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<br>Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://dimans.mx) research, making released research more easily reproducible [24] [144] while offering users with a simple user interface for connecting with these environments. In 2022, new advancements of Gym have actually been moved to the library Gymnasium. [145] [146]
<br>Gym Retro<br>
<br>Released in 2018, Gym Retro is a platform for support learning (RL) research study on [video games](http://gsrl.uk) [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to solve single tasks. Gym Retro offers the capability to generalize between games with comparable principles however various appearances.<br>
<br>RoboSumo<br>
<br>Released in 2017, RoboSumo is a virtual world where [humanoid metalearning](http://82.19.55.40443) robot agents initially lack knowledge of how to even stroll, but are provided the objectives of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents discover how to adjust to altering conditions. When an agent is then removed from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, recommending it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could create 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 team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high skill level entirely through trial-and-error algorithms. Before becoming a group of 5, the first public presentation took place at The International 2017, the annual premiere championship tournament for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of real time, which the learning software was a step in the instructions of creating software application that can handle intricate jobs like a cosmetic surgeon. [152] [153] The system uses a kind of support learning, as the bots learn in time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
<br>By June 2018, the capability of the bots expanded to play together as a full team of 5, and they were able to beat groups of [amateur](https://armconnection.com) and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against [professional](http://xn--mf0bm6uh9iu3avi400g.kr) players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 total 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 difficulties of [AI](http://euhope.com) systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated the usage of deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
<br>Dactyl<br>
<br>Developed in 2018, Dactyl utilizes maker discovering to train a Shadow Hand, a human-like robotic hand, to control physical objects. [167] It discovers completely in simulation using the very same RL algorithms and [training](http://sintec-rs.com.br) code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking electronic cameras, likewise has RGB cams to allow the robotic to control an approximate object by seeing it. In 2018, OpenAI revealed that the system was able to [manipulate](https://oerdigamers.info) a cube and an octagonal prism. [168]
<br>In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot was able to the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex [physics](https://hortpeople.com) that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating progressively harder environments. ADR varies from manual domain randomization by not needing a human to [define randomization](https://git.xjtustei.nteren.net) 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://git.mm-music.cn) models developed by OpenAI" to let developers get in touch with it for "any English language [AI](http://git.jishutao.com) task". [170] [171]
<br>Text generation<br>
<br>The business has actually promoted generative [pretrained](http://gitlab.flyingmonkey.cn8929) 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 model was composed by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language could obtain world understanding 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 an unsupervised transformer [language](https://gitea.ashcloud.com) design and the successor to [OpenAI's initial](https://pl.velo.wiki) GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions at first released to the general public. The complete version of GPT-2 was not immediately released due to concern about potential abuse, including applications for [composing phony](https://www.h2hexchange.com) news. [174] Some experts revealed uncertainty that GPT-2 posed a considerable threat.<br>
<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of various 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 modern accuracy and perplexity on 7 of 8 zero-shot tasks (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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain problems [encoding vocabulary](http://aiot7.com3000) with word tokens by utilizing byte pair encoding. This permits representing 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 an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated 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 variation of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were likewise trained). [186]
<br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and might generalize the [purpose](https://119.29.170.147) of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184]
<br>GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the public for concerns of possible abuse, although OpenAI planned to permit 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 exclusively 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](https://playtube.ann.az) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in private beta. [194] According to OpenAI, the model can develop working code in over a lots programming languages, many efficiently in Python. [192]
<br>Several problems with problems, style flaws and security vulnerabilities were mentioned. [195] [196]
<br>GitHub Copilot has actually been implicated of giving off copyrighted code, with no author attribution or license. [197]
<br>OpenAI announced that they would terminate 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 innovation passed a [simulated law](http://www.gbape.com) school bar test 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 also check out, analyze or create as much as 25,000 words of text, and write code in all major programs languages. [200]
<br>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 revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose different technical details and stats about GPT-4, such as the accurate size of the design. [203]
<br>GPT-4o<br>
<br>On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting edge [outcomes](http://git.info666.com) in voice, multilingual, and vision standards, setting brand-new [records](http://artsm.net) in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
<br>On July 18, 2024, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:MargueriteBoelke) OpenAI launched GPT-4o mini, a smaller version of GPT-4o changing 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 expects it to be particularly useful for business, start-ups and developers seeking to automate services with [AI](https://thebigme.cc:3000) agents. [208]
<br>o1<br>
<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been designed to take more time to think of their reactions, causing greater precision. These models are particularly reliable in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]
<br>o3<br>
<br>On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking design. OpenAI also revealed o3-mini, a lighter and quicker variation 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 scientists had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]
<br>Deep research<br>
<br>Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of [OpenAI's](https://www.kenpoguy.com) o3 model to perform extensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With [browsing](https://157.56.180.169) and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
<br>Image classification<br>
<br>CLIP<br>
<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to evaluate the [semantic resemblance](http://gitlab.y-droid.com) between text and images. It can notably be utilized 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 variation of GPT-3 to analyze natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can develop pictures of sensible things ("a stained-glass window with an image of a blue strawberry") along with objects that do not exist in [reality](https://gitlab.rails365.net) ("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 upgraded version of the model with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new simple system for converting a text description into a 3-dimensional design. [220]
<br>DALL-E 3<br>
<br>In September 2023, OpenAI announced DALL-E 3, a more effective design better able to create images from complex descriptions without manual prompt engineering and render complex details like hands and text. [221] It was launched 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 upon short detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of created videos is unknown.<br>
<br>Sora's advancement team called it after the Japanese word for "sky", to signify its "limitless creative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos accredited for that function, however did not expose the number or the specific sources of the videos. [223]
<br>OpenAI demonstrated some [Sora-created high-definition](https://fromkorea.kr) videos to the general public on February 15, 2024, mentioning that it might generate videos up to one minute long. It also shared a technical report highlighting the approaches used to train the model, and the design's capabilities. [225] It acknowledged some of its imperfections, including struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however noted that they need to have been cherry-picked and might not represent Sora's normal output. [225]
<br>Despite [uncertainty](https://ashawo.club) from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's ability to generate reasonable video from text descriptions, citing its potential to reinvent storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause strategies for expanding his Atlanta-based motion picture 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 big dataset of varied audio and is also a [multi-task](https://www.h2hexchange.com) model that can perform multilingual speech acknowledgment in addition to 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 files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a song generated by [MuseNet](https://noarjobs.info) tends to start fairly but then fall under chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized 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](http://142.93.151.79) on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "show regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge mentioned "It's technologically impressive, even if the results seem like mushy variations of tunes that may feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are memorable and sound genuine". [234] [235] [236]
<br>Interface<br>
<br>Debate Game<br>
<br>In 2018, OpenAI launched the Debate Game, which teaches machines to discuss toy issues in front of a human judge. The purpose is to research whether such a [technique](http://gitfrieds.nackenbox.xyz) may help in auditing [AI](http://gitlab.solyeah.com) decisions and in developing explainable [AI](https://git.parat.swiss). [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 designs which are typically studied in interpretability. [240] Microscope was developed to examine the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
<br>ChatGPT<br>
<br>Launched in November 2022, ChatGPT is an artificial intelligence tool built on top of GPT-3 that provides a conversational interface that allows users to ask questions in natural language. The system then responds with a response within seconds.<br>