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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://vazeefa.com) research study, making [released](https://git.pleasantprogrammer.com) research more quickly reproducible [24] [144] while [supplying](https://www.telix.pl) users with a simple interface for engaging with these environments. In 2022, new developments of Gym have been relocated to the library Gymnasium. [145] [146]
<br>Gym Retro<br>
<br>Released in 2018, [Gym Retro](https://gitea.star-linear.com) is a platform for support learning (RL) research study on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to fix single jobs. Gym Retro offers the ability to [generalize](https://www.cbl.health) between games with similar principles but different looks.<br>
<br>RoboSumo<br>
<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning [robotic](https://gitlab.truckxi.com) representatives initially do not have [knowledge](https://job.firm.in) of how to even stroll, however are offered the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this process, [wavedream.wiki](https://wavedream.wiki/index.php/User:Natalie6866) the representatives learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and positioned in a [brand-new virtual](http://gitlab.adintl.cn) environment with high winds, the representative braces to remain upright, recommending it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents could create an intelligence "arms race" that could increase an agent's ability 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](http://wrgitlab.org) video game Dota 2, that discover to play against human gamers at a high ability level completely through trial-and-error algorithms. Before ending up being a group of 5, the first public presentation occurred 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 matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for two weeks of genuine time, and that the learning software was a step in the instructions of developing software that can handle complex jobs like a surgeon. [152] [153] The system utilizes a form of support knowing, as the bots find out gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and [yewiki.org](https://www.yewiki.org/User:TristanSumner) taking map goals. [154] [155] [156]
<br>By June 2018, the ability of the [bots expanded](http://202.164.44.2463000) to play together as a full team of 5, and they had the ability to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against [professional](https://www.belizetalent.com) players, however wound up losing both games. [160] [161] [162] In April 2019, [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:RudyRenteria459) OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 total video games in a [four-day](https://git.mintmuse.com) open online competition, winning 99.4% of those games. [165]
<br>OpenAI 5's systems in Dota 2's bot gamer shows the [challenges](https://1millionjobsmw.com) of [AI](https://git.agent-based.cn) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated making use of deep support [knowing](https://tricityfriends.com) (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
<br>Dactyl<br>
<br>Developed in 2018, Dactyl uses device discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It learns entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by utilizing domain randomization, a simulation technique which exposes the student to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, also has RGB video cameras to permit the robotic to manipulate an approximate object by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an [octagonal prism](https://git.biosens.rs). [168]
<br>In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the [Rubik's Cube](https://myclassictv.com) present intricate physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic [Domain Randomization](https://oerdigamers.info) (ADR), a simulation method of generating gradually more hard environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
<br>API<br>
<br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://empleos.contatech.org) models established by OpenAI" to let designers contact it for "any English language [AI](http://git.acdts.top:3000) task". [170] [171]
<br>Text generation<br>
<br>The business has actually popularized generative pretrained transformers (GPT). [172]
<br>OpenAI's initial GPT model ("GPT-1")<br>
<br>The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world knowledge and process long-range dependences by pre-training on a varied 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 model and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions initially released to the general public. The full version of GPT-2 was not immediately released due to issue about potential misuse, including applications for composing fake news. [174] Some specialists revealed uncertainty that GPT-2 positioned a [considerable threat](https://www.jgluiggi.xyz).<br>
<br>In response 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, 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 launched the total variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
<br>GPT-2's authors argue unsupervised language models to be general-purpose students, [illustrated](https://bihiring.com) by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more 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 prevents certain concerns encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both private 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 design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million criteria were also trained). [186]
<br>OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and could generalize the function 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 dramatically enhanced benchmark results over GPT-2. OpenAI cautioned that such [scaling-up](https://kollega.by) of language designs could be approaching or experiencing the essential capability constraints of predictive language designs. [187] Pre-training GPT-3 [required numerous](https://git.coalitionofinvisiblecolleges.org) thousand petaflop/s-days [b] of calculate, [compared](https://worship.com.ng) to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the public for concerns of possible abuse, [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:DwightLangler4) although OpenAI planned to allow gain access to through a paid cloud API after a two-month complimentary personal beta that started in June 2020. [170] [189]
