Announced in 2016, Gym is an open-source Python library developed to help with the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research study, making released research study more easily reproducible [24] [144] while providing users with a simple user interface for engaging with these environments. In 2022, new advancements 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 focused mainly on optimizing agents to solve single jobs. Gym Retro offers the ability to generalize in between games with comparable principles but different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have knowledge of how to even stroll, but 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 adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had actually discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could develop an intelligence "arms race" that could increase an agent's capability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team 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 team of 5, the first public presentation took place at The International 2017, the annual best championship competition for the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually matchup. [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, which the knowing software application was a step in the direction of creating software application that can manage intricate jobs like a surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots learn over time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a complete group of 5, and they were able to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional players, but wound 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 exhibit match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 overall 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 player reveals the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated using deep reinforcement knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It finds out totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation method which exposes the student to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking electronic cameras, likewise has RGB video cameras to allow the robotic to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system was able 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 fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of generating gradually more tough environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [169]
API

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

The company has promoted 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 coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and procedure long-range reliances by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative variations at first released to the public. The complete variation of GPT-2 was not immediately launched due to issue about possible misuse, consisting of applications for writing phony news. [174] Some professionals expressed uncertainty that GPT-2 posed a considerable danger.

In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the technology to completely 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 released the complete variation of the GPT-2 language model. [177] Several sites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose learners, shown by GPT-2 attaining modern 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 prevents certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both individual 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 model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version 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 could generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
GPT-3 drastically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or coming across the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of 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 design was not instantly released to the general public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually 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 design can create working code in over a dozen programs languages, many successfully in Python. [192]
Several problems with glitches, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been accused of producing copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would cease support 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), capable of accepting text or image inputs. [199] They revealed that the upgraded technology passed a simulated law school bar test 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 read, analyze or create up to 25,000 words of text, and write code in all major shows 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 modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different technical details and statistics about GPT-4, such as the precise size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition 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 variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user 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 business, start-ups and designers 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 developed to take more time to consider their actions, resulting in greater accuracy. These designs are especially effective in science, coding, and reasoning 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 follower of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and much faster variation 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 models. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications companies O2. [215]
Deep research

Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [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 especially be utilized 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 translate natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can create pictures of practical items ("a stained-glass window with an image of a blue strawberry") along with objects that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an upgraded version of the design with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new rudimentary system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful design much better able to generate images from complicated descriptions without manual prompt engineering and render complicated 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 generate videos based upon short detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora's development group named it after the Japanese word for "sky", to signify its "unlimited innovative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos accredited for that function, but did not expose the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could generate videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the model, and the model's capabilities. [225] It acknowledged a few of its imperfections, consisting of battles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but noted that they must have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to generate reasonable video from text descriptions, citing its prospective to change storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly plans for broadening his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of varied audio and is likewise a multi-task model that can perform 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 anticipate subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 designs. According to The Verge, a tune created by MuseNet tends to begin fairly however then fall into chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to produce music for disgaeawiki.info 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 snippet of lyrics and outputs song samples. OpenAI specified the tunes "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a substantial gap" between Jukebox and human-generated music. The Verge mentioned "It's technically impressive, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider stated "remarkably, a few of the resulting songs are catchy and sound genuine". [234] [235] [236]
User 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 study whether such a technique might assist in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of 8 neural network models which are often studied in interpretability. [240] was produced to evaluate the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an artificial intelligence 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.