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<br>Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in [AI](https://deadlocked.wiki) research study, making published research study more quickly reproducible [24] [144] while supplying users with a simple user interface for connecting with these environments. In 2022, new developments of Gym have been relocated to the library Gymnasium. [145] [146]
<br>Gym Retro<br>
<br>Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to fix single jobs. Gym Retro offers the ability to generalize between video games with comparable principles but different looks.<br>
<br>RoboSumo<br>
<br>Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack knowledge of how to even stroll, however are provided the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents discover how to adjust to altering conditions. When a representative is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had 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 could increase an agent's capability to operate even outside the context of the competitors. [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://wiki.ragnaworld.net) 2, that [discover](https://zeustrahub.osloop.com) to play against human gamers at a high ability level completely through experimental algorithms. Before becoming a team of 5, the very first public demonstration happened at The International 2017, the yearly best champion competition for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman [explained](http://git.the-archive.xyz) that the bot had found out by playing against itself for 2 weeks of genuine time, which the learning software was a step in the instructions of producing software application that can handle intricate jobs like a surgeon. [152] [153] The system utilizes a type of [support](https://git.goatwu.com) knowing, as the bots learn 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]
<br>By June 2018, the capability of the bots expanded to play together as a full group of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert gamers, however wound up losing both [video games](http://shiningon.top). [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world [champions](https://git.hxps.ru) 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 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 difficulties of [AI](https://git.tool.dwoodauto.com) systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown making use of deep support knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
<br>Dactyl<br>
<br>Developed in 2018, [wavedream.wiki](https://wavedream.wiki/index.php/User:Dave48B57946) Dactyl utilizes machine learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It discovers entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by using domain randomization, a simulation approach which exposes the student to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, likewise has RGB video cameras to enable the robot to control an [approximate item](https://gitlab.xfce.org) by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
<br>In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a [simulation technique](https://bence.net) of creating gradually more difficult environments. 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://skytube.skyinfo.in) designs developed by OpenAI" to let developers contact it for "any English language [AI](https://rubius-qa-course.northeurope.cloudapp.azure.com) job". [170] [171]
<br>Text generation<br>
<br>The company has actually popularized 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 published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of contiguous text.<br>
<br>GPT-2<br>
<br>Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative [variations initially](http://ufidahz.com.cn9015) launched to the public. The complete version of GPT-2 was not instantly launched due to concern about possible misuse, consisting of applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 presented a considerable hazard.<br>
<br>In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural phony 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 hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several sites host interactive presentations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
<br>GPT-2's authors argue not being watched language designs to be general-purpose students, shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not further 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 with word tokens by utilizing byte pair encoding. This permits representing 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 a without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion specifications, [184] two orders of [magnitude larger](https://umindconsulting.com) than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as few as 125 million specifications were also trained). [186]
<br>OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" tasks 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 in between English and Romanian, and between English and German. [184]
<br>GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across the [essential ability](http://www.jedge.top3000) constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, [wiki.myamens.com](http://wiki.myamens.com/index.php/User:IndiraMrd9371508) 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 instantly released to the public for concerns of possible abuse, although OpenAI prepared to [enable gain](https://nkaebang.com) access to through a paid cloud API after a two-month free private 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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the [AI](https://willingjobs.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 working code in over a dozen programming languages, many successfully in Python. [192]
<br>Several problems with glitches, design flaws and security vulnerabilities were pointed out. [195] [196]
<br>GitHub Copilot has been accused of producing copyrighted code, with no author attribution or license. [197]
<br>OpenAI revealed that they would stop support 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 announced that the updated technology passed a simulated law school bar examination 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 might likewise check out, evaluate or generate approximately 25,000 words of text, and compose code in all major programming languages. [200]
<br>Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on [ChatGPT](https://ddsbyowner.com). [202] OpenAI has decreased to reveal various 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 advanced lead to voice, multilingual, and vision criteria, setting new records in audio speech acknowledgment and translation. [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 version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user interface. Its [API costs](https://ari-sound.aurumai.io) $0.15 per million input tokens and $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be especially beneficial for enterprises, startups and developers looking for to automate services with [AI](http://hoteltechnovalley.com) representatives. [208]
<br>o1<br>
<br>On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been designed to take more time to think about their actions, causing greater accuracy. These designs are especially efficient in science, coding, and reasoning tasks, 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 unveiled o3, the follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and much faster version of OpenAI o3. Since 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 researchers had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to avoid confusion with [telecommunications providers](https://mobishorts.com) O2. [215]
<br>Deep research study<br>
<br>Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [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 analyze the semantic similarity 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 design that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can produce images of reasonable things ("a stained-glass window with an image of a blue strawberry") along with things that do not exist in truth ("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 version of the model with more sensible results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new primary 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 [effective design](https://jobiaa.com) much better able to create images from intricate descriptions without manual timely engineering and render intricate details like hands and text. [221] It was [released](http://repo.fusi24.com3000) 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 model that can generate videos based upon short detailed triggers [223] in addition to extend existing videos [forwards](https://abadeez.com) or in [reverse](https://smaphofilm.com) in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.<br>
<br>Sora's advancement team named it after the Japanese word for "sky", to [signify](http://xiaomaapp.top3000) its "unlimited imaginative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system publicly-available videos along with copyrighted videos licensed for that purpose, but did not reveal the number or the exact sources of the videos. [223]
<br>OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it might create videos approximately one minute long. It also shared a technical report highlighting the approaches used to train the design, and the model's capabilities. [225] It acknowledged a few of its imperfections, including struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but kept in mind that they need to have been cherry-picked and might not represent Sora's common output. [225]
<br>Despite uncertainty from some [academic leaders](https://git.kundeng.us) following Sora's public demo, significant entertainment-industry figures have shown [considerable](https://incomash.com) interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to create sensible video from text descriptions, citing its potential to revolutionize storytelling and content production. He said that his excitement about [Sora's possibilities](http://parasite.kicks-ass.org3000) was so strong that he had chosen to pause 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 model that can carry out 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 tune created by MuseNet tends to start fairly however then fall into [turmoil](https://cchkuwait.com) the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned 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 snippet of lyrics and [outputs song](https://www.noagagu.kr) samples. OpenAI specified the songs "reveal local musical coherence [and] follow standard chord patterns" however [acknowledged](http://106.55.234.1783000) that the songs do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a substantial gap" in between Jukebox and human-generated music. The Verge specified "It's highly outstanding, even if the results seem like mushy variations of songs that may feel familiar", while [Business Insider](http://gogsb.soaringnova.com) specified "remarkably, a few of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
<br>User user interfaces<br>
<br>Debate Game<br>
<br>In 2018, OpenAI introduced the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The purpose is to research whether such a technique may help in auditing [AI](https://friendfairs.com) decisions and in establishing explainable [AI](https://www.megahiring.com). [237] [238]
<br>Microscope<br>
<br>Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are typically studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. 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 developed on top of GPT-3 that supplies a conversational interface that enables users to ask concerns in natural language. The system then responds with an answer within seconds.<br>
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