GitLab

Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research study, making released research more easily reproducible [24] [144] while providing users with a simple user interface for interacting with these environments. In 2022, brand-new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to fix single tasks. Gym Retro provides the capability to generalize between games with comparable principles but different 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 objectives of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to altering conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors 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 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 ability level totally through experimental algorithms. Before becoming a team of 5, the first public presentation happened at The International 2017, the annual 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 found out by playing against itself for 2 weeks of actual time, and wiki-tb-service.com that the knowing software application was an action in the direction of producing software application that can handle intricate jobs like a cosmetic surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots find out 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 goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full team of 5, and they were able to defeat 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 beat OG, the reigning 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 overall games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown using deep support knowing (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker discovering to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It learns completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a simulation method which exposes the learner to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB cameras to allow the robot to manipulate an arbitrary item by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of producing gradually more challenging environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
API

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

The company has actually 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 design was composed by Alec Radford and his colleagues, and published 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 dependencies by pre-training on a diverse 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 follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative variations at first released to the general public. The full variation of GPT-2 was not right away launched due to concern about possible abuse, including applications for composing phony 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 researchers, 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 impossible to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language model. [177] Several sites host interactive presentations of various instances of GPT-2 and other . [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose learners, illustrated by GPT-2 attaining state-of-the-art accuracy and wiki.asexuality.org perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more 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 avoids certain issues encoding vocabulary with word tokens by using 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 stated that the complete version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186]
OpenAI specified that GPT-3 was successful at certain "meta-learning" jobs and forum.altaycoins.com might 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 significantly enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or encountering the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for it-viking.ch the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the public for issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month complimentary private beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified solely 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 released in private beta. [194] According to OpenAI, the design can develop working code in over a lots shows languages, most effectively in Python. [192]
Several concerns with problems, style defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of emitting copyrighted code, without any 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), it-viking.ch capable of 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 check out, analyze or create up to 25,000 words of text, and write code in all major programs languages. [200]
Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and data about GPT-4, such as the exact size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition 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 sized 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 anticipates it to be especially helpful for enterprises, startups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been created to take more time to think of their reactions, causing higher accuracy. These designs are particularly efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and 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 design is called o3 rather than o2 to avoid confusion with telecoms companies O2. [215]
Deep research

Deep research is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out extensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image classification

CLIP

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

DALL-E

Revealed in 2021, DALL-E is a Transformer design that produces 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 bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can create pictures of reasonable items ("a stained-glass window with a picture of a blue strawberry") along with 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, OpenAI announced DALL-E 2, an updated version of the model with more realistic results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new primary system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful design much better able to create images from complex descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released 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 create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.

Sora's advancement group named it after the Japanese word for "sky", to signify its "unlimited imaginative capacity". [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 as well as copyrighted videos accredited for that function, but did not expose the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it could create videos as much as one minute long. It likewise shared a technical report highlighting the approaches utilized to train the model, and the design's abilities. [225] It acknowledged a few of its shortcomings, including battles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", but kept in mind that they need to have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have actually revealed substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to create practical video from text descriptions, citing its prospective to revolutionize storytelling and material development. 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 design. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can carry out 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 forecast subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to begin fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, initial 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, pediascape.science Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the songs "reveal regional musical coherence [and] follow traditional chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" which "there is a considerable space" in between Jukebox and human-generated music. The Verge stated "It's highly impressive, even if the outcomes seem like mushy versions of tunes that may feel familiar", while Business Insider specified "remarkably, some of the resulting tunes are appealing and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches devices to dispute toy problems in front of a human judge. The purpose is to research study whether such an approach 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 substantial layer and neuron of 8 neural network models which are typically studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different variations of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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