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 released research study more quickly reproducible [24] [144] while offering users with an easy user interface for interacting 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 support knowing (RL) research study on video games [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing agents to solve single tasks. Gym Retro offers the ability to generalize between games with comparable principles but different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially lack understanding of how to even stroll, but are offered the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives learn how to adjust to altering 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, recommending it had actually learned how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might create 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 team of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human players at a high skill level completely through experimental algorithms. Before ending up being a team of 5, the very first public demonstration took place at The International 2017, the yearly premiere champion tournament for the video game, where Dendi, a professional 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 real time, and that the learning software was a step in the direction of developing software application that can deal with intricate jobs like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots find out with time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a complete group of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 total video 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 reveals the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated the use of deep support knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It finds out entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation method which exposes the learner to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB cams to allow the robotic to manipulate an arbitrary item by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing gradually more challenging 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 designs established by OpenAI" to let designers get in touch with it for "any English language AI task". [170] [171]
Text generation

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

The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative model of language could obtain world knowledge and procedure long-range dependences 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 successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions at first launched to the public. The full variation of GPT-2 was not right away released due to issue about possible misuse, consisting of applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 postured a substantial risk.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to absolutely 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 launched the total variation of the GPT-2 language model. [177] Several sites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue not being watched language models to be general-purpose learners, illustrated by GPT-2 attaining modern 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 at least 3 upvotes. It prevents certain problems 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 a not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were also trained). [186]
OpenAI stated that GPT-3 prospered at certain "meta-learning" tasks and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning 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 models might be approaching or experiencing the basic capability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous 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 instantly 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 private beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has additionally 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 private beta. [194] According to OpenAI, the model can develop working code in over a dozen programming languages, many successfully in Python. [192]
Several concerns with problems, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of discharging copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would discontinue 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 revealed that the upgraded 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 likewise check out, examine or approximately 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 caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose different technical details and statistics about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained modern lead to voice, multilingual, and vision standards, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized version 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 especially beneficial for enterprises, startups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been developed to take more time to consider their responses, resulting in higher accuracy. These designs are particularly effective 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 follower of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and much faster variation 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, safety and security researchers had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications services company O2. [215]
Deep research

Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out comprehensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, it reached an accuracy 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 analyze the semantic resemblance in between text and images. It can significantly be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret 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 create images of sensible items ("a stained-glass window with a picture of a blue strawberry") in addition to items that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, trademarketclassifieds.com no API or code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, an upgraded variation of the model with more practical results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new fundamental 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 design better able to generate images from complicated descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released to the public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can generate videos based on brief detailed prompts [223] along with 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 produced videos is unidentified.

Sora's advancement team named it after the Japanese word for "sky", to represent its "limitless imaginative potential". [223] Sora's technology 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 certified for that purpose, however did not reveal the number or the specific 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 create videos as much as one minute long. It also shared a technical report highlighting the approaches utilized to train the design, and the design's abilities. [225] It acknowledged some of its drawbacks, including battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they need to have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have actually shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to produce practical video from text descriptions, mentioning its possible to reinvent storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to stop briefly prepare for broadening his Atlanta-based movie 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 varied audio and is also a multi-task model 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 create tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web psychological 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 category, artist, and a snippet of lyrics and outputs tune samples. OpenAI mentioned the tunes "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" which "there is a substantial space" between Jukebox and human-generated music. The Verge mentioned "It's technically remarkable, even if the outcomes seem like mushy variations of songs that may feel familiar", while Business Insider specified "surprisingly, a few of the resulting songs are memorable and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches devices to dispute 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 developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was developed to analyze the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various variations 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 user interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.