Announced in 2016, Gym is an open-source Python library developed to help with the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research study, making published research more quickly reproducible [24] [144] while offering users with a simple user interface for engaging 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 learning (RL) research on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to resolve single jobs. Gym Retro provides the ability to generalize between video games with similar concepts but different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have knowledge of how to even stroll, however are given the objectives of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to altering conditions. When an agent is then removed from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had learned how to balance in a generalized way. [148] [149] OpenAI’s Igor Mordatch argued that competition in between agents might produce an intelligence “arms race” that could increase a representative’s capability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of 5 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 skill level entirely through trial-and-error algorithms. Before becoming a team of 5, the first public demonstration occurred at The International 2017, the annual premiere championship tournament for the 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 actually found out by playing against itself for two weeks of genuine time, and that the learning software application was a step in the instructions of developing software application that can manage complicated tasks like a surgeon. [152] [153] The system utilizes a type of reinforcement knowing, as the bots learn in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a full team of 5, and they were able 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, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots’ last public look came later that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5’s mechanisms in Dota 2’s bot gamer shows the obstacles of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown using deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device learning to train a Shadow Hand, disgaeawiki.info 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 dealt with the things orientation issue by using domain randomization, a simulation approach which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB video cameras to permit the robotic to control an approximate things by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik’s Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik’s Cube present intricate physics that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of producing gradually more difficult environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [169]
API

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

The business has popularized generative pretrained transformers (GPT). [172]
OpenAI’s initial GPT model (“GPT-1”)

The original paper on generative pre-training of a transformer-based language model 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 model of language could obtain world understanding and process long-range reliances by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 (“GPT-2”) is a without supervision transformer language design and the successor to OpenAI’s initial GPT design (“GPT-1”). GPT-2 was announced in February 2019, with just minimal demonstrative versions at first released to the general public. The full version of GPT-2 was not immediately released due to issue about possible abuse, including applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 postured a substantial danger.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect “neural fake news”. [175] Other scientists, such as Jeremy Howard, alerted of “the innovation to completely 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 demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2’s authors argue unsupervised language models to be general-purpose students, highlighted by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not additional trained on any task-specific input-output examples).

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 prevents certain concerns 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 a without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were likewise trained). [186]
OpenAI mentioned that GPT-3 was successful at certain “meta-learning” jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
GPT-3 dramatically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or encountering the essential ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [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 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 specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition 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 design can develop working code in over a lots programming languages, the majority of efficiently in Python. [192]
Several problems with glitches, design flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has actually been implicated of discharging copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would cease assistance 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 updated 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 could likewise read, analyze or create up to 25,000 words of text, and write code in all major programming languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and data about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision benchmarks, setting 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 user 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 seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been developed to take more time to think of their reactions, causing greater precision. These models are especially effective in science, coding, and thinking jobs, 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 unveiled o3, the successor of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this design 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 researchers had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215]
Deep research

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI’s o3 model to carry out substantial web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, 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 model that is trained to examine the semantic resemblance in between text and images. It can significantly be utilized for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that creates 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 bag shaped like a pentagon” or “an isometric view of an unfortunate capybara”) and create matching images. It can create pictures of realistic things (“a stained-glass window with a picture of a blue strawberry”) along with objects that do not exist in truth (“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 upgraded version of the model with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new basic system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

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

Sora

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

Sora’s development team named it after the Japanese word for “sky”, to represent its “endless innovative potential”. [223] Sora’s innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos licensed for that function, but did not reveal the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it might create videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the design, and the design’s abilities. [225] It acknowledged some of its shortcomings, consisting of struggles imitating complicated physics. [226] Will Heaven of the MIT Technology Review called the presentation videos “excellent”, but noted that they should have been cherry-picked and may not represent Sora’s common output. [225]
Despite uncertainty from some scholastic leaders following Sora’s public demo, significant entertainment-industry figures have revealed considerable interest in the innovation’s capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation’s capability to generate practical video from text descriptions, mentioning its potential to revolutionize storytelling and material development. He said that his enjoyment about Sora’s possibilities was so strong that he had actually decided to pause plans for expanding his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a large dataset of varied audio and is likewise a multi-task design that can perform multilingual speech acknowledgment along with speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to start fairly but then fall into mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to produce music for the titular character. [232] [233]
Jukebox

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 category, artist, and a snippet of lyrics and outputs song samples. OpenAI mentioned the tunes “reveal regional musical coherence [and] follow conventional chord patterns” however acknowledged that the tunes lack “familiar larger musical structures such as choruses that duplicate” which “there is a substantial gap” between Jukebox and human-generated music. The Verge specified “It’s technically remarkable, even if the results sound like mushy variations of tunes that may feel familiar”, while Business Insider specified “surprisingly, a few of the resulting songs are appealing and sound legitimate”. [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches devices to dispute toy issues in front of a human judge. The purpose is to research whether such a method might help in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of eight neural network models which are typically studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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