Announced in 2016, Gym is an open-source Python library created to facilitate 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, brand-new developments of Gym have actually been relocated 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 research study generalization. Prior RL research study focused mainly on enhancing representatives to resolve single tasks. Gym Retro gives the ability to generalize between video games with similar principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially do not have understanding of how to even stroll, however are offered the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives learn how to adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, recommending it had actually learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might develop an intelligence "arms race" that might increase an agent's ability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human players at a high skill level entirely through experimental algorithms. Before ending up being a group of 5, the first public demonstration took place at The International 2017, the yearly premiere championship competition for the game, where Dendi, 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 found out by playing against itself for 2 weeks of actual time, which the knowing software application was an action in the instructions of developing software application that can handle complicated tasks like a surgeon. [152] [153] The system utilizes a form of support learning, as the bots discover gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a full team of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the 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 systems in Dota 2's bot player shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown making use of deep reinforcement learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It learns totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB video cameras to allow the robotic to control an approximate object by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate 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 creating progressively more challenging environments. ADR differs from manual domain randomization by not requiring 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 models established by OpenAI" to let developers get in touch with it for "any English language AI job". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world understanding and process 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 follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations at first launched to the general public. The full variation of GPT-2 was not right away released due to issue about possible abuse, including applications for composing phony news. [174] Some specialists revealed uncertainty that GPT-2 postured a considerable danger.

In action 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, cautioned 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 launched the total variation of the GPT-2 language model. [177] Several websites host interactive demonstrations 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 cutting edge accuracy 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 a minimum of 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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 successor to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were also trained). [186]
OpenAI stated that GPT-3 was successful at certain "meta-learning" tasks and might generalize the function 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 drastically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the essential capability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary 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 actually 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 personal beta. [194] According to OpenAI, the design can create working code in over a dozen shows languages, the majority of successfully in Python. [192]
Several problems with glitches, style flaws and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been accused of producing copyrighted code, without any 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded innovation 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 read, examine or produce approximately 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has declined to reveal different 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 modern lead to 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) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller version 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 expects it to be especially helpful 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 models, which have actually been designed to take more time to believe about their reactions, causing greater accuracy. These designs are particularly efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning model. OpenAI likewise revealed o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications companies O2. [215]
Deep research study

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image classification

CLIP

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

DALL-E

Revealed in 2021, DALL-E is a Transformer design that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can create pictures of sensible things ("a stained-glass window with a picture of a blue strawberry") as well as objects that do not exist in reality ("a cube with the texture of a porcupine"). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, an updated variation of the model with more reasonable results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new simple 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 produce images from complex descriptions without manual prompt 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 design that can produce videos based upon brief detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.

Sora's advancement team called it after the Japanese word for "sky", to represent its "unlimited innovative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [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 precise 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 generate videos approximately one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the model's capabilities. [225] It acknowledged a few of its drawbacks, including struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they must have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have revealed significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to create practical video from text descriptions, mentioning its possible to revolutionize storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly strategies for expanding his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of varied audio and is also a multi-task design that can carry out multilingual speech recognition as well as speech translation and language identification. [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 generated by MuseNet tends to begin fairly but then fall into turmoil the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for bytes-the-dust.com the internet 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 stated the tunes "show regional musical coherence [and] follow conventional chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial gap" between Jukebox and human-generated music. The Verge specified "It's technologically excellent, even if the results seem like mushy variations of songs that might feel familiar", while Business Insider mentioned "remarkably, a few of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to debate toy problems in front of a human judge. The function is to research study whether such a method may 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 considerable layer and neuron of 8 neural network models which are typically studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various versions of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that offers a conversational user interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.