Announced in 2016, Gym is an open-source Python library developed to help with the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research, making published research study more easily reproducible [24] [144] while providing users with an easy user interface for interacting with these environments. In 2022, brand-new developments of Gym have been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support learning (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to resolve single jobs. Gym Retro provides the ability to generalize between video games with comparable principles but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack knowledge of how to even stroll, however are given the objectives of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adjust to changing conditions. When an agent is then removed from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might develop an intelligence "arms race" that might increase an agent's ability to work even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that find out to play against human players at a high ability level entirely through experimental algorithms. Before becoming a group of 5, the first public demonstration occurred at The International 2017, the yearly best champion competition for the game, where Dendi, it-viking.ch an expert Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of genuine time, which the knowing software was an action in the direction of producing software that can handle complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a form of support learning, as the bots find out gradually by playing against themselves hundreds of 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 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 professional players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs 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 on that month, systemcheck-wiki.de where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot gamer reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated making use of deep reinforcement learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker discovering to train a Shadow Hand, a human-like robotic hand, kousokuwiki.org to manipulate physical items. [167] It learns totally in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB cams to permit the robotic to control an arbitrary item by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated 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 technique of generating gradually harder environments. ADR differs from manual domain randomization by not requiring 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 developed by OpenAI" to let developers contact it for "any English language AI task". [170] [171]
Text generation

The business 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 model was composed by Alec Radford and his coworkers, and published in preprint on OpenAI's website 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 diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative variations at first released to the general public. The complete variation of GPT-2 was not right away launched due to concern about prospective abuse, consisting of applications for composing phony news. [174] Some specialists expressed uncertainty that GPT-2 presented a substantial hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted 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 difficult to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language design. [177] Several websites host interactive presentations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not additional 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 avoids certain concerns encoding vocabulary with word tokens by using 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 stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were likewise trained). [186]
OpenAI mentioned that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]
GPT-3 dramatically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or coming across the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away launched to the general public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month totally free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed solely 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 released in personal beta. [194] According to OpenAI, the model can develop working code in over a lots programs languages, a lot of efficiently in Python. [192]
Several issues with problems, design flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been accused of releasing copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would discontinue 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 revealed that the upgraded technology passed a simulated law school bar examination with a score 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 create up to 25,000 words of text, and compose code in all significant shows languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various technical details and statistics 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 standards, setting new records in audio speech recognition 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 released GPT-4o mini, a smaller sized 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 expects it to be particularly helpful for enterprises, startups and designers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been created to take more time to think of their reactions, leading to higher accuracy. These designs are especially reliable 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 unveiled o3, the follower of the o1 thinking model. OpenAI also unveiled o3-mini, a lighter and quicker variation of OpenAI o3. Since December 21, 2024, this design 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 chance to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecoms services O2. [215]
Deep research

Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out extensive web surfing, 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 an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic similarity in between text and images. It can significantly be used for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce pictures of realistic items ("a stained-glass window with a picture of a blue strawberry") in addition to objects that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or genbecle.com code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, genbecle.com an updated variation of the design with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new primary system for transforming 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 timely engineering and render complex 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 produce videos based on short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora's development team named it after the Japanese word for "sky", to represent its "limitless innovative potential". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that function, 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, stating that it might generate videos as much as one minute long. It also shared a technical report highlighting the approaches used to train the model, and the model's abilities. [225] It acknowledged some of its imperfections, including battles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", however noted that they need to have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have actually shown substantial interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to produce practical video from text descriptions, citing its potential to transform storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause plans for broadening his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment 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 along with 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 create songs with 10 instruments in 15 designs. According to The Verge, a tune produced by MuseNet tends to begin fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web psychological 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 mentioned the tunes "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a considerable space" between Jukebox and human-generated music. The Verge specified "It's technologically excellent, even if the results sound like mushy versions of songs that might feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are appealing and sound legitimate". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to discuss toy problems in front of a human judge. The function is to research whether such a technique may assist 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 neuron of 8 neural network designs which are typically studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

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