Announced in 2016, Gym is an open-source Python library designed to help with the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research study, making published research study more easily reproducible [24] [144] while supplying users with an easy user interface for interacting with these environments. In 2022, new advancements of Gym have been relocated 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 study generalization. Prior RL research study focused mainly on enhancing agents to resolve single jobs. Gym Retro provides the ability to generalize between games with similar concepts but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack knowledge of how to even walk, but are provided the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents find out how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might create an intelligence "arms race" that could increase an agent's ability to work 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 video game Dota 2, that learn to play against human gamers at a high ability level entirely through trial-and-error algorithms. Before becoming a group of 5, the first public presentation occurred at The International 2017, the yearly best championship tournament for the 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 real time, and that the knowing software application was a step in the direction of developing software that can manage intricate jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the bots find out in time by playing against themselves numerous 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 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 two exhibition matches against professional gamers, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot gamer shows the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown using deep support knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device learning to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It learns totally in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation problem by utilizing domain randomization, a simulation method which exposes the student 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 cameras to permit the robotic to manipulate an approximate 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 could resolve 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 model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually harder environments. ADR differs from manual domain randomization by not requiring a human to define randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI models developed by OpenAI" to let designers call on 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 model ("GPT-1")

The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative design of language might obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions at first released to the public. The full variation of GPT-2 was not instantly launched due to concern about potential misuse, including applications for composing fake news. [174] Some professionals expressed uncertainty that GPT-2 presented a significant 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 technology to absolutely 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 launched the complete version of the GPT-2 language model. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose students, highlighted by GPT-2 attaining modern precision 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 a minimum of 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing 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 a without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the purpose of a single input-output pair. The GPT-3 release paper gave 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 cautioned that such scaling-up of language designs could be approaching or encountering the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the general public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month complimentary personal 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 produce working code in over a dozen programs languages, the majority of successfully in Python. [192]
Several problems with glitches, style defects and security vulnerabilities were mentioned. [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 support 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 announced that the upgraded innovation passed a simulated law school bar exam 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 likewise read, examine or generate as much as 25,000 words of text, and compose code in all significant shows languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined to expose numerous technical details and data about GPT-4, such as the precise 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 cutting edge lead to voice, multilingual, and vision criteria, setting brand-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 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 helpful 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 been created to take more time to consider their actions, causing greater accuracy. These designs are especially reliable in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the successor of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and much faster version of OpenAI o3. Since December 21, 2024, this design 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 researchers had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]
Deep research study

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out comprehensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, 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 model that is trained to analyze the semantic similarity in between text and images. It can especially be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces 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 bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can develop images of reasonable things ("a stained-glass window with an image of a blue strawberry") in addition to 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 variation of the design with more practical outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new fundamental 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 better able to generate images from complicated 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 function in October. [222]
Text-to-video

Sora

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

Sora's advancement group named it after the Japanese word for "sky", to symbolize its "limitless imaginative 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 function, wiki.whenparked.com but did not reveal the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might create videos up to one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the design's capabilities. [225] It acknowledged a few of its drawbacks, including battles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", however kept in mind that they should have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, notable entertainment-industry figures have actually shown substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's ability to generate practical video from text descriptions, mentioning its possible to change storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually decided to stop briefly prepare 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 big dataset of diverse audio and is likewise a multi-task design that can perform multilingual speech acknowledgment along with speech translation and language identification. [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 designs. According to The Verge, a song created by MuseNet tends to start fairly however then fall under turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to develop 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, and a bit of lyrics and outputs tune samples. OpenAI stated the tunes "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" and that "there is a significant space" in between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the results seem like mushy versions of songs that might feel familiar", while Business Insider mentioned "remarkably, some 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 devices to discuss toy problems in front of a human judge. The purpose is to research study whether such a technique may 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 considerable layer and nerve cell of eight neural network models which are typically studied in interpretability. [240] Microscope was developed to examine the features 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 offers a conversational interface that enables users to ask questions in natural language. The system then responds with a response within seconds.