GitLab

Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of reinforcement knowing 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 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 study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to solve single tasks. Gym Retro gives the ability to generalize between video games with similar principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack knowledge of how to even stroll, but are offered the goals of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives find out how to adjust to changing conditions. When a representative is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had 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 a representative's capability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high ability level completely through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation occurred at The International 2017, the yearly premiere championship competition for raovatonline.org the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of actual time, and that the knowing software application was a step in the instructions of developing software that can manage complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a form of support knowing, as the bots learn over 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 capability of the bots broadened to play together as a full 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 2 exhibit matches against professional players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 total 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 player shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown using deep reinforcement knowing (DRL) agents to attain superhuman skills 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 discovers totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB electronic cameras to permit the robot to manipulate an arbitrary item 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 might solve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually more hard environments. ADR varies from manual domain randomization by not requiring 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 developers contact it for "any English language AI job". [170] [171]
Text generation

The business has actually 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 model was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language could obtain world knowledge and process long-range reliances by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations initially launched to the general public. The complete variation of GPT-2 was not immediately launched due to concern about possible abuse, including applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 positioned a significant danger.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to absolutely 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 total variation 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 not being watched language designs to be general-purpose students, illustrated by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not further 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 a minimum of 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both private 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 mentioned that the complete version of GPT-3 contained 175 billion criteria, [184] 2 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 parameters were likewise trained). [186]
OpenAI stated that GPT-3 was successful at certain "meta-learning" tasks and might generalize the function of a pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing 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 could be approaching or coming across the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to tens 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 permit 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 furthermore 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 dozen programs languages, most efficiently in Python. [192]
Several concerns with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been accused of discharging copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would terminate support 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 upgraded innovation passed a simulated law school bar test 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 might likewise check out, evaluate or create up to 25,000 words of text, and write code in all major programming languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement 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 likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased 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 revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision criteria, 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 sized 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 particularly useful for business, startups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been designed to take more time to think of their reactions, leading to greater accuracy. These models are particularly effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [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 thinking design. OpenAI likewise unveiled o3-mini, a lighter and quicker variation 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, safety and security scientists had the opportunity to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecoms companies O2. [215]
Deep research study

Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, it reached an accuracy 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 design that is trained to analyze the semantic resemblance 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 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 bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can develop images of practical items ("a stained-glass window with an image of a blue strawberry") along with things that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.

DALL-E 2

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

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

Sora

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

Sora's development team named it after the Japanese word for "sky", to represent its "limitless creative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that purpose, however did not expose the number or the precise sources of the videos. [223]
OpenAI showed 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 likewise shared a technical report highlighting the methods utilized to train the model, and the model's capabilities. [225] It acknowledged a few of its imperfections, consisting of struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but noted that they must 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 shown considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to generate reasonable video from text descriptions, citing its potential to transform storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had decided to pause prepare for expanding his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of varied audio and is likewise 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 anticipate subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to begin 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 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 create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the songs "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" which "there is a significant space" between Jukebox and human-generated music. The Verge stated "It's highly impressive, even if the outcomes sound like mushy variations of tunes that may feel familiar", while Business Insider mentioned "remarkably, some of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

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

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of 8 neural network designs which are typically studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks easily. The models included 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 built on top of GPT-3 that provides a conversational user interface that permits users to ask concerns in natural language. The system then responds with an answer within seconds.