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, making released research study more easily reproducible [24] [144] while providing users with an easy interface for communicating with these environments. In 2022, brand-new advancements of Gym have actually been transferred to the . [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to solve single tasks. Gym Retro gives the capability to generalize in between games with comparable concepts but different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first lack knowledge of how to even stroll, however are provided the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adapt to changing conditions. When an agent is then gotten rid of from this virtual environment and placed in a brand-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 between agents might develop an intelligence "arms race" that might increase an agent's ability to work even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group 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 trial-and-error algorithms. Before becoming a team of 5, the first public presentation happened at The International 2017, the yearly premiere championship competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of real time, and that the learning software application was a step in the instructions of developing software application that can deal with complex jobs like a surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots learn in time by playing against themselves numerous 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 ability of the bots expanded to play together as a complete group of 5, and they had the ability to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional players, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look 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 video games. [165]
OpenAI 5's mechanisms in Dota 2's bot player reveals the challenges of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep reinforcement knowing (DRL) representatives to attain superhuman competence 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 things. [167] It learns completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by using domain randomization, a simulation method which exposes the student to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB cameras to enable the robot to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system was able to control a cube and garagesale.es an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of producing gradually harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new AI designs established by OpenAI" to let designers call on it for "any English language AI job". [170] [171]
Text generation

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

The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language might 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 an unsupervised transformer language design and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative variations initially released to the general public. The full variation of GPT-2 was not instantly launched due to concern about possible abuse, including applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 presented a considerable hazard.

In action to GPT-2, the Allen Institute for disgaeawiki.info Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several sites host interactive presentations of various circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose students, highlighted by GPT-2 attaining state-of-the-art 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 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 private characters and wiki.lafabriquedelalogistique.fr 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 follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were likewise trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
GPT-3 dramatically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or encountering the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched 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 began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed solely 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 private beta. [194] According to OpenAI, the design can produce working code in over a dozen programs languages, many efficiently in Python. [192]
Several concerns with glitches, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been accused of emitting copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would stop support 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 announced that the updated technology passed a simulated law school bar test with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, analyze or create as much as 25,000 words of text, and write code in all major shows languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose different technical details and data 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 produce text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, and vision standards, setting new records in audio speech recognition and 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 launched GPT-4o mini, a smaller version 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 particularly beneficial for enterprises, start-ups and designers 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 created to take more time to think of their responses, resulting in greater precision. These designs are especially efficient in science, coding, and reasoning tasks, 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, surgiteams.com 2024, OpenAI revealed o3, the successor of the o1 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. 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 designs. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]
Deep research

Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform extensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
Image classification

CLIP

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

DALL-E

Revealed in 2021, systemcheck-wiki.de 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 handbag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and create matching images. It can create images of sensible items ("a stained-glass window with an image of a blue strawberry") along with items 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 design with more realistic results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new primary system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective design better able to create images from intricate descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was released 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 upon brief detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.

Sora's development team called it after the Japanese word for "sky", to symbolize its "endless imaginative potential". [223] Sora's innovation is an adaptation 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 licensed for that purpose, but 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, specifying that it could generate videos up to one minute long. It also shared a technical report highlighting the approaches utilized to train the design, and the model's abilities. [225] It acknowledged some of its imperfections, consisting of battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", however noted that they should have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's ability to produce practical video from text descriptions, mentioning its prospective to reinvent storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause strategies for expanding 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 diverse audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment as well as 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 generate tunes with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to start fairly however then fall under mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to produce 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, wiki.dulovic.tech the system accepts a category, artist, and wiki.snooze-hotelsoftware.de a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a substantial space" between Jukebox and human-generated music. The Verge specified "It's highly outstanding, even if the outcomes sound like mushy variations of songs that may feel familiar", while Business Insider mentioned "remarkably, some of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches machines to discuss toy issues in front of a human judge. The function is to research study whether such a method might help in auditing AI choices and in developing 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 created to examine the features that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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