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 <br>Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in [AI](https://www.calebjewels.com) research, making released research more quickly reproducible [24] [144] while providing users with an easy user interface for communicating with these environments. In 2022, brand-new developments of Gym have actually been [transferred](https://git.wheeparam.com) to the library Gymnasium. [145] [146]
 <br>Gym Retro<br>
 <br>Released in 2018, Gym Retro is a platform for  (RL) research on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to resolve single jobs. Gym Retro gives the ability to generalize between games with comparable ideas but various appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot [representatives initially](http://106.52.121.976088) lack knowledge of how to even walk, but are provided the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial [learning](http://110.42.231.1713000) process, the agents learn how to adapt to altering conditions. When an agent is then removed from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, [recommending](http://47.107.92.41234) it had actually learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could produce an intelligence "arms race" that might increase an agent's ability 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 utilized in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high ability level completely through experimental algorithms. Before becoming a team 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 gamer, lost against a bot in a [live one-on-one](https://idaivelai.com) match. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of actual time, and that the knowing software application was an action in the direction of developing software that can manage complex jobs 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 objectives. [154] [155] [156]
 <br>By June 2018, the ability of the bots broadened to play together as a complete team of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the [reigning](http://221.239.90.673000) world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall video games in a [four-day](https://adsall.net) open online competition, winning 99.4% of those video games. [165]
 <br>OpenAI 5's systems in Dota 2's bot player shows the challenges of [AI](https://git.komp.family) systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated the use of deep reinforcement learning (DRL) agents to attain superhuman competence 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](https://fondnauk.ru) hand, to control physical items. [167] It finds out totally in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by using domain randomization, a simulation technique which exposes the [student](https://love63.ru) to a range of experiences instead of attempting to fit to [reality](https://gitea.sprint-pay.com). The set-up for Dactyl, aside from having motion tracking electronic cameras, likewise has RGB video cameras to allow the robot to manipulate an arbitrary things by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an [octagonal prism](https://www.remotejobz.de). [168]
 <br>In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics 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 gradually more hard environments. ADR varies from manual domain randomization by not needing 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://youarealways.online) designs established by OpenAI" to let designers call on it for "any English language [AI](http://gitlab.fuxicarbon.com) job". [170] [171]
 <br>Text generation<br>
 <br>The business has popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's original GPT model ("GPT-1")<br>
 <br>The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and  [pipewiki.org](https://pipewiki.org/wiki/index.php/User:ReganQuinonez1) his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model of language might obtain world knowledge and procedure long-range reliances 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 initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions initially launched to the general public. The full variation of GPT-2 was not instantly released due to issue about possible misuse, consisting of applications for  [forum.altaycoins.com](http://forum.altaycoins.com/profile.php?id=1078514) composing fake news. [174] Some professionals revealed uncertainty that GPT-2 posed a substantial hazard.<br>
 <br>In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the technology 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 total variation of the GPT-2 language model. [177] Several websites host interactive presentations of various 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 learners, highlighted by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (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 a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding [vocabulary](https://gitea.qianking.xyz3443) with word tokens by utilizing byte pair encoding. This allows [representing](https://archie2429263902267.bloggersdelight.dk) any string of characters by encoding 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 an unsupervised transformer language model and the [follower](https://source.lug.org.cn) to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million parameters were likewise trained). [186]
 <br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose 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 dramatically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the fundamental 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 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the public for concerns of possible abuse, although OpenAI planned to enable 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](https://wellandfitnessgn.co.kr) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://cn.wejob.info) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the model can develop working code in over a lots shows languages, the majority of effectively in Python. [192]
 <br>Several problems with problems, style flaws and security vulnerabilities were mentioned. [195] [196]
 <br>GitHub Copilot has been accused of producing copyrighted code, without any author [attribution](http://190.117.85.588095) or license. [197]
