Announced in 2016, Gym is an open-source Python library developed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making released research more quickly reproducible [24] [144] while supplying users with a basic interface for connecting with these environments. In 2022, brand-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 learning (RL) research on video games [147] using RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to resolve single jobs. Gym Retro offers the ability to generalize in between video games with similar ideas however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially lack knowledge of how to even walk, but are provided the goals of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adjust to changing conditions. When an agent is then removed from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might produce an intelligence "arms race" that could increase an agent's capability to function 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 discover to play against human players at a high skill level totally through experimental algorithms. Before ending up being a team of 5, the very first public demonstration occurred at The International 2017, the annual premiere championship competition for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for two weeks of actual time, which the learning software application was an action in the instructions of producing software application that can deal with intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots find out over time by playing against themselves numerous times a day for months, and are rewarded for surgiteams.com actions such as killing an enemy and higgledy-piggledy.xyz taking map objectives. [154] [155] [156]
By June 2018, the capability 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 players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 total games in a four-day open online competitors, pipewiki.org winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer shows the obstacles of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep support learning (DRL) representatives to attain superhuman skills 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 items. [167] It learns completely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation method which exposes the student to a variety of experiences rather than trying to fit to truth. The set-up for it-viking.ch Dactyl, aside from having motion tracking electronic cameras, also has RGB cameras to enable the robotic to control an arbitrary things by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated 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 approach of producing gradually more tough environments. ADR varies from manual domain randomization by not requiring a human to specify randomization varieties. [169]
API

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

The business has actually promoted 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 written by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world knowledge and procedure long-range reliances by pre-training on a varied corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative versions at first released to the general public. The complete version of GPT-2 was not immediately launched due to issue about prospective abuse, including applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 presented a substantial hazard.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural phony news". [175] Other scientists, 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 muffle all other speech and be difficult to filter". [176] In November 2019, engel-und-waisen.de OpenAI released the total version of the GPT-2 language model. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose students, illustrated by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more 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 a minimum of 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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 a not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million specifications were also trained). [186]
OpenAI specified that GPT-3 prospered 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 between English and German. [184]
GPT-3 considerably enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous 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 model was not immediately launched to the general public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month totally free 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 launched in personal beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, most effectively in Python. [192]
Several problems with problems, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of releasing copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would terminate assistance 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), 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 likewise check out, evaluate or produce approximately 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement 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 capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and data about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, bytes-the-dust.com OpenAI announced and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision criteria, setting 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 launched GPT-4o mini, a smaller sized version 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 useful for business, startups and designers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been created 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 Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking design. OpenAI also unveiled o3-mini, a lighter and quicker variation of OpenAI o3. Since 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, security and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid 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 model to carry out substantial web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty 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) criteria. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic similarity in 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 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 generate matching images. It can create pictures of reasonable objects ("a stained-glass window with an image of a blue strawberry") in addition to things 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 revealed DALL-E 2, an upgraded version of the model with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new primary system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective model much better able to generate 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 create videos based upon short detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.

Sora's advancement team called it after the Japanese word for "sky", to represent its "unlimited imaginative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing 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 public on February 15, 2024, stating that it could produce videos approximately one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the model's abilities. [225] It acknowledged a few of its drawbacks, including struggles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however kept in mind that they must have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's capability to produce sensible video from text descriptions, mentioning its potential to change storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to stop briefly plans 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 design that can carry out 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 predict subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 styles. According to The Verge, ratemywifey.com a song produced by MuseNet tends to begin fairly however 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 web psychological thriller Ben Drowned to create 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, artist, and a bit of lyrics and outputs song samples. OpenAI stated the songs "show local musical coherence [and] follow standard chord patterns" but acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" and that "there is a significant space" in between Jukebox and human-generated music. The Verge mentioned "It's technically impressive, even if the results sound like mushy versions of songs that might feel familiar", while Business Insider specified "remarkably, some of the resulting songs are memorable and sound legitimate". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to dispute toy problems in front of a human judge. The purpose is to research study whether such a technique may help in auditing AI decisions 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 eight neural network designs which are frequently studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

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