Announced in 2016, Gym is an open-source Python library created to assist in the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research, making released research study more easily reproducible [24] [144] while providing users with an easy user interface for connecting with these environments. In 2022, new developments of Gym have 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 research study generalization. Prior RL research focused mainly on enhancing representatives to solve single jobs. Gym Retro provides the capability to generalize between video games with comparable principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first do not have knowledge of how to even walk, but are offered the objectives of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives find out how to adapt to changing conditions. When a representative is then removed from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives could develop an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high ability level totally through experimental algorithms. Before becoming a group of 5, the very first public presentation happened at The International 2017, the yearly best champion tournament for the game, where Dendi, a professional 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 learned by playing against itself for two weeks of genuine time, and that the knowing software was an action in the direction of creating software that can manage complicated tasks like a surgeon. [152] [153] The system utilizes 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 expanded to play together as a full group 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 two exhibition matches against professional gamers, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the challenges of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown the use of deep reinforcement knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It learns completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by utilizing domain randomization, a simulation method which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, also has RGB cameras to allow the robotic 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 showed that Dactyl might solve a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI designs established by OpenAI" to let developers get in touch with it for "any English language AI task". [170] [171]
Text generation

The company has actually popularized generative pretrained transformers (GPT). [172]
OpenAI's original GPT design ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative model of language might obtain world understanding and process long-range dependences 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 model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just minimal demonstrative versions at first launched to the public. The full variation of GPT-2 was not instantly launched due to issue about potential abuse, including applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 presented a significant danger.

In response 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, alerted of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be difficult to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language design. [177] Several websites host interactive presentations of various circumstances of GPT-2 and surgiteams.com other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose learners, illustrated by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more 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 at least 3 upvotes. It prevents certain concerns 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 an unsupervised transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were likewise trained). [186]
OpenAI stated that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the purpose 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 drastically enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or experiencing the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month free private beta that started 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 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 develop working code in over a lots shows languages, many efficiently in Python. [192]
Several concerns with problems, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of emitting copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would stop 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 revealed that the upgraded technology passed a simulated law school bar examination 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 read, examine or create as much as 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and stats about GPT-4, such as the exact size of the model. [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 advanced outcomes 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) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller variation of GPT-4o replacing 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 especially beneficial for enterprises, startups and developers 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 been designed to take more time to consider their actions, causing greater precision. These models are particularly effective in science, coding, and thinking jobs, 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 unveiled o3, the successor of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this model 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 avoid confusion with telecoms providers O2. [215]
Deep research study

Deep research study is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web browsing, data analysis, and synthesis, delivering 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 classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic resemblance in between text and images. It can be used for image category. [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 handbag shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can develop images of reasonable things ("a stained-glass window with an image of a blue strawberry") in addition to objects 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 announced DALL-E 2, an updated version of the design with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new basic 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 powerful model better able to create images from intricate descriptions without manual timely engineering and render complex details like hands and text. [221] It was released to the general public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can create videos based on short detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's development team named it after the Japanese word for "sky", to represent its "endless innovative capacity". [223] Sora's technology 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 licensed for that purpose, however did not expose the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could produce videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the model, and the design's capabilities. [225] It acknowledged some of its shortcomings, including battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but kept in mind that they need to have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have revealed significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to generate sensible video from text descriptions, citing its potential to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly prepare for broadening his Atlanta-based film 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 varied audio and is likewise a multi-task model that can perform multilingual speech recognition 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 generate tunes with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, initial 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 mentioned the songs "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" which "there is a considerable space" in between Jukebox and human-generated music. The Verge specified "It's highly excellent, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider stated "surprisingly, 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 discuss toy issues in front of a human judge. The purpose is to research study whether such a technique might help in auditing AI decisions 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 eight neural network models which are typically studied in interpretability. [240] Microscope was developed to evaluate the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

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