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<br>Announced in 2016, Gym is an open-source Python library developed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://bryggeriklubben.se) research study, making published research more easily reproducible [24] [144] while supplying users with a basic interface for connecting with these environments. In 2022, new developments of Gym have been transferred to the [library Gymnasium](https://source.brutex.net). [145] [146]
<br>Gym Retro<br>
<br>Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on video games [147] using RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to fix single tasks. Gym Retro offers the [capability](http://anggrek.aplikasi.web.id3000) to generalize between video games with similar concepts but various looks.<br>
<br>RoboSumo<br>
<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first do not have understanding of how to even walk, but are offered the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the agents find out how to adjust to altering conditions. When an agent is then gotten rid of from this virtual environment and put in a environment with high winds, the agent braces to remain upright, [recommending](http://119.3.70.2075690) it had learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could produce an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competitors. [148]
<br>OpenAI 5<br>
<br>OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that discover to play against [human gamers](https://equijob.de) at a high skill level totally through trial-and-error algorithms. Before becoming a group of 5, the first public presentation took place at The International 2017, the yearly best championship competition for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of genuine time, and that the learning software [application](https://dreamcorpsllc.com) was an action in the instructions of developing software application that can manage intricate tasks like a surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots learn with 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. [154] [155] [156]
<br>By June 2018, the ability of the bots expanded to play together as a full team of 5, and they were able to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional 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 exhibition match in San Francisco. [163] [164] The bots' final public look came later that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those games. [165]
<br>OpenAI 5's systems in Dota 2's bot gamer shows the [challenges](https://git.sunqida.cn) of [AI](https://git.ivran.ru) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually shown making use of deep reinforcement knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
<br>Dactyl<br>
<br>Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It learns entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1118914) aside from having movement tracking cams, likewise has RGB cameras to allow the robotic to manipulate an [arbitrary object](http://64.227.136.170) by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
<br>In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to design. OpenAI did this by enhancing the [toughness](http://39.105.128.46) of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of [producing gradually](https://canadasimple.com) harder environments. ADR differs from manual domain randomization by not needing a human to define randomization ranges. [169]
<br>API<br>
<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://www.womplaz.com) designs developed by OpenAI" to let developers call on it for "any English language [AI](https://git-web.phomecoming.com) job". [170] [171]
<br>Text generation<br>
<br>The company has popularized generative pretrained transformers (GPT). [172]
<br>OpenAI's initial GPT design ("GPT-1")<br>
<br>The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his associates, and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11860868) published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language might obtain world understanding and [process long-range](https://praca.e-logistyka.pl) dependences by pre-training on a diverse corpus with long stretches of contiguous text.<br>
<br>GPT-2<br>
<br>Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions at first released to the general public. The full version of GPT-2 was not instantly launched due to concern about possible abuse, consisting of applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 posed a significant threat.<br>
<br>In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the innovation to completely 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 sites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
<br>GPT-2's authors argue unsupervised language designs to be general-purpose learners, highlighted by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model 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 avoids certain concerns encoding vocabulary with word tokens by [utilizing byte](https://socials.chiragnahata.is-a.dev) pair encoding. This permits representing any string of characters by [encoding](https://gitlab.ngser.com) both specific 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 not being watched transformer language model and [pediascape.science](https://pediascape.science/wiki/User:Kristina1835) the follower to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion criteria, [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 criteria were likewise trained). [186]
<br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered [examples](https://handsfarmers.fr) of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]
<br>GPT-3 considerably enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across the fundamental capability constraints of predictive language designs. [187] [Pre-training](http://64.227.136.170) GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 design. [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 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]
<br>On September 23, 2020, GPT-3 was certified 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://www.fun-net.co.kr) 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 produce working code in over a lots programming languages, the majority of successfully in Python. [192]
<br>Several concerns with problems, style defects and security vulnerabilities were mentioned. [195] [196]
<br>GitHub Copilot has been [implicated](http://boiler.ttoslinux.org8888) of giving off copyrighted code, with no author attribution or license. [197]
