Announced in 2016, Gym is an open-source Python library created to facilitate the advancement of support knowing algorithms. It aimed to standardize how environments are defined in AI research, making published research study more quickly reproducible [24] [144] while supplying users with an easy user interface for communicating with these environments. In 2022, systemcheck-wiki.de 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 reinforcement learning (RL) research study on computer game [147] using RL algorithms and study generalization. Prior RL research focused mainly on enhancing representatives to resolve single jobs. Gym Retro provides the ability to generalize between video games with similar ideas however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first do not have knowledge of how to even walk, however are provided the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adjust to changing conditions. When a representative is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the agent 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 agents might create an intelligence "arms race" that could increase a representative's capability 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, systemcheck-wiki.de that find out to play against human gamers at a high skill level completely through trial-and-error algorithms. Before becoming a group of 5, the first public demonstration occurred at The International 2017, the annual best championship tournament for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for two weeks of genuine time, and that the learning software was an action in the instructions of developing software application that can handle intricate tasks like a cosmetic surgeon. [152] [153] The system uses a type of support learning, as the bots find out gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a full 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 two exhibit matches against professional gamers, pipewiki.org however wound up losing both 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 competition, 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 fight arena (MOBA) video games and how OpenAI Five has demonstrated the usage of deep reinforcement knowing (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It learns completely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation approach 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, likewise has RGB cameras to allow the robot to manipulate an arbitrary item 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 demonstrated that Dactyl might fix a Rubik's Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing gradually harder environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new AI models 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 initial GPT model ("GPT-1")

The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language could obtain world knowledge and procedure long-range dependencies by pre-training on a varied corpus with long stretches of adjoining 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 announced in February 2019, with just restricted demonstrative variations initially launched to the public. The full version of GPT-2 was not right away released due to concern about potential misuse, including applications for writing fake news. [174] Some professionals expressed uncertainty that GPT-2 presented a substantial danger.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to totally 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, OpenAI released the complete version of the GPT-2 language design. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose students, highlighted by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not further trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains slightly 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 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 without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186]
OpenAI specified that GPT-3 was successful at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and 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 needed several 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 right away released to the general public for concerns of possible abuse, although OpenAI prepared to enable 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 exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually additionally 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 create working code in over a lots shows languages, most effectively in Python. [192]
Several issues with problems, design defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of giving off copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would terminate 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), capable of accepting text or image inputs. [199] They revealed that the upgraded technology passed a simulated law school bar exam with a score around the leading 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 as much as 25,000 words of text, and write code in all major programs languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose numerous technical details and data about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision benchmarks, setting new records in audio speech acknowledgment 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 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 helpful for enterprises, startups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been created to take more time to consider their responses, causing higher precision. These models are particularly 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 replaced by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, disgaeawiki.info the follower of the o1 reasoning model. OpenAI also unveiled o3-mini, a lighter and 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 opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecoms services service provider O2. [215]
Deep research

Deep research study is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform substantial web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached a precision 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 examine the semantic resemblance between text and images. It can significantly be used for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce images of realistic things ("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 revealed DALL-E 2, an upgraded variation of the model with more reasonable results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new basic 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 better able to produce images from complex descriptions without manual timely engineering and render complicated 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 model that can generate videos based on brief detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.

Sora's development team named it after the Japanese word for "sky", to symbolize its "limitless innovative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that function, however did not expose the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could produce videos up to one minute long. It likewise shared a technical report highlighting the approaches utilized to train the design, and the model's abilities. [225] It acknowledged a few of its imperfections, including battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they need to have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to produce reasonable video from text descriptions, mentioning its potential to transform storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had decided to pause prepare for expanding his Atlanta-based motion picture 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 also a multi-task design that can perform multilingual speech acknowledgment in addition to 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 produce tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to develop 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, the system accepts a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "show regional musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a substantial gap" between Jukebox and human-generated music. The Verge specified "It's technologically remarkable, even if the results seem like mushy versions of tunes that may feel familiar", while Business Insider mentioned "surprisingly, 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 makers 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 decisions and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of of every substantial layer and nerve cell of 8 neural network designs which are typically studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various versions 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 offers a conversational user interface that allows users to ask questions in natural language. The system then responds with an answer within seconds.