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 <br>Announced in 2016, Gym is an open-source Python library created to help with the development of support learning [algorithms](https://git.lmh5.com). It aimed to standardize how environments are specified in [AI](https://cinetaigia.com) research, making released research study more quickly reproducible [24] [144] while providing users with a basic user [interface](http://59.37.167.938091) for engaging with these environments. In 2022, brand-new developments 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 support knowing (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to resolve single jobs. Gym Retro offers the ability to generalize in between video games with similar ideas however various looks.<br>
 <br>RoboSumo<br>
 <br>Released in 2017, RoboSumo is a virtual world where [humanoid metalearning](https://git.elder-geek.net) robot representatives at first lack knowledge of how to even stroll, but are given the goals of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, [suggesting](https://croart.net) it had discovered 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 could increase an agent's capability to work 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 Dota 2, that find out to play against human gamers at a high ability level completely through experimental algorithms. Before ending up being a group of 5,  [hb9lc.org](https://www.hb9lc.org/wiki/index.php/User:NorbertoPlayford) the first public demonstration occurred at The International 2017, the annual best championship tournament 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 2 weeks of actual time, and that the [knowing software](http://59.37.167.938091) was an action in the instructions of creating software application that can [handle complicated](http://47.119.27.838003) jobs like a surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots learn with time by playing against themselves numerous times a day for months, and are [rewarded](https://www.srapo.com) for  [pipewiki.org](https://pipewiki.org/wiki/index.php/User:Alfie04M080) actions such as [killing](https://itheadhunter.vn) an [opponent](http://httelecom.com.cn3000) 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 beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The [International](https://pattondemos.com) 2018, OpenAI Five played in two exhibition matches against expert gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last [public appearance](https://hortpeople.com) came later on that month, where they played in 42,729 total video games in a four-day open online competitors, winning 99.4% of those games. [165]
 <br>OpenAI 5's mechanisms in Dota 2's bot player shows the challenges of [AI](https://wiki.communitydata.science) systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown the use of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
 <br>Dactyl<br>
 <br>Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robot hand, to control physical objects. [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](http://git.armrus.org) the learner to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has RGB electronic cameras to permit the robotic to control an arbitrary object by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
 <br>In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating progressively more tough environments. ADR differs from manual domain randomization by not requiring a human to [define randomization](https://event.genie-go.com) ranges. [169]
 <br>API<br>
 <br>In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new [AI](https://www.scikey.ai) models established by OpenAI" to let developers contact it for "any English language [AI](https://git.buzhishi.com:14433) task". [170] [171]
 <br>Text generation<br>
 <br>The company has actually popularized generative pretrained transformers (GPT). [172]
 <br>OpenAI's original 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 site on June 11, 2018. [173] It showed how a generative design of language could obtain world knowledge and process 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 an unsupervised transformer language design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative variations at first released to the public. The complete variation of GPT-2 was not instantly launched due to issue about potential misuse, consisting of applications for writing fake news. [174] Some experts revealed uncertainty that GPT-2 presented a substantial threat.<br>
 <br>In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural phony news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to totally 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 total variation of the GPT-2 [language model](https://tv.lemonsocial.com). [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
 <br>GPT-2['s authors](https://www.lakarjobbisverige.se) argue unsupervised language models to be general-purpose students, illustrated by GPT-2 attaining cutting edge accuracy 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by using byte pair encoding. This permits representing any string of characters by encoding both [individual characters](https://ruofei.vip) and multiple-character tokens. [181]
 <br>GPT-3<br>
 <br>First explained in May 2020, Generative Pre-trained [a] [Transformer](http://221.182.8.1412300) 3 (GPT-3) is an unsupervised transformer language design and the successor to GPT-2. [182] [183] [184] [OpenAI stated](https://studiostilesandtotalfitness.com) that the full version of GPT-3 contained 175 billion specifications, [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 likewise trained). [186]
 <br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs 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 considerably enhanced [benchmark outcomes](https://catvcommunity.com.tr) over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or coming across the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the public for concerns of possible abuse, although [OpenAI prepared](https://energypowerworld.co.uk) to [enable gain](https://gigen.net) access to through a paid cloud API after a two-month complimentary personal beta that began in June 2020. [170] [189]
 <br>On September 23, 2020, GPT-3 was certified solely to [Microsoft](https://pakalljobs.live). [190] [191]
 <br>Codex<br>
