GitLab

Announced in 2016, Gym is an open-source Python developed to help with the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research, making published research more quickly reproducible [24] [144] while offering users with an easy user interface for connecting with these environments. In 2022, brand-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 on computer game [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to resolve single tasks. Gym Retro provides the ability to generalize between video games with comparable concepts however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have knowledge of how to even walk, however are given the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives discover how to adapt to changing conditions. When an agent is then gotten rid of from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had actually discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might create an intelligence "arms race" that could increase an agent's ability to operate even outside the context of the competitors. [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 totally through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation took place at The International 2017, the yearly premiere championship tournament for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of genuine time, and that the knowing software application was an action in the direction of producing software application that can manage complicated tasks like a surgeon. [152] [153] The system utilizes a form of reinforcement knowing, as the bots find out over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy and taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a complete group of 5, and they were able to beat 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 video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 total video 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 shows the obstacles of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep reinforcement knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker finding out to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It learns totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB cams to allow the robot to manipulate an arbitrary item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and trademarketclassifieds.com an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complicated 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 gradually more tough environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
API

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

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

The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative design of language could obtain world knowledge 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 an unsupervised transformer language model and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative versions at first released to the general public. The full variation of GPT-2 was not instantly released due to concern about prospective misuse, including applications for composing fake news. [174] Some professionals expressed uncertainty that GPT-2 posed a substantial hazard.

In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural fake news". [175] Other scientists, trademarketclassifieds.com such as Jeremy Howard, warned of "the innovation to absolutely 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 version of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose students, shown by GPT-2 attaining advanced 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain issues 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 model and the successor engel-und-waisen.de to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation 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 designs with as few as 125 million specifications were also trained). [186]
OpenAI specified 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 offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184]
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or coming across the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched to the public for concerns of possible abuse, although OpenAI prepared 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 certified 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 launched in private beta. [194] According to OpenAI, the design can develop working code in over a dozen shows languages, many efficiently in Python. [192]
Several issues with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been implicated of giving off copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would cease 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 revealed that the updated innovation passed a simulated law school bar exam 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 might also read, evaluate or wiki.asexuality.org generate up to 25,000 words of text, and compose code in all significant programming languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various technical details and statistics about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and disgaeawiki.info released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision standards, setting brand-new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria 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 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 useful for business, startups and developers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been developed to take more time to think about their reactions, causing higher 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, 2024, OpenAI unveiled o3, the successor of the o1 thinking design. OpenAI likewise revealed o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these models. [214] The design 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, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out substantial web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools enabled, it reached an accuracy 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 model that is trained to evaluate the semantic similarity between text and yewiki.org images. It can significantly be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can produce pictures of sensible items ("a stained-glass window with a picture of a blue strawberry") in addition to 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 variation of the model with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software application 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 announced DALL-E 3, a more powerful design better able to generate images from complicated descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the general public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can generate videos based on brief detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's development group called it after the Japanese word for "sky", to symbolize its "limitless creative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos accredited for that function, but did not reveal the number or the exact 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 generate videos up to one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the design's capabilities. [225] It acknowledged a few of its imperfections, including struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", 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, noteworthy entertainment-industry figures have actually revealed significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to create sensible video from text descriptions, citing its prospective to change storytelling and material creation. He said that his enjoyment 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 model. [228] It is trained on a big dataset of diverse audio and is likewise a multi-task design that can perform multilingual speech acknowledgment as well as 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 songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized 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 create music with vocals. After training on 1.2 million samples, the system accepts a genre, surgiteams.com artist, and a snippet of lyrics and outputs song samples. OpenAI specified the songs "show regional musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" which "there is a considerable gap" between Jukebox and human-generated music. The Verge specified "It's highly remarkable, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting songs are memorable and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches makers to discuss toy problems in front of a human judge. The purpose is to research study whether such a technique 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 considerable layer and nerve cell of 8 neural network models which are typically studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks quickly. The designs included 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 interface that enables users to ask concerns in natural language. The system then reacts with a response within seconds.