GitLab

Announced in 2016, Gym is an open-source Python library developed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research study, making released research study more easily reproducible [24] [144] while providing users with a basic interface for engaging with these environments. In 2022, new developments of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support knowing (RL) research on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to fix single jobs. Gym Retro offers the capability to generalize in between games with similar principles however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack understanding of how to even walk, however are given the goals of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adapt to altering 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, suggesting it had actually discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might create an intelligence "arms race" that could increase an agent's ability to function even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human players at a high skill level entirely through experimental algorithms. Before becoming a team of 5, the first public demonstration took place at The International 2017, the annual best championship tournament for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for two weeks of actual time, which the learning software was a step in the direction of producing software that can manage intricate tasks like a cosmetic surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots discover with time by playing against themselves hundreds of 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 team of 5, and they were able to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms 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 actually demonstrated making use of deep reinforcement learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine learning to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [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 utilizing domain randomization, a simulation approach which exposes the student to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB video cameras to enable the robotic to manipulate an approximate object by seeing it. In 2018, OpenAI revealed that the system was able to manipulate 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 resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of generating progressively more challenging environments. ADR differs from manual domain randomization by not requiring a human to define 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 designers contact it for "any English language AI task". [170] [171]
Text generation

The business has actually 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 design was written by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language could obtain world understanding and procedure long-range dependencies 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 original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations at first launched to the public. The complete variation of GPT-2 was not instantly launched due to issue about possible abuse, consisting of applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 posed a substantial threat.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural phony news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation to completely 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 released the total version of the GPT-2 language design. [177] Several sites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, shown by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not more 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 at least 3 upvotes. It avoids certain issues 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 not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete variation 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 specifications were likewise trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the purpose 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 between English and German. [184]
GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, 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 general public for concerns of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month complimentary private beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified solely 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 released in personal beta. [194] According to OpenAI, the model can create working code in over a lots shows languages, the majority of successfully in Python. [192]
Several issues with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of releasing copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would terminate 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 upgraded innovation 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 could also check out, analyze or produce as much as 25,000 words of text, and compose code in all significant programs languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal different technical details and data about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, and vision criteria, 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 anticipates it to be particularly beneficial for enterprises, start-ups and designers looking for to automate services with AI representatives. [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 think of their actions, leading to greater accuracy. These models are particularly efficient in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Staff member. [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 variation of OpenAI o3. Since December 21, 2024, this design is not available for . According to OpenAI, they are checking 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 a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out substantial web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, 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 analyze the semantic similarity in between text and images. It can notably be used for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that produces 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 shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate matching images. It can create pictures of practical items ("a stained-glass window with a picture of a blue strawberry") as well as things 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 revealed DALL-E 2, an upgraded version of the model with more reasonable results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new simple system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective design much better able to produce images from complicated descriptions without manual timely engineering and render intricate details like hands and text. [221] It was released to the 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 brief detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.

Sora's advancement group named it after the Japanese word for "sky", to signify its "unlimited innovative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for that function, but did not expose 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 likewise shared a technical report highlighting the approaches used to train the model, and the design's abilities. [225] It acknowledged a few of its shortcomings, including battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however noted that they need to have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have revealed substantial interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to generate practical video from text descriptions, citing its potential to transform storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to stop briefly strategies for expanding 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 design that can perform multilingual speech recognition 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 songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to create 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, pediascape.science and a snippet of lyrics and outputs tune samples. OpenAI mentioned the tunes "show local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" and that "there is a substantial space" in between Jukebox and human-generated music. The Verge stated "It's highly outstanding, even if the results seem like mushy versions of songs that might feel familiar", while Business Insider stated "remarkably, a few of the resulting songs are memorable and sound genuine". [234] [235] [236]
Interface

Debate Game

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

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

Launched in November 2022, ChatGPT is an artificial intelligence tool built on top of GPT-3 that provides a conversational user interface that allows users to ask concerns in natural language. The system then reacts with an answer within seconds.