Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of support learning algorithms. It aimed to standardize how environments are specified in AI research, making published research more quickly reproducible [24] [144] while offering users with a basic interface for connecting with these environments. In 2022, brand-new advancements of Gym have actually been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support learning (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to fix single jobs. Gym Retro offers the capability to generalize between video games with similar ideas however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack understanding of how to even stroll, but are given the objectives of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives find out how to adjust to altering conditions. When an agent is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually learned how to stabilize in a generalized method. [148] [149] OpenAI’s Igor Mordatch argued that competitors in between representatives might create an intelligence “arms race” that might increase an agent’s capability to function even outside the context of the competition. [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 totally through experimental algorithms. Before becoming a group of 5, the very first public demonstration took place at The International 2017, the yearly best championship tournament for the 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 learned by against itself for two weeks of actual time, which the learning software application was a step in the instructions of creating software that can deal with intricate tasks like a surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots find out over time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a complete group of 5, and they were able to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots’ final public appearance came later on that month, where they played in 42,729 overall games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5’s mechanisms in Dota 2’s bot player reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated making use of deep reinforcement knowing (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB video cameras to enable the robotic to control an approximate object by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could fix a Rubik’s Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik’s Cube introduce complicated physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing gradually harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization ranges. [169]
API

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

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

The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his coworkers, and published in preprint on OpenAI’s site on June 11, 2018. [173] It revealed how a generative model of language might obtain world knowledge and process long-range reliances by pre-training on a varied corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 (“GPT-2”) is an unsupervised transformer language design and the follower to OpenAI’s initial GPT design (“GPT-1”). GPT-2 was revealed in February 2019, with only minimal demonstrative variations initially released to the public. The complete version of GPT-2 was not immediately launched due to concern about potential abuse, consisting of applications for writing fake news. [174] Some experts revealed uncertainty that GPT-2 presented a significant hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find “neural fake news”. [175] Other researchers, such as Jeremy Howard, warned of “the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be difficult to filter”. [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several websites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2’s authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining cutting edge 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain problems 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]
GPT-3

First explained in May 2020, Generative Pre-trained [a] Transformer 3 (GPT-3) is a not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186]
OpenAI mentioned that GPT-3 was successful at certain “meta-learning” jobs and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or encountering the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of compute, 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 launched 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 complimentary personal beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed solely to Microsoft. [190] [191]
Codex

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 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 create working code in over a lots programming languages, a lot of effectively in Python. [192]
Several issues with glitches, design defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of discharging copyrighted code, without any 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They announced that the updated innovation passed a simulated law school bar exam 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 likewise read, evaluate or raovatonline.org produce approximately 25,000 words of text, and compose code in all significant shows languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few 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 expose different technical details and stats about GPT-4, such as the precise size of the model. [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 modern outcomes in voice, multilingual, and vision standards, 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]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized variation of GPT-4o replacing 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 anticipates it to be particularly useful for business, startups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been developed to take more time to think of their actions, leading to higher precision. These models are especially effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this design 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 telecommunications companies O2. [215]
Deep research

Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI’s o3 model to carry out extensive web surfing, 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) standard. [120]
Image category

CLIP

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

DALL-E

Revealed in 2021, DALL-E is a Transformer design that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as “a green leather purse shaped like a pentagon” or “an isometric view of a sad capybara”) and produce matching images. It can create images of reasonable objects (“a stained-glass window with a picture of a blue strawberry”) as well as 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 updated variation of the model with more reasonable outcomes. [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 design. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more effective model much better able to create images from complex descriptions without manual prompt engineering and render intricate 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 upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.

Sora’s advancement team called it after the Japanese word for “sky”, to signify its “endless creative potential”. [223] Sora’s innovation is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that purpose, but did not reveal the number or the precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could create videos approximately one minute long. It also shared a technical report highlighting the approaches used to train the design, and the design’s capabilities. [225] It acknowledged a few of its shortcomings, including struggles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos “impressive”, but noted that they should have been cherry-picked and may not represent Sora’s common output. [225]
Despite uncertainty from some academic leaders following Sora’s public demo, noteworthy entertainment-industry figures have actually revealed considerable interest in the innovation’s potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation’s ability to generate practical video from text descriptions, mentioning its prospective to revolutionize storytelling and content production. He said that his excitement about Sora’s possibilities was so strong that he had chosen to stop briefly plans for broadening his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can perform multilingual speech recognition as well as speech translation and language identification. [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 create tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to begin fairly however then fall into mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned to develop music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs tune samples. OpenAI specified the tunes “show regional musical coherence [and] follow traditional chord patterns” but acknowledged that the tunes lack “familiar larger musical structures such as choruses that duplicate” and that “there is a considerable space” in between Jukebox and human-generated music. The Verge stated “It’s highly remarkable, even if the results seem like mushy variations of songs that may feel familiar”, while Business Insider mentioned “surprisingly, a few of the resulting songs are catchy and sound genuine”. [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The function is to research whether such a technique 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 designs which are typically studied in interpretability. [240] Microscope was developed to examine the features that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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