Announced in 2016, Gym is an open-source Python library designed to assist in the development of support learning algorithms. It aimed to standardize how environments are defined in AI research study, making published research more easily reproducible [24] [144] while providing users with a basic interface for communicating with these environments. In 2022, brand-new developments of Gym have been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on video games [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to solve single tasks. Gym Retro provides the capability to generalize between video games with comparable principles however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack knowledge of how to even stroll, but are given the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents could create an intelligence "arms race" that could increase an agent's capability to work even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human players at a high skill level totally through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation took place at The International 2017, the annual best champion competition for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of actual time, and that the knowing software was an action in the instructions of developing software that can manage complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement knowing, as the bots discover in time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a complete team of 5, and they had the ability to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot player reveals the obstacles of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It finds out totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, also has RGB electronic cameras to permit the robot to manipulate an approximate things by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
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 design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually harder environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new AI models developed by OpenAI" to let developers call on it for "any English language AI task". [170] [171]
Text generation

The business has promoted generative pretrained transformers (GPT). [172]
OpenAI's original GPT model ("GPT-1")

The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, it-viking.ch and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world knowledge and procedure long-range dependences by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions at first released to the general public. The complete version of GPT-2 was not right away launched due to issue about possible misuse, systemcheck-wiki.de consisting of applications for news. [174] Some specialists expressed uncertainty that GPT-2 postured a significant hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural phony news". [175] Other researchers, 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 muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language design. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose students, shown by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not additional trained on any task-specific input-output examples).

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 avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both specific characters and multiple-character tokens. [181]
GPT-3

First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186]
OpenAI specified 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 learning in between English and Romanian, and between English and German. [184]
GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the essential ability constraints of predictive language models. [187] Pre-training GPT-3 required a number of 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 immediately released to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free personal beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally been trained on code from 54 million GitHub repositories, [192] [193] and is the AI 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 programming languages, many effectively in Python. [192]
Several concerns with glitches, design defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of releasing copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would discontinue support for Codex API on March 23, 2023. [198]
GPT-4

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 announced that the upgraded 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 could also read, analyze or create as much as 25,000 words of text, and compose code in all major programs languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose numerous technical details and stats about GPT-4, such as the precise size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and create 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) criteria compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller variation of GPT-4o changing 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 anticipates it to be especially useful for business, startups and developers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been created to take more time to think of their actions, leading to greater precision. These models are particularly effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI likewise revealed o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215]
Deep research study

Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out comprehensive web browsing, data analysis, fishtanklive.wiki and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic similarity in between text and images. It can especially be used for image classification. [217]
Text-to-image

DALL-E

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 language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can develop pictures of sensible things ("a stained-glass window with a picture of a blue strawberry") along with objects that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an updated version of the model with more practical results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new fundamental system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful model much better able to generate images from complex descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can create videos based on brief detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora's development group named it after the Japanese word for "sky", to signify its "limitless imaginative potential". [223] Sora's innovation is an adjustment of the innovation 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 exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might produce videos up to one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the design's capabilities. [225] It acknowledged some of its drawbacks, consisting of battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but noted that they must have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to produce reasonable video from text descriptions, citing its prospective to transform storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had decided to pause plans for broadening his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment as well as speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to develop music for the titular character. [232] [233]
Jukebox

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 genre, artist, and a bit of lyrics and surgiteams.com outputs tune samples. OpenAI stated the songs "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" which "there is a substantial space" in between Jukebox and human-generated music. The Verge stated "It's highly remarkable, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are memorable and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, wiki.myamens.com OpenAI released the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The purpose is to research whether such a method might help in auditing AI choices and in developing explainable AI. [237] [238]
Microscope

Released in 2020, pediascape.science Microscope [239] is a collection of visualizations of every substantial layer and nerve cell of eight neural network models which are often studied in interpretability. [240] Microscope was produced to analyze the features that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions of Inception, and garagesale.es various versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that supplies a conversational interface that allows users to ask questions in natural language. The system then responds with an answer within seconds.