tuxpa

Announced in 2016, Gym is an open-source Python library created to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research, making released research study more quickly reproducible [24] [144] while offering users with an easy interface for interacting with these environments. In 2022, 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 knowing (RL) research study on video games [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to resolve single tasks. Gym Retro gives the ability to generalize in between video games with comparable principles however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack understanding of how to even walk, but are offered the objectives of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing process, the agents learn how to adjust to altering conditions. When a representative is then removed from this virtual environment and placed in a new virtual environment with high winds, the representative 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 in between representatives might create an intelligence "arms race" that could increase a representative's capability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human players at a high ability level entirely through trial-and-error algorithms. Before ending up being a group of 5, the very first public demonstration happened at The International 2017, the yearly premiere championship tournament for the video game, where Dendi, a gamer, lost against a bot in a live individually match. [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, which the knowing software application was a step in the instructions of creating software application that can handle complicated tasks like a surgeon. [152] [153] The system utilizes a kind of support knowing, as the bots discover gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a complete team of 5, and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot player shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has demonstrated making use of deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It learns completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by using domain randomization, a simulation approach which exposes the learner to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB electronic cameras to enable the robot to control an arbitrary item 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 showed that Dactyl might fix a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing progressively harder environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [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 developers get in touch with it for "any English language AI task". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and procedure long-range dependencies 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 model and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative versions initially released to the public. The full version of GPT-2 was not right away released due to issue about possible misuse, including applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 presented a substantial risk.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language design. [177] Several websites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, shown by GPT-2 attaining advanced accuracy 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 prevents certain problems 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 an unsupervised transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the full variation of GPT-3 contained 175 billion parameters, [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 couple of as 125 million parameters were also trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or encountering the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released 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 personal beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has 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 model can develop working code in over a dozen programs languages, most effectively in Python. [192]
Several issues with glitches, design defects and security vulnerabilities were cited. [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 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), efficient in accepting text or image inputs. [199] They revealed that the upgraded technology passed a simulated law school bar exam 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 might likewise check out, analyze or produce up to 25,000 words of text, and compose code in all significant shows languages. [200]
Observers reported that the model of ChatGPT using 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 modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal different technical details and stats about GPT-4, such as the accurate size of the model. [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 advanced outcomes in voice, multilingual, and vision standards, 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 changing 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 enterprises, startups and developers 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 think of their reactions, resulting in higher accuracy. These models are especially reliable in science, coding, and thinking 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, the follower of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and faster version of OpenAI o3. As of December 21, setiathome.berkeley.edu 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
Deep research

Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform extensive web surfing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, 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 design that is trained to examine the semantic resemblance in between text and images. It can especially be utilized 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 analyze natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can create pictures of realistic things ("a stained-glass window with a picture of a blue strawberry") in addition to objects 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 updated variation of the design with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new basic system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful model 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 general public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can produce videos based upon short detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's development team called it after the Japanese word for "sky", to signify its "limitless innovative potential". [223] Sora's technology 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 purpose, but did not expose the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it might generate videos up to one minute long. It likewise shared a technical report highlighting the approaches used to train the model, and the design's abilities. [225] It acknowledged a few of its drawbacks, including struggles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but kept in mind 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 demonstration, noteworthy entertainment-industry figures have shown significant interest in the innovation'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 prospective to change storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to pause strategies for broadening 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 big dataset of diverse audio and is also a multi-task model that can carry out multilingual speech recognition along with speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly but then fall into chaos the longer it plays. [230] [231] In pop 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 bit of lyrics and outputs tune samples. OpenAI stated the songs "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge stated "It's technically excellent, 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 legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers to debate toy problems in front of a human judge. The purpose is to research study whether such a method 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 designs which are typically studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, different variations 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 enables users to ask questions in natural language. The system then reacts with an answer within seconds.