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 defined in AI research study, making published research more quickly reproducible [24] [144] while supplying users with a simple user interface for connecting with these environments. In 2022, new advancements of Gym have been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support knowing (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to solve single tasks. Gym Retro provides the ability 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 initially do not have knowledge of how to even stroll, however are provided the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the agents learn how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could produce an intelligence "arms race" that might increase a representative's capability to function even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high skill level entirely through experimental algorithms. Before ending up being a group of 5, the very first public demonstration took place at The International 2017, the yearly best championship competition for the video game, where Dendi, an expert Ukrainian player, 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 2 weeks of genuine time, and that the learning software was a step in the direction of creating software that can handle complex jobs like a cosmetic surgeon. [152] [153] The system uses a form of support 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 killing an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a complete group of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video 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 video games in a four-day open online competition, kousokuwiki.org winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated the usage of deep reinforcement learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It discovers entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by utilizing domain randomization, a simulation method which exposes the student to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cams to enable the robotic to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to design. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing gradually harder environments. ADR varies from manual domain randomization by not requiring a human to specify randomization varieties. [169]
API

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

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

The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and process long-range reliances by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative variations initially released to the public. The complete version of GPT-2 was not immediately released due to concern about possible misuse, including applications for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 posed a significant danger.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the technology 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 total version of the GPT-2 language design. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose students, shown by GPT-2 attaining modern 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by using byte pair encoding. This allows 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 without supervision transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million criteria were also trained). [186]
OpenAI stated that GPT-3 prospered at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184]
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across the fundamental capability 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 the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for issues of possible abuse, although OpenAI planned to allow 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 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 lots programming languages, the majority of efficiently in Python. [192]
Several issues with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been implicated of releasing copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would cease assistance 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 announced that the upgraded technology passed a simulated law school bar examination with a rating around the top 10% of test takers. (By contrast, wakewiki.de GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also check out, examine or produce as much as 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the version of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few 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 different technical details and statistics about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision criteria, 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 released GPT-4o mini, a smaller sized 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 anticipates it to be particularly beneficial for business, startups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been developed to take more time to consider their reactions, causing higher precision. These designs are particularly effective in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning model. OpenAI also revealed o3-mini, demo.qkseo.in a lighter and faster variation of OpenAI o3. Since 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 chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications companies O2. [215]
Deep research

Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching 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 design that is trained to examine the semantic resemblance between text and images. It can especially be used for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can develop images of sensible objects ("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"). Since March 2021, no API or bytes-the-dust.com 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 published on GitHub software application for Point-E, a brand-new primary 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 intricate details like hands and text. [221] It was launched to the public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can generate videos based upon brief detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with approximately 1920x1080 or 1080x1920. The maximal length of created videos is unknown.

Sora's development team called it after the Japanese word for "sky", to represent its "unlimited innovative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos certified for that purpose, but did not expose the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it might create videos up to one minute long. It likewise shared a technical report highlighting the approaches used to train the model, and the model's capabilities. [225] It acknowledged some of its imperfections, consisting of struggles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", but kept in mind that they must have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have shown significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to create reasonable video from text descriptions, citing its potential to revolutionize storytelling and material creation. 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 recognition model. [228] It is trained on a large dataset of varied audio and is also a multi-task design that can perform multilingual speech recognition in addition to speech translation and language identification. [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 tunes with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start fairly but then fall into mayhem the longer it plays. [230] [231] In pop culture, initial 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 generate music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song samples. OpenAI stated the tunes "reveal local musical coherence [and] follow traditional chord patterns" however acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the outcomes sound like mushy versions of songs that might feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are catchy and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to discuss toy issues in front of a human judge. The purpose is to research whether such a technique might assist in auditing AI choices and in establishing explainable AI. [237] [238]
Microscope

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

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