Announced in 2016, Gym is an open-source Python library developed to assist in the development of support knowing algorithms. It aimed to standardize how environments are specified in AI research, making released research study more quickly reproducible [24] [144] while providing users with a basic user interface for interacting with these environments. In 2022, new advancements of Gym have actually 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 computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to resolve single jobs. Gym Retro offers the capability to generalize in between games with comparable concepts but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack understanding of how to even stroll, however are given the objectives of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adjust to altering conditions. When a representative is then gotten rid of from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered 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 function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that learn to play against human players at a high skill level completely through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation took place at The International 2017, the yearly premiere champion competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for two weeks of actual time, which the learning software application was an action in the direction of producing software application that can manage complicated jobs like a cosmetic surgeon. [152] [153] The system uses a type of support knowing, as the bots discover gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a complete team of 5, and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional players, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot player reveals the difficulties of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown making use of deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It finds out completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by utilizing domain randomization, a simulation method which exposes the student to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cameras to enable the robot to control an arbitrary item 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 might solve a Rubik's Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated 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 more challenging 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 brand-new AI models established by OpenAI" to let designers contact it for "any English language AI job". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language might obtain world knowledge and procedure long-range reliances 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 initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations initially launched to the public. The full version of GPT-2 was not immediately launched due to concern about potential abuse, including applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 postured a significant danger.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to absolutely 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 total version of the GPT-2 language design. [177] Several sites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, illustrated by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains slightly 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 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 an unsupervised transformer language design 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 variation of GPT-2 (although GPT-3 models with as few as 125 million specifications were likewise trained). [186]
OpenAI specified that GPT-3 prospered at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]
GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could 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 complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for concerns of possible abuse, although OpenAI planned to enable 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 actually 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 private beta. [194] According to OpenAI, the model can produce working code in over a lots programs languages, most successfully in Python. [192]
Several concerns with glitches, design flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of discharging copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would stop 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 technology passed a simulated law school bar test 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 might likewise check out, analyze or create as much as 25,000 words of text, and compose code in all major programs languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal various technical details and stats about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern outcomes in voice, multilingual, and vision benchmarks, setting 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 released GPT-4o mini, a smaller variation 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 business, 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 designs, which have been designed to take more time to think of their reactions, trademarketclassifieds.com causing higher precision. These models are particularly efficient in science, coding, and thinking 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 revealed o3, the follower of the o1 reasoning model. OpenAI also unveiled o3-mini, a lighter and much faster version of OpenAI o3. As of 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 opportunity to obtain early access to these models. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications services service provider O2. [215]
Deep research study

Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out substantial web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, higgledy-piggledy.xyz 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 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 model that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can develop pictures of practical items ("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"). As of March 2021, no API or code is available.

DALL-E 2

In April 2022, yewiki.org OpenAI announced DALL-E 2, an updated version of the design with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new rudimentary 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 create images from complicated descriptions without manual timely engineering and render complicated 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 model that can create videos based on short detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.

Sora's development group called it after the Japanese word for "sky", to symbolize its "unlimited innovative potential". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos certified for that function, however did not expose 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, mentioning that it could produce videos up to one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the design's abilities. [225] It acknowledged a few of its drawbacks, including battles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", however noted that they must have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have shown significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's capability to create sensible video from text descriptions, citing its possible to reinvent storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had actually decided to stop briefly plans for expanding his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a big dataset of diverse audio and is also 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 generate tunes with 10 instruments in 15 designs. According to The Verge, a song produced 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 psychological thriller Ben Drowned to develop music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to produce 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 mentioned the songs "show regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge specified "It's technically excellent, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider specified "remarkably, some of the resulting songs are appealing and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to debate toy problems in front of a human judge. The purpose is to research study whether such a technique may help in auditing AI choices and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of 8 neural network designs which are typically studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool built on top of GPT-3 that supplies a conversational user interface that enables users to ask questions in natural language. The system then responds with an answer within seconds.