Announced in 2016, Gym is an open-source Python library created to help with the development of support learning algorithms. It aimed to standardize how environments are defined in AI research study, making published research more easily reproducible [24] [144] while offering users with an easy user interface for communicating with these environments. In 2022, brand-new developments 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 study on video games [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing representatives to fix single jobs. Gym Retro provides the ability to generalize in between games with comparable concepts however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially do not have knowledge of how to even walk, however are given the objectives of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the agents find out how to adapt to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had actually learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could produce an intelligence "arms race" that could increase a representative'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 computer game Dota 2, that discover to play against human gamers at a high ability level completely 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 game, where Dendi, a professional 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 discovered by playing against itself for two weeks of genuine time, and that the learning software application was a step in the direction of producing software that can handle intricate jobs like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots find out in time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a complete team of 5, and they were able to beat groups of amateur and bytes-the-dust.com semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two against expert players, however wound up losing both games. [160] [161] [162] In April 2019, pipewiki.org OpenAI Five beat 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' final public appearance came later on that month, where they played in 42,729 total video games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot player reveals the obstacles of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown making use of deep reinforcement knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It learns completely in simulation using the exact 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 rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, also has RGB cameras to enable the robot to manipulate an approximate things by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might solve a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating progressively harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization ranges. [169]
API

In June 2020, OpenAI revealed 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 task". [170] [171]
Text generation

The company has popularized 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 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 process long-range dependences 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 initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative variations at first released to the general public. The full variation of GPT-2 was not right away launched due to issue about potential abuse, including applications for composing phony news. [174] Some professionals expressed uncertainty that GPT-2 posed a substantial hazard.

In response 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, cautioned of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language model. [177] Several sites host interactive demonstrations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose students, illustrated by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not additional 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 concerns encoding vocabulary with word tokens by using byte pair encoding. This permits 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 without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were also trained). [186]
OpenAI mentioned that GPT-3 was successful 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 knowing between English and Romanian, and between English and German. [184]
GPT-3 significantly improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could 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 tens of petaflop/s-days for the complete 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 prepared to permit 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 licensed exclusively 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 released in personal beta. [194] According to OpenAI, the model can produce working code in over a dozen programming languages, the majority of successfully in Python. [192]
Several problems with problems, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of emitting copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would discontinue assistance for larsaluarna.se 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 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, analyze or produce approximately 25,000 words of text, and write code in all major programming languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems 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 exact size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision benchmarks, setting brand-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 launched GPT-4o mini, a smaller version of GPT-4o changing 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 designers 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 developed to take more time to consider their reactions, pipewiki.org resulting in greater precision. These models are especially effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was changed 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 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, security and security researchers had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215]
Deep research study

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out substantial web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, pediascape.science 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 design that is trained to evaluate the semantic resemblance in between text and images. It can especially 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 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 create corresponding images. It can develop images of realistic things ("a stained-glass window with an image of a blue strawberry") along with objects 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 updated version of the design with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new simple 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 create images from complex descriptions without manual prompt engineering and pipewiki.org render intricate details like hands and text. [221] It was released to the public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can produce videos based on short detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora's development team called it after the Japanese word for "sky", to signify its "unlimited innovative capacity". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that function, however did not reveal the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could produce videos approximately one minute long. It likewise shared a technical report highlighting the methods utilized to train the model, and the design's abilities. [225] It acknowledged some of its shortcomings, including battles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they need to have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have shown significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to generate reasonable video from text descriptions, mentioning its potential to change storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause plans 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 diverse audio and is likewise a multi-task model that can carry out multilingual speech acknowledgment as well as 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 create songs with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to start fairly but then fall under turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized 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 generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI stated the tunes "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" and that "there is a significant gap" between Jukebox and human-generated music. The Verge specified "It's highly impressive, even if the results seem like mushy versions of tunes that might feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting tunes are catchy and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches makers to debate toy problems in front of a human judge. The function is to research study whether such an approach may assist 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 often 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, various variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

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