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Announced in 2016, Gym is an open-source Python library designed to help with the advancement of support knowing algorithms. It aimed to [standardize](https://idaivelai.com) how environments are specified in [AI](https://xn--114-2k0oi50d.com) research, making published research study more easily reproducible [24] [144] while offering users with a simple user interface for engaging with these [environments](https://www.cartoonistnetwork.com). In 2022, new [developments](https://git.starve.space) of Gym have actually been moved to the [library Gymnasium](https://www.olindeo.net). [145] [146] +
Gym Retro
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Released in 2018, Gym Retro is a platform for support learning (RL) research on computer game [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to fix single jobs. Gym Retro gives the capability to generalize in between video games with comparable ideas however different looks.
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RoboSumo
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Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first lack knowledge of how to even stroll, however are given the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives learn how to adjust to altering conditions. When an agent is then [eliminated](https://dispatchexpertscudo.org.uk) from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents could develop an [intelligence](http://47.90.83.1323000) "arms race" that could increase an agent's ability to function even outside the context of the competitors. [148] +
OpenAI 5
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OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive [five-on-five](https://farmwoo.com) computer game Dota 2, that discover to play against human players at a high ability level entirely through trial-and-error algorithms. Before becoming a group of 5, the first public presentation occurred at The [International](https://www.weben.online) 2017, the annual best champion competition for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for 2 weeks of actual time, and that the learning software application was a step in the [instructions](https://careers.webdschool.com) of creating software that can handle complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement knowing, as the bots learn in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156] +
By June 2018, the ability of the bots broadened to play together as a full group of 5, and they had the ability to defeat teams of amateur and [semi-professional players](https://git.apps.calegix.net). [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games. [165] +
OpenAI 5's mechanisms in Dota 2's bot player reveals the challenges of [AI](http://git.morpheu5.net) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown the use of deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166] +
Dactyl
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Developed in 2018, Dactyl uses maker finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It finds out entirely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences rather than [attempting](https://namoshkar.com) to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cams to allow the robot to manipulate an arbitrary object by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168] +
In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing progressively harder environments. ADR varies from manual domain randomization by not needing a human to define randomization ranges. [169] +
API
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In June 2020, OpenAI announced a [multi-purpose API](https://desarrollo.skysoftservicios.com) which it said was "for accessing brand-new [AI](https://www.chinami.com) models established by OpenAI" to let designers call on it for "any English language [AI](https://kiaoragastronomiasocial.com) task". [170] [171] +
Text generation
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The company has promoted generative [pretrained transformers](http://www.forwardmotiontx.com) (GPT). [172] +
OpenAI's original GPT model ("GPT-1")
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The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and process long-range dependences by pre-training on a diverse corpus with long [stretches](https://www.lingualoc.com) of adjoining text.
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GPT-2
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Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative versions at first released to the general public. The complete version of GPT-2 was not right away released due to issue about prospective misuse, consisting of applications for writing phony news. [174] Some professionals revealed uncertainty that GPT-2 postured a significant risk.
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In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural phony 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 muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language design. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180] +
GPT-2's authors argue without supervision language designs to be general-purpose students, highlighted by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not further trained on any task-specific input-output examples).
