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 <br>Announced in 2016, Gym is an [open-source Python](http://140.143.208.1273000) library designed to facilitate the development of reinforcement learning [algorithms](http://47.120.20.1583000). It aimed to standardize how environments are defined in [AI](https://socialsnug.net) research, making released research more quickly reproducible [24] [144] while providing users with a basic interface for communicating with these environments. In 2022, new advancements of Gym have 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 on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to solve single tasks. Gym Retro gives the capability to generalize between games with similar principles but various appearances.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even stroll, however are provided the goals of discovering to move and to push the opposing representative 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 removed from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might produce an intelligence "arms race" that might increase a representative's capability to function even outside the context of the competition. [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](http://git.liuhung.com) Dota 2, that learn to play against human players at a high skill level totally through experimental algorithms. Before ending up being a team of 5, the first public demonstration happened at The International 2017, the annual best champion competition for the game, where Dendi, an expert Ukrainian gamer, 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 2 weeks of real time, and that the knowing software application was an action in the instructions of producing software that can handle complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of support learning, as the bots discover with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing 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 group of 5, and they were able to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world [champions](https://moztube.com) of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 total games in a [four-day](http://175.178.199.623000) open online competitors, winning 99.4% of those games. [165]
 <br>OpenAI 5's systems in Dota 2's bot gamer shows the difficulties of [AI](http://1.94.30.1:3000) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has demonstrated the usage of deep support [learning](http://101.52.220.1708081) (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl utilizes device finding out to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It learns entirely in simulation using the very same [RL algorithms](http://39.108.86.523000) and training code as OpenAI Five. OpenAI tackled the things orientation problem by using domain randomization, a simulation approach which exposes the learner to a variety of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB cameras to permit the robot to control an arbitrary object by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI showed that Dactyl could fix 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 model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing progressively harder environments. ADR varies from manual domain randomization by not requiring a human to specify randomization ranges. [169]
 <br>API<br>
 <br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new [AI](https://southwales.com) models developed by OpenAI" to let developers call on it for "any English language [AI](http://124.221.76.28:13000) task". [170] [171]
 <br>Text generation<br>
 <br>The [business](https://8.129.209.127) has actually popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's original 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 colleagues, and published 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 dependencies 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 an unsupervised transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative variations at first launched to the general public. The full variation of GPT-2 was not instantly released due to concern about possible misuse, consisting of applications for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 positioned a substantial danger.<br>
 <br>In reaction to GPT-2, the Allen [Institute](https://git2.nas.zggsong.cn5001) for Artificial Intelligence [responded](http://www.evmarket.co.kr) with a tool to identify "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation to totally 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 complete version of the GPT-2 language model. [177] Several websites host interactive demonstrations of various [instances](https://awaz.cc) 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, illustrated by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not additional 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](http://modulysa.com). It avoids certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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 a without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were also trained). [186]
 <br>OpenAI stated that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
 <br>GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or encountering the essential ability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the general public for issues of possible abuse, although OpenAI prepared to permit  to through a paid cloud API after a two-month totally free personal 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 actually in addition been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://e-gitlab.isyscore.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI,  [wavedream.wiki](https://wavedream.wiki/index.php/User:GenevievePrada) the model can develop working code in over a lots programming languages, many effectively in Python. [192]
 <br>Several problems with glitches, design flaws and security vulnerabilities were pointed out. [195] [196]
 <br>[GitHub Copilot](https://celflicks.com) has actually been accused of emitting copyrighted code, with no author attribution or license. [197]
 <br>OpenAI announced that they would [cease assistance](https://nukestuff.co.uk) 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), capable of [accepting text](https://code.flyingtop.cn) or image inputs. [199] They [revealed](https://git-dev.xyue.zip8443) that the updated innovation passed a simulated law school bar examination 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 likewise read, evaluate or generate approximately 25,000 words of text, and write code in all major shows languages. [200]
 <br>Observers reported that the iteration of [ChatGPT utilizing](http://106.15.120.1273000) GPT-4 was an [enhancement](https://supardating.com) on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and stats about GPT-4, such as the exact size of the design. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision standards, setting brand-new records in audio speech acknowledgment and [translation](https://twoo.tr). [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, OpenAI launched GPT-4o mini, a smaller sized variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT 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 useful for enterprises, start-ups and designers looking for to automate services with [AI](http://git.yang800.cn) representatives. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been created to take more time to think about their actions, causing greater precision. 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 revealed o3, the follower of the o1 reasoning design. OpenAI also revealed o3-mini, a [lighter](http://www.larsaluarna.se) and much faster version 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](https://www.thewaitersacademy.com) and security scientists had the [opportunity](https://careers.webdschool.com) to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
 <br>Deep research<br>
 <br>Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the [capabilities](http://www.grainfather.eu) of OpenAI's o3 model to perform extensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty 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 classification<br>
 <br>CLIP<br>
 <br>[Revealed](https://tweecampus.com) in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze the semantic resemblance in 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 design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can produce images of [reasonable items](http://39.99.158.11410080) ("a stained-glass window with an image of a blue strawberry") in addition to items that do not exist in [reality](http://kyeongsan.co.kr) ("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 model with more reasonable results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new simple system for transforming 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 powerful design much better able to produce images from complicated descriptions without manual timely engineering and render complicated details like hands and text. [221] It was launched to the 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 create videos based on brief detailed prompts [223] along with [extend existing](http://8.211.134.2499000) videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.<br>
 <br>Sora's development group named it after the [Japanese](http://vk-mix.ru) word for "sky", to symbolize its "endless innovative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that purpose, but did not reveal the number or the exact sources of the videos. [223]
 <br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it might generate videos up to one minute long. It likewise shared a technical report highlighting the methods utilized to train the design, and the model's abilities. [225] It acknowledged a few of its shortcomings, [consisting](https://members.advisorist.com) of struggles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but noted that they must have been cherry-picked and might not represent Sora's common output. [225]
 <br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's capability to create reasonable video from text descriptions, citing its possible to change storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause strategies for broadening 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 large dataset of varied audio and is likewise a multi-task design that can perform multilingual speech acknowledgment in addition to 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 designs. According to The Verge, a song produced by MuseNet tends to start fairly however 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://tweecampus.com) to produce 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 bit of lyrics and outputs song samples. OpenAI specified the songs "show local musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" which "there is a considerable gap" between Jukebox and human-generated music. The Verge [mentioned](http://xn--80azqa9c.xn--p1ai) "It's technically impressive, even if the results seem like mushy versions of tunes that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting tunes are catchy 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 debate toy problems in front of a human judge. The function is to research study whether such a technique might assist in auditing [AI](http://president-park.co.kr) decisions and in establishing explainable [AI](https://www.sociopost.co.uk). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020, Microscope [239] is a collection of visualizations of every [substantial layer](http://121.4.70.43000) and neuron of 8 neural network designs which are often studied in interpretability. [240] Microscope was developed to examine the functions that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different versions of Inception, and different [versions](https://starfc.co.kr) of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is a synthetic intelligence tool developed 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 an answer within seconds.<br>