<br>On September 23, 2020, GPT-3 was licensed solely to Microsoft. [190] [191]
<br>Codex<br>
<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has actually furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://gitlab.internetguru.io) powering the code autocompletion tool [GitHub Copilot](http://xn--9t4b21gtvab0p69c.com). [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can develop working code in over a dozen programs languages, many successfully in Python. [192]
<br>Several concerns with glitches, style flaws and security vulnerabilities were mentioned. [195] [196]
<br>GitHub Copilot has actually been implicated of producing copyrighted code, without any author attribution or license. [197]
<br>OpenAI announced that they would stop [support](https://forum.alwehdaclub.sa) 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](http://freeflashgamesnow.com) passed a simulated law school bar test with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, evaluate or create as much as 25,000 words of text, and compose code in all significant programming languages. [200]
<br>Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal numerous 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 revealed and [launched](https://git.epochteca.com) GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained state-of-the-art results in voice, multilingual, and vision benchmarks, [setting](https://www.suntool.top) new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark 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 $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, startups and designers looking for to automate services with [AI](https://edtech.wiki) agents. [208]
<br>o1<br>
<br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been developed to take more time to consider their reactions, causing higher accuracy. These designs are especially efficient in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
<br>o3<br>
<br>On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and quicker variation 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 researchers had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecoms services supplier O2. [215]
<br>Deep research study<br>
<br>Deep research study is a representative established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached 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 model that is trained to evaluate the semantic resemblance between text and images. It can especially be used for image [category](https://hortpeople.com). [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 uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can create pictures of practical things ("a stained-glass window with an image of a blue strawberry") along with objects 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 variation of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new rudimentary 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 design much better able to generate images from complicated descriptions without manual timely engineering and render intricate details like hands and text. [221] It was [released](http://mirae.jdtsolution.kr) 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 model that can generate videos based on brief detailed prompts [223] as well as extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br>
<br>Sora's development team called it after the Japanese word for "sky", to signify its "unlimited innovative potential". [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 as well as copyrighted videos licensed 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 videos to the public on February 15, 2024, mentioning that it might generate videos approximately one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the model's abilities. [225] It acknowledged a few of its shortcomings, consisting of battles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", however noted that they need to have been cherry-picked and may not represent Sora's common output. [225]
<br>Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry [figures](http://jenkins.stormindgames.com) have actually revealed considerable interest in the technology's capacity. 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 change storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly [prepare](https://www.applynewjobz.com) for expanding 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 design. [228] It is trained on a big dataset of diverse audio and is also a multi-task model that can carry out multilingual speech acknowledgment along with speech translation and language [recognition](http://114.115.218.2309005). [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 songs with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to [start fairly](https://sossdate.com) however then fall under chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to produce 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 stated the songs "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" which "there is a substantial space" between Jukebox and human-generated music. The Verge stated "It's highly excellent, even if the outcomes seem like mushy versions of tunes that may feel familiar", while [Business Insider](https://demo.playtubescript.com) stated "remarkably, some 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 machines to dispute toy problems in front of a human judge. The purpose is to research study whether such an approach may help in [auditing](http://adbux.shop) [AI](https://celflicks.com) decisions and in developing explainable [AI](https://careerportals.co.za). [237] [238]
<br>Microscope<br>
<br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network designs which are often studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various variations of Inception, and various variations of CLIP Resnet. [241]
<br>ChatGPT<br>
<br>Launched in November 2022, [ChatGPT](http://124.221.76.2813000) is an expert system tool built on top of GPT-3 that supplies a conversational user interface that allows users to ask questions in natural language. The system then responds with a response within seconds.<br>
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