Announced in 2016, Gym is an open-source Python library designed to help with the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making published research study more easily reproducible [24] [144] while providing users with a simple interface for engaging with these environments. In 2022, 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 study focused mainly on optimizing representatives to resolve single tasks. Gym Retro provides the ability to generalize in between games with comparable ideas however various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have understanding of how to even stroll, however are offered the goals of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives find out how to adapt to changing conditions. When a is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives might produce an intelligence "arms race" that might increase an agent's ability to function even outside the context of the competition. [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 learn to play against human players at a high skill level totally through experimental algorithms. Before ending up being a group of 5, the first public presentation happened at The International 2017, the yearly premiere champion tournament for the game, where Dendi, wiki.whenparked.com an expert Ukrainian player, 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 learned by playing against itself for two weeks of actual time, and that the learning software application was an action in the direction of creating software application that can manage complex tasks like a cosmetic surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the bots discover over time by playing against themselves numerous 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 expanded to play together as a full group of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional players, but wound up losing both games. [160] [161] [162] In April 2019, 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 that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot player reveals the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has demonstrated the use of deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses device finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It discovers totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB electronic cameras to allow the robotic to control an approximate things by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating gradually harder environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI designs established by OpenAI" to let developers get in touch with it for "any English language AI job". [170] [171]
Text generation

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

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world understanding and procedure long-range dependences by pre-training on a diverse corpus with long stretches of contiguous 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 limited demonstrative versions at first released to the general public. The complete variation of GPT-2 was not immediately launched due to concern about prospective misuse, consisting of applications for writing fake news. [174] Some experts revealed uncertainty that GPT-2 positioned a significant hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation 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 design. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, shown 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 at least 3 upvotes. It avoids certain concerns 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 not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were also trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and could generalize the purpose 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 in between English and German. [184]
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or experiencing the basic ability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away 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 free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified solely to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has 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 private beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, many efficiently in Python. [192]
Several issues with glitches, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of producing copyrighted code, without any author attribution or license. [197]
OpenAI revealed 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), capable of accepting text or image inputs. [199] They revealed that the updated 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 might also check out, examine or produce approximately 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, 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 declined to expose different technical details and stats 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 produce text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized 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 expects it to be particularly useful for business, yewiki.org startups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been developed to take more time to think about their reactions, resulting in greater precision. These designs are especially reliable in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning model. OpenAI likewise revealed o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215]
Deep research

Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform comprehensive web browsing, data analysis, and wiki.snooze-hotelsoftware.de synthesis, providing 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 category

CLIP

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

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can develop images of reasonable objects ("a stained-glass window with an image of a blue strawberry") as well as objects that do not exist in reality ("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 version of the design with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new simple 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 effective design much better able to produce images from intricate descriptions without manual timely engineering and render complicated 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 create videos based upon brief detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of created videos is unknown.

Sora's development group named it after the Japanese word for "sky", to signify its "endless imaginative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that purpose, however did not expose 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, mentioning that it might generate videos up to one minute long. It also shared a technical report highlighting the methods used to train the model, and the model's abilities. [225] It acknowledged a few of its shortcomings, including battles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", however noted that they must have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's capability to produce reasonable video from text descriptions, mentioning its prospective to revolutionize storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly strategies for expanding his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of diverse audio and is also a multi-task design that can perform multilingual speech recognition as well as 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 produce songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly but then fall into turmoil the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to develop 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 snippet of lyrics and outputs song samples. OpenAI stated the songs "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a significant space" between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider stated "surprisingly, some of the resulting songs are memorable 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 purpose is to research whether such a method may help in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of eight neural network models which are typically studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks quickly. The designs consisted of 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 developed on top of GPT-3 that offers a conversational interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.