 <br>Announced in mid-2021, Codex is a descendant of GPT-3 that has furthermore been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://www.myjobsghana.com) powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in personal beta. [194] According to OpenAI, the design can create working code in over a dozen programs languages, the majority of effectively in Python. [192]
 <br>Several problems with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
 <br>GitHub Copilot has been implicated of discharging copyrighted code, without any author attribution or license. [197]
 <br>OpenAI announced that they would cease support for Codex API on March 23, 2023. [198]
 <br>GPT-4<br>
 <br>On March 14, 2023, [OpenAI revealed](https://spiritustv.com) the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar examination 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, examine or generate up to 25,000 words of text, and compose code in all major programs languages. [200]
 <br>Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, 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 various technical details and stats about GPT-4,  [wiki.rolandradio.net](https://wiki.rolandradio.net/index.php?title=User:MaxMcAulay008) such as the precise size of the design. [203]
 <br>GPT-4o<br>
 <br>On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision criteria, setting brand-new records in audio speech 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 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 beneficial for enterprises, start-ups and designers seeking to automate services with [AI](http://128.199.161.91:3000) agents. [208]
 <br>o1<br>
 <br>On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been created to take more time to think of their reactions, resulting in greater accuracy. These designs are particularly efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
 <br>o3<br>
 <br>On December 20, 2024, OpenAI unveiled o3, the successor of the o1 thinking model. OpenAI also unveiled o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024,  [wiki.dulovic.tech](https://wiki.dulovic.tech/index.php/User:KatrinaPolding1) this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to [prevent confusion](https://topdubaijobs.ae) with telecoms services provider O2. [215]
 <br>Deep research study<br>
 <br>Deep research is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out extensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and [Python tools](http://lethbridgegirlsrockcamp.com) made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
 <br>Image category<br>
 <br>CLIP<br>
 <br>Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic resemblance in between text and images. It can significantly be utilized for image category. [217]
 <br>Text-to-image<br>
 <br>DALL-E<br>
 <br>Revealed in 2021, DALL-E is a Transformer model that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and create matching images. It can create pictures of reasonable things ("a stained-glass window with an image of a blue strawberry") in addition to objects 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 revealed](https://copyrightcontest.com) DALL-E 2, an updated version of the model with more sensible results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new basic 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 powerful model better able to produce images from intricate descriptions without manual [timely engineering](https://bantooplay.com) and render complex details like hands and text. [221] It was launched 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 design that can produce videos based upon short detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce 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 word for "sky", to represent its "endless creative capacity". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that purpose, 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, specifying that it could produce videos up to one minute long. It also shared a technical report highlighting the methods used to train the design, and the model's abilities. [225] It acknowledged some of its imperfections, consisting of battles simulating [complicated physics](https://abstaffs.com). [226] Will Douglas Heaven of the MIT Technology Review called the [demonstration](https://ubuntushows.com) videos "impressive", however noted that they must have been cherry-picked and may not represent Sora's typical output. [225]
 <br>Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have actually shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to  video from text descriptions, mentioning its potential to reinvent storytelling and content creation. He said that his excitement about [Sora's possibilities](https://topdubaijobs.ae) was so strong that he had chosen to pause prepare for expanding his Atlanta-based film studio. [227]
 <br>Speech-to-text<br>
 <br>Whisper<br>
 <br>Released in 2022, [Whisper](https://wooshbit.com) is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of varied audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. [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 tunes with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to create 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 snippet of lyrics and outputs song samples. [OpenAI stated](https://thefreedommovement.ca) the songs "reveal local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" which "there is a significant space" in between Jukebox and human-generated music. The Verge mentioned "It's technologically remarkable, even if the results seem like mushy versions of tunes that may feel familiar", while Business Insider stated "remarkably, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
 <br>Interface<br>
 <br>Debate Game<br>
 <br>In 2018, OpenAI introduced the Debate Game, which teaches devices to debate toy issues in front of a human judge. The function is to research study whether such a technique may help in auditing [AI](https://www.womplaz.com) choices and in establishing explainable [AI](http://encocns.com:30001). [237] [238]
 <br>Microscope<br>
 <br>Released in 2020,  [yewiki.org](https://www.yewiki.org/User:Nichol9618) Microscope [239] is a [collection](https://app.galaxiesunion.com) of visualizations of every [considerable layer](https://ramique.kr) and [nerve cell](http://git.risi.fun) of eight neural network designs which are often 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 versions of CLIP Resnet. [241]
 <br>ChatGPT<br>
 <br>Launched in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that provides a conversational user interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.<br>