GitLab

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 study, making released research study more quickly reproducible [24] [144] while supplying users with a basic interface for interacting with these environments. In 2022, setiathome.berkeley.edu new advancements of Gym have been relocated 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 study generalization. Prior RL research study focused mainly on optimizing agents to solve single tasks. Gym Retro provides the ability to generalize in between games with similar principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first lack understanding of how to even stroll, however are offered the objectives of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives could develop an intelligence "arms race" that might increase an agent's capability to work 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 video game Dota 2, that discover to play against human gamers at a high ability level entirely through trial-and-error algorithms. Before becoming a team of 5, the very first public demonstration happened at The International 2017, the annual best champion tournament 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 actually found out by playing against itself for 2 weeks of actual time, which the knowing software application was a step in the direction of developing software that can handle complex jobs like a cosmetic surgeon. [152] [153] The system uses a kind of support learning, as the bots learn 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 goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a complete team of 5, and they were able to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert gamers, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot gamer reveals the difficulties of AI systems in multiplayer online (MOBA) video games and how OpenAI Five has actually demonstrated the use of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device finding out to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It finds out totally in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by using domain randomization, a simulation technique which exposes the student to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB electronic cameras to allow the robotic to manipulate an arbitrary object 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 could fix a Rubik's Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing gradually more hard 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 brand-new AI designs established by OpenAI" to let designers contact it for "any English language AI job". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language might obtain world understanding and procedure 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 follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just minimal demonstrative versions initially launched to the public. The full version of GPT-2 was not immediately released due to issue about prospective misuse, consisting of applications for writing fake news. [174] Some experts revealed uncertainty that GPT-2 positioned a considerable danger.

In reaction to GPT-2, the Allen Institute for pediascape.science Artificial Intelligence responded with a tool to detect "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted 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 launched the complete version of the GPT-2 language design. [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose learners, illustrated by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not additional 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 prevents 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 successor to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion parameters, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were likewise trained). [186]
OpenAI stated that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]
GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the essential capability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away released to the public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month complimentary personal beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually in addition 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 personal beta. [194] According to OpenAI, the model can produce working code in over a dozen programs languages, many successfully in Python. [192]
Several concerns with problems, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been implicated of giving off copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would discontinue 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), efficient in accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar examination 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 also read, evaluate or produce up to 25,000 words of text, and compose code in all significant shows languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and data about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern results in voice, multilingual, and vision standards, 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 launched GPT-4o mini, a smaller 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 especially useful for business, startups and surgiteams.com 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 developed to take more time to think of their reactions, causing greater precision. These designs are particularly 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 revealed o3, the successor of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and much 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, security and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 instead of o2 to prevent confusion with telecoms companies O2. [215]
Deep research

Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out extensive web surfing, data 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 an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to evaluate the semantic similarity between text and images. It can especially be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that creates 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 purse formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can create pictures of reasonable objects ("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 upgraded version of the model with more sensible results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new primary system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful design much better able to produce images from complex descriptions without manual prompt engineering and render intricate 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 on short detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.

Sora's development group called it after the Japanese word for "sky", to represent its "endless imaginative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for that purpose, however did not reveal the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it could create videos as much as one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the model's abilities. [225] It acknowledged a few of its imperfections, consisting of battles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they should have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to create practical video from text descriptions, mentioning its possible to revolutionize storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had decided to pause prepare for broadening his Atlanta-based film 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 diverse audio and is likewise a multi-task design that can perform multilingual speech recognition as well as speech translation and language recognition. [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 generate tunes with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly but then fall under turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to produce 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 specified the songs "show regional musical coherence [and] follow standard chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" and that "there is a substantial space" between Jukebox and human-generated music. The Verge specified "It's technologically outstanding, even if the outcomes seem like mushy versions of tunes that might feel familiar", while Business Insider stated "remarkably, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to debate toy issues in front of a human judge. The function is to research whether such an approach 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 nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks easily. 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 interface that enables users to ask questions in natural language. The system then responds with a response within seconds.