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<br>Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in [AI](http://cloud-repo.sdt.services) research, making published research more quickly reproducible [24] [144] while supplying users with a simple user interface for engaging with these environments. In 2022, brand-new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
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<br>Gym Retro<br>
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<br>Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to resolve single tasks. Gym Retro offers the capability to generalize in between video games with comparable concepts but various appearances.<br>
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<br>RoboSumo<br>
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<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack knowledge of how to even stroll, however are given the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives discover how to adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and positioned in a brand-new virtual [environment](https://www.guidancetaxdebt.com) with high winds, the representative braces to remain upright, recommending it had actually learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might produce an intelligence "arms race" that could increase an agent's ability to operate even outside the context of the competitors. [148]
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<br>OpenAI 5<br>
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<br>OpenAI Five is a group of five 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 totally through experimental algorithms. Before ending up being a team of 5, the very first public presentation happened at The International 2017, the yearly premiere champion competition for [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:SantosDeBernales) the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of actual time, and that the learning software application was an action in the direction of producing software that can manage complex tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of support learning, as the bots discover gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
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<br>By June 2018, the capability of the bots broadened to play together as a complete group of 5, and they were able to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two [exhibit matches](https://akrs.ae) against expert players, but ended up losing both 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](https://remoterecruit.com.au). [163] [164] The bots' final public look came later that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those video games. [165]
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<br>OpenAI 5's systems in Dota 2's bot player shows the challenges of [AI](https://collegejobportal.in) systems in multiplayer online [fight arena](http://www.hnyqy.net3000) (MOBA) video games and how OpenAI Five has actually shown making use of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
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<br>Dactyl<br>
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<br>Developed in 2018, Dactyl uses device learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out completely in simulation [utilizing](https://altaqm.nl) the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a [simulation method](https://bpx.world) which exposes the learner to a variety of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cameras to enable the robot to control an approximate object by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168]
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<br>In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the [Rubik's Cube](https://uspublicsafetyjobs.com) present complex physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of producing progressively harder environments. ADR varies from manual domain randomization by not requiring a human to define randomization varieties. [169]
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<br>API<br>
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<br>In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new [AI](https://wiki.whenparked.com) models developed by OpenAI" to let developers call on it for "any English language [AI](https://www.armeniapedia.org) task". [170] [171]
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<br>Text generation<br>
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<br>The business has actually [popularized generative](https://dispatchexpertscudo.org.uk) pretrained transformers (GPT). [172]
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<br>OpenAI's initial GPT model ("GPT-1")<br>
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<br>The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world knowledge and procedure long-range dependencies by pre-training on a diverse corpus with long [stretches](http://121.28.134.382039) of contiguous text.<br>
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<br>GPT-2<br>
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<br>Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative versions at first launched to the general public. The full variation of GPT-2 was not instantly launched due to concern about potential misuse, consisting of applications for composing fake news. [174] Some specialists revealed uncertainty that GPT-2 presented a considerable threat.<br>
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<br>In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to absolutely 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 sites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
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<br>GPT-2's authors argue without supervision language designs to be general-purpose students, highlighted by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more trained on any task-specific input-output examples).<br>
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<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 avoids certain problems encoding vocabulary with word tokens by [utilizing byte](https://apyarx.com) pair encoding. This permits representing any string of characters by encoding both individual characters and multiple-character tokens. [181]
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<br>GPT-3<br>
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<br>First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion parameters, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full [variation](https://git.opskube.com) of GPT-2 (although GPT-3 models with as few as 125 million parameters were also trained). [186]
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<br>OpenAI stated that GPT-3 was successful at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]
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<br>GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the essential ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly launched to the public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month totally free personal beta that began in June 2020. [170] [189]
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<br>On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191]
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<br>Codex<br>
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<br>Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition been [trained](https://antoinegriezmannclub.com) on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://nepalijob.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was released in personal beta. [194] According to OpenAI, the design can [develop](https://nepalijob.com) working code in over a dozen programming languages, a lot of efficiently in Python. [192]
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<br>Several issues with problems, style flaws and security vulnerabilities were cited. [195] [196]
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<br>GitHub Copilot has been implicated of releasing copyrighted code, with no author attribution or license. [197]
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<br>OpenAI revealed that they would stop assistance for Codex API on March 23, 2023. [198]
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<br>GPT-4<br>
