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 <br>Announced in 2016, Gym is an [open-source Python](http://aircrew.co.kr) library developed to help with the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://120.77.209.176:3000) research, making released research more easily reproducible [24] [144] while supplying users with a simple interface for connecting with these environments. In 2022, brand-new advancements of Gym have actually been transferred to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to fix single tasks. Gym Retro gives the capability to generalize between video games with similar principles however different looks.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have knowledge of how to even walk, but are [offered](https://ouptel.com) the goals of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the agents find out 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 discovered how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might produce an intelligence "arms race" that might increase an agent's ability to operate even outside the context of the [competition](https://gitea.linuxcode.net). [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](https://duniareligi.com) 2, that find out to play against human players at a high skill level entirely through experimental algorithms. Before ending up being a team of 5, the very first public presentation took place at The International 2017, the yearly best [championship competition](https://git.pawott.de) 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 Brockman explained that the bot had actually learned by playing against itself for two weeks of genuine time, and that the knowing software application was an action in the instructions of [creating software](https://laborando.com.mx) application that can manage intricate tasks like a surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots learn gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as [eliminating](https://thathwamasijobs.com) an enemy and taking map goals. [154] [155] [156]
 <br>By June 2018, the capability of the bots expanded to play together as a complete group of 5, and they had the ability to defeat teams of amateur and  [wavedream.wiki](https://wavedream.wiki/index.php/User:JaysonSpahn7) semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those games. [165]
 <br>OpenAI 5['s systems](http://udyogservices.com) in Dota 2's bot gamer shows the obstacles of [AI](https://satitmattayom.nrru.ac.th) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 [matches](https://golz.tv). [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl utilizes machine discovering to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It learns entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by using domain randomization, a simulation approach which exposes the learner to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB cams to enable the robot to manipulate an [arbitrary object](https://denis.usj.es) by seeing it. In 2018, OpenAI showed that the system was able to [manipulate](http://forum.moto-fan.pl) a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI [demonstrated](http://47.100.17.114) that Dactyl could fix a Rubik's Cube. The robotic had the ability to [resolve](https://gitstud.cunbm.utcluj.ro) the puzzle 60% of the time. Objects like the [Rubik's Cube](https://wiki.lafabriquedelalogistique.fr) introduce complicated physics that is harder to design. OpenAI did this by enhancing the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of creating gradually more challenging [environments](https://gitea.thuispc.dynu.net). ADR varies from manual domain randomization by not requiring a human to define randomization ranges. [169]
 <br>API<br>
 <br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new [AI](https://jobsthe24.com) designs developed by OpenAI" to let developers get in touch with it for "any English language [AI](https://git.fanwikis.org) task". [170] [171]
 <br>Text generation<br>
 <br>The business has actually promoted generative pretrained transformers (GPT). [172]
 <br>OpenAI's original GPT design ("GPT-1")<br>
 <br>The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a [generative design](https://git.iidx.ca) of language might obtain world understanding and process long-range dependencies 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 a not being watched transformer language design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative variations [initially launched](https://systemcheck-wiki.de) to the general public. The full version of GPT-2 was not instantly launched due to issue about prospective misuse, consisting of applications for composing 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 detect "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the technology to absolutely 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 complete variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
 <br>GPT-2's authors argue not being watched language models to be general-purpose students, highlighted by GPT-2 attaining state-of-the-art precision and [perplexity](https://901radio.com) on 7 of 8 zero-shot jobs (i.e. the model was not additional 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 at least 3 upvotes. It prevents certain concerns encoding [vocabulary](http://gamebizdev.ru) with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both [private characters](https://alldogssportspark.com) 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 follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion parameters, [184] two orders of [magnitude larger](https://coopervigrj.com.br) than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were likewise trained). [186]
 <br>OpenAI mentioned that GPT-3 prospered at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]
 <br>GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the basic capability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched 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 personal beta that began 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](http://115.236.37.105:30011) powering the code autocompletion tool [GitHub Copilot](https://hub.bdsg.academy). [193] In August 2021, an API was released in [personal](https://drshirvany.ir) beta. [194] According to OpenAI, the model can [produce](http://tanpoposc.com) working code in over a [dozen programs](https://itheadhunter.vn) languages, most successfully in Python. [192]
