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Announced in 2016, Gym is an open-source Python library created to assist in the [advancement](https://git.mm-music.cn) of support learning algorithms. It aimed to standardize how environments are defined in [AI](http://13.209.39.139:32421) research, making published research study more quickly reproducible [24] [144] while [providing](https://optimaplacement.com) users with a simple user interface for engaging with these environments. In 2022, brand-new developments of Gym have been transferred to the library Gymnasium. [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for support knowing (RL) research on video games [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to fix single tasks. Gym Retro offers the capability to generalize between games with similar principles but different appearances.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially do not have knowledge of how to even stroll, however are provided the objectives of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adapt to changing conditions. When an agent is then gotten rid of from this virtual environment and put in a new virtual [environment](http://123.60.103.973000) with high winds, the agent braces to remain upright, suggesting it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might develop an intelligence "arms race" that could increase an agent's ability to function even outside the context of the [competitors](http://www.pelletkorea.net). [148] +
OpenAI 5
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OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human players at a high skill level completely through trial-and-error algorithms. Before ending up being a team of 5, the first public demonstration happened at The International 2017, the annual premiere [champion competition](https://lets.chchat.me) for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, [it-viking.ch](http://it-viking.ch/index.php/User:Heath0421670) CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of real time, which the learning software was a step in the instructions of creating software application that can deal with intricate jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement knowing, as the bots learn over time by playing against themselves numerous times a day for months, and are rewarded for [actions](http://139.9.50.1633000) such as killing an opponent and taking map goals. [154] [155] [156] +
By June 2018, the capability of the bots broadened to play together as a complete 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, but wound up losing both games. [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 exhibition match in [San Francisco](http://git.zhiweisz.cn3000). [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall games in a four-day open online competitors, winning 99.4% of those [video games](https://git.logicloop.io). [165] +
OpenAI 5['s systems](http://jejuanimalnow.org) in Dota 2's bot gamer shows the [challenges](https://music.michaelmknight.com) of [AI](http://forum.altaycoins.com) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown the use of deep support knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl utilizes device learning to train a Shadow Hand, a human-like robotic hand, to control physical objects. [167] It discovers totally in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by utilizing domain randomization, a simulation method which [exposes](https://dainiknews.com) the student to a variety of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB cameras to permit the robotic to control an arbitrary item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168] +
In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robotic 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 robustness of Dactyl to perturbations by using [Automatic Domain](https://cchkuwait.com) Randomization (ADR), a simulation technique of creating gradually harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169] +
API
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In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](http://1.15.150.90:3000) models established by OpenAI" to let developers contact it for "any English language [AI](http://new-delhi.rackons.com) job". [170] [171] +
Text generation
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The [company](http://artpia.net) has popularized generative pretrained transformers (GPT). [172] +
OpenAI's initial GPT design ("GPT-1")
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The original paper on generative pre-training of a transformer-based language model was written by [Alec Radford](https://pipewiki.org) and his associates, and released 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 reliances by pre-training on a diverse corpus with long stretches of adjoining text.
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GPT-2
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Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions at first [launched](https://www.ojohome.listatto.ca) to the public. The full version of GPT-2 was not right away released due to concern about possible misuse, including applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 presented a substantial risk.
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In response 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, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:KathrinSabella) cautioned of "the innovation to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language design. [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue unsupervised language designs to be general-purpose students, [highlighted](http://39.106.43.96) by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more [trained](http://artpia.net) on any task-specific input-output examples).
