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 <br>Announced in 2016, Gym is an open-source Python library developed to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are specified in [AI](https://www.sewosoft.de) research, making published research study more easily reproducible [24] [144] while supplying users with a basic user interface for [connecting](https://forum.petstory.ge) with these environments. In 2022, new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, [Gym Retro](https://neejobs.com) is a platform for [support learning](https://www.ssecretcoslab.com) (RL) research study on computer game [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to fix single jobs. Gym Retro gives the capability to generalize in between video games with comparable principles but various appearances.<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, but are offered the goals of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives learn how to adapt to changing conditions. When an agent is then removed from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor  [systemcheck-wiki.de](https://systemcheck-wiki.de/index.php?title=Benutzer:HaiKethel19755) Mordatch argued that competition between representatives could produce an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competition. [148]
 <br>OpenAI 5<br>
 <br>OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high skill level entirely through trial-and-error algorithms. Before becoming a team of 5, the first public demonstration occurred at The International 2017, the yearly best championship tournament for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, [CTO Greg](https://aladin.social) Brockman explained that the bot had actually learned by playing against itself for 2 weeks of genuine time, and that the knowing software was an action in the direction of producing software application that can handle complex tasks like a cosmetic surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots find out in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking [map goals](http://47.108.140.33). [154] [155] [156]
 <br>By June 2018, the capability of the bots expanded to play together as a complete team of 5, and they were able to [beat teams](https://git.jordanbray.com) of amateur and  [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11857434) semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final [public appearance](https://storage.sukazyo.cc) 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. [165]
 <br>OpenAI 5's systems in Dota 2's bot gamer shows the challenges of [AI](https://fassen.net) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep support knowing (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses device finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It discovers completely in simulation using the exact 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 learner to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB cameras to allow the robotic to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated 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](http://121.41.31.1463000) present intricate physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of generating progressively harder environments. ADR varies from manual domain randomization by not requiring a human to specify randomization ranges. [169]
 <br>API<br>
 <br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](http://1024kt.com:3000) models established by OpenAI" to let developers get in touch with it for "any English language [AI](https://netgork.com) task". [170] [171]
 <br>Text generation<br>
 <br>The company has popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's original GPT design ("GPT-1")<br>
 <br>The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, 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 dependencies by pre-training on a varied 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 follower to OpenAI's original [GPT model](https://realhindu.in) ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative versions initially launched to the general public. The complete variation of GPT-2 was not instantly launched due to issue about potential misuse, including applications for composing fake news. [174] Some professionals expressed uncertainty that GPT-2 presented a significant danger.<br>
 <br>In  to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural fake 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 drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language model. [177] Several websites host interactive demonstrations of different [instances](https://pl.velo.wiki) of GPT-2 and other transformer models. [178] [179] [180]
 <br>GPT-2's authors argue without supervision language models to be general-purpose learners, shown by GPT-2 attaining state-of-the-art 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows [representing](https://nurseportal.io) any string of characters by encoding both individual 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 without supervision transformer language design and the successor  [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:ForrestHilton45) to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186]
 <br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]
 <br>GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or experiencing the basic capability [constraints](http://rm.runfox.com) of predictive language designs. [187] [Pre-training](http://gite.limi.ink) GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away 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]
 <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 furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://netgork.com) powering the code autocompletion tool GitHub [Copilot](http://47.101.187.298081). [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can develop working code in over a lots shows languages, many effectively in Python. [192]
 <br>Several concerns with glitches, design flaws and security vulnerabilities were mentioned. [195] [196]
 <br>GitHub Copilot has been implicated of producing copyrighted code, with no author attribution or license. [197]
 <br>OpenAI announced that they would cease 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 technology passed a simulated law school bar exam with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, examine or produce approximately 25,000 words of text, and write code in all significant programming languages. [200]
 <br>Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is also [capable](http://139.9.60.29) of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal numerous technical details and data about GPT-4, such as the precise size of the model. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision standards, setting brand-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]
 <br>On July 18, 2024, OpenAI launched GPT-4o mini, a smaller version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT interface. Its [API costs](https://mixup.wiki) $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, startups and developers looking for to automate services with [AI](https://www.infiniteebusiness.com) 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 consider their reactions, leading to higher precision. These designs are particularly effective in science, coding, and reasoning tasks,  [garagesale.es](https://www.garagesale.es/author/lyndoncoove/) and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
 <br>o3<br>
 <br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI likewise revealed o3-mini, a [lighter](https://xhandler.com) and faster version of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecoms [providers](https://iamtube.jp) O2. [215]
 <br>Deep research<br>
 <br>Deep research study is an agent developed by OpenAI, revealed on February 2,  [pipewiki.org](https://pipewiki.org/wiki/index.php/User:LynwoodBolling) 2025. It leverages the abilities of OpenAI's o3 design to carry out substantial web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With [searching](https://git.purplepanda.cc) and Python tools allowed, it [reached](https://gitlab-mirror.scale.sc) a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
 <br>Image classification<br>
 <br>CLIP<br>
 <br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic resemblance between text and images. It can significantly 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 develops 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 handbag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can develop images of sensible items ("a stained-glass window with a picture of a blue strawberry") as well as objects that do not exist in reality ("a cube with the texture of a porcupine"). As of 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 variation of the design with more sensible results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new simple system for transforming 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 design much better able to create 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 feature in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video design that can produce videos based on short detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br>
 <br>Sora's development team named it after the Japanese word for "sky", to signify its "limitless imaginative capacity". [223] Sora's technology is an [adaptation](https://git.parat.swiss) of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos accredited for that purpose, however did not reveal the number or the specific sources of the videos. [223]
 <br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, [stating](http://121.40.234.1308899) that it might create videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, consisting of battles replicating intricate physics. [226] Will [Douglas Heaven](https://wino.org.pl) of the MIT Technology Review called the presentation videos "impressive", but noted that they need to have been cherry-picked and might not represent Sora's normal output. [225]
 <br>Despite [uncertainty](https://app.zamow-kontener.pl) from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's ability to produce [reasonable video](https://schanwoo.com) from text descriptions, citing its prospective to reinvent storytelling and content [creation](https://git.arachno.de). He said that his excitement about Sora's possibilities was so strong that he had decided to pause prepare for expanding his Atlanta-based movie studio. [227]
 <br>Speech-to-text<br>
 <br>Whisper<br>
 <br>Released in 2022, Whisper is a [general-purpose speech](http://121.196.13.116) recognition model. [228] It is trained on a large dataset of varied audio and is also a multi-task design that can carry out multilingual speech recognition along with speech translation and language recognition. [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 files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to start fairly however then fall into chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the internet psychological 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](https://ratemywifey.com) a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the tunes "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" and  [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:JWOPearline) that "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's highly excellent, even if the outcomes sound like mushy variations of songs that might feel familiar", while Business Insider [mentioned](https://hyped4gamers.com) "remarkably, some of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI released the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The function is to research study whether such an approach might assist in auditing [AI](https://www.activeline.com.au) [decisions](https://acrohani-ta.com) and in establishing explainable [AI](https://sea-crew.ru). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a [collection](https://just-entry.com) of visualizations of every substantial layer and nerve cell of eight neural network models which are frequently studied in interpretability. [240] Microscope was developed to evaluate the functions that form inside these [neural networks](https://gitlab.vp-yun.com) quickly. The designs included are AlexNet, VGG-19, various variations of Inception, and various versions of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that offers a conversational user interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.<br>