Announced in 2016, Gym is an open-source Python library developed to assist in the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making published research more easily reproducible [24] [144] while offering users with a basic interface for connecting with these environments. In 2022, 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 study on computer game [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to resolve single tasks. Gym Retro offers the ability to generalize between games with comparable principles but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first lack knowledge of how to even walk, but are provided the objectives of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents learn how to adjust to changing conditions. When an agent is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could create an intelligence "arms race" that might increase a representative's ability to work even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high ability level totally through experimental algorithms. Before ending up being a team of 5, the very first public presentation occurred at The International 2017, the yearly best 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 learned by playing against itself for 2 weeks of real time, and that the learning software application was an action in the instructions of creating software application that can handle complex tasks like a surgeon. [152] [153] The system utilizes a type of support learning, as the bots discover with time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a complete team of 5, and they were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional players, however 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' final public appearance came later that month, where they played in 42,729 total 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 shows the obstacles of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown using deep reinforcement knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses device learning to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It discovers completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation method which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB cams to enable the robot to manipulate an arbitrary things by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating gradually more tough environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
API

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

The business has actually 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 design was written by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language could obtain world understanding and process long-range dependencies by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions at first released to the general public. The complete version of GPT-2 was not instantly launched due to concern about possible abuse, consisting of applications for writing phony news. [174] Some experts revealed uncertainty that GPT-2 presented a significant danger.

In reaction 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, 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 impossible to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language design. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, highlighted by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further 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 issues encoding vocabulary with word tokens by using 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 without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as few as 125 million criteria were likewise trained). [186]
OpenAI stated that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the function 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 in between English and German. [184]
GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or encountering the essential capability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [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 planned to enable gain access to through a paid cloud API after a two-month complimentary 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 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 private beta. [194] According to OpenAI, the design can develop working code in over a dozen shows languages, a lot of effectively in Python. [192]
Several issues with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been implicated of discharging copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would stop 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), efficient in accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar test with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might likewise check out, examine or produce approximately 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and stats about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision standards, setting 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 launched GPT-4o mini, a smaller version 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 especially beneficial for enterprises, start-ups and developers seeking to services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been designed to take more time to consider their responses, causing greater accuracy. These models are especially reliable in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking design. OpenAI also unveiled o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications services supplier O2. [215]
Deep research study

Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web surfing, information analysis, 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 a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to evaluate the semantic similarity 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 develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can develop images of realistic things ("a stained-glass window with a picture of a blue strawberry") as well as items that do not exist in truth ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.

DALL-E 2

In April 2022, setiathome.berkeley.edu OpenAI revealed DALL-E 2, an upgraded version of the design with more practical outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new rudimentary 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 effective design better able to generate images from complex descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the general public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can generate videos based on short detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of created videos is unknown.

Sora's advancement team named it after the Japanese word for "sky", to represent its "limitless innovative potential". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos certified for that purpose, however did not reveal the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could produce videos up to one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the model's abilities. [225] It acknowledged a few of its drawbacks, including struggles mimicing complicated 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 may not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have revealed considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to generate practical video from text descriptions, citing its potential to reinvent storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to stop briefly plans for expanding 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 large dataset of diverse audio and is also a multi-task model that can carry out multilingual speech acknowledgment in addition to 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 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 pop culture, initial applications of this tool were used 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 create 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 songs "reveal local musical coherence [and] follow standard chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge mentioned "It's technically impressive, even if the results seem like mushy versions of songs that may feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are catchy and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches makers to debate toy issues in front of a human judge. The function is to research whether such an approach might assist in auditing AI choices and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of 8 neural network models which are often studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

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