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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 with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both private 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 without supervision transformer [language](https://www.cdlcruzdasalmas.com.br) model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full variation of GPT-3 contained 175 billion parameters, [184] two orders of [magnitude larger](http://118.195.204.2528080) than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as few as 125 million specifications were likewise trained). [186] +
OpenAI stated that GPT-3 was successful at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184] +
GPT-3 significantly improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the basic ability [constraints](http://101.43.135.2349211) of predictive [language models](https://selfyclub.com). [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the general public for concerns of possible abuse, although OpenAI prepared to enable 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 exclusively](https://gitlab.interjinn.com) to Microsoft. [190] [191] +
Codex
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Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](http://app.ruixinnj.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 programs languages, a lot of successfully in Python. [192] +
Several issues with problems, style defects and security vulnerabilities were pointed out. [195] [196] +
GitHub Copilot has been accused of [discharging copyrighted](https://git.mm-music.cn) code, with no author attribution or license. [197] +
OpenAI announced that they would cease assistance for Codex API on March 23, 2023. [198] +
GPT-4
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On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They [revealed](https://gitlab.mnhn.lu) that the updated technology passed a [simulated law](https://droidt99.com) school bar test 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 read, evaluate or produce approximately 25,000 words of text, and write code in all major programs languages. [200] +
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and stats 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 launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207] +
On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o [changing](https://www.cbtfmytube.com) 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 especially helpful for business, start-ups and developers looking for to automate services with [AI](http://119.167.221.14:60000) [representatives](http://101.200.33.643000). [208] +
o1
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On September 12, 2024, OpenAI released the o1[-preview](https://jobs1.unifze.com) and o1-mini designs, which have been [designed](https://jobs.constructionproject360.com) to take more time to consider their reactions, leading to greater 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 was replaced by o1. [211] +
o3
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On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and [faster variation](https://gitea.qi0527.com) of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecoms services provider O2. [215] +
Deep research
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Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It [leverages](https://cheere.org) the capabilities of OpenAI's o3 design to perform comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached a precision of 26.6 percent on HLE ([Humanity's](https://cphallconstlts.com) Last Exam) standard. [120] +
Image classification
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CLIP
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Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic similarity in between text and images. It can especially be used for image category. [217] +
Text-to-image
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DALL-E
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Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze [natural language](https://evove.io) inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can produce images of reasonable items ("a stained-glass window with a picture of a blue strawberry") as well as objects that do not exist in truth ("a cube with the texture of a porcupine"). As of 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 [updated variation](https://ces-emprego.com) of the model with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new basic system for transforming a text description into a 3-dimensional model. [220] +
DALL-E 3
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In September 2023, [OpenAI revealed](https://kiaoragastronomiasocial.com) DALL-E 3, a more powerful design better able to generate images from complicated descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was released to the general public as a ChatGPT Plus feature in October. [222] +
Text-to-video
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Sora
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Sora is a text-to-video design that can create videos based on 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 generated videos is unidentified.
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Sora's advancement group called it after the Japanese word for "sky", to represent its "unlimited imaginative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL ยท E 3 [text-to-image design](https://testing-sru-git.t2t-support.com). [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that function, but did not reveal the number or the [exact sources](https://empregos.acheigrandevix.com.br) of the videos. [223] +
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it could produce videos up to one minute long. It likewise shared a [technical report](https://media.motorsync.co.uk) highlighting the methods utilized to train the design, and the model's abilities. [225] It [acknowledged](http://175.6.124.2503100) a few of its drawbacks, including struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they must have been cherry-picked and may not represent Sora's typical output. [225] +
Despite uncertainty from some [academic leaders](https://lastpiece.co.kr) following Sora's public demonstration, significant entertainment-industry figures have revealed substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to produce reasonable video from text descriptions, citing its prospective to revolutionize storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly strategies for broadening his Atlanta-based film studio. [227] +
Speech-to-text
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Whisper
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Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a big dataset of diverse audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment as well as 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 anticipate subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to produce music for [trademarketclassifieds.com](https://trademarketclassifieds.com/user/profile/2672496) the [titular](http://saehanfood.co.kr) character. [232] [233] +
Jukebox
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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 mentioned the songs "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" which "there is a substantial gap" in between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the results sound like mushy variations of tunes that may feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are catchy and sound legitimate". [234] [235] [236] +
User user interfaces
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Debate Game
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In 2018, OpenAI introduced the Debate Game, which [teaches makers](https://git.lewis.id) to discuss toy problems in front of a human judge. The function is to research study whether such a technique might help in auditing [AI](http://www.yfgame.store) decisions and in developing explainable [AI](http://1.117.194.115:10080). [237] [238] +
Microscope
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Released in 2020, Microscope [239] is a collection of [visualizations](https://tikness.com) of every significant layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was created to examine the [functions](https://droidt99.com) that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various versions of CLIP Resnet. [241] +
ChatGPT
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Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that offers a conversational user interface that allows users to ask concerns in natural language. The system then reacts with a response within seconds.
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