional players, however ended 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 exhibit match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those video games. [165] |
||||
<br>OpenAI 5's mechanisms in Dota 2's bot gamer reveals the difficulties of [AI](https://aiviu.app) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown making use of deep reinforcement learning (DRL) agents to attain superhuman skills in Dota 2 matches. [166] |
||||
<br>Dactyl<br> |
||||
<br>Developed in 2018, Dactyl uses [maker learning](https://git2.ujin.tech) to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It learns totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by utilizing domain randomization, a simulation method which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB cameras to allow the robotic to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system had the [ability](http://hammer.x0.to) to control a cube and an octagonal prism. [168] |
||||
<br>In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the [Rubik's Cube](https://social.mirrororg.com) introduce complex physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating progressively harder environments. ADR differs from manual domain randomization by not needing a human to define 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](http://www.xn--9m1b66aq3oyvjvmate.com) models established by OpenAI" to let developers get in touch with it for "any English language [AI](https://genzkenya.co.ke) task". [170] [171] |
||||
<br>Text generation<br> |
||||
<br>The company has popularized generative pretrained transformers (GPT). [172] |
||||
<br>OpenAI's original GPT model ("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 revealed how a generative model of language might obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.<br> |
||||
<br>GPT-2<br> |
||||
<br>Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to [OpenAI's original](https://www.armeniapedia.org) GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative versions at first launched to the public. The full version of GPT-2 was not [instantly launched](https://social1776.com) due to concern about potential misuse, including applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 posed a considerable threat.<br> |
||||
<br>In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the technology to absolutely 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 version of the GPT-2 language model. [177] Several sites [host interactive](https://dsspace.co.kr) demonstrations of various instances of GPT-2 and other transformer designs. [178] [179] [180] |
||||
<br>GPT-2's authors argue not being watched language models to be general-purpose students, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not further 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 a minimum of 3 upvotes. It avoids certain concerns encoding [vocabulary](https://career.abuissa.com) with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both [specific characters](https://livy.biz) and multiple-character tokens. [181] |
||||
<br>GPT-3<br> |
||||
<br>First [explained](https://xhandler.com) in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language model and [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:NorbertoPlayford) the follower to GPT-2. [182] [183] [184] OpenAI mentioned 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 full variation of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186] |
||||
<br>OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs and could generalize the purpose 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 drastically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or encountering the essential ability 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 full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a [paid cloud](https://justhired.co.in) API after a two-month free private beta that began in June 2020. [170] [189] |
||||
<br>On September 23, 2020, GPT-3 was certified specifically 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](https://zapinacz.pl) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, [higgledy-piggledy.xyz](https://higgledy-piggledy.xyz/index.php/User:Maryjo3973) an API was launched in private beta. [194] According to OpenAI, the model can develop working code in over a dozen programs languages, many efficiently in Python. [192] |
||||
<br>Several issues with problems, design defects and security vulnerabilities were cited. [195] [196] |
||||
<br>GitHub Copilot has been accused of discharging copyrighted code, without any author attribution or license. [197] |
||||
<br>OpenAI revealed that they would cease assistance for Codex API on March 23, 2023. [198] |
||||
<br>GPT-4<br> |
||||
<br>On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of [accepting text](https://uspublicsafetyjobs.com) or image inputs. [199] They announced that the updated technology 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 check out, analyze or create approximately 25,000 words of text, and write 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 a few of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on [ChatGPT](https://rca.co.id). [202] OpenAI has actually decreased to expose numerous technical details and data about GPT-4, such as the exact size of the design. [203] |
||||
<br>GPT-4o<br> |
||||
<br>On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained modern outcomes in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207] |
||||
<br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized variation 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 expects it to be especially beneficial for business, start-ups and designers seeking to automate services with [AI](http://f225785a.80.robot.bwbot.org) representatives. [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 reactions, leading to greater accuracy. These designs are especially effective in science, coding, and reasoning jobs, and were made available to Plus and Team members. [209] [210] In December 2024, [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2684771) o1-preview was replaced by o1. [211] |