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<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 revealed that the updated technology passed a [simulated law](https://es-africa.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 could also check out, evaluate or create up to 25,000 words of text, and compose code in all major programming languages. [200]
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<br>Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal various [technical details](http://steriossimplant.com) and data about GPT-4, such as the accurate size of the model. [203]
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<br>GPT-4o<br>
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<br>On May 13, 2024, OpenAI announced and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207]
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<br>On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized version 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 particularly useful for enterprises, [start-ups](https://git.desearch.cc) and developers looking for to automate services with [AI](http://xiaomu-student.xuetangx.com) agents. [208]
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<br>o1<br>
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<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been created to take more time to think about their responses, leading to higher accuracy. These designs 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]
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<br>o3<br>
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<br>On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking model. OpenAI also unveiled o3-mini, a lighter and faster version 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, safety and security scientists had the opportunity to obtain early access to these models. [214] The model is called o3 rather than o2 to prevent confusion with O2. [215]
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<br>Deep research<br>
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<br>Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform extensive web browsing, information analysis, and synthesis, delivering detailed reports within a [timeframe](https://15.164.25.185) of 5 to thirty minutes. [216] With browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
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<br>Image category<br>
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<br>CLIP<br>
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<br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is [trained](https://git.limework.net) to evaluate the semantic similarity between text and images. It can significantly be used for image classification. [217]
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<br>Text-to-image<br>
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<br>DALL-E<br>
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<br>Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to [translate natural](http://charge-gateway.com) language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can create images of [reasonable objects](https://www.bluedom.fr) ("a stained-glass window with an image of a blue strawberry") along with things 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>
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<br>DALL-E 2<br>
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<br>In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the model with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new simple system for converting a text description into a 3-dimensional design. [220]
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<br>DALL-E 3<br>
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<br>In September 2023, OpenAI announced DALL-E 3, a more effective design better able to create images from intricate descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was released to the public as a ChatGPT Plus function in October. [222]
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<br>Text-to-video<br>
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<br>Sora<br>
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<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](https://git.vicagroup.com.cn) with resolution up to 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.<br>
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<br>Sora's development group named it after the Japanese word for "sky", to [signify](https://gitea.ochoaprojects.com) its "limitless imaginative 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 using publicly-available videos along with copyrighted videos certified for that function, but did not reveal the number or the precise sources of the videos. [223]
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<br>OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it might generate videos up to one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the model's capabilities. [225] It acknowledged a few of its shortcomings, consisting of struggles mimicing intricate [physics](https://gitlab.dituhui.com). [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they need to have been cherry-picked and may not represent Sora's normal output. [225]
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<br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to create practical video from text descriptions, citing its prospective to reinvent storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to stop briefly prepare for expanding his Atlanta-based film studio. [227]
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<br>Speech-to-text<br>
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<br>Whisper<br>
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<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 likewise a [multi-task model](https://forum.elaivizh.eu) that can carry out multilingual speech acknowledgment in addition to speech translation and language identification. [229]
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<br>Music generation<br>
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<br>MuseNet<br>
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<br>Released in 2019, MuseNet is a deep neural net trained to [predict subsequent](http://codaip.co.kr) musical notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly however then fall under mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the [internet psychological](https://audioedu.kyaikkhami.com) thriller Ben [Drowned](http://zerovalueentertainment.com3000) to create music for the [titular character](https://fondnauk.ru). [232] [233]
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<br>Jukebox<br>
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<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](https://bewerbermaschine.de). OpenAI mentioned the songs "show regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial space" between Jukebox and human-generated music. The Verge stated "It's technologically impressive, even if the results sound 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]
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<br>Interface<br>
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<br>Debate Game<br>
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<br>In 2018, OpenAI released the Debate Game, which [teaches makers](https://10-4truckrecruiting.com) to debate toy problems in front of a human judge. The function is to research study whether such an approach may help in auditing [AI](http://metis.lti.cs.cmu.edu:8023) decisions and in establishing explainable [AI](https://10-4truckrecruiting.com). [237] [238]
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<br>Microscope<br>
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<br>Released in 2020, Microscope [239] is a collection of [visualizations](https://2t-s.com) of every substantial layer and nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, various variations of Inception, and different variations of CLIP Resnet. [241]
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<br>ChatGPT<br>
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<br>Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that provides a conversational user interface that permits users to ask [concerns](https://truejob.co) in natural language. The system then reacts with an answer within seconds.<br>
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