Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research study, making published research more quickly reproducible [24] [144] while offering users with an easy user interface for communicating with these environments. In 2022, brand-new advancements of Gym have been transferred 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] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to fix single jobs. Gym Retro gives the capability to generalize in between games with similar principles however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first do not have knowledge of how to even walk, however are offered the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives learn how to adjust to changing conditions. When a representative is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might develop an intelligence "arms race" that could increase a representative's ability to work even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high skill level entirely through trial-and-error algorithms. Before ending up being a team of 5, the very first public demonstration took place at The International 2017, the yearly best championship competition for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for 2 weeks of real time, and that the learning software application was an action in the instructions of developing software that can deal with complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of reinforcement knowing, as the bots learn with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a complete group of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert gamers, but 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 exhibit match in San Francisco. [163] [164] The bots' last public appearance came later on 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 gamer reveals the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown the usage of deep reinforcement knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, genbecle.com Dactyl utilizes device learning to train a Shadow Hand, a human-like robot hand, it-viking.ch to manipulate physical items. [167] It learns entirely 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 method which exposes the learner to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB cams to allow the robot to control an approximate object by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The robot had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated 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 approach of producing progressively more hard 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 models established by OpenAI" to let developers call on it for "any English language AI job". [170] [171]
Text generation

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

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language could obtain world knowledge 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 a without supervision transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative variations at first released to the general public. The complete version of GPT-2 was not right away released due to concern about possible misuse, consisting of applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 presented a significant risk.

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, warned of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be difficult 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 designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining cutting edge 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 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 mentioned that the complete variation of GPT-3 contained 175 billion criteria, [184] 2 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 parameters were also trained). [186]
OpenAI mentioned that GPT-3 prospered 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 learning in between English and Romanian, and between English and German. [184]
GPT-3 dramatically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or coming across the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, 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 right away released to the public for issues of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month complimentary personal beta that began 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 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 launched in private beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, most efficiently in Python. [192]
Several issues with glitches, design flaws and security vulnerabilities were cited. [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 announced that the upgraded technology passed a simulated law school bar exam with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise read, evaluate or create as much as 25,000 words of text, and compose 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 version, with the caution that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose different technical details and statistics about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, archmageriseswiki.com 2024, OpenAI revealed and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision criteria, setting brand-new records in audio speech acknowledgment and archmageriseswiki.com 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 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 especially beneficial for business, startups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been developed to take more time to think of their actions, leading to higher precision. These models are especially effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and much faster version of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety 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 providers O2. [215]
Deep research study

Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, 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 similarity in between text and images. It can notably be used 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 uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can produce images of realistic things ("a stained-glass window with an image of a blue strawberry") as well as items that do not exist in truth ("a cube with the texture of a porcupine"). Since March 2021, no API or garagesale.es code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, an updated variation of the design with more reasonable results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new basic 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 model better able to produce images from intricate descriptions without manual timely engineering and render complex details like hands and text. [221] It was launched 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 upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unknown.

Sora's advancement team called it after the Japanese word for "sky", to signify its "unlimited creative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for that function, however did not reveal 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 could generate videos approximately one minute long. It also shared a technical report highlighting the approaches used to train the model, and the design's capabilities. [225] It acknowledged some of its drawbacks, consisting of struggles replicating complex physics. [226] Will Douglas Heaven of the MIT called the demonstration videos "excellent", however kept in mind that they should have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have revealed substantial interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to generate reasonable video from text descriptions, citing its possible to change storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had decided to pause prepare 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 large dataset of diverse audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment in addition to 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 styles. According to The Verge, a song generated by MuseNet tends to start fairly however 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 mental 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, systemcheck-wiki.de artist, and a bit of lyrics and outputs tune samples. OpenAI specified the songs "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial space" between Jukebox and human-generated music. The Verge stated "It's technically remarkable, 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]
Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research study whether such an approach may 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 substantial layer and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, various variations of Inception, and various versions 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 enables users to ask questions in natural language. The system then reacts with a response within seconds.