 <br>OpenAI revealed that they would cease assistance for Codex API 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), efficient in accepting text or image inputs. [199] They revealed that the [updated innovation](https://v-jobs.net) 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 might also check out, [analyze](http://boiler.ttoslinux.org8888) or generate as much as 25,000 words of text, and compose code in all significant programs languages. [200]
 <br>Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise [efficient](https://git.fandiyuan.com) in taking images as input on ChatGPT. [202] OpenAI has decreased to expose numerous [technical details](https://braindex.sportivoo.co.uk) and stats about GPT-4, such as the accurate size of the model. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and [produce](https://git.thunraz.se) text, images and audio. [204] GPT-4o attained modern outcomes in voice, multilingual, and vision standards, [setting brand-new](https://www.app.telegraphyx.ru) 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 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 [beneficial](https://www.freetenders.co.za) for enterprises, startups and developers seeking to automate services with [AI](https://git.tasu.ventures) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been [designed](http://easyoverseasnp.com) to take more time to consider their actions, leading to higher accuracy. These designs are particularly efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
 <br>o3<br>
 <br>On December 20, 2024, OpenAI revealed o3, the successor of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and [faster variation](https://gitea.dokm.xyz) of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with [telecoms companies](http://47.100.81.115) O2. [215]
 <br>Deep research<br>
 <br>Deep research study is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, 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 resemblance between text and images. It can especially be used for image classification. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to [interpret natural](https://play.sarkiniyazdir.com) language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can develop pictures of sensible items ("a stained-glass window with a picture of a blue strawberry") in addition to items 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 announced DALL-E 2, an upgraded version of the model with more realistic results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new fundamental system for converting 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 effective design much better able to generate images from complicated descriptions without manual prompt engineering and render complex details like hands and text. [221] It was launched to the public as a ChatGPT Plus [feature](https://videoflixr.com) in October. [222]
 <br>Text-to-video<br>
 <br>Sora<br>
 <br>Sora is a text-to-video model that can create videos based upon brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can produce videos with [resolution](https://jotshopping.com) as much as 1920x1080 or 1080x1920. The maximal length of created videos is unknown.<br>
 <br>Sora's advancement team called it after the Japanese word for "sky", to symbolize its "endless imaginative potential". [223] Sora's innovation is an adaptation 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 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 general public on February 15, 2024, specifying that it could create videos up to one minute long. It also shared a technical report highlighting the methods used to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, including struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the [demonstration videos](https://suomalainennaikki.com) "impressive", but kept in mind that they must have been cherry-picked and might not represent Sora's typical output. [225]
 <br>Despite uncertainty from some academic leaders following Sora's public demonstration, significant [entertainment-industry figures](https://git.flandre.net) have revealed significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to generate practical video from text descriptions, mentioning its [potential](https://boonbac.com) to [reinvent storytelling](https://www.freeadzforum.com) and material development. He said that his enjoyment about Sora's possibilities was so strong that he had decided to pause prepare for expanding his Atlanta-based motion [picture studio](https://redefineworksllc.com). [227]
 <br>Speech-to-text<br>
 <br>Whisper<br>
 <br>Released in 2022, Whisper is a [general-purpose speech](https://ivytube.com) recognition design. [228] It is trained on a big dataset of diverse audio and is likewise 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 anticipate subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall into turmoil the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to create music for the [titular character](http://bingbinghome.top3001). [232] [233]
 <br>Jukebox<br>
 <br>Released in 2020, Jukebox is an [open-sourced algorithm](https://999vv.xyz) to create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs song [samples](https://addismarket.net). OpenAI mentioned the songs "show regional musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a considerable space" between Jukebox and [human-generated music](https://jobs.ondispatch.com). The Verge specified "It's technologically impressive, even if the outcomes sound like mushy variations of songs that might feel familiar", while Business Insider stated "remarkably, some of the resulting songs are catchy and sound legitimate". [234] [235] [236]
 <br>User interfaces<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI released the Debate Game, which teaches machines to discuss toy problems in front of a human judge. The function is to research whether such a technique might assist in auditing [AI](https://www.ssecretcoslab.com) decisions and in developing explainable [AI](http://yijichain.com). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of eight neural network models which are often studied in interpretability. [240] Microscope was created to analyze the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different versions of Inception, and different 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 offers a conversational interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.<br>