Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of support knowing algorithms. It aimed to standardize how environments are defined in AI research study, making published research study more easily reproducible [24] [144] while offering users with a simple user interface for connecting 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 reinforcement knowing (RL) research study on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to resolve single jobs. Gym Retro offers the ability to generalize between video games with comparable principles however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first lack knowledge of how to even stroll, but are offered the goals of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives find out how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could develop an intelligence "arms race" that could increase an agent's capability 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 computer game Dota 2, surgiteams.com that find out to play against human players at a high ability level entirely through trial-and-error algorithms. Before becoming a team of 5, the first public demonstration took place at The International 2017, the yearly premiere champion tournament for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of actual time, and that the knowing software application was a step in the direction of producing software application that can handle complex jobs like a surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots discover gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map objectives. [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 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 gamers, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later 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 shows the challenges of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated using deep reinforcement learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses device learning to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It finds out entirely in simulation utilizing the 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 range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB electronic cameras to permit the robotic to manipulate an arbitrary things 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 showed 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 intricate physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating gradually more challenging environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [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 contact it for "any English language AI job". [170] [171]
Text generation

The company has 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 composed by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world knowledge and procedure 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 a without supervision transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions at first launched to the public. The full version of GPT-2 was not right away released due to issue about possible abuse, including applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 postured a substantial hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, illustrated by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (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 private 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 successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were also trained). [186]
OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184]
GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or coming across the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required 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 general public for issues of possible abuse, although OpenAI prepared to allow 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 licensed specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually 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 produce working code in over a dozen programming languages, most successfully in Python. [192]
Several concerns with problems, 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 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), efficient in accepting text or image inputs. [199] They revealed that the updated 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 might also read, examine or create as much as 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various technical details and stats about GPT-4, such as the exact size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained modern outcomes in 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 sized version of GPT-4o changing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million input tokens and pediascape.science $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI anticipates it to be particularly helpful for business, start-ups and developers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been created to take more time to consider their actions, causing higher precision. These designs are particularly reliable 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 unveiled o3, the successor of the o1 thinking model. OpenAI also unveiled o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this model 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 scientists had the chance to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications companies O2. [215]
Deep research

Deep research study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out extensive web browsing, information analysis, and synthesis, delivering 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) 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 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 model 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 produce corresponding images. It can create images of practical objects ("a stained-glass window with an image of a blue strawberry") as well as things 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, OpenAI revealed DALL-E 2, an upgraded variation of the design with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-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 better able to create images from complex 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 model that can produce videos based upon short detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.

Sora's development team called it after the Japanese word for "sky", to represent its "limitless creative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos accredited for that purpose, but did not expose the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could create videos as much as one minute long. It likewise shared a technical report highlighting the methods used to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, consisting of struggles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", however kept in mind that they need to have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to create realistic video from text descriptions, mentioning its prospective to reinvent storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly prepare for broadening his 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 diverse audio and is also a multi-task design that can carry out multilingual speech acknowledgment in addition to 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 produce songs with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to start fairly but then fall under turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the web 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 genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the songs "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" and that "there is a significant space" between Jukebox and human-generated music. The Verge mentioned "It's technologically outstanding, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider mentioned "remarkably, a few of the resulting tunes are catchy and sound genuine". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The function is to research study whether such a method may 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 substantial layer and nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different versions of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool constructed 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 a response within seconds.