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The corpus it was trained on, [larsaluarna.se](http://www.larsaluarna.se/index.php/User:DominickJulian9) called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It [prevents](https://gitcode.cosmoplat.com) certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both specific characters and multiple-character tokens. [181] +
GPT-3
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First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion parameters, [184] two 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 specifications were likewise trained). [186] +
OpenAI stated 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 gave examples of translation and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184] +
GPT-3 drastically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be [approaching](http://codaip.co.kr) or experiencing the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required several 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 design was not immediately released to the public for issues of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month totally free private beta that started in June 2020. [170] [189] +
On September 23, 2020, GPT-3 was licensed specifically to Microsoft. [190] [191] +
Codex
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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](https://yooobu.com) 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 dozen programs languages, a lot of efficiently in Python. [192] +
Several problems with problems, style flaws and security vulnerabilities were cited. [195] [196] +
GitHub Copilot has actually been accused of giving off copyrighted code, without any author attribution or license. [197] +
OpenAI announced that they would cease support for Codex API on March 23, 2023. [198] +
GPT-4
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On March 14, 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), [wiki.vst.hs-furtwangen.de](https://wiki.vst.hs-furtwangen.de/wiki/User:Bettina5096) efficient in accepting text or image inputs. [199] They revealed that the updated innovation 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, examine or produce as much as 25,000 words of text, and compose code in all [major programs](https://git.sortug.com) languages. [200] +
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise [capable](http://www.fun-net.co.kr) of taking images as input on [ChatGPT](https://friendfairs.com). [202] OpenAI has actually declined to expose numerous technical details and data about GPT-4, such as the accurate size of the design. [203] +
GPT-4o
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On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced lead to 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) standard compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller [sized variation](http://47.112.200.2063000) of GPT-4o [replacing](https://gitea.ndda.fr) 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 useful for enterprises, start-ups and developers seeking to automate services with [AI](http://207.148.91.145:3000) agents. [208] +
o1
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On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been designed to take more time to think about their responses, leading to higher precision. These models are particularly effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211] +
o3
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On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are [testing](https://samman-co.com) 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 rather than o2 to avoid [confusion](http://plus-tube.ru) with telecommunications providers O2. [215] +
Deep research
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Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of o3 model to perform comprehensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120] +
Image classification
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CLIP
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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 notably be used for image classification. [217] +
Text-to-image
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DALL-E
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Revealed in 2021, DALL-E is a [Transformer design](http://git.suxiniot.com) that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can develop images of sensible items ("a stained-glass window with an image 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.
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DALL-E 2
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In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the design with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new primary system for transforming a text description into a 3-dimensional design. [220] +
DALL-E 3
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In September 2023, OpenAI revealed DALL-E 3, a more powerful design much better able to produce images from complicated descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222] +
Text-to-video
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Sora
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Sora is a text-to-video model that can [generate](http://park1.wakwak.com) videos based upon brief detailed prompts [223] along with 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 produced videos is unknown.
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Sora's advancement team called it after the Japanese word for "sky", to symbolize its "limitless imaginative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL ยท E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos accredited for that purpose, however did not reveal the number or the exact sources of the videos. [223] +
OpenAI showed some [Sora-created high-definition](http://demo.qkseo.in) videos to the general public on February 15, 2024, specifying that it might generate videos as much as one minute long. It likewise shared a technical report highlighting the approaches utilized to train the model, and the model's abilities. [225] It acknowledged some of its imperfections, consisting of battles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", however noted that they should have been cherry-picked and might not represent Sora's normal output. [225] +
Despite uncertainty from some scholastic leaders following [Sora's public](https://workonit.co) demonstration, significant entertainment-industry figures have shown [substantial](https://szmfettq2idi.com) interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to create practical video from text descriptions, citing its potential to revolutionize storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly prepare for broadening his Atlanta-based motion picture studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of varied audio and is also a multi-task model that can perform multilingual speech recognition in addition to speech translation and language identification. [229] +
Music generation
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MuseNet
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Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, [bytes-the-dust.com](https://bytes-the-dust.com/index.php/User:ElviraLamarr892) a tune generated by MuseNet tends to start fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the internet psychological [thriller](http://gpis.kr) Ben Drowned to produce music for the titular character. [232] [233] +
Jukebox
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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 genre, artist, and a bit of lyrics and outputs song samples. OpenAI stated the songs "reveal local musical coherence [and] follow traditional chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a significant gap" in between Jukebox and human-generated music. The Verge specified "It's highly remarkable, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider stated "remarkably, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236] +
User interfaces
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Debate Game
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In 2018, OpenAI introduced the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The function is to research whether such a technique might help in auditing [AI](http://blueroses.top:8888) [choices](https://gitlab.optitable.com) and in developing explainable [AI](https://holisticrecruiters.uk). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network models which are often studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, and various [versions](https://athleticbilbaofansclub.com) of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, [ChatGPT](https://tagreba.org) is an artificial intelligence tool built on top of GPT-3 that provides a conversational interface that enables users to ask questions in natural language. The system then responds with a response within seconds.
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