<br>OpenAI revealed that they would stop 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), capable of accepting text or image inputs. [199] They announced that the [updated innovation](http://git.aiotools.ovh) 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 likewise check out, analyze or produce approximately 25,000 words of text, and compose code in all major programs languages. [200]
<br>Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various [technical details](http://39.99.134.1658123) and data about GPT-4, such as the exact size of the model. [203]
<br>GPT-4o<br>
<br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and create 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](https://charin-issuedb.elaad.io) Language Understanding (MMLU) standard 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 changing GPT-3.5 Turbo on the ChatGPT user interface. Its [API costs](https://nepaxxtube.com) $0.15 per million input tokens and $0.60 per million output tokens, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:JacintoVroland9) compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly beneficial for business, startups and designers looking for to automate services with [AI](https://saghurojobs.com) representatives. [208]
<br>o1<br>
<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been created to take more time to consider their reactions, resulting in greater accuracy. These models are especially reliable in science, coding, and thinking jobs, and were made available to [ChatGPT](http://27.128.240.723000) Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
<br>o3<br>
<br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and much faster version of OpenAI o3. Since 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](https://www.viewtubs.com) to obtain early access to these models. [214] The model is called o3 instead of o2 to avoid confusion with telecoms services supplier O2. [215]
<br>Deep research study<br>
<br>Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform substantial web browsing, data 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 an accuracy 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 design that is trained to examine the semantic similarity in 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 creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can develop images of reasonable objects ("a stained-glass window with an image of a blue strawberry") along with items 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 announced DALL-E 2, an updated variation of the design with more practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new basic system for transforming a text description into a 3-dimensional design. [220]
<br>DALL-E 3<br>
<br>In September 2023, OpenAI revealed DALL-E 3, a more powerful model better able to generate images from complex descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the general public as a ChatGPT Plus function in October. [222]
<br>Text-to-video<br>
<br>Sora<br>
<br>Sora is a text-to-video design that can produce videos based upon short detailed prompts [223] along with extend existing videos forwards or in [reverse](https://rosaparks-ci.com) in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.<br>
<br>Sora's advancement team called it after the Japanese word for "sky", to symbolize its "limitless creative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] [OpenAI trained](https://www.jobindustrie.ma) the system utilizing publicly-available videos in addition to copyrighted videos accredited for that purpose, but did not expose the number or the exact sources of the videos. [223]
<br>OpenAI demonstrated some Sora-created [high-definition videos](http://121.43.121.1483000) to the general public on February 15, 2024, specifying that it might create videos approximately one minute long. It also shared a technical report highlighting the techniques used to train the design, and the model's abilities. [225] It acknowledged some of its drawbacks, including struggles [imitating](http://expertsay.blog) complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but kept in mind that they need to have been cherry-picked and may not represent Sora's typical output. [225]
<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, [engel-und-waisen.de](http://www.engel-und-waisen.de/index.php/Benutzer:KiaraThompson) noteworthy entertainment-industry figures have actually revealed considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to [generate](https://dandaelitetransportllc.com) practical video from text descriptions, mentioning its prospective to transform storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly prepare for expanding his Atlanta-based motion [picture](https://gitea.lelespace.top) studio. [227]
<br>Speech-to-text<br>
<br>Whisper<br>
<br>Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a big dataset of diverse audio and is also a multi-task design that can perform multilingual speech acknowledgment 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 predict subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by [MuseNet](https://wooshbit.com) tends to begin fairly however then fall under mayhem the longer it plays. [230] [231] In popular culture, [initial applications](https://insta.tel) of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to develop music for the titular character. [232] [233]
<br>Jukebox<br>
<br>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 category, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the songs "show regional musical coherence [and] follow traditional chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" which "there is a significant gap" between Jukebox and human-generated music. The Verge mentioned "It's technologically excellent, even if the results seem like mushy variations of songs that might feel familiar", while Business Insider [mentioned](http://118.31.167.22813000) "surprisingly, a few of the resulting tunes are catchy and sound genuine". [234] [235] [236]
<br>User user interfaces<br>
<br>Debate Game<br>
<br>In 2018, OpenAI released the Debate Game, which teaches devices to debate toy issues in front of a human judge. The function is to research whether such a technique may help in auditing [AI](http://104.248.138.208) choices and in establishing explainable [AI](https://gitea.ochoaprojects.com). [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](https://git.joystreamstats.live) designs which are typically studied in interpretability. [240] Microscope was produced 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 variations of CLIP Resnet. [241]
<br>ChatGPT<br>
<br>Launched in November 2022, ChatGPT is a synthetic intelligence tool constructed on top of GPT-3 that supplies a conversational user interface that enables users to ask concerns in natural language. The system then responds with an answer within seconds.<br>