Announced in 2016, Gym is an open-source Python library developed to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research study, making released research more easily reproducible [24] [144] while supplying users with a basic interface for interacting with these environments. In 2022, new developments of Gym have been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to resolve single tasks. Gym Retro offers the ability to generalize between video games with similar principles but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially lack understanding of how to even walk, however are provided the goals of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives find out how to adjust to changing conditions. When an agent is then gotten rid of from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had discovered 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 operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human players at a high ability level totally through experimental algorithms. Before becoming a team of 5, the first public demonstration took place at The International 2017, the annual best 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 two weeks of actual time, and that the learning software was a step in the instructions of producing software that can manage intricate tasks like a surgeon. [152] [153] The system utilizes a type of reinforcement learning, as the bots learn with time by playing against themselves numerous 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 capability of the bots broadened to play together as a full group of 5, and they were able 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 expert gamers, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public look 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 mechanisms in Dota 2's bot gamer shows the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown using deep reinforcement learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker discovering 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 tackled the things orientation problem by using domain randomization, a simulation method which exposes the student to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB cams to permit the robot to control an approximate item by seeing it. In 2018, OpenAI showed 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 robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating progressively more tough environments. ADR differs from manual domain randomization by not needing a human to define randomization ranges. [169]
API

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

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

The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative design of language could obtain world understanding and procedure long-range dependences by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative versions at first released to the general public. The full version of GPT-2 was not immediately launched due to concern about prospective abuse, including applications for writing phony news. [174] Some professionals expressed uncertainty that GPT-2 positioned a substantial risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to totally 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 total variation of the GPT-2 language model. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose learners, shown by GPT-2 attaining state-of-the-art 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 a little 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 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 specified that the complete version of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as few as 125 million criteria were also trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs and could generalize the purpose 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 in between English and German. [184]
GPT-3 dramatically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away launched to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month totally free personal beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has 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 released in private beta. [194] According to OpenAI, the model can develop working code in over a dozen shows languages, the majority of efficiently in Python. [192]
Several concerns with problems, design defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been accused of giving off copyrighted code, with no author attribution or license. [197]
OpenAI announced 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 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 could also check out, analyze or produce as much as 25,000 words of text, and compose code in all significant programming languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose various technical details and stats about GPT-4, such as the accurate size of the design. [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 criteria, 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 released GPT-4o mini, a smaller 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 particularly helpful for business, start-ups and designers seeking to automate services with AI agents. [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, leading to greater precision. These models are especially 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 changed by o1. [211]
o3

On December 20, yewiki.org 2024, OpenAI revealed o3, the successor of the o1 thinking design. OpenAI also unveiled o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating 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 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 model to perform comprehensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [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 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 utilizes a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can develop pictures of reasonable objects ("a stained-glass window with a picture of a blue strawberry") along with 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 updated version of the model with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new fundamental system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more effective model much better able to produce images from complicated descriptions without manual timely engineering and render complicated details like hands and text. [221] It was launched to the general public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can produce videos based upon brief detailed prompts [223] as well as extend existing videos forwards or backwards in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora's development team called it after the Japanese word for "sky", to symbolize its "endless imaginative potential". [223] Sora's technology 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 precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could produce videos as much as one minute long. It also shared a technical report highlighting the methods used to train the design, and the design's abilities. [225] It acknowledged some of its shortcomings, consisting of battles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but noted that they must have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have revealed considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to create sensible video from text descriptions, mentioning its possible to reinvent storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly strategies for broadening his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of varied audio and is also a multi-task model that can carry out 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 predict 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 begin fairly but then fall under turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the internet psychological thriller Ben Drowned to develop music for 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 category, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the songs "show local musical coherence [and] follow traditional chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that duplicate" and that "there is a substantial gap" in between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider specified "remarkably, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches devices to debate toy problems in front of a human judge. The purpose is to research study whether such a method might assist in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and neuron of eight neural network designs which are typically studied in interpretability. [240] Microscope was created to evaluate the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various variations of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool developed on top of GPT-3 that supplies a conversational user interface that enables users to ask concerns in natural language. The system then responds with a response within seconds.