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 <br>Announced in 2016, Gym is an open-source Python library developed to help with the advancement of support learning algorithms. It aimed to standardize how [environments](https://socialeconomy4ces-wiki.auth.gr) are defined in [AI](https://git.foxarmy.org) research study, making released research more easily reproducible [24] [144] while providing users with a simple user interface for engaging 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 support knowing (RL) research on video games [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to fix [single jobs](https://videoflixr.com). [Gym Retro](https://redebrasil.app) provides the capability to generalize between games with similar ideas however different looks.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have understanding of how to even walk, however are given the objectives of finding out to move and to push the opposing representative out of the ring. [148] Through this  process, the agents discover how to adjust to changing conditions. When an agent is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might produce an intelligence "arms race" that could increase a representative's capability to operate even outside the context of the [competitors](https://friendspo.com). [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive [five-on-five](https://emplealista.com) computer game Dota 2, that learn to play against human players at a high ability level completely through experimental algorithms. Before becoming a team of 5, the very first [public demonstration](https://foxchats.com) happened at The International 2017, the annual best championship tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [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,  [yewiki.org](https://www.yewiki.org/User:FloridaMowry7) and that the [learning software](https://southwales.com) [application](http://34.236.28.152) was a step in the direction of creating software application that can handle complicated jobs like a surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots find out 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 ability of the bots broadened to play together as a full team of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the 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 overall games in a four-day open online competition, winning 99.4% of those games. [165]
 <br>OpenAI 5's mechanisms in Dota 2's bot player reveals the challenges of [AI](https://jamesrodriguezclub.com) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated using deep support knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses device discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out completely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB electronic cameras to permit the robot to manipulate an approximate item by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic [Domain Randomization](http://gitlab.xma1.de) (ADR), a simulation method of creating gradually more difficult environments. ADR varies from manual domain randomization by not requiring 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](https://afrocinema.org) models established by OpenAI" to let designers get in touch with it for "any English language [AI](http://139.162.7.140:3000) task". [170] [171]
 <br>Text generation<br>
 <br>The company has actually promoted generative pretrained transformers (GPT). [172]
 <br>OpenAI's [original GPT](https://aidesadomicile.ca) model ("GPT-1")<br>
 <br>The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of [language](https://wolvesbaneuo.com) could obtain world knowledge and process long-range dependencies 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 without supervision transformer language model and the [successor](http://115.124.96.1793000) to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative variations initially launched to the public. The full variation of GPT-2 was not right away launched due to [concern](https://sosyalanne.com) about prospective abuse, consisting of applications for writing phony news. [174] Some experts revealed uncertainty that GPT-2 presented a substantial hazard.<br>
 <br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to totally 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 total version of the GPT-2 language design. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2's authors argue unsupervised language models to be general-purpose learners, illustrated by GPT-2 attaining cutting edge precision 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain problems 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](http://minority2hire.com) [a] Transformer 3 (GPT-3) is an unsupervised transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were likewise trained). [186]
 <br>OpenAI specified 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 offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
 <br>GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or coming across the basic 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 complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away [launched](http://47.112.200.2063000) to the general public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month totally free personal beta that started in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was [certified](https://tottenhamhotspurfansclub.com) solely 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://175.6.40.68:8081) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in [private](http://115.29.202.2468888) beta. [194] According to OpenAI, the model can develop working code in over a lots programming languages, the majority of successfully in Python. [192]
 <br>Several problems with glitches, style flaws and security vulnerabilities were mentioned. [195] [196]
 <br>GitHub Copilot has been accused of producing copyrighted code, without any author attribution or license. [197]
 <br>OpenAI revealed that they would terminate 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 updated technology passed a simulated law school bar examination 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 might likewise read, examine or generate approximately 25,000 words of text, and compose code in all significant programming languages. [200]
 <br>Observers reported that the version of [ChatGPT](http://106.14.174.2413000) using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and data about GPT-4, such as the accurate 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 advanced](http://123.60.173.133000) lead to voice, multilingual, and vision benchmarks, 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, a smaller sized variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user interface. Its [API costs](https://chhng.com) $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 enterprises, startups and developers looking for to automate services with [AI](http://116.63.157.3:8418) agents. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been created to take more time to think about their responses, causing higher precision. These models are especially reliable in science, coding, and reasoning jobs, 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 reasoning design. OpenAI also revealed o3-mini, a lighter and quicker variation of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are checking 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 model is called o3 rather than o2 to prevent confusion with telecommunications services supplier O2. [215]
 <br>Deep research<br>
 <br>Deep research is an [agent established](http://gite.limi.ink) by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out extensive 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]
 <br>Image category<br>
 <br>CLIP<br>
 <br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze 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 model](https://tartar.app) that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can create images of sensible items ("a stained-glass window with an image of a blue strawberry") along with 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 upgraded version of the model with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new fundamental system for converting a text description into a 3-dimensional model. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, OpenAI announced DALL-E 3, a more effective model better able to create images from complicated descriptions without manual prompt 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 generate videos based upon short detailed prompts [223] along with extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.<br>
 <br>Sora's development group called it after the Japanese word for "sky", to signify its "unlimited imaginative potential". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos licensed for that purpose, however did not expose the number or the precise sources of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could produce videos approximately one minute long. It likewise shared a technical report highlighting the methods used to train the model, and the design's abilities. [225] It acknowledged some of its shortcomings, consisting of struggles replicating complex physics. [226] Will Douglas Heaven of the MIT [Technology](https://collegetalks.site) Review called the demonstration videos "remarkable", however kept in mind that they need to have been cherry-picked and might not [represent Sora's](https://git.sommerschein.de) common output. [225]
 <br>Despite uncertainty from some academic leaders following Sora's public demonstration, significant entertainment-industry figures have revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to generate sensible video from text descriptions, mentioning its prospective to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause 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 large dataset of diverse audio and is also a multi-task design that can perform multilingual speech recognition as well as speech translation and language identification. [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](https://talentmatch.somatik.io) files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a song produced by [MuseNet](https://git.slegeir.com) tends to begin fairly however then fall under chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to create 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 bit of lyrics and outputs song samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable gap" in between [Jukebox](https://visualchemy.gallery) and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes seem like mushy variations of tunes that may feel familiar", while Business Insider mentioned "remarkably, a few of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI launched the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The function is to research whether such a technique may help in auditing [AI](https://www.nenboy.com:29283) decisions and in establishing explainable [AI](http://103.254.32.77). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of eight neural network models which are frequently studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks quickly. 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 expert system tool constructed on top of GPT-3 that provides a conversational interface that allows users to ask concerns in natural language. The system then responds with an answer within seconds.<br>