||||
<br>o3<br> |
||||
<br>On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning model. OpenAI also revealed o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this design is not available for [public usage](https://jp.harmonymart.in). According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1099984) 2025, safety and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecoms services supplier O2. [215] |
||||
<br>Deep research<br> |
||||
<br>Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to [perform comprehensive](https://manpoweradvisors.com) web browsing, information analysis, and synthesis, delivering detailed reports 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] |
||||
<br>Image category<br> |
||||
<br>CLIP<br> |
||||
<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is [trained](https://www.h2hexchange.com) to examine the semantic resemblance in between text and images. It can especially be used for image classification. [217] |
||||
<br>Text-to-image<br> |
||||
<br>DALL-E<br> |
||||
<br>Revealed in 2021, DALL-E is a Transformer design that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can develop pictures of practical items ("a stained-glass window with a picture of a blue strawberry") as well as objects that do not exist in [reality](https://carvidoo.com) ("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 upgraded variation of the design with more realistic results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new primary system for converting a [text description](http://jibedotcompany.com) into a 3-dimensional design. [220] |
||||
<br>DALL-E 3<br> |
||||
<br>In September 2023, OpenAI revealed DALL-E 3, a more powerful design much better able to produce images from intricate 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](https://video.chops.com) in October. [222] |
||||
<br>Text-to-video<br> |
||||
<br>Sora<br> |
||||
<br>Sora is a text-to-video model that can produce videos based upon brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.<br> |
||||
<br>Sora's advancement group called it after the Japanese word for "sky", to represent its "unlimited creative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that function, however did not reveal the number or the specific sources of the videos. [223] |
||||
<br>OpenAI demonstrated some Sora-created [high-definition videos](http://git.maxdoc.top) to the general public on February 15, 2024, specifying that it could produce videos as much as one minute long. It also shared a technical report highlighting the methods utilized to train the design, and the design's capabilities. [225] It acknowledged some of its drawbacks, including struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however noted that they need to have been cherry-picked and might not represent Sora's typical output. [225] |
||||
<br>Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have shown significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to generate practical video from text descriptions, citing its prospective to reinvent storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly prepare 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 recognition model. [228] It is trained on a big dataset of diverse audio and [ratemywifey.com](https://ratemywifey.com/author/xjstrudi716/) is also a multi-task model that can carry out multilingual speech recognition in addition to speech translation and [language recognition](https://essencialponto.com.br). [229] |
||||
<br>Music generation<br> |
||||
<br>MuseNet<br> |
||||
<br>Released in 2019, MuseNet is a deep neural net trained to [predict subsequent](https://www.rybalka.md) musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, a [song generated](https://suomalainennaikki.com) by MuseNet tends to start fairly however then fall under mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the [internet psychological](http://120.79.211.1733000) thriller Ben Drowned to create music for [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:NigelMei567) the titular character. [232] [233] |
||||
<br>Jukebox<br> |
||||
<br>Released in 2020, Jukebox is an [open-sourced algorithm](https://textasian.com) to create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the songs "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" and that "there is a significant space" between Jukebox and human-generated music. The Verge mentioned "It's highly impressive, even if the results seem like mushy versions of tunes that may feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting tunes are catchy and sound legitimate". [234] [235] [236] |
||||
<br>Interface<br> |
||||
<br>Debate Game<br> |
||||
<br>In 2018, OpenAI released the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research study whether such a technique may help in auditing [AI](https://git.purwakartakab.go.id) decisions and in developing explainable [AI](https://www.viewtubs.com). [237] [238] |
||||
<br>Microscope<br> |
||||
<br>Released in 2020, [Microscope](http://pinetree.sg) [239] is a collection of visualizations of every significant layer and neuron of 8 neural network designs which are typically studied in interpretability. [240] Microscope was produced to [examine](http://platform.kuopu.net9999) the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and various versions of CLIP Resnet. [241] |
||||
<br>ChatGPT<br> |
||||
<br>Launched in November 2022, ChatGPT is an expert system tool built on top of GPT-3 that offers a conversational interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.<br> |
||||
Loading…
Reference in new issue