 <br>Several issues with glitches, [style flaws](https://www.miptrucking.net) and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has actually been accused of emitting copyrighted code, without any author attribution or license. [197]
 <br>OpenAI announced that they would discontinue support for [Codex API](https://xn--114-2k0oi50d.com) on March 23, 2023. [198]
 <br>GPT-4<br>
 <br>On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar exam 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 also check out, analyze or produce as much as 25,000 words of text, and write code in all major programming languages. [200]
 <br>Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the issues with earlier [revisions](http://www.maxellprojector.co.kr). [201] GPT-4 is likewise [efficient](https://gogs.tyduyong.com) in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal 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 revealed and released GPT-4o, which can process and  text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, and vision criteria, setting brand-new records in [audio speech](http://aircrew.co.kr) recognition 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 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 especially helpful for enterprises, startups and developers seeking to automate services with [AI](https://git.rankenste.in) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been designed to take more time to consider their responses, leading to higher precision. These models are especially effective 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 reasoning design. OpenAI also unveiled o3-mini, a lighter and much faster variation of OpenAI o3. Since 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 telecommunications providers O2. [215]
 <br>Deep research<br>
 <br>Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to [perform extensive](https://git.itbcode.com) web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached a [precision](http://www.thegrainfather.com.au) 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 examine the semantic similarity between text and images. It can especially be used for image category. [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 variation of GPT-3 to interpret natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can create pictures of realistic items ("a stained-glass window with a picture of a blue strawberry") along with things that do not exist in reality ("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 variation of the model with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new primary system for converting a text description into a 3-dimensional model. [220]
 <br>DALL-E 3<br>
 <br>In September 2023, [OpenAI revealed](http://n-f-l.jp) DALL-E 3, a more powerful design better able to produce images from complicated descriptions without manual timely engineering and render complicated details like hands and text. [221] It was [released](https://thathwamasijobs.com) to the 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 on brief 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 generated videos is unknown.<br>
 <br>Sora's development group named it after the Japanese word for "sky", to signify its "endless innovative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos licensed for that purpose, however did not reveal the number or the specific sources of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could create videos as much as one minute long. It also shared a technical report highlighting the approaches utilized to train the design, and the model's capabilities. [225] It acknowledged some of its imperfections, including battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the [presentation](http://81.70.25.1443000) videos "impressive", but noted that they need to have been cherry-picked and might not represent Sora's typical output. [225]
 <br>Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's ability to create reasonable video from text descriptions, citing its potential to reinvent storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly 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 acknowledgment model. [228] It is trained on a big dataset of diverse audio and is likewise a [multi-task](https://www.passadforbundet.se) 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 forecast subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a song created by [MuseNet](http://135.181.29.1743001) tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web psychological thriller Ben [Drowned](https://dimans.mx) 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 genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "show local musical coherence [and] follow traditional chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" and that "there is a considerable space" between Jukebox and human-generated music. The Verge mentioned "It's technically remarkable, even if the outcomes sound like mushy versions of songs that may feel familiar", while Business Insider specified "surprisingly, a few of the resulting tunes are catchy 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 study whether such an approach may assist in auditing [AI](http://git.jetplasma-oa.com) decisions and in developing explainable [AI](https://git.xutils.co). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and [nerve cell](https://git.fanwikis.org) of 8 neural network [designs](https://manilall.com) which are frequently studied in [interpretability](http://115.236.37.10530011). [240] Microscope was developed to examine the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and various variations of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is an expert system tool built on top of GPT-3 that offers a conversational user interface that enables users to ask concerns in natural language. The system then reacts with an answer